Prevalence and management of hypertension in the inpatient setting: A systematic review

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Prevalence and management of hypertension in the inpatient setting: A systematic review

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Educational Objectives

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  • To describe the correlation between inpatient and outpatient blood pressure measurements.

  • To assess the potential benefits of prescribing antihypertensive medication in hospitalized patients with hypertension.

This manuscript underwent peer review in line with the standards of editorial integrity and publication ethics maintained by Journal of Hospital Medicine. The peer reviewers have no relevant financial relationships. The peer review process for Journal of Hospital Medicine is single‐blinded. As such, the identities of the reviewers are not disclosed in line with the standard accepted practices of medical journal peer review.

Conflicts of interest have been identified and resolved in accordance with Blackwell Futura Media Services's Policy on Activity Disclosure and Conflict of Interest. The primary resolution method used was peer review and review by a non‐conflicted expert.

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If you wish to receive credit for this activity, please refer to the website: www.wileyblackwellcme.com.

Accreditation and Designation Statement

Blackwell Futura Media Services designates this journal‐based CME activity for a maximum of 1 AMA PRA Category 1 Credit.. Physicians should only claim credit commensurate with the extent of their participation in the activity.

Blackwell Futura Media Services is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

Educational Objectives

The objectives need to be changed. Please remove the existing ones, and include these two:

  • To describe the correlation between inpatient and outpatient blood pressure measurements.

  • To assess the potential benefits of prescribing antihypertensive medication in hospitalized patients with hypertension.

This manuscript underwent peer review in line with the standards of editorial integrity and publication ethics maintained by Journal of Hospital Medicine. The peer reviewers have no relevant financial relationships. The peer review process for Journal of Hospital Medicine is single‐blinded. As such, the identities of the reviewers are not disclosed in line with the standard accepted practices of medical journal peer review.

Conflicts of interest have been identified and resolved in accordance with Blackwell Futura Media Services's Policy on Activity Disclosure and Conflict of Interest. The primary resolution method used was peer review and review by a non‐conflicted expert.

Instructions on Receiving Credit

For information on applicability and acceptance of CME credit for this activity, please consult your professional licensing board.

This activity is designed to be completed within an hour; physicians should claim only those credits that reflect the time actually spent in the activity. To successfully earn credit, participants must complete the activity during the valid credit period, which is up to two years from initial publication.

Follow these steps to earn credit:

  • Log on to www.wileyblackwellcme.com

  • Read the target audience, learning objectives, and author disclosures.

  • Read the article in print or online format.

  • Reflect on the article.

  • Access the CME Exam, and choose the best answer to each question.

  • Complete the required evaluation component of the activity.

This activity will be available for CME credit for twelve months following its publication date. At that time, it will be reviewed and potentially updated and extended for an additional twelve months.

If you wish to receive credit for this activity, please refer to the website: www.wileyblackwellcme.com.

Accreditation and Designation Statement

Blackwell Futura Media Services designates this journal‐based CME activity for a maximum of 1 AMA PRA Category 1 Credit.. Physicians should only claim credit commensurate with the extent of their participation in the activity.

Blackwell Futura Media Services is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

Educational Objectives

The objectives need to be changed. Please remove the existing ones, and include these two:

  • To describe the correlation between inpatient and outpatient blood pressure measurements.

  • To assess the potential benefits of prescribing antihypertensive medication in hospitalized patients with hypertension.

This manuscript underwent peer review in line with the standards of editorial integrity and publication ethics maintained by Journal of Hospital Medicine. The peer reviewers have no relevant financial relationships. The peer review process for Journal of Hospital Medicine is single‐blinded. As such, the identities of the reviewers are not disclosed in line with the standard accepted practices of medical journal peer review.

Conflicts of interest have been identified and resolved in accordance with Blackwell Futura Media Services's Policy on Activity Disclosure and Conflict of Interest. The primary resolution method used was peer review and review by a non‐conflicted expert.

Instructions on Receiving Credit

For information on applicability and acceptance of CME credit for this activity, please consult your professional licensing board.

This activity is designed to be completed within an hour; physicians should claim only those credits that reflect the time actually spent in the activity. To successfully earn credit, participants must complete the activity during the valid credit period, which is up to two years from initial publication.

Follow these steps to earn credit:

  • Log on to www.wileyblackwellcme.com

  • Read the target audience, learning objectives, and author disclosures.

  • Read the article in print or online format.

  • Reflect on the article.

  • Access the CME Exam, and choose the best answer to each question.

  • Complete the required evaluation component of the activity.

This activity will be available for CME credit for twelve months following its publication date. At that time, it will be reviewed and potentially updated and extended for an additional twelve months.

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Prevalence and management of hypertension in the inpatient setting: A systematic review
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OSTE for Hospitalist Teaching During FCR

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Using observed structured teaching exercises (OSTE) to enhance hospitalist teaching during family centered rounds

Providing family centered care has been identified as a goal in the Institute of Medicine's report Crossing the Quality Chiasm1 and endorsed by the American Academy of Pediatrics.2 Traditionally, rounds are the central organizing structure for clinical work, decision making, and teaching in the inpatient setting. Patient care and educational goals emanate from rounds. Over the past several decades rounds have migrated from the patient's bedside to the privacy of the conference room. In our experience, although conference room rounds offer some advantages, patients and families are not privy to the data or decision‐making process used to determine their diagnosis and plan of care. The ritual that frequently occurs after conference‐room rounds is that the team members (medical students, residents, nurses, attending) visit the patient and family independently throughout the course of the day, communicating their understanding of medical and affective issues in a manner that families often view as providing confusing, if not contradictory information.

Conducting rounds entirely at the bedside can bypass this systemic flaw, allowing parents and patients to correct inaccurate data, and enable them to make their values and concerns known to the team. This model can help to connect the caregivers and receivers of care, and represents a collaborative communication process, the foundation for effective family‐centered rounds (FCR). When team members discuss how they interpret clinical data in the presence of the family it helps them to understand how and why a management plan is conceived. The care team develops an alliance of trust with the family through this transparent communication and joint decision‐making.

Despite the potential for enhancing patient/family satisfaction and endorsements by public and professional organizations, in a recent study less than half of pediatric hospitalists reported conducting FCR.3 Trainees and attending physicians raised concerns about the potential for FCR to waste time and diminish teaching.4 Trainees' perceptions of the educational value of FCR has not been well studied, but a recent qualitative study of pediatric residents reported that if conducted well, FCRs enhance education and clinical skills by increasing the number of patients seen by each team member, and by offering opportunities to improve physical examination skills. Trainees appreciated role‐modeling and realtime feedback by attending physicians. Senior residents reported enhanced leadership and teaching opportunities.5

The aim of this study was to design and implement a faculty development program to address the need of our junior hospitalist faculty members to enhance teaching during FCR.

Methods

We determined, based upon direct observation, a focus group and survey feedback from our pediatric residents, that for inpatient teaching during FCR to be successful, our faculty needed training in the following areas: orienting learners, providing feedback, teaching assessment of key physical exam findings, correcting errors in clinical reasoning, and promoting the role of the senior resident as team leader. We developed the Observed Structured Teaching Exercises (OSTE)6 and related workshops to promote key behaviors identified from the literature for each of the areas.

All of the Children's National Medical Center (CNMC) Pediatric hospitalists (N = 14) who were not investigators in the study were asked to participate. They were informed of the study design and the overall goal of making inpatient rounds more effective and efficient through better teaching skills. The study was approved by the CNMC institutional review board and was conducted from August to September 2007 in the CLASS (Clinical Learning and Simulation Skills) at The George Washington University School of Medicine and Health Sciences.

To assess faculty and fellow baseline knowledge and skills, the authors conducted a preintervention OSTE consisting of 4 stations: 1) physical exam interpretation and promoting PL‐3 autonomy (Established Patient), 2) stimulating clinical reasoning (New Patient), 3) feedback, and 4) facilitating an orientation. This exercise was followed within 2 weeks by four 90‐minute interactive workshops that focused on the topic areas as evaluated in the OSTEs. Each workshop consisted of a brief evidence‐based didactic component, interactive discussion, and skill building exercises to practice desired teaching behaviors. Two weeks following the workshops, the group participated in postintervention OSTEs similar to the preintervention scenarios, with minor changes, such as presenting diagnoses, to avoid pattern recognition.

Development of the Evaluation Process

The authors reviewed the literature on providing effective feedback7 and orientation8; in teaching a skill9; promoting senior resident autonomy10 and clinical reasoning.11 We also reviewed the faculty development literature12, 13 to determine which behaviors were found to be effective specifically for promoting teaching during FCR, but no studies specifically addressed evaluation of teaching skills during FCR. Checklists were created based on the evidence in the literature and supplemented by the consensus of the investigators when there was no evidence available (see Supp. Appendix S1, which is available online).

Two stations simulating FCR (physical exam interpretation and promoting PL‐3 autonomy [Established Patient]; and stimulating clinical reasoning [New Patient]), each used 2 Standardized Learners (SL) and 1 Standardized Parent (SP). The patient was portrayed using a poster or simulator. The stations simulating feedback and orientation used 1 SL. To conduct 14 pre‐ and post‐OSTEs, we used a total of 5 SPs and 20 SLs. The SPs were recruited from a cohort of individuals that regularly participate in OSCE teaching and evaluation scenarios in the CLASS Center. The SLs were 4th year medical students enrolled in the TALKS (Teaching and Learning Communication Skills) elective and trained how to portray SLs.14

Training consisted of advanced distribution of specific scripts to SPs/SLs and practice through role playing the scenarios with study investigators acting as the attending hospitalist. The SP/SLs and investigators tried to anticipate several possible ways participants might react to the scenarios so that SPs and SLs could standardize their responses and interrater reliability for rating checklists of desired teaching behaviors. SLs rated faculty according to the teaching behavior template during a 5‐minute interval immediately after each OSTE. Different SLs were used for pre‐ versus postintervention OSTEs and were unaware of the intervention itself or whether faculty participants were pre‐ or postintervention.

Each of the 4 OSTE stations began with the hospitalist reading a brief paragraph describing the scenario and the overall goals for the OSTE. SLs/SPs acted out scripts designed by investigators to provide opportunities for hospitalists to demonstrate desired teaching behaviors. Each OSTE was designed to be completed within 10 minutes.

Development of the Intervention Workshops

Five Hospitalist faculty members with extensive training in faculty development facilitated four 90‐minute workshops, each focused on the goals of a particular pretest OSTE session. The learning objectives for each workshop are listed in Table 1. Each interactive workshop included a brief, evidence‐based didactic portion followed by a presentation of the evaluation checklists and an aggregate summary of hospitalist pretest ratings on the corresponding OSTE.

Workshop Objectives
  • Abbreviations: OSTE, Observed Structured Teaching Exercises; PE, physical examination.

Established Patient Workshop: Promoting the Senior Resident Leadership Role and Physical Exam Assessment
1.Identify barriers to teaching PE skills/emnterpretation at bedside
2.Identify barriers to promoting the role of the senior resident as leader
3.Discuss strategies for overcoming 1 & 2
4.State what is meant by Deliberate Practice
5.State the key aspects of Activated Demonstration
6.Practice Activated Demonstration through deliberate practice using the OSTE scoring template in role plays
Feedback Workshop
1.State the value of feedback to learners
2.Identify barriers to giving feedback, especially corrective feedback
3.Discuss strategies for promoting reflective self‐assessment
4.Describe examples that represent effective strategies for reinforcing behaviors
5.Describe examples that represent effective strategies for correcting behaviors
6.Practice through role play (using the OSTE scoring template):
a.Developing a learner‐centered action plan
b.Eliciting learner's feelings about feedback and action plan
c.Exploring the learner's readiness to implement plan
Workshop Promoting Clinical Reasoning‐Correcting Wrong New Patient Diagnosis
1.Identify barriers to trainees giving focused oral presentations
2.Identify barriers to teaching clinical reasoning
3.Identify barriers inherent in discussing diagnostic uncertainty and misdiagnosis in front of families
4.Discuss strategies for overcoming 1‐3
5.Describe the theoretical framework behind Problem Representation
6.Describe the key behaviors that comprise the OMP model
7.Practice using abstractions of the key features to represent the problem
8.Practice identifying knowledge/synthesis gaps and correcting learner mistakes using the OSTE scoring template in role‐plays
Orientation Workshop
1.State the value of orientation to learners
2.State the key elements for an effective orientation
3.Identify barriers to providing an orientation
4.Discuss strategies for effectively orienting learners
5.Practice orienting a learner through role play using the OSTE scoring template

After facilitators explained the theory behind determining the checklist behaviors, participants discussed the checklists and agreed on the validity of the rating instruments. The participants determined strategies to consistently remember to incorporate the desired behaviors, such as using mnemonics on pocket‐sized laminated cards and then practiced desired behaviors using roleplay.

Analysis

The percentage of total points possible on each of the pretest and posttest OSTE scoring templates was compared using a paired Student t test for each of the 14 participants.

Results

All 14 eligible hospitalists voluntarily participated. Their mean year postcompletion of residency training for the faculty was 17 months 14 months; 71% were female. None of the participants experienced previous training in the areas proposed in the study.

Participants assigned high scores to the quality of the workshops, the OSTE experience and their learning from the participating in the faculty development exercise. The differences between pre‐ and post‐OSTE scenario as well as overall scores for the 4 stations were statistically significant (P < .0001). Particular improvements were noted in the correction of incorrect new admission diagnoses (56% pre, 86% post) and orientation (65% pre, 95% post; see Table 2).

Pre‐ and Post‐OSTE t Test Results
OSTE stationPrePostDFt value
  • P < .0001.

  • Abbreviations: OSTE, Observed Structured Teaching Exercises; PE, physical examination.

PE skill/ leadership70%91%129.07*
Feedback71%94%127.40*
Clinical reasoning56%86%1312.40*
Orientation65%95%137.56*
Overall64%90%1317.58*

Discussion

If FCR are to be universally adopted in the academic pediatric inpatient setting, faculty must successfully balance the educational needs of trainees as well as efficiently negotiating a plan of management with patients and families. The ability of faculty to consistently orchestrate rounds so that they meet educational needs of varied levels of learners, while ensuring that patient management is correct and well communicated to families is a very complex task.

We found using OSTEs to frame desired behavior, supplemented with background information to validate the desired behaviors followed by deliberate practice opportunities during the workshops to be an effective faculty development strategy. We not only provided participants with feedback on the group's performance according to the rating scale, but also gave them the opportunity to practice rating each other using the scale so that they could reflect on the elements of their performance that merited a specific rating.

This strategy for training faculty to perform well in the complex environment within the patient's room during FCR is similar in some respects with training military personnel for complex battle situations.15 Desired behaviors are broken down and packaged within a framework to be implemented in a specific context. For example, we combined aspects of the One Minute Preceptor model (OMP)12 with Bordage's Problem Representation model to create a framework of behaviors to promote and correct errors in clinical reasoning.16, 17 Another framework was created and practiced to promote assessment of the physical exam at the bedside. Orientation and feedback, although not frequently used components of actual FCR, are necessary to set expectations and calibrate learner's performance during FCR.

The OSTE is an observed examination that has been validated for evaluating the teaching skills of faculty and residents.18 We planned to use learner‐centered, interactive workshops as the key component of the training intervention with the pre‐ and posttest OSTE as a measure of their effectiveness. However, we found in faculty feedback that the OSTEs were actually a key adjuvant, to the workshop training in that they provided a major source of feedback and learning opportunities in addition to their inherent evaluative qualities.

Each of 4 workshops was designed to teach participants the behaviors assessed in the 4 OSTE stations. The pretest OSTE provided a baseline for participant performance and served to activate the participants to focus on key teaching behaviors during the workshop. During the workshop following the pretest OSTE, participants were given copies of the rating scales and feedback on the performance of the group as a whole on each rated behavior. The evidence used to create the rating instruments was presented and participants had the opportunity to debate and agree on the instrument's construct validity. They then had the opportunity to engage in deliberate practice during role plays depicting challenges to orienting a learner, providing feedback, and to family centered rounds. The posttest OSTE served as summative evaluation of the participants' ability to perform the practiced behaviors effectively in a simulated teaching environment.

We chose to focus the FCR scenarios on correcting mistakes in clinical reasoning for a new patient and on teaching key parts of the physical exam during rounds for an established patient. Errors in clinical reasoning lead to misdirected patient management and are the number 1 cause of medical errors.19, 20 Bedside rounds are a perfect venue for reinforcing and fine‐tuning diagnostic reasoning because all the crucial sources of data are present: the patient, the parent, the nurse, and the computer with lab and imaging results. Faculty members and trainees have both expressed discomfort at correcting errors in clinical reasoning in front of families, leading to missed learning opportunities.21

During the workshop on clinical reasoning, we taught faculty how to use the Problem Representation method to analyze and correct errors in clinical reasoning. The method, studied by Bordage and associates22 forces learners to identify the key features of a presentation and relate their interpretation of the findings by using semantic qualifiers. We trained faculty to deliberately listen for the learner's interpretation of the key features to determine how a misdiagnosis occurred. They were also trained to walk trainees back through their thought process in an objective way, correcting the misinterpretation of data, so that the trainee's competence is not compromised in the eyes of the team or the parents. Teaching the trainee to think correctly about a clinical problem benefits the other members of the team, as well as providing the parents with a better understanding of the rationale for the management plan.

Correct interpretation of the physical exam findings is crucial to making the correct diagnosis. However, there have been several articles chronicling the lost art of eliciting and interpreting physical exam findings, ranging from the cardiac exam to neurological exam.23 A minority of physical exam teaching occurs at the patient's bedside, partly attributed to faculty members discomfort with this type of teaching.24, 25 To enhance the comfort of our faculty members, we included behaviors referenced in articles on teaching a skill,26 activated demonstration,10 and effective bedside teaching27 to guide faculty to incorporate eliciting and interpreting focused aspects of the physical exam during rounds.

Our FCR evaluation templates awarded the highest scores if the hospitalist encouraged senior residents to model clinical reasoning or physical exam skills for junior learners. Hospitalists' presence, especially on work rounds, can diminish the senior resident's opportunity to gain experience and confidence in leading the team.28 We therefore explicitly directed hospitalists to promote the role of the senior resident as the team leader during workshops, while priming faculty to assume the role of educational coach.29

We hypothesize that several factors contributed to the success of the OSTE workshopOSTE intervention. First, faculty members willingly volunteered to participate because they recognized gaps in their own knowledge and skill at leading FCR. They found the ability to deliberately practice the desired behaviors in the OSTE exercises to be the most useful part of the exercise, because the scenarios were authentic and SLs were real trainees.

Although we included all the junior faculty members of our large Pediatric Hospitalist Division in our study, our sample size is still small; limiting our ability to generalize our findings. We found the scheduling of 14 hospitalists to attend 4 different events in close succession to be problematic. Conducting the OSTE sessions at the GW CLASS center 5 miles away from our hospital was also logistically challenging.

We plan to simplify the logistics so that we can incorporate this model in the training of new hospitalists in the division. We still plan to use preintervention FCR OSTEs, but instead of workshops, will provide background information by means of self‐directed Web‐based modules. We will also videotape the OSTEs and provide faculty with a template to rate their own performance and then compare it with ratings from SLs. This individualized feedback and self‐reflection could result in better performance30 than the summary group feedback we gave during the workshops.

Another limitation of this study is the lack of data regarding the consistency of our faculty participants' performance in real FCR. Finally, we did not study the impact of the desired behaviors on patient, trainee, or nursing satisfaction, learning, or efficiency.

Conclusion

In conclusion, we found incorporating OSTEs into a faculty development program to improve FCR to be an effective strategy for changing faculty behavior in leading FCR. Additional study is needed to determine if replacing the workshops with Web‐based tutorials is equally effective and to determine if this faculty development strategy results in long‐term consistent practice in conducting rounds in real inpatient settings.

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References
  1. Institute of Medicine of the National Academies.Crossing the Quality Chasm: A New Health System for the 21st Century. March 1,2001.Washington, DC:National Academy of Science.
  2. Committee on Hospital Care.American Academy of Pediatrics. Family‐centered care and the pediatrician's role.Pediatrics.2003;112:691696.
  3. Mittal V,Sigrest T,Roy L, et al.Current trends in practice of family centered rounds: a study from the pediatric research in inpatient settings (PRIS) network.Pediatrics.2010. In press.
  4. Muething SE,Uma RK,Schoettker PJ,Gonzalez del Rey J,DeWitt TG.Family‐centered bedside rounds: A new approach to patient care and teaching.Pediatrics.2007;119:829832.
  5. Mittal V,Lee B,Patel R, et al.Do Family‐Centered Rounds (FCRs) Enhance Resident's Clinical and Educational Experiences and Improve Patient Outcomes? A Qualitative Study. In: Proceedings of the 2010 Pediatric Academic Societies Annual Meeting, May 1–4,2010, Vancouver, BC, Canada.
  6. Stone S,Mazor K,Devaney‐O'Neil S, et al.Development and implementation of an objective structured teaching exercise (OSTE) to evaluate improvement in feedback skills following a faculty development workshop.Teach Learn Med.2003;15:713.
  7. Ende J.Feedback in clinical medical education.JAMA.1983;250:777781.
  8. Ferenchick G,Simpson D,Blackman J,DaRosa D,Dunnington G.Strategies for efficient and effective teaching in the ambulatory care setting.Acad Med.1997;72:277280.
  9. Wilkerson L,Sarkin RT.Arrows in the quiver: evaluation of a workshop on ambulatory teaching.Acad Med.1998;73(Suppl):S67hyphen.
  10. Babitch LA.Teaching practice management skills to pediatric residents.Clin Pediatr.2006;45:846849.
  11. Bowen JL.Educational strategies to promote clinical diagnostic reasoning.N Engl J Med.2006;355:22172225.
  12. Heidenreich C,Lye P,Simpson D,Lourich M.The search for effective and efficient ambulatory teaching methods through the literature.Pediatrics.2000;105:231237.
  13. Irby DM,Aagaard E,Teherani A.Teaching points identified by preceptors observing one‐minute preceptor and traditional preceptor encounters.Acad Med.2004;79:5055.
  14. Blatt B,Greenberg L,Kallenberg G,Confessore G,Confessore S.The Talks Manual: A Guide to Teaching Senior Students in the Health Professions to be Educators.Washington, DC:George Washington University;2000.
  15. Chatman R.20th‐century revolution in military training. In: Ericsson KA, editor.Development of Professional Expertise Toward Measurement of Expert Performance and Design of Optimal Learning Environments.New York:Cambridge University Press;2009:2759.
  16. Bordage G.“Why did I miss the diagnosis? Some cognitive explanations and educational implications.”Acad Med.1999;74:S138S143.
  17. Nendaz MR,Bordage G.Promoting diagnostic problem.Med Educ.2002;36:760766.
  18. Morrison EH,Boker JR,Hollingshead J,Prislin MD,Hitchcock MA,Litzleman DK.Reliability and validity of an objective structured teaching examination for generalist resident teachers.Acad Med.2002;77(suppl):S29.
  19. Schiff GD,Kim S,Abrams R, et al.Diagnosing diagnostic errors: lessons from a multi‐institutional collaborative project.Adv Patient Safety.2005;2:255278.
  20. Newman‐Toker DE,Pronovost PJ.“Diagnostic errors – the next frontier for patient safety.”JAMA.2009;301:10601062.
  21. Janicik RW,Fletcher KE.Teaching at the bedside: a new model.Med Teach.2003;25:127130.
  22. Bordage G.Elaborated knowledge: a key to successful diagnostic thinking.Acad Med.1994;69:883889.
  23. LaCombe MA.On bedside teaching.Ann Intern Med.1997;126:217220.
  24. Gonzalo JD,Masters PA,Simons RJ,Chuang CH.Attending rounds and bedside case presentations: medical student and medicine resident experiences and attitudes.Teach Learn Med.2009;21:105110.
  25. Ramani S,Orlander JD,Strunin L,Barber TW.Whither bedside teaching? A focus‐group study of clinical teachers.Acad Med.2003;78:384390.
  26. Smee S.ABC of learning and teaching in medicine: skill based assessment.BMJ.2003;326:703706.
  27. Ramani S.Twelve tips to improve bedside teaching.Med Teach.2003;25:112115.
  28. Landrigan CP,Muret‐Wagstaff S,Chiang VW, et al.Effect of a pediatric hospitalist system on house staff education and experience.Arch Pediatr Adolesc Med.2002;156:877883.
  29. Orlander JDWipf JE,Lew RA.Development of a tool to assess the team leadership skills of medical residents.Med Educ Online.2006:11;1127.
  30. Gelula MH,Yudkowsky R.Using standardised students in faculty development workshops to improve clinical teaching skills.Med Educ.2003;37:621629.
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Providing family centered care has been identified as a goal in the Institute of Medicine's report Crossing the Quality Chiasm1 and endorsed by the American Academy of Pediatrics.2 Traditionally, rounds are the central organizing structure for clinical work, decision making, and teaching in the inpatient setting. Patient care and educational goals emanate from rounds. Over the past several decades rounds have migrated from the patient's bedside to the privacy of the conference room. In our experience, although conference room rounds offer some advantages, patients and families are not privy to the data or decision‐making process used to determine their diagnosis and plan of care. The ritual that frequently occurs after conference‐room rounds is that the team members (medical students, residents, nurses, attending) visit the patient and family independently throughout the course of the day, communicating their understanding of medical and affective issues in a manner that families often view as providing confusing, if not contradictory information.

Conducting rounds entirely at the bedside can bypass this systemic flaw, allowing parents and patients to correct inaccurate data, and enable them to make their values and concerns known to the team. This model can help to connect the caregivers and receivers of care, and represents a collaborative communication process, the foundation for effective family‐centered rounds (FCR). When team members discuss how they interpret clinical data in the presence of the family it helps them to understand how and why a management plan is conceived. The care team develops an alliance of trust with the family through this transparent communication and joint decision‐making.

Despite the potential for enhancing patient/family satisfaction and endorsements by public and professional organizations, in a recent study less than half of pediatric hospitalists reported conducting FCR.3 Trainees and attending physicians raised concerns about the potential for FCR to waste time and diminish teaching.4 Trainees' perceptions of the educational value of FCR has not been well studied, but a recent qualitative study of pediatric residents reported that if conducted well, FCRs enhance education and clinical skills by increasing the number of patients seen by each team member, and by offering opportunities to improve physical examination skills. Trainees appreciated role‐modeling and realtime feedback by attending physicians. Senior residents reported enhanced leadership and teaching opportunities.5

The aim of this study was to design and implement a faculty development program to address the need of our junior hospitalist faculty members to enhance teaching during FCR.

Methods

We determined, based upon direct observation, a focus group and survey feedback from our pediatric residents, that for inpatient teaching during FCR to be successful, our faculty needed training in the following areas: orienting learners, providing feedback, teaching assessment of key physical exam findings, correcting errors in clinical reasoning, and promoting the role of the senior resident as team leader. We developed the Observed Structured Teaching Exercises (OSTE)6 and related workshops to promote key behaviors identified from the literature for each of the areas.

All of the Children's National Medical Center (CNMC) Pediatric hospitalists (N = 14) who were not investigators in the study were asked to participate. They were informed of the study design and the overall goal of making inpatient rounds more effective and efficient through better teaching skills. The study was approved by the CNMC institutional review board and was conducted from August to September 2007 in the CLASS (Clinical Learning and Simulation Skills) at The George Washington University School of Medicine and Health Sciences.

To assess faculty and fellow baseline knowledge and skills, the authors conducted a preintervention OSTE consisting of 4 stations: 1) physical exam interpretation and promoting PL‐3 autonomy (Established Patient), 2) stimulating clinical reasoning (New Patient), 3) feedback, and 4) facilitating an orientation. This exercise was followed within 2 weeks by four 90‐minute interactive workshops that focused on the topic areas as evaluated in the OSTEs. Each workshop consisted of a brief evidence‐based didactic component, interactive discussion, and skill building exercises to practice desired teaching behaviors. Two weeks following the workshops, the group participated in postintervention OSTEs similar to the preintervention scenarios, with minor changes, such as presenting diagnoses, to avoid pattern recognition.

Development of the Evaluation Process

The authors reviewed the literature on providing effective feedback7 and orientation8; in teaching a skill9; promoting senior resident autonomy10 and clinical reasoning.11 We also reviewed the faculty development literature12, 13 to determine which behaviors were found to be effective specifically for promoting teaching during FCR, but no studies specifically addressed evaluation of teaching skills during FCR. Checklists were created based on the evidence in the literature and supplemented by the consensus of the investigators when there was no evidence available (see Supp. Appendix S1, which is available online).

Two stations simulating FCR (physical exam interpretation and promoting PL‐3 autonomy [Established Patient]; and stimulating clinical reasoning [New Patient]), each used 2 Standardized Learners (SL) and 1 Standardized Parent (SP). The patient was portrayed using a poster or simulator. The stations simulating feedback and orientation used 1 SL. To conduct 14 pre‐ and post‐OSTEs, we used a total of 5 SPs and 20 SLs. The SPs were recruited from a cohort of individuals that regularly participate in OSCE teaching and evaluation scenarios in the CLASS Center. The SLs were 4th year medical students enrolled in the TALKS (Teaching and Learning Communication Skills) elective and trained how to portray SLs.14

Training consisted of advanced distribution of specific scripts to SPs/SLs and practice through role playing the scenarios with study investigators acting as the attending hospitalist. The SP/SLs and investigators tried to anticipate several possible ways participants might react to the scenarios so that SPs and SLs could standardize their responses and interrater reliability for rating checklists of desired teaching behaviors. SLs rated faculty according to the teaching behavior template during a 5‐minute interval immediately after each OSTE. Different SLs were used for pre‐ versus postintervention OSTEs and were unaware of the intervention itself or whether faculty participants were pre‐ or postintervention.

Each of the 4 OSTE stations began with the hospitalist reading a brief paragraph describing the scenario and the overall goals for the OSTE. SLs/SPs acted out scripts designed by investigators to provide opportunities for hospitalists to demonstrate desired teaching behaviors. Each OSTE was designed to be completed within 10 minutes.

Development of the Intervention Workshops

Five Hospitalist faculty members with extensive training in faculty development facilitated four 90‐minute workshops, each focused on the goals of a particular pretest OSTE session. The learning objectives for each workshop are listed in Table 1. Each interactive workshop included a brief, evidence‐based didactic portion followed by a presentation of the evaluation checklists and an aggregate summary of hospitalist pretest ratings on the corresponding OSTE.

Workshop Objectives
  • Abbreviations: OSTE, Observed Structured Teaching Exercises; PE, physical examination.

Established Patient Workshop: Promoting the Senior Resident Leadership Role and Physical Exam Assessment
1.Identify barriers to teaching PE skills/emnterpretation at bedside
2.Identify barriers to promoting the role of the senior resident as leader
3.Discuss strategies for overcoming 1 & 2
4.State what is meant by Deliberate Practice
5.State the key aspects of Activated Demonstration
6.Practice Activated Demonstration through deliberate practice using the OSTE scoring template in role plays
Feedback Workshop
1.State the value of feedback to learners
2.Identify barriers to giving feedback, especially corrective feedback
3.Discuss strategies for promoting reflective self‐assessment
4.Describe examples that represent effective strategies for reinforcing behaviors
5.Describe examples that represent effective strategies for correcting behaviors
6.Practice through role play (using the OSTE scoring template):
a.Developing a learner‐centered action plan
b.Eliciting learner's feelings about feedback and action plan
c.Exploring the learner's readiness to implement plan
Workshop Promoting Clinical Reasoning‐Correcting Wrong New Patient Diagnosis
1.Identify barriers to trainees giving focused oral presentations
2.Identify barriers to teaching clinical reasoning
3.Identify barriers inherent in discussing diagnostic uncertainty and misdiagnosis in front of families
4.Discuss strategies for overcoming 1‐3
5.Describe the theoretical framework behind Problem Representation
6.Describe the key behaviors that comprise the OMP model
7.Practice using abstractions of the key features to represent the problem
8.Practice identifying knowledge/synthesis gaps and correcting learner mistakes using the OSTE scoring template in role‐plays
Orientation Workshop
1.State the value of orientation to learners
2.State the key elements for an effective orientation
3.Identify barriers to providing an orientation
4.Discuss strategies for effectively orienting learners
5.Practice orienting a learner through role play using the OSTE scoring template

After facilitators explained the theory behind determining the checklist behaviors, participants discussed the checklists and agreed on the validity of the rating instruments. The participants determined strategies to consistently remember to incorporate the desired behaviors, such as using mnemonics on pocket‐sized laminated cards and then practiced desired behaviors using roleplay.

Analysis

The percentage of total points possible on each of the pretest and posttest OSTE scoring templates was compared using a paired Student t test for each of the 14 participants.

Results

All 14 eligible hospitalists voluntarily participated. Their mean year postcompletion of residency training for the faculty was 17 months 14 months; 71% were female. None of the participants experienced previous training in the areas proposed in the study.

Participants assigned high scores to the quality of the workshops, the OSTE experience and their learning from the participating in the faculty development exercise. The differences between pre‐ and post‐OSTE scenario as well as overall scores for the 4 stations were statistically significant (P < .0001). Particular improvements were noted in the correction of incorrect new admission diagnoses (56% pre, 86% post) and orientation (65% pre, 95% post; see Table 2).

Pre‐ and Post‐OSTE t Test Results
OSTE stationPrePostDFt value
  • P < .0001.

  • Abbreviations: OSTE, Observed Structured Teaching Exercises; PE, physical examination.

PE skill/ leadership70%91%129.07*
Feedback71%94%127.40*
Clinical reasoning56%86%1312.40*
Orientation65%95%137.56*
Overall64%90%1317.58*

Discussion

If FCR are to be universally adopted in the academic pediatric inpatient setting, faculty must successfully balance the educational needs of trainees as well as efficiently negotiating a plan of management with patients and families. The ability of faculty to consistently orchestrate rounds so that they meet educational needs of varied levels of learners, while ensuring that patient management is correct and well communicated to families is a very complex task.

We found using OSTEs to frame desired behavior, supplemented with background information to validate the desired behaviors followed by deliberate practice opportunities during the workshops to be an effective faculty development strategy. We not only provided participants with feedback on the group's performance according to the rating scale, but also gave them the opportunity to practice rating each other using the scale so that they could reflect on the elements of their performance that merited a specific rating.

This strategy for training faculty to perform well in the complex environment within the patient's room during FCR is similar in some respects with training military personnel for complex battle situations.15 Desired behaviors are broken down and packaged within a framework to be implemented in a specific context. For example, we combined aspects of the One Minute Preceptor model (OMP)12 with Bordage's Problem Representation model to create a framework of behaviors to promote and correct errors in clinical reasoning.16, 17 Another framework was created and practiced to promote assessment of the physical exam at the bedside. Orientation and feedback, although not frequently used components of actual FCR, are necessary to set expectations and calibrate learner's performance during FCR.

The OSTE is an observed examination that has been validated for evaluating the teaching skills of faculty and residents.18 We planned to use learner‐centered, interactive workshops as the key component of the training intervention with the pre‐ and posttest OSTE as a measure of their effectiveness. However, we found in faculty feedback that the OSTEs were actually a key adjuvant, to the workshop training in that they provided a major source of feedback and learning opportunities in addition to their inherent evaluative qualities.

Each of 4 workshops was designed to teach participants the behaviors assessed in the 4 OSTE stations. The pretest OSTE provided a baseline for participant performance and served to activate the participants to focus on key teaching behaviors during the workshop. During the workshop following the pretest OSTE, participants were given copies of the rating scales and feedback on the performance of the group as a whole on each rated behavior. The evidence used to create the rating instruments was presented and participants had the opportunity to debate and agree on the instrument's construct validity. They then had the opportunity to engage in deliberate practice during role plays depicting challenges to orienting a learner, providing feedback, and to family centered rounds. The posttest OSTE served as summative evaluation of the participants' ability to perform the practiced behaviors effectively in a simulated teaching environment.

We chose to focus the FCR scenarios on correcting mistakes in clinical reasoning for a new patient and on teaching key parts of the physical exam during rounds for an established patient. Errors in clinical reasoning lead to misdirected patient management and are the number 1 cause of medical errors.19, 20 Bedside rounds are a perfect venue for reinforcing and fine‐tuning diagnostic reasoning because all the crucial sources of data are present: the patient, the parent, the nurse, and the computer with lab and imaging results. Faculty members and trainees have both expressed discomfort at correcting errors in clinical reasoning in front of families, leading to missed learning opportunities.21

During the workshop on clinical reasoning, we taught faculty how to use the Problem Representation method to analyze and correct errors in clinical reasoning. The method, studied by Bordage and associates22 forces learners to identify the key features of a presentation and relate their interpretation of the findings by using semantic qualifiers. We trained faculty to deliberately listen for the learner's interpretation of the key features to determine how a misdiagnosis occurred. They were also trained to walk trainees back through their thought process in an objective way, correcting the misinterpretation of data, so that the trainee's competence is not compromised in the eyes of the team or the parents. Teaching the trainee to think correctly about a clinical problem benefits the other members of the team, as well as providing the parents with a better understanding of the rationale for the management plan.

Correct interpretation of the physical exam findings is crucial to making the correct diagnosis. However, there have been several articles chronicling the lost art of eliciting and interpreting physical exam findings, ranging from the cardiac exam to neurological exam.23 A minority of physical exam teaching occurs at the patient's bedside, partly attributed to faculty members discomfort with this type of teaching.24, 25 To enhance the comfort of our faculty members, we included behaviors referenced in articles on teaching a skill,26 activated demonstration,10 and effective bedside teaching27 to guide faculty to incorporate eliciting and interpreting focused aspects of the physical exam during rounds.

Our FCR evaluation templates awarded the highest scores if the hospitalist encouraged senior residents to model clinical reasoning or physical exam skills for junior learners. Hospitalists' presence, especially on work rounds, can diminish the senior resident's opportunity to gain experience and confidence in leading the team.28 We therefore explicitly directed hospitalists to promote the role of the senior resident as the team leader during workshops, while priming faculty to assume the role of educational coach.29

We hypothesize that several factors contributed to the success of the OSTE workshopOSTE intervention. First, faculty members willingly volunteered to participate because they recognized gaps in their own knowledge and skill at leading FCR. They found the ability to deliberately practice the desired behaviors in the OSTE exercises to be the most useful part of the exercise, because the scenarios were authentic and SLs were real trainees.

Although we included all the junior faculty members of our large Pediatric Hospitalist Division in our study, our sample size is still small; limiting our ability to generalize our findings. We found the scheduling of 14 hospitalists to attend 4 different events in close succession to be problematic. Conducting the OSTE sessions at the GW CLASS center 5 miles away from our hospital was also logistically challenging.

We plan to simplify the logistics so that we can incorporate this model in the training of new hospitalists in the division. We still plan to use preintervention FCR OSTEs, but instead of workshops, will provide background information by means of self‐directed Web‐based modules. We will also videotape the OSTEs and provide faculty with a template to rate their own performance and then compare it with ratings from SLs. This individualized feedback and self‐reflection could result in better performance30 than the summary group feedback we gave during the workshops.

Another limitation of this study is the lack of data regarding the consistency of our faculty participants' performance in real FCR. Finally, we did not study the impact of the desired behaviors on patient, trainee, or nursing satisfaction, learning, or efficiency.

Conclusion

In conclusion, we found incorporating OSTEs into a faculty development program to improve FCR to be an effective strategy for changing faculty behavior in leading FCR. Additional study is needed to determine if replacing the workshops with Web‐based tutorials is equally effective and to determine if this faculty development strategy results in long‐term consistent practice in conducting rounds in real inpatient settings.

Providing family centered care has been identified as a goal in the Institute of Medicine's report Crossing the Quality Chiasm1 and endorsed by the American Academy of Pediatrics.2 Traditionally, rounds are the central organizing structure for clinical work, decision making, and teaching in the inpatient setting. Patient care and educational goals emanate from rounds. Over the past several decades rounds have migrated from the patient's bedside to the privacy of the conference room. In our experience, although conference room rounds offer some advantages, patients and families are not privy to the data or decision‐making process used to determine their diagnosis and plan of care. The ritual that frequently occurs after conference‐room rounds is that the team members (medical students, residents, nurses, attending) visit the patient and family independently throughout the course of the day, communicating their understanding of medical and affective issues in a manner that families often view as providing confusing, if not contradictory information.

Conducting rounds entirely at the bedside can bypass this systemic flaw, allowing parents and patients to correct inaccurate data, and enable them to make their values and concerns known to the team. This model can help to connect the caregivers and receivers of care, and represents a collaborative communication process, the foundation for effective family‐centered rounds (FCR). When team members discuss how they interpret clinical data in the presence of the family it helps them to understand how and why a management plan is conceived. The care team develops an alliance of trust with the family through this transparent communication and joint decision‐making.

Despite the potential for enhancing patient/family satisfaction and endorsements by public and professional organizations, in a recent study less than half of pediatric hospitalists reported conducting FCR.3 Trainees and attending physicians raised concerns about the potential for FCR to waste time and diminish teaching.4 Trainees' perceptions of the educational value of FCR has not been well studied, but a recent qualitative study of pediatric residents reported that if conducted well, FCRs enhance education and clinical skills by increasing the number of patients seen by each team member, and by offering opportunities to improve physical examination skills. Trainees appreciated role‐modeling and realtime feedback by attending physicians. Senior residents reported enhanced leadership and teaching opportunities.5

The aim of this study was to design and implement a faculty development program to address the need of our junior hospitalist faculty members to enhance teaching during FCR.

Methods

We determined, based upon direct observation, a focus group and survey feedback from our pediatric residents, that for inpatient teaching during FCR to be successful, our faculty needed training in the following areas: orienting learners, providing feedback, teaching assessment of key physical exam findings, correcting errors in clinical reasoning, and promoting the role of the senior resident as team leader. We developed the Observed Structured Teaching Exercises (OSTE)6 and related workshops to promote key behaviors identified from the literature for each of the areas.

All of the Children's National Medical Center (CNMC) Pediatric hospitalists (N = 14) who were not investigators in the study were asked to participate. They were informed of the study design and the overall goal of making inpatient rounds more effective and efficient through better teaching skills. The study was approved by the CNMC institutional review board and was conducted from August to September 2007 in the CLASS (Clinical Learning and Simulation Skills) at The George Washington University School of Medicine and Health Sciences.

To assess faculty and fellow baseline knowledge and skills, the authors conducted a preintervention OSTE consisting of 4 stations: 1) physical exam interpretation and promoting PL‐3 autonomy (Established Patient), 2) stimulating clinical reasoning (New Patient), 3) feedback, and 4) facilitating an orientation. This exercise was followed within 2 weeks by four 90‐minute interactive workshops that focused on the topic areas as evaluated in the OSTEs. Each workshop consisted of a brief evidence‐based didactic component, interactive discussion, and skill building exercises to practice desired teaching behaviors. Two weeks following the workshops, the group participated in postintervention OSTEs similar to the preintervention scenarios, with minor changes, such as presenting diagnoses, to avoid pattern recognition.

Development of the Evaluation Process

The authors reviewed the literature on providing effective feedback7 and orientation8; in teaching a skill9; promoting senior resident autonomy10 and clinical reasoning.11 We also reviewed the faculty development literature12, 13 to determine which behaviors were found to be effective specifically for promoting teaching during FCR, but no studies specifically addressed evaluation of teaching skills during FCR. Checklists were created based on the evidence in the literature and supplemented by the consensus of the investigators when there was no evidence available (see Supp. Appendix S1, which is available online).

Two stations simulating FCR (physical exam interpretation and promoting PL‐3 autonomy [Established Patient]; and stimulating clinical reasoning [New Patient]), each used 2 Standardized Learners (SL) and 1 Standardized Parent (SP). The patient was portrayed using a poster or simulator. The stations simulating feedback and orientation used 1 SL. To conduct 14 pre‐ and post‐OSTEs, we used a total of 5 SPs and 20 SLs. The SPs were recruited from a cohort of individuals that regularly participate in OSCE teaching and evaluation scenarios in the CLASS Center. The SLs were 4th year medical students enrolled in the TALKS (Teaching and Learning Communication Skills) elective and trained how to portray SLs.14

Training consisted of advanced distribution of specific scripts to SPs/SLs and practice through role playing the scenarios with study investigators acting as the attending hospitalist. The SP/SLs and investigators tried to anticipate several possible ways participants might react to the scenarios so that SPs and SLs could standardize their responses and interrater reliability for rating checklists of desired teaching behaviors. SLs rated faculty according to the teaching behavior template during a 5‐minute interval immediately after each OSTE. Different SLs were used for pre‐ versus postintervention OSTEs and were unaware of the intervention itself or whether faculty participants were pre‐ or postintervention.

Each of the 4 OSTE stations began with the hospitalist reading a brief paragraph describing the scenario and the overall goals for the OSTE. SLs/SPs acted out scripts designed by investigators to provide opportunities for hospitalists to demonstrate desired teaching behaviors. Each OSTE was designed to be completed within 10 minutes.

Development of the Intervention Workshops

Five Hospitalist faculty members with extensive training in faculty development facilitated four 90‐minute workshops, each focused on the goals of a particular pretest OSTE session. The learning objectives for each workshop are listed in Table 1. Each interactive workshop included a brief, evidence‐based didactic portion followed by a presentation of the evaluation checklists and an aggregate summary of hospitalist pretest ratings on the corresponding OSTE.

Workshop Objectives
  • Abbreviations: OSTE, Observed Structured Teaching Exercises; PE, physical examination.

Established Patient Workshop: Promoting the Senior Resident Leadership Role and Physical Exam Assessment
1.Identify barriers to teaching PE skills/emnterpretation at bedside
2.Identify barriers to promoting the role of the senior resident as leader
3.Discuss strategies for overcoming 1 & 2
4.State what is meant by Deliberate Practice
5.State the key aspects of Activated Demonstration
6.Practice Activated Demonstration through deliberate practice using the OSTE scoring template in role plays
Feedback Workshop
1.State the value of feedback to learners
2.Identify barriers to giving feedback, especially corrective feedback
3.Discuss strategies for promoting reflective self‐assessment
4.Describe examples that represent effective strategies for reinforcing behaviors
5.Describe examples that represent effective strategies for correcting behaviors
6.Practice through role play (using the OSTE scoring template):
a.Developing a learner‐centered action plan
b.Eliciting learner's feelings about feedback and action plan
c.Exploring the learner's readiness to implement plan
Workshop Promoting Clinical Reasoning‐Correcting Wrong New Patient Diagnosis
1.Identify barriers to trainees giving focused oral presentations
2.Identify barriers to teaching clinical reasoning
3.Identify barriers inherent in discussing diagnostic uncertainty and misdiagnosis in front of families
4.Discuss strategies for overcoming 1‐3
5.Describe the theoretical framework behind Problem Representation
6.Describe the key behaviors that comprise the OMP model
7.Practice using abstractions of the key features to represent the problem
8.Practice identifying knowledge/synthesis gaps and correcting learner mistakes using the OSTE scoring template in role‐plays
Orientation Workshop
1.State the value of orientation to learners
2.State the key elements for an effective orientation
3.Identify barriers to providing an orientation
4.Discuss strategies for effectively orienting learners
5.Practice orienting a learner through role play using the OSTE scoring template

After facilitators explained the theory behind determining the checklist behaviors, participants discussed the checklists and agreed on the validity of the rating instruments. The participants determined strategies to consistently remember to incorporate the desired behaviors, such as using mnemonics on pocket‐sized laminated cards and then practiced desired behaviors using roleplay.

Analysis

The percentage of total points possible on each of the pretest and posttest OSTE scoring templates was compared using a paired Student t test for each of the 14 participants.

Results

All 14 eligible hospitalists voluntarily participated. Their mean year postcompletion of residency training for the faculty was 17 months 14 months; 71% were female. None of the participants experienced previous training in the areas proposed in the study.

Participants assigned high scores to the quality of the workshops, the OSTE experience and their learning from the participating in the faculty development exercise. The differences between pre‐ and post‐OSTE scenario as well as overall scores for the 4 stations were statistically significant (P < .0001). Particular improvements were noted in the correction of incorrect new admission diagnoses (56% pre, 86% post) and orientation (65% pre, 95% post; see Table 2).

Pre‐ and Post‐OSTE t Test Results
OSTE stationPrePostDFt value
  • P < .0001.

  • Abbreviations: OSTE, Observed Structured Teaching Exercises; PE, physical examination.

PE skill/ leadership70%91%129.07*
Feedback71%94%127.40*
Clinical reasoning56%86%1312.40*
Orientation65%95%137.56*
Overall64%90%1317.58*

Discussion

If FCR are to be universally adopted in the academic pediatric inpatient setting, faculty must successfully balance the educational needs of trainees as well as efficiently negotiating a plan of management with patients and families. The ability of faculty to consistently orchestrate rounds so that they meet educational needs of varied levels of learners, while ensuring that patient management is correct and well communicated to families is a very complex task.

We found using OSTEs to frame desired behavior, supplemented with background information to validate the desired behaviors followed by deliberate practice opportunities during the workshops to be an effective faculty development strategy. We not only provided participants with feedback on the group's performance according to the rating scale, but also gave them the opportunity to practice rating each other using the scale so that they could reflect on the elements of their performance that merited a specific rating.

This strategy for training faculty to perform well in the complex environment within the patient's room during FCR is similar in some respects with training military personnel for complex battle situations.15 Desired behaviors are broken down and packaged within a framework to be implemented in a specific context. For example, we combined aspects of the One Minute Preceptor model (OMP)12 with Bordage's Problem Representation model to create a framework of behaviors to promote and correct errors in clinical reasoning.16, 17 Another framework was created and practiced to promote assessment of the physical exam at the bedside. Orientation and feedback, although not frequently used components of actual FCR, are necessary to set expectations and calibrate learner's performance during FCR.

The OSTE is an observed examination that has been validated for evaluating the teaching skills of faculty and residents.18 We planned to use learner‐centered, interactive workshops as the key component of the training intervention with the pre‐ and posttest OSTE as a measure of their effectiveness. However, we found in faculty feedback that the OSTEs were actually a key adjuvant, to the workshop training in that they provided a major source of feedback and learning opportunities in addition to their inherent evaluative qualities.

Each of 4 workshops was designed to teach participants the behaviors assessed in the 4 OSTE stations. The pretest OSTE provided a baseline for participant performance and served to activate the participants to focus on key teaching behaviors during the workshop. During the workshop following the pretest OSTE, participants were given copies of the rating scales and feedback on the performance of the group as a whole on each rated behavior. The evidence used to create the rating instruments was presented and participants had the opportunity to debate and agree on the instrument's construct validity. They then had the opportunity to engage in deliberate practice during role plays depicting challenges to orienting a learner, providing feedback, and to family centered rounds. The posttest OSTE served as summative evaluation of the participants' ability to perform the practiced behaviors effectively in a simulated teaching environment.

We chose to focus the FCR scenarios on correcting mistakes in clinical reasoning for a new patient and on teaching key parts of the physical exam during rounds for an established patient. Errors in clinical reasoning lead to misdirected patient management and are the number 1 cause of medical errors.19, 20 Bedside rounds are a perfect venue for reinforcing and fine‐tuning diagnostic reasoning because all the crucial sources of data are present: the patient, the parent, the nurse, and the computer with lab and imaging results. Faculty members and trainees have both expressed discomfort at correcting errors in clinical reasoning in front of families, leading to missed learning opportunities.21

During the workshop on clinical reasoning, we taught faculty how to use the Problem Representation method to analyze and correct errors in clinical reasoning. The method, studied by Bordage and associates22 forces learners to identify the key features of a presentation and relate their interpretation of the findings by using semantic qualifiers. We trained faculty to deliberately listen for the learner's interpretation of the key features to determine how a misdiagnosis occurred. They were also trained to walk trainees back through their thought process in an objective way, correcting the misinterpretation of data, so that the trainee's competence is not compromised in the eyes of the team or the parents. Teaching the trainee to think correctly about a clinical problem benefits the other members of the team, as well as providing the parents with a better understanding of the rationale for the management plan.

Correct interpretation of the physical exam findings is crucial to making the correct diagnosis. However, there have been several articles chronicling the lost art of eliciting and interpreting physical exam findings, ranging from the cardiac exam to neurological exam.23 A minority of physical exam teaching occurs at the patient's bedside, partly attributed to faculty members discomfort with this type of teaching.24, 25 To enhance the comfort of our faculty members, we included behaviors referenced in articles on teaching a skill,26 activated demonstration,10 and effective bedside teaching27 to guide faculty to incorporate eliciting and interpreting focused aspects of the physical exam during rounds.

Our FCR evaluation templates awarded the highest scores if the hospitalist encouraged senior residents to model clinical reasoning or physical exam skills for junior learners. Hospitalists' presence, especially on work rounds, can diminish the senior resident's opportunity to gain experience and confidence in leading the team.28 We therefore explicitly directed hospitalists to promote the role of the senior resident as the team leader during workshops, while priming faculty to assume the role of educational coach.29

We hypothesize that several factors contributed to the success of the OSTE workshopOSTE intervention. First, faculty members willingly volunteered to participate because they recognized gaps in their own knowledge and skill at leading FCR. They found the ability to deliberately practice the desired behaviors in the OSTE exercises to be the most useful part of the exercise, because the scenarios were authentic and SLs were real trainees.

Although we included all the junior faculty members of our large Pediatric Hospitalist Division in our study, our sample size is still small; limiting our ability to generalize our findings. We found the scheduling of 14 hospitalists to attend 4 different events in close succession to be problematic. Conducting the OSTE sessions at the GW CLASS center 5 miles away from our hospital was also logistically challenging.

We plan to simplify the logistics so that we can incorporate this model in the training of new hospitalists in the division. We still plan to use preintervention FCR OSTEs, but instead of workshops, will provide background information by means of self‐directed Web‐based modules. We will also videotape the OSTEs and provide faculty with a template to rate their own performance and then compare it with ratings from SLs. This individualized feedback and self‐reflection could result in better performance30 than the summary group feedback we gave during the workshops.

Another limitation of this study is the lack of data regarding the consistency of our faculty participants' performance in real FCR. Finally, we did not study the impact of the desired behaviors on patient, trainee, or nursing satisfaction, learning, or efficiency.

Conclusion

In conclusion, we found incorporating OSTEs into a faculty development program to improve FCR to be an effective strategy for changing faculty behavior in leading FCR. Additional study is needed to determine if replacing the workshops with Web‐based tutorials is equally effective and to determine if this faculty development strategy results in long‐term consistent practice in conducting rounds in real inpatient settings.

References
  1. Institute of Medicine of the National Academies.Crossing the Quality Chasm: A New Health System for the 21st Century. March 1,2001.Washington, DC:National Academy of Science.
  2. Committee on Hospital Care.American Academy of Pediatrics. Family‐centered care and the pediatrician's role.Pediatrics.2003;112:691696.
  3. Mittal V,Sigrest T,Roy L, et al.Current trends in practice of family centered rounds: a study from the pediatric research in inpatient settings (PRIS) network.Pediatrics.2010. In press.
  4. Muething SE,Uma RK,Schoettker PJ,Gonzalez del Rey J,DeWitt TG.Family‐centered bedside rounds: A new approach to patient care and teaching.Pediatrics.2007;119:829832.
  5. Mittal V,Lee B,Patel R, et al.Do Family‐Centered Rounds (FCRs) Enhance Resident's Clinical and Educational Experiences and Improve Patient Outcomes? A Qualitative Study. In: Proceedings of the 2010 Pediatric Academic Societies Annual Meeting, May 1–4,2010, Vancouver, BC, Canada.
  6. Stone S,Mazor K,Devaney‐O'Neil S, et al.Development and implementation of an objective structured teaching exercise (OSTE) to evaluate improvement in feedback skills following a faculty development workshop.Teach Learn Med.2003;15:713.
  7. Ende J.Feedback in clinical medical education.JAMA.1983;250:777781.
  8. Ferenchick G,Simpson D,Blackman J,DaRosa D,Dunnington G.Strategies for efficient and effective teaching in the ambulatory care setting.Acad Med.1997;72:277280.
  9. Wilkerson L,Sarkin RT.Arrows in the quiver: evaluation of a workshop on ambulatory teaching.Acad Med.1998;73(Suppl):S67hyphen.
  10. Babitch LA.Teaching practice management skills to pediatric residents.Clin Pediatr.2006;45:846849.
  11. Bowen JL.Educational strategies to promote clinical diagnostic reasoning.N Engl J Med.2006;355:22172225.
  12. Heidenreich C,Lye P,Simpson D,Lourich M.The search for effective and efficient ambulatory teaching methods through the literature.Pediatrics.2000;105:231237.
  13. Irby DM,Aagaard E,Teherani A.Teaching points identified by preceptors observing one‐minute preceptor and traditional preceptor encounters.Acad Med.2004;79:5055.
  14. Blatt B,Greenberg L,Kallenberg G,Confessore G,Confessore S.The Talks Manual: A Guide to Teaching Senior Students in the Health Professions to be Educators.Washington, DC:George Washington University;2000.
  15. Chatman R.20th‐century revolution in military training. In: Ericsson KA, editor.Development of Professional Expertise Toward Measurement of Expert Performance and Design of Optimal Learning Environments.New York:Cambridge University Press;2009:2759.
  16. Bordage G.“Why did I miss the diagnosis? Some cognitive explanations and educational implications.”Acad Med.1999;74:S138S143.
  17. Nendaz MR,Bordage G.Promoting diagnostic problem.Med Educ.2002;36:760766.
  18. Morrison EH,Boker JR,Hollingshead J,Prislin MD,Hitchcock MA,Litzleman DK.Reliability and validity of an objective structured teaching examination for generalist resident teachers.Acad Med.2002;77(suppl):S29.
  19. Schiff GD,Kim S,Abrams R, et al.Diagnosing diagnostic errors: lessons from a multi‐institutional collaborative project.Adv Patient Safety.2005;2:255278.
  20. Newman‐Toker DE,Pronovost PJ.“Diagnostic errors – the next frontier for patient safety.”JAMA.2009;301:10601062.
  21. Janicik RW,Fletcher KE.Teaching at the bedside: a new model.Med Teach.2003;25:127130.
  22. Bordage G.Elaborated knowledge: a key to successful diagnostic thinking.Acad Med.1994;69:883889.
  23. LaCombe MA.On bedside teaching.Ann Intern Med.1997;126:217220.
  24. Gonzalo JD,Masters PA,Simons RJ,Chuang CH.Attending rounds and bedside case presentations: medical student and medicine resident experiences and attitudes.Teach Learn Med.2009;21:105110.
  25. Ramani S,Orlander JD,Strunin L,Barber TW.Whither bedside teaching? A focus‐group study of clinical teachers.Acad Med.2003;78:384390.
  26. Smee S.ABC of learning and teaching in medicine: skill based assessment.BMJ.2003;326:703706.
  27. Ramani S.Twelve tips to improve bedside teaching.Med Teach.2003;25:112115.
  28. Landrigan CP,Muret‐Wagstaff S,Chiang VW, et al.Effect of a pediatric hospitalist system on house staff education and experience.Arch Pediatr Adolesc Med.2002;156:877883.
  29. Orlander JDWipf JE,Lew RA.Development of a tool to assess the team leadership skills of medical residents.Med Educ Online.2006:11;1127.
  30. Gelula MH,Yudkowsky R.Using standardised students in faculty development workshops to improve clinical teaching skills.Med Educ.2003;37:621629.
References
  1. Institute of Medicine of the National Academies.Crossing the Quality Chasm: A New Health System for the 21st Century. March 1,2001.Washington, DC:National Academy of Science.
  2. Committee on Hospital Care.American Academy of Pediatrics. Family‐centered care and the pediatrician's role.Pediatrics.2003;112:691696.
  3. Mittal V,Sigrest T,Roy L, et al.Current trends in practice of family centered rounds: a study from the pediatric research in inpatient settings (PRIS) network.Pediatrics.2010. In press.
  4. Muething SE,Uma RK,Schoettker PJ,Gonzalez del Rey J,DeWitt TG.Family‐centered bedside rounds: A new approach to patient care and teaching.Pediatrics.2007;119:829832.
  5. Mittal V,Lee B,Patel R, et al.Do Family‐Centered Rounds (FCRs) Enhance Resident's Clinical and Educational Experiences and Improve Patient Outcomes? A Qualitative Study. In: Proceedings of the 2010 Pediatric Academic Societies Annual Meeting, May 1–4,2010, Vancouver, BC, Canada.
  6. Stone S,Mazor K,Devaney‐O'Neil S, et al.Development and implementation of an objective structured teaching exercise (OSTE) to evaluate improvement in feedback skills following a faculty development workshop.Teach Learn Med.2003;15:713.
  7. Ende J.Feedback in clinical medical education.JAMA.1983;250:777781.
  8. Ferenchick G,Simpson D,Blackman J,DaRosa D,Dunnington G.Strategies for efficient and effective teaching in the ambulatory care setting.Acad Med.1997;72:277280.
  9. Wilkerson L,Sarkin RT.Arrows in the quiver: evaluation of a workshop on ambulatory teaching.Acad Med.1998;73(Suppl):S67hyphen.
  10. Babitch LA.Teaching practice management skills to pediatric residents.Clin Pediatr.2006;45:846849.
  11. Bowen JL.Educational strategies to promote clinical diagnostic reasoning.N Engl J Med.2006;355:22172225.
  12. Heidenreich C,Lye P,Simpson D,Lourich M.The search for effective and efficient ambulatory teaching methods through the literature.Pediatrics.2000;105:231237.
  13. Irby DM,Aagaard E,Teherani A.Teaching points identified by preceptors observing one‐minute preceptor and traditional preceptor encounters.Acad Med.2004;79:5055.
  14. Blatt B,Greenberg L,Kallenberg G,Confessore G,Confessore S.The Talks Manual: A Guide to Teaching Senior Students in the Health Professions to be Educators.Washington, DC:George Washington University;2000.
  15. Chatman R.20th‐century revolution in military training. In: Ericsson KA, editor.Development of Professional Expertise Toward Measurement of Expert Performance and Design of Optimal Learning Environments.New York:Cambridge University Press;2009:2759.
  16. Bordage G.“Why did I miss the diagnosis? Some cognitive explanations and educational implications.”Acad Med.1999;74:S138S143.
  17. Nendaz MR,Bordage G.Promoting diagnostic problem.Med Educ.2002;36:760766.
  18. Morrison EH,Boker JR,Hollingshead J,Prislin MD,Hitchcock MA,Litzleman DK.Reliability and validity of an objective structured teaching examination for generalist resident teachers.Acad Med.2002;77(suppl):S29.
  19. Schiff GD,Kim S,Abrams R, et al.Diagnosing diagnostic errors: lessons from a multi‐institutional collaborative project.Adv Patient Safety.2005;2:255278.
  20. Newman‐Toker DE,Pronovost PJ.“Diagnostic errors – the next frontier for patient safety.”JAMA.2009;301:10601062.
  21. Janicik RW,Fletcher KE.Teaching at the bedside: a new model.Med Teach.2003;25:127130.
  22. Bordage G.Elaborated knowledge: a key to successful diagnostic thinking.Acad Med.1994;69:883889.
  23. LaCombe MA.On bedside teaching.Ann Intern Med.1997;126:217220.
  24. Gonzalo JD,Masters PA,Simons RJ,Chuang CH.Attending rounds and bedside case presentations: medical student and medicine resident experiences and attitudes.Teach Learn Med.2009;21:105110.
  25. Ramani S,Orlander JD,Strunin L,Barber TW.Whither bedside teaching? A focus‐group study of clinical teachers.Acad Med.2003;78:384390.
  26. Smee S.ABC of learning and teaching in medicine: skill based assessment.BMJ.2003;326:703706.
  27. Ramani S.Twelve tips to improve bedside teaching.Med Teach.2003;25:112115.
  28. Landrigan CP,Muret‐Wagstaff S,Chiang VW, et al.Effect of a pediatric hospitalist system on house staff education and experience.Arch Pediatr Adolesc Med.2002;156:877883.
  29. Orlander JDWipf JE,Lew RA.Development of a tool to assess the team leadership skills of medical residents.Med Educ Online.2006:11;1127.
  30. Gelula MH,Yudkowsky R.Using standardised students in faculty development workshops to improve clinical teaching skills.Med Educ.2003;37:621629.
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Using observed structured teaching exercises (OSTE) to enhance hospitalist teaching during family centered rounds
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Hospitalist System Performance in Taiwan

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Evaluating the performance of a hospitalist system in Taiwan: A pioneer study for nationwide health insurance in Asia

In Taiwan, the national health insurance (NHI) implemented since 19951 has extended its coverage to almost the entire population. It may serve as a model for other countries looking to implement a universal health insurance system.2, 3 However, due to the low copayment for services, there are increasing admission rates and hospitalizations.2, 4 Admission rates, in particular, have nearly tripled for those who have been previously uninsured prior to the NHI program.2 In terms of hospital care, internal medicine and surgery are not favorite areas of specialty in the NHI system because inpatient care has a high workload but relatively low salaries.2, 5, 6 Consequently, there is now a shortage of primary inpatient care staff in Taiwan. The hospitalist system may be a solution to this problem.

The role of a hospitalist system has been discussed since 1996.7 Although its pros and cons are still debatable,8 the hospitalist system has grown in recent decades and there is a wide acceptance that hospitalists can efficiently care for inpatients.4, 9, 10 However, most related studies are in Western countries.4, 6, 11 It has rarely been studied in Asian countries and in those with NHI programs.

This study therefore aimed to investigate whether the hospitalist system, working within the NHI system in Taiwan, can be efficient in saving costs, maintaining quality care, and managing a high volume of in patients.

Materials and Methods

This prospective observational study was conducted in the National Taiwan University Hospital (NTUH), a tertiary‐care referral center in northern Taiwan, and approved by the hospital's Institutional Review Board. The program was also registered on Clinicaltrial.gov (identifier NCT00997646). A 36‐bed hospitalist‐run ward (HW) was set up in October 2009 in NTUH. For performance comparison, two 36‐bed internist‐run wards (IWs) were selected. The three wards were geographically separated.

Study Subjects

All patients age >18 years from the emergency department (ED) were admitted into one of the three wards based on the diagnosis category determined by the ED physicians. A patient was admitted by bed managers who were blinded to the study. Cases were categorized as diseases of general medicine, such as congestive heart failure, pneumonia, exacerbation of chronic obstructive pulmonary disease, cellulitis, ischemic stroke, urinary tract infection, and gastrointestinal bleeding.

Patients with severe illnesses requiring admission to intensive care units were excluded. Research assistants who were blinded to the patient stratification performed the patients' identification and data collection. Patient care was determined by the respective medical teams without any interference from this study.

Care‐Team Structure

The HW was set up with 3 attending physicians certified by a board of internal medicine and 6 nurse practitioners. All staff members worked full‐time to provide primary inpatient care. For comparison (Table 1), each IW had a set‐up of 3 attending physicians licensed by a board of internal medicine, one chief resident, 3 junior residents, and 3 interns. The attending physicians of the IWs visited their inpatients every workday and delegated primary care to residents on night shifts and weekends.

Comparison of Care‐Team Setting Between the Hospitalist‐Run Ward and Internist‐Run Ward in a Medical Center
 Hospitalist‐Run WardInternist‐Run Wards
  • Abbreviations: AP, attending physician; CR, chief resident; JR, junior resident; NP, nurse practitioner.

Team member, per ward3 AP, 6 NP3 AP, 1 CR, 3 JR, 3 intern
Beds, per ward3636
Inpatient care of APFull timeOnce daily
Who prescribes care order?APAP, CR, JR
Who executes order?NPJR, intern
AP dutyInpatient care; researchInpatient/outpatient care; work of subspecialty; research
Bed managerNP/APCR

Clinical Characteristics

The patients' clinical characteristics, laboratory data, hospital course, and outcomes were recorded. The clinical characteristics included age, gender, underlying comorbidities, activities of daily living, and admission diagnosis. Charlson scores and Barthel's scores represented underlying comorbidities and activities of daily living, respectively. These were calculated as described in previous studies.11, 12 Admission costs paid for by the Taiwan NHI was defined as an inpatient's expenditure paid to the hospital by the institute of NHI. Total admission cost included expenses paid for by NHI and the patient's out‐of‐pocket expenditure not covered by NHI. A primary care physician was defined if the patient had visited the same doctor's clinic three times or more within one year prior to admission.8 Patients were followed‐up for 30 days after discharge by telephone, or until readmission.

Propensity Score Methods

Propensity score‐matching was used to balance observed covariates between the 2 care groups. It was defined as the conditional probability for being admitted to the HW, as a binary dependent variable, under a set of measurements. Factors that were significantly different (P < 0.05) between the 2 groups in univariate analysis were included in a multivariable logistic regression model to predict HW admission. The predicted probability derived from the logistic equation was used as the propensity score for each individual.

Patients in the HW and IWs were pooled and sorted according to their propensity score in ascending order. The selection process began from the first two cases with the lowest propensity score. If one was admitted to the HW and the other to an IW, both were selected as a matched pair. If this was not the case, then four cases were included. If there were two HW patients and two IW cases, the four were selected as two matched pairs. In the same way, HW and IW cases were matched by their propensity score in 1:1, 2:2, or 3:3 blocks. A patient who did not have a suitable match within the acceptable rank range was excluded from further analysis. The matching process moved down the sort list until all possible matched pairs were included and the selected patients formed a matched 1:1 pair in both groups.

Statistical Analysis

Intergroup differences were compared using independent t test for numerical variables and chi‐square test for categorical variables. Curves of probability of staying in the hospital within 30 days were generated using the Kaplan‐Meier method and compared using the log‐rank test. A logistic regression model was used for the propensity score match using the SPSS software version 13.0 (SPSS, Chicago, IL). The probability that indicated patient admission to the HW in both groups was used to draw box‐plots. After the 1:1 matched groups were assembled, the clinical characteristics were compared accordingly.

Results

From November 2009 to January 2010, 810 patients admitted from the ED to the study wards were enrolled. Among them, 377 were admitted to the HW and 433 to the IWs. Analysis of admission days showed that 84 (22%) and 53 (12%) patients were admitted to the HW and IWs, respectively, on weekends (P < 0.001).

Compared to the IW patients, the HW patients were older (age >65 years) and had poorer functional status by Barthel's scores (Table 2). Admission diagnosis was similar in both groups, except for pneumonia and urinary tract infection, which were higher in the HW patients. There was a primary care physician in 242 (64%) HW and 282 (65%) IW patients (P = 0.781).

The Charlson score, representing underlying comorbidity, was higher in the HW group (P = 0.002). Moreover, patients with severe liver cirrhosis (Child‐Pugh class C) were more frequently admitted to the HW (P = 0.018). Underlying malignancy, severe chronic kidney disease (estimated creatinine clearance <30 mL/min), and chronic respiratory failure requiring mechanical ventilator support were more associated with HW admission, although not statistically significantly (P = 0.064, 0.072, and 0.104, respectively).

The average admission cost was lower in HW patients than in IW patients, whether paid for by NHI ($1640.2 vs $2933.8 per patient, P = 0.001) or by the total admission cost ($2223.4 vs $3700.8 per patient, P = 0.001) (Table 3). Similarly, there was a shorter average length of stay (LOS) in the HW patients (9.3 vs 13.1 days, P < 0.001), who were discharged earlier than IW patients (Figure 1A). Regarding cost per patient‐day, the total daily cost was similar between the two groups (P = 0.560).

Clinical Characteristics and Laboratory Data of Hospitalized Patients by Their Admission Wards
 Hospitalist‐Run Ward (n = 377)Internist‐Run Wards (n = 433)P‐Value
  • NOTE: Data are no. (%) or mean standard deviation unless otherwise indicated.

  • Abbreviations: COPD, chronic obstructive pulmonary disease.

  • Other diagnoses included pleural effusion, hemoptysis, arrhythmia, hepatitis, obstructive jaundice, lower gastrointestinal bleeding, severe diarrhea, ileus, and renal failure.

Age >65 years old237 (63)240 (55)0.032
Gender, male210 (56)243 (56)0.905
Barthel's score61 3570 33<0.001
Charlson score3.7 3.43.0 3.20.002
Admission diagnosis   
Pneumonia106 (28)88 (20)0.010
Exacerbation of COPD18 (5)15 (3)0.347
Congestive heart failure12 (3)19 (4)0.373
Upper gastrointestinal bleeding55 (15)58 (13)0.625
Intra‐abdominal infection36 (10)47 (11)0.541
Urinary tract infection85 (23)69 (16)0.017
Cellulitis20 (5)18 (4)0.441
Ischemic stroke12 (3)21 (5)0.231
Others*117 (31)164 (38)0.041
Laboratory data in the initial admission
Leukocyte count, /L11372 796210377 64220.050
Hemoglobin, g/dL12.7 12.812.3 8.60.714
Platelet count, K/L219 124205 1080.102
Blood urea nitrogen, mg/dL33.2 27.724.1 17.4<0.001
Creatinine, mg/dL1.9 2.91.6 2.80.080
Total bilirubin, mg/dL2.2 3.72.3 3.60.826
C‐reactive protein, mg/dL8.0 7.76.0 6.40.008
Figure 1
Probability of hospitalization plotted by the Kaplan‐Meier method and compared by log‐rank test according to (A) the total number of patients in the internist‐run wards (IW) and hospitalist‐run ward (HW); and (B) matched‐patients in the IW (IW‐M) and HW (HW‐M).

More patients in the HW group signed the do‐not‐resuscitate (DNR) consent (P < 0.001) and died during the hospital course, although the difference was not statistically significant (P = 0.068). Among those who expired during hospitalization, DNR consent was signed by 42 (90%) HW and 27 (68%) IW patients (P = 0.014). Among those discharged, 57 (17.2%) HW and 70 (17.6%) IW patients were lost to follow‐up. There was no difference in the 30‐day readmission for any cause between the two groups (P = 0.992).

Due to baseline differences, propensity score‐matching was performed and 101 pairs of patients were selected according to the probability generated from factors significantly different in univariate analysis (ie, age >65 years, pneumonia or urinary tract infections, Charlson score, Barthel's score, and blood urea nitrogen and C‐reactive protein levels on initial admission). The clinical characteristics of the 202 patients were shown in Table 4.

Course and Outcome of the Hospitalized Patients by Their Admission Wards
 Hospitalist‐Run Ward (n = 377)Internist‐Run Wards (n = 433)P‐Value
  • NOTE: Data are no. (%) or mean standard deviation unless otherwise indicated. All costs were at exchange rates of $1.00 = 31.90 yuan (Taiwan dollar) as of July 1, 2010.

  • Abbreviations: NHI, national health insurance (Taiwan).

  • The denominator of the percentage is the number of patients discharged alive.

  • P‐value by the log‐rank test.

Length of hospital stay, days9.3 6.713.1 12.4<0.001
Total admission cost: $ per patient2223.4 3428.23700.8 8010.70.001
Admission cost paid by NHI: $ per patient1640.2 2403.32933.8 7460.70.001
In‐hospital mortality47 (12)37 (9)0.068
Do‐not‐resuscitate consent74 (20)34 (8)<0.001
30‐Day readmission*71 (22)83 (21)0.922

0

Clinical Characteristics, Hospital Course, and Outcome of Propensity Score‐Matched Patients by Their Admission Wards
 Hospitalist‐Run Ward (n = 101)Internist‐Run Wards (n = 101)P‐Value
  • NOTE: Data are no. (%) or mean standard deviation unless otherwise indicated. All costs are at exchange rates of $1.00 = 31.90 yuan (Taiwan dollar) as of July 1, 2010.

  • Abbreviations: COPD, chronic obstructive pulmonary disease; NHI, national health insurance (Taiwan).

  • Other diagnoses included pleural effusion, hemoptysis, arrhythmia, hepatitis, obstruction jaundice, lower gastrointestinal bleeding, severe diarrhea, ileus, and renal failure.

  • The denominator of the percentage is the number of patients discharged alive.

  • P‐value by the log‐rank test.

Age >65 years old59 (58)59 (58)1.000
Gender, Male55 (54)50 (50)0.481
Barthel's score66 3465 350.897
Charlson score3.2 3.23.6 3.50.437
Admission diagnosis   
Pneumonia31 (31)27 (27)0.534
Exacerbation of COPD4 (4)5 (5)0.733
Congestive heart failure2 (2)2 (2)1.000
Gastrointestinal bleeding10 (10)8 (8)0.621
Intra‐abdominal infection18 (18)10 (10)0.103
Urinary tract infection22 (22)21 (21)0.864
Cellulitis6 (6)5 (5)0.757
Ischemic stroke2 (2)00.155
Others*39 (39)30 (30)0.182
Laboratory data in the initial admission   
Leukocyte count, /L12487 628811430 77180.287
Hemoglobin, g/dL12.8 13.712.5 7.50.803
Platelet count, K/L212 102207 1030.710
Blood urea nitrogen, mg/dL25.5 19.724.7 17.50.773
Creatinine, mg/dL1.5 1.21.6 1.50.979
Total bilirubin, mg/dL2.0 7.02.0 6.90.963
C‐reactive protein, mg/dL6.9 7.77.0 6.40.859
Length of hospital stay, days9.2 6.415.2 13.8<0.001
Do‐not‐resuscitate consent18 (18)6 (6)0.009
Total admission cost: $ per patient2019.4 1709.35608.9 14244.80.013
Cost paid by NHI: $ per patient1463.4 1404.64665.8 13553.30.019
In‐hospital mortality9 (9)7 (7)0.602
30‐Day postdischarge readmission17 (18)21 (22)0.492

Both groups had almost the same propensity scores (P = 0.970; see online Supporting Information). Patients in the HW group had significantly lower admission cost, shorter LOS (Figure 1B), and more DNR consent, but similar in‐hospital mortality and readmission rates (Table 4).

DISCUSSION

The hospitalist system, which has been practiced for years in the United States, has not really been reported in Asia.13 Under the universal NHI system, this system has been studied in terms of treating patients in a Taiwan referral center. This study is the first to report on a hospitalist system in an Asian country with an NHI program. The hospitalist system in this study demonstrates efficient performance even though the patients have multiple comorbidities, compared to those in the general medical wards. By propensity score‐matching, admission costs of the hospitalist‐run ward are significantly lower than those of the internist‐run wards despite similar mortality and readmission rates.

The average LOS is reduced by 29% in HW patients and this plays a major role in cost reduction.14, 15 The reason may be the hospitalist's full‐time care, which allows for prompt decision‐making and close interaction with the patients' families.16 These families thus understand the treatment planning and prognosis. Furthermore, the hospitalist system continues working on weekends. As a result, patients are discharged without delay, even on holidays.

The aim of reducing LOS and costs is important because hospital income will decrease under the payment by disease‐related group (DRG) being implemented by the NHI system.17 A shortage of inpatient physicians may also develop due to the high workload but relatively low remuneration.2, 18 In contrast, a hospitalist care system that integrates nurse practitioners demands less human resources and saves on costs. In the future, it may be one of the solutions for hospitals aiming to maintain financial balance.

Another important issue in the NHI coverage is the increasing number of patients in the ED, which seems to be overflowing.19 In a previous Taiwan report, there are 7.1 patients per day who are staying in the ED for more than 72 hours, despite indications for admission.20 The delay is possibly due to the lack of available beds in the inpatient department.21 Amidst increasing demands for admission under the NHI and an aging society,2, 20 experience suggests that a hospitalist care system is a promising alternative to address the high ED patient volumes, especially on holidays. Howell et al. have also demonstrated that hospitalist‐driven bed management enhances the bed utility rate.21, 22 Since the current study also shows reduced LOS in the HW, patients will have a faster turn‐over rate and thereby assist in alleviating ED overcrowding.

Although the LOS of the patients here is comparable to that reported by the Taiwan NHI,2 it is far longer than that reported in the United States (around 4.75.2 days).4, 23 One possible explanation is the social and cultural determinants, including hospital‐ or physician‐dependence.24 In literature from Japan and Taiwan, hospitalization is as long as 13 weeks.25, 26 In addition, the average admission cost is reportedly around $1540 per patient‐day in the US, around 6 times that in this study ($266.6 per patient‐day).4 In the aging society of Taiwan,27 the NHI‐required copayment for admission may be relatively low, such that patients (or their families) may be misled that hospital care is better and hesitate to be discharged.2830

Regarding quality of care and patient safety, the in‐hospital mortality and the 30‐day readmission rates are similar in both groups, although disease severity and underlying comorbidities are worse in the HW at the start. This is consistent with previous reports that hospitalists can manage inpatient as well as internist care systems.4, 23 However, because this study has been performed in a tertiary referral center, patients may be more severely ill, such that the inpatient mortality and 30‐day readmission rates are as high as 10.3% and 21.11%, respectively.31, 32 Nonetheless, generalizing the hospitalist system to regional or district hospitals remains a concern, and this warrants further study.

This study has two other limitations. First, it is an observational study and patients have different demographics even though propensity score‐matching has been performed. Second, the patients were hospitalized without a standardized treatment protocol.

In conclusion, under the NHI system in Taiwan, a hospitalist system can have higher efficiency in shortening LOS and reducing cost than an internist care system, and still have similar hospital mortality and readmission rates. A hospitalist system may address the issue of high patient volume by increasing ward utilization. It can be recommended in a country with NHI that has a shortage of inpatient care staff.

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References
  1. Peabody JW,Yu JC,Wang YR,Bickel SR.Health system reform in the Republic of China. Formulating policy in a market‐based health system.JAMA.1995;273:777781.
  2. Wen CP,Tsai SP,Chung WS.A 10‐year experience with universal health insurance in Taiwan: measuring changes in health and health disparity.Ann Intern Med.2008;148:258267.
  3. Davis K,Huang AT.Learning from Taiwan: experience with universal health insurance.Ann Intern Med.2008;148:313314.
  4. Lindenauer PK,Rothberg MB,Pekow PS,Kenwood C,Benjamin EM,Auerbach AD.Outcomes of care by hospitalists, general internists, and family physicians.N Engl J Med.2007;357:25892600.
  5. Cheng SH,Chiang TL.The effect of universal health insurance on health care utilization in Taiwan. Results from a natural experiment.JAMA.1997;278:8993.
  6. Tokuda Y,Hayano K,Ozaki M,Bito S,Yanai H,Koizumi S.The interrelationships between working conditions, job satisfaction, burnout and mental health among hospital physicians in Japan: a path analysis.Ind Health.2009;47:166172.
  7. Wachter RM,Goldman L.The emerging role of “hospitalists” in the American health care system.N Engl J Med.1996;335:514517.
  8. Sharma G,Fletcher KE,Zhang D,Kuo YF,Freeman JL,Goodwin JS.Continuity of outpatient and inpatient care by primary care physicians for hospitalized older adults.JAMA.2009;301:16711680.
  9. Lopez L,Hicks LS,Cohen AP,McKean S,Weissman JS.Hospitalists and the quality of care in hospitals.Arch Intern Med.2009;169:13891394.
  10. Kuo YF,Sharma G,Freeman JL,Goodwin JS.Growth in the care of older patients by hospitalists in the United States.N Engl J Med.2009;360:11021112.
  11. Wang HY,Chew G,Kung CT,Chung KJ,Lee WH.The use of Charlson comorbidity index for patients revisiting the emergency department within 72 hours.Chang Gung Med J.2007;30:437444.
  12. Sainsbury A,Seebass G,Bansal A,Young JB.Reliability of the Barthel Index when used with older people.Age Ageing.2005;34:228232.
  13. Lee KH.The hospitalist movement—a complex adaptive response to fragmentation of care in hospitals.Ann Acad Med Singapore.2008;37:145150.
  14. Dynan L,Stein R,David G,Kenny LC,Eckman M,Short AD.Determinants of hospitalist efficiency: a qualitative and quantitative study.Med Care Res Rev.2009;66:682702.
  15. Mitchell DM.The critical role of hospitalists in controlling healthcare costs.J Hosp Med.2010;5:127132.
  16. Wachter RM.The relationship between hospitalists and primary care physicians.Ann Intern Med.2010;152:474.
  17. Lungen M,Dredge B,Rose A,Roebuck C,Plamper E,Lauterbach K.Using diagnosis‐related groups. The situation in the United Kingdom National Health Service and in Germany.Eur J Health Econ.2004;5:287289.
  18. Chang PY,Hung CY,Wang KI,Huang YH,Chang KJ.Factors influencing medical students' choice of specialty.J Formos Med Assoc.2006;105:489496.
  19. Huang JA,Tsai WC,Chen YC,Hu WH,Yang DY.Factors associated with frequent use of emergency services in a medical center.J Formos Med Assoc.2003;102:222228.
  20. Shih FY,Ma MH,Chen SC, et al.ED overcrowding in Taiwan: facts and strategies.Am J Emerg Med.1999;17:198202.
  21. Howell E,Bessman E,Kravet S,Kolodner K,Marshall R,Wright S.Active bed management by hospitalists and emergency department throughput.Ann Intern Med.2008;149:804811.
  22. Howell E,Bessman E,Marshall R,Wright S.Hospitalist bed management effecting throughput from the emergency department to the intensive care unit.J Crit Care.2010;25:184189.
  23. Halasyamani LK,Valenstein PN,Friedlander MP,Cowen ME.A comparison of two hospitalist models with traditional care in a community teaching hospital.Am J Med.2005;118:536543.
  24. Ma KZ,Norton EC,Tsai EM,Lee SY.Factors associated with tocolytic hospitalizations in Taiwan: evidence from a population‐based and longitudinal study from 1997 to 2004.BMC Pregnancy Childbirth.2009;9:59.
  25. Shen YH,Hwang KP,Niu CK.Complicated parapneumonic effusion and empyema in children.J Microbiol Immunol Infect.2006;39:483488.
  26. Mikami K,Suzuki M,Kitagawa H, et al.Efficacy of corticosteroids in the treatment of community‐acquired pneumonia requiring hospitalization.Lung.2007;185:249255.
  27. Ling Wang Y‐ML,Fan S‐C,Chao W‐T.Analysis of population projections for Taiwan area: 2008 to 2056.Taiwan Economic Forum.2009;7:3669.
  28. Hu WY,Chiu TY,Cheng YR,Chuang RB,Chen CY.Why Taiwanese hospice patients want to stay in hospital: health‐care professionals' beliefs and solutions.Support Care Cancer.2004;12:285292.
  29. Wu CY,Lai HJ,Chen RC.Patient characteristics predict occurrence and outcome of complaints against physicians: a study from a medical center in central Taiwan.J Formos Med Assoc.2009;108:126134.
  30. Lin YH,Chen WY.The demand for healthcare under Taiwan's national health insurance: a count data model approach.Expert Rev Pharmacoecon Outcomes Res.2009;9:1322.
  31. Yang CM,Reinke W.Feasibility and validity of International Classification of Diseases based case mix indices.BMC Health Serv Res.2006;6:125.
  32. Shu CC,Lee LN,Wang JT,Chien YJ,Wang JY,Yu CJ.Non‐tuberculous mycobacterial pleurisy: an 8‐year single‐centre experience in Taiwan.Int J Tuberc Lung Dis.2010;14:635641, p 634 follows 641.
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In Taiwan, the national health insurance (NHI) implemented since 19951 has extended its coverage to almost the entire population. It may serve as a model for other countries looking to implement a universal health insurance system.2, 3 However, due to the low copayment for services, there are increasing admission rates and hospitalizations.2, 4 Admission rates, in particular, have nearly tripled for those who have been previously uninsured prior to the NHI program.2 In terms of hospital care, internal medicine and surgery are not favorite areas of specialty in the NHI system because inpatient care has a high workload but relatively low salaries.2, 5, 6 Consequently, there is now a shortage of primary inpatient care staff in Taiwan. The hospitalist system may be a solution to this problem.

The role of a hospitalist system has been discussed since 1996.7 Although its pros and cons are still debatable,8 the hospitalist system has grown in recent decades and there is a wide acceptance that hospitalists can efficiently care for inpatients.4, 9, 10 However, most related studies are in Western countries.4, 6, 11 It has rarely been studied in Asian countries and in those with NHI programs.

This study therefore aimed to investigate whether the hospitalist system, working within the NHI system in Taiwan, can be efficient in saving costs, maintaining quality care, and managing a high volume of in patients.

Materials and Methods

This prospective observational study was conducted in the National Taiwan University Hospital (NTUH), a tertiary‐care referral center in northern Taiwan, and approved by the hospital's Institutional Review Board. The program was also registered on Clinicaltrial.gov (identifier NCT00997646). A 36‐bed hospitalist‐run ward (HW) was set up in October 2009 in NTUH. For performance comparison, two 36‐bed internist‐run wards (IWs) were selected. The three wards were geographically separated.

Study Subjects

All patients age >18 years from the emergency department (ED) were admitted into one of the three wards based on the diagnosis category determined by the ED physicians. A patient was admitted by bed managers who were blinded to the study. Cases were categorized as diseases of general medicine, such as congestive heart failure, pneumonia, exacerbation of chronic obstructive pulmonary disease, cellulitis, ischemic stroke, urinary tract infection, and gastrointestinal bleeding.

Patients with severe illnesses requiring admission to intensive care units were excluded. Research assistants who were blinded to the patient stratification performed the patients' identification and data collection. Patient care was determined by the respective medical teams without any interference from this study.

Care‐Team Structure

The HW was set up with 3 attending physicians certified by a board of internal medicine and 6 nurse practitioners. All staff members worked full‐time to provide primary inpatient care. For comparison (Table 1), each IW had a set‐up of 3 attending physicians licensed by a board of internal medicine, one chief resident, 3 junior residents, and 3 interns. The attending physicians of the IWs visited their inpatients every workday and delegated primary care to residents on night shifts and weekends.

Comparison of Care‐Team Setting Between the Hospitalist‐Run Ward and Internist‐Run Ward in a Medical Center
 Hospitalist‐Run WardInternist‐Run Wards
  • Abbreviations: AP, attending physician; CR, chief resident; JR, junior resident; NP, nurse practitioner.

Team member, per ward3 AP, 6 NP3 AP, 1 CR, 3 JR, 3 intern
Beds, per ward3636
Inpatient care of APFull timeOnce daily
Who prescribes care order?APAP, CR, JR
Who executes order?NPJR, intern
AP dutyInpatient care; researchInpatient/outpatient care; work of subspecialty; research
Bed managerNP/APCR

Clinical Characteristics

The patients' clinical characteristics, laboratory data, hospital course, and outcomes were recorded. The clinical characteristics included age, gender, underlying comorbidities, activities of daily living, and admission diagnosis. Charlson scores and Barthel's scores represented underlying comorbidities and activities of daily living, respectively. These were calculated as described in previous studies.11, 12 Admission costs paid for by the Taiwan NHI was defined as an inpatient's expenditure paid to the hospital by the institute of NHI. Total admission cost included expenses paid for by NHI and the patient's out‐of‐pocket expenditure not covered by NHI. A primary care physician was defined if the patient had visited the same doctor's clinic three times or more within one year prior to admission.8 Patients were followed‐up for 30 days after discharge by telephone, or until readmission.

Propensity Score Methods

Propensity score‐matching was used to balance observed covariates between the 2 care groups. It was defined as the conditional probability for being admitted to the HW, as a binary dependent variable, under a set of measurements. Factors that were significantly different (P < 0.05) between the 2 groups in univariate analysis were included in a multivariable logistic regression model to predict HW admission. The predicted probability derived from the logistic equation was used as the propensity score for each individual.

Patients in the HW and IWs were pooled and sorted according to their propensity score in ascending order. The selection process began from the first two cases with the lowest propensity score. If one was admitted to the HW and the other to an IW, both were selected as a matched pair. If this was not the case, then four cases were included. If there were two HW patients and two IW cases, the four were selected as two matched pairs. In the same way, HW and IW cases were matched by their propensity score in 1:1, 2:2, or 3:3 blocks. A patient who did not have a suitable match within the acceptable rank range was excluded from further analysis. The matching process moved down the sort list until all possible matched pairs were included and the selected patients formed a matched 1:1 pair in both groups.

Statistical Analysis

Intergroup differences were compared using independent t test for numerical variables and chi‐square test for categorical variables. Curves of probability of staying in the hospital within 30 days were generated using the Kaplan‐Meier method and compared using the log‐rank test. A logistic regression model was used for the propensity score match using the SPSS software version 13.0 (SPSS, Chicago, IL). The probability that indicated patient admission to the HW in both groups was used to draw box‐plots. After the 1:1 matched groups were assembled, the clinical characteristics were compared accordingly.

Results

From November 2009 to January 2010, 810 patients admitted from the ED to the study wards were enrolled. Among them, 377 were admitted to the HW and 433 to the IWs. Analysis of admission days showed that 84 (22%) and 53 (12%) patients were admitted to the HW and IWs, respectively, on weekends (P < 0.001).

Compared to the IW patients, the HW patients were older (age >65 years) and had poorer functional status by Barthel's scores (Table 2). Admission diagnosis was similar in both groups, except for pneumonia and urinary tract infection, which were higher in the HW patients. There was a primary care physician in 242 (64%) HW and 282 (65%) IW patients (P = 0.781).

The Charlson score, representing underlying comorbidity, was higher in the HW group (P = 0.002). Moreover, patients with severe liver cirrhosis (Child‐Pugh class C) were more frequently admitted to the HW (P = 0.018). Underlying malignancy, severe chronic kidney disease (estimated creatinine clearance <30 mL/min), and chronic respiratory failure requiring mechanical ventilator support were more associated with HW admission, although not statistically significantly (P = 0.064, 0.072, and 0.104, respectively).

The average admission cost was lower in HW patients than in IW patients, whether paid for by NHI ($1640.2 vs $2933.8 per patient, P = 0.001) or by the total admission cost ($2223.4 vs $3700.8 per patient, P = 0.001) (Table 3). Similarly, there was a shorter average length of stay (LOS) in the HW patients (9.3 vs 13.1 days, P < 0.001), who were discharged earlier than IW patients (Figure 1A). Regarding cost per patient‐day, the total daily cost was similar between the two groups (P = 0.560).

Clinical Characteristics and Laboratory Data of Hospitalized Patients by Their Admission Wards
 Hospitalist‐Run Ward (n = 377)Internist‐Run Wards (n = 433)P‐Value
  • NOTE: Data are no. (%) or mean standard deviation unless otherwise indicated.

  • Abbreviations: COPD, chronic obstructive pulmonary disease.

  • Other diagnoses included pleural effusion, hemoptysis, arrhythmia, hepatitis, obstructive jaundice, lower gastrointestinal bleeding, severe diarrhea, ileus, and renal failure.

Age >65 years old237 (63)240 (55)0.032
Gender, male210 (56)243 (56)0.905
Barthel's score61 3570 33<0.001
Charlson score3.7 3.43.0 3.20.002
Admission diagnosis   
Pneumonia106 (28)88 (20)0.010
Exacerbation of COPD18 (5)15 (3)0.347
Congestive heart failure12 (3)19 (4)0.373
Upper gastrointestinal bleeding55 (15)58 (13)0.625
Intra‐abdominal infection36 (10)47 (11)0.541
Urinary tract infection85 (23)69 (16)0.017
Cellulitis20 (5)18 (4)0.441
Ischemic stroke12 (3)21 (5)0.231
Others*117 (31)164 (38)0.041
Laboratory data in the initial admission
Leukocyte count, /L11372 796210377 64220.050
Hemoglobin, g/dL12.7 12.812.3 8.60.714
Platelet count, K/L219 124205 1080.102
Blood urea nitrogen, mg/dL33.2 27.724.1 17.4<0.001
Creatinine, mg/dL1.9 2.91.6 2.80.080
Total bilirubin, mg/dL2.2 3.72.3 3.60.826
C‐reactive protein, mg/dL8.0 7.76.0 6.40.008
Figure 1
Probability of hospitalization plotted by the Kaplan‐Meier method and compared by log‐rank test according to (A) the total number of patients in the internist‐run wards (IW) and hospitalist‐run ward (HW); and (B) matched‐patients in the IW (IW‐M) and HW (HW‐M).

More patients in the HW group signed the do‐not‐resuscitate (DNR) consent (P < 0.001) and died during the hospital course, although the difference was not statistically significant (P = 0.068). Among those who expired during hospitalization, DNR consent was signed by 42 (90%) HW and 27 (68%) IW patients (P = 0.014). Among those discharged, 57 (17.2%) HW and 70 (17.6%) IW patients were lost to follow‐up. There was no difference in the 30‐day readmission for any cause between the two groups (P = 0.992).

Due to baseline differences, propensity score‐matching was performed and 101 pairs of patients were selected according to the probability generated from factors significantly different in univariate analysis (ie, age >65 years, pneumonia or urinary tract infections, Charlson score, Barthel's score, and blood urea nitrogen and C‐reactive protein levels on initial admission). The clinical characteristics of the 202 patients were shown in Table 4.

Course and Outcome of the Hospitalized Patients by Their Admission Wards
 Hospitalist‐Run Ward (n = 377)Internist‐Run Wards (n = 433)P‐Value
  • NOTE: Data are no. (%) or mean standard deviation unless otherwise indicated. All costs were at exchange rates of $1.00 = 31.90 yuan (Taiwan dollar) as of July 1, 2010.

  • Abbreviations: NHI, national health insurance (Taiwan).

  • The denominator of the percentage is the number of patients discharged alive.

  • P‐value by the log‐rank test.

Length of hospital stay, days9.3 6.713.1 12.4<0.001
Total admission cost: $ per patient2223.4 3428.23700.8 8010.70.001
Admission cost paid by NHI: $ per patient1640.2 2403.32933.8 7460.70.001
In‐hospital mortality47 (12)37 (9)0.068
Do‐not‐resuscitate consent74 (20)34 (8)<0.001
30‐Day readmission*71 (22)83 (21)0.922

0

Clinical Characteristics, Hospital Course, and Outcome of Propensity Score‐Matched Patients by Their Admission Wards
 Hospitalist‐Run Ward (n = 101)Internist‐Run Wards (n = 101)P‐Value
  • NOTE: Data are no. (%) or mean standard deviation unless otherwise indicated. All costs are at exchange rates of $1.00 = 31.90 yuan (Taiwan dollar) as of July 1, 2010.

  • Abbreviations: COPD, chronic obstructive pulmonary disease; NHI, national health insurance (Taiwan).

  • Other diagnoses included pleural effusion, hemoptysis, arrhythmia, hepatitis, obstruction jaundice, lower gastrointestinal bleeding, severe diarrhea, ileus, and renal failure.

  • The denominator of the percentage is the number of patients discharged alive.

  • P‐value by the log‐rank test.

Age >65 years old59 (58)59 (58)1.000
Gender, Male55 (54)50 (50)0.481
Barthel's score66 3465 350.897
Charlson score3.2 3.23.6 3.50.437
Admission diagnosis   
Pneumonia31 (31)27 (27)0.534
Exacerbation of COPD4 (4)5 (5)0.733
Congestive heart failure2 (2)2 (2)1.000
Gastrointestinal bleeding10 (10)8 (8)0.621
Intra‐abdominal infection18 (18)10 (10)0.103
Urinary tract infection22 (22)21 (21)0.864
Cellulitis6 (6)5 (5)0.757
Ischemic stroke2 (2)00.155
Others*39 (39)30 (30)0.182
Laboratory data in the initial admission   
Leukocyte count, /L12487 628811430 77180.287
Hemoglobin, g/dL12.8 13.712.5 7.50.803
Platelet count, K/L212 102207 1030.710
Blood urea nitrogen, mg/dL25.5 19.724.7 17.50.773
Creatinine, mg/dL1.5 1.21.6 1.50.979
Total bilirubin, mg/dL2.0 7.02.0 6.90.963
C‐reactive protein, mg/dL6.9 7.77.0 6.40.859
Length of hospital stay, days9.2 6.415.2 13.8<0.001
Do‐not‐resuscitate consent18 (18)6 (6)0.009
Total admission cost: $ per patient2019.4 1709.35608.9 14244.80.013
Cost paid by NHI: $ per patient1463.4 1404.64665.8 13553.30.019
In‐hospital mortality9 (9)7 (7)0.602
30‐Day postdischarge readmission17 (18)21 (22)0.492

Both groups had almost the same propensity scores (P = 0.970; see online Supporting Information). Patients in the HW group had significantly lower admission cost, shorter LOS (Figure 1B), and more DNR consent, but similar in‐hospital mortality and readmission rates (Table 4).

DISCUSSION

The hospitalist system, which has been practiced for years in the United States, has not really been reported in Asia.13 Under the universal NHI system, this system has been studied in terms of treating patients in a Taiwan referral center. This study is the first to report on a hospitalist system in an Asian country with an NHI program. The hospitalist system in this study demonstrates efficient performance even though the patients have multiple comorbidities, compared to those in the general medical wards. By propensity score‐matching, admission costs of the hospitalist‐run ward are significantly lower than those of the internist‐run wards despite similar mortality and readmission rates.

The average LOS is reduced by 29% in HW patients and this plays a major role in cost reduction.14, 15 The reason may be the hospitalist's full‐time care, which allows for prompt decision‐making and close interaction with the patients' families.16 These families thus understand the treatment planning and prognosis. Furthermore, the hospitalist system continues working on weekends. As a result, patients are discharged without delay, even on holidays.

The aim of reducing LOS and costs is important because hospital income will decrease under the payment by disease‐related group (DRG) being implemented by the NHI system.17 A shortage of inpatient physicians may also develop due to the high workload but relatively low remuneration.2, 18 In contrast, a hospitalist care system that integrates nurse practitioners demands less human resources and saves on costs. In the future, it may be one of the solutions for hospitals aiming to maintain financial balance.

Another important issue in the NHI coverage is the increasing number of patients in the ED, which seems to be overflowing.19 In a previous Taiwan report, there are 7.1 patients per day who are staying in the ED for more than 72 hours, despite indications for admission.20 The delay is possibly due to the lack of available beds in the inpatient department.21 Amidst increasing demands for admission under the NHI and an aging society,2, 20 experience suggests that a hospitalist care system is a promising alternative to address the high ED patient volumes, especially on holidays. Howell et al. have also demonstrated that hospitalist‐driven bed management enhances the bed utility rate.21, 22 Since the current study also shows reduced LOS in the HW, patients will have a faster turn‐over rate and thereby assist in alleviating ED overcrowding.

Although the LOS of the patients here is comparable to that reported by the Taiwan NHI,2 it is far longer than that reported in the United States (around 4.75.2 days).4, 23 One possible explanation is the social and cultural determinants, including hospital‐ or physician‐dependence.24 In literature from Japan and Taiwan, hospitalization is as long as 13 weeks.25, 26 In addition, the average admission cost is reportedly around $1540 per patient‐day in the US, around 6 times that in this study ($266.6 per patient‐day).4 In the aging society of Taiwan,27 the NHI‐required copayment for admission may be relatively low, such that patients (or their families) may be misled that hospital care is better and hesitate to be discharged.2830

Regarding quality of care and patient safety, the in‐hospital mortality and the 30‐day readmission rates are similar in both groups, although disease severity and underlying comorbidities are worse in the HW at the start. This is consistent with previous reports that hospitalists can manage inpatient as well as internist care systems.4, 23 However, because this study has been performed in a tertiary referral center, patients may be more severely ill, such that the inpatient mortality and 30‐day readmission rates are as high as 10.3% and 21.11%, respectively.31, 32 Nonetheless, generalizing the hospitalist system to regional or district hospitals remains a concern, and this warrants further study.

This study has two other limitations. First, it is an observational study and patients have different demographics even though propensity score‐matching has been performed. Second, the patients were hospitalized without a standardized treatment protocol.

In conclusion, under the NHI system in Taiwan, a hospitalist system can have higher efficiency in shortening LOS and reducing cost than an internist care system, and still have similar hospital mortality and readmission rates. A hospitalist system may address the issue of high patient volume by increasing ward utilization. It can be recommended in a country with NHI that has a shortage of inpatient care staff.

In Taiwan, the national health insurance (NHI) implemented since 19951 has extended its coverage to almost the entire population. It may serve as a model for other countries looking to implement a universal health insurance system.2, 3 However, due to the low copayment for services, there are increasing admission rates and hospitalizations.2, 4 Admission rates, in particular, have nearly tripled for those who have been previously uninsured prior to the NHI program.2 In terms of hospital care, internal medicine and surgery are not favorite areas of specialty in the NHI system because inpatient care has a high workload but relatively low salaries.2, 5, 6 Consequently, there is now a shortage of primary inpatient care staff in Taiwan. The hospitalist system may be a solution to this problem.

The role of a hospitalist system has been discussed since 1996.7 Although its pros and cons are still debatable,8 the hospitalist system has grown in recent decades and there is a wide acceptance that hospitalists can efficiently care for inpatients.4, 9, 10 However, most related studies are in Western countries.4, 6, 11 It has rarely been studied in Asian countries and in those with NHI programs.

This study therefore aimed to investigate whether the hospitalist system, working within the NHI system in Taiwan, can be efficient in saving costs, maintaining quality care, and managing a high volume of in patients.

Materials and Methods

This prospective observational study was conducted in the National Taiwan University Hospital (NTUH), a tertiary‐care referral center in northern Taiwan, and approved by the hospital's Institutional Review Board. The program was also registered on Clinicaltrial.gov (identifier NCT00997646). A 36‐bed hospitalist‐run ward (HW) was set up in October 2009 in NTUH. For performance comparison, two 36‐bed internist‐run wards (IWs) were selected. The three wards were geographically separated.

Study Subjects

All patients age >18 years from the emergency department (ED) were admitted into one of the three wards based on the diagnosis category determined by the ED physicians. A patient was admitted by bed managers who were blinded to the study. Cases were categorized as diseases of general medicine, such as congestive heart failure, pneumonia, exacerbation of chronic obstructive pulmonary disease, cellulitis, ischemic stroke, urinary tract infection, and gastrointestinal bleeding.

Patients with severe illnesses requiring admission to intensive care units were excluded. Research assistants who were blinded to the patient stratification performed the patients' identification and data collection. Patient care was determined by the respective medical teams without any interference from this study.

Care‐Team Structure

The HW was set up with 3 attending physicians certified by a board of internal medicine and 6 nurse practitioners. All staff members worked full‐time to provide primary inpatient care. For comparison (Table 1), each IW had a set‐up of 3 attending physicians licensed by a board of internal medicine, one chief resident, 3 junior residents, and 3 interns. The attending physicians of the IWs visited their inpatients every workday and delegated primary care to residents on night shifts and weekends.

Comparison of Care‐Team Setting Between the Hospitalist‐Run Ward and Internist‐Run Ward in a Medical Center
 Hospitalist‐Run WardInternist‐Run Wards
  • Abbreviations: AP, attending physician; CR, chief resident; JR, junior resident; NP, nurse practitioner.

Team member, per ward3 AP, 6 NP3 AP, 1 CR, 3 JR, 3 intern
Beds, per ward3636
Inpatient care of APFull timeOnce daily
Who prescribes care order?APAP, CR, JR
Who executes order?NPJR, intern
AP dutyInpatient care; researchInpatient/outpatient care; work of subspecialty; research
Bed managerNP/APCR

Clinical Characteristics

The patients' clinical characteristics, laboratory data, hospital course, and outcomes were recorded. The clinical characteristics included age, gender, underlying comorbidities, activities of daily living, and admission diagnosis. Charlson scores and Barthel's scores represented underlying comorbidities and activities of daily living, respectively. These were calculated as described in previous studies.11, 12 Admission costs paid for by the Taiwan NHI was defined as an inpatient's expenditure paid to the hospital by the institute of NHI. Total admission cost included expenses paid for by NHI and the patient's out‐of‐pocket expenditure not covered by NHI. A primary care physician was defined if the patient had visited the same doctor's clinic three times or more within one year prior to admission.8 Patients were followed‐up for 30 days after discharge by telephone, or until readmission.

Propensity Score Methods

Propensity score‐matching was used to balance observed covariates between the 2 care groups. It was defined as the conditional probability for being admitted to the HW, as a binary dependent variable, under a set of measurements. Factors that were significantly different (P < 0.05) between the 2 groups in univariate analysis were included in a multivariable logistic regression model to predict HW admission. The predicted probability derived from the logistic equation was used as the propensity score for each individual.

Patients in the HW and IWs were pooled and sorted according to their propensity score in ascending order. The selection process began from the first two cases with the lowest propensity score. If one was admitted to the HW and the other to an IW, both were selected as a matched pair. If this was not the case, then four cases were included. If there were two HW patients and two IW cases, the four were selected as two matched pairs. In the same way, HW and IW cases were matched by their propensity score in 1:1, 2:2, or 3:3 blocks. A patient who did not have a suitable match within the acceptable rank range was excluded from further analysis. The matching process moved down the sort list until all possible matched pairs were included and the selected patients formed a matched 1:1 pair in both groups.

Statistical Analysis

Intergroup differences were compared using independent t test for numerical variables and chi‐square test for categorical variables. Curves of probability of staying in the hospital within 30 days were generated using the Kaplan‐Meier method and compared using the log‐rank test. A logistic regression model was used for the propensity score match using the SPSS software version 13.0 (SPSS, Chicago, IL). The probability that indicated patient admission to the HW in both groups was used to draw box‐plots. After the 1:1 matched groups were assembled, the clinical characteristics were compared accordingly.

Results

From November 2009 to January 2010, 810 patients admitted from the ED to the study wards were enrolled. Among them, 377 were admitted to the HW and 433 to the IWs. Analysis of admission days showed that 84 (22%) and 53 (12%) patients were admitted to the HW and IWs, respectively, on weekends (P < 0.001).

Compared to the IW patients, the HW patients were older (age >65 years) and had poorer functional status by Barthel's scores (Table 2). Admission diagnosis was similar in both groups, except for pneumonia and urinary tract infection, which were higher in the HW patients. There was a primary care physician in 242 (64%) HW and 282 (65%) IW patients (P = 0.781).

The Charlson score, representing underlying comorbidity, was higher in the HW group (P = 0.002). Moreover, patients with severe liver cirrhosis (Child‐Pugh class C) were more frequently admitted to the HW (P = 0.018). Underlying malignancy, severe chronic kidney disease (estimated creatinine clearance <30 mL/min), and chronic respiratory failure requiring mechanical ventilator support were more associated with HW admission, although not statistically significantly (P = 0.064, 0.072, and 0.104, respectively).

The average admission cost was lower in HW patients than in IW patients, whether paid for by NHI ($1640.2 vs $2933.8 per patient, P = 0.001) or by the total admission cost ($2223.4 vs $3700.8 per patient, P = 0.001) (Table 3). Similarly, there was a shorter average length of stay (LOS) in the HW patients (9.3 vs 13.1 days, P < 0.001), who were discharged earlier than IW patients (Figure 1A). Regarding cost per patient‐day, the total daily cost was similar between the two groups (P = 0.560).

Clinical Characteristics and Laboratory Data of Hospitalized Patients by Their Admission Wards
 Hospitalist‐Run Ward (n = 377)Internist‐Run Wards (n = 433)P‐Value
  • NOTE: Data are no. (%) or mean standard deviation unless otherwise indicated.

  • Abbreviations: COPD, chronic obstructive pulmonary disease.

  • Other diagnoses included pleural effusion, hemoptysis, arrhythmia, hepatitis, obstructive jaundice, lower gastrointestinal bleeding, severe diarrhea, ileus, and renal failure.

Age >65 years old237 (63)240 (55)0.032
Gender, male210 (56)243 (56)0.905
Barthel's score61 3570 33<0.001
Charlson score3.7 3.43.0 3.20.002
Admission diagnosis   
Pneumonia106 (28)88 (20)0.010
Exacerbation of COPD18 (5)15 (3)0.347
Congestive heart failure12 (3)19 (4)0.373
Upper gastrointestinal bleeding55 (15)58 (13)0.625
Intra‐abdominal infection36 (10)47 (11)0.541
Urinary tract infection85 (23)69 (16)0.017
Cellulitis20 (5)18 (4)0.441
Ischemic stroke12 (3)21 (5)0.231
Others*117 (31)164 (38)0.041
Laboratory data in the initial admission
Leukocyte count, /L11372 796210377 64220.050
Hemoglobin, g/dL12.7 12.812.3 8.60.714
Platelet count, K/L219 124205 1080.102
Blood urea nitrogen, mg/dL33.2 27.724.1 17.4<0.001
Creatinine, mg/dL1.9 2.91.6 2.80.080
Total bilirubin, mg/dL2.2 3.72.3 3.60.826
C‐reactive protein, mg/dL8.0 7.76.0 6.40.008
Figure 1
Probability of hospitalization plotted by the Kaplan‐Meier method and compared by log‐rank test according to (A) the total number of patients in the internist‐run wards (IW) and hospitalist‐run ward (HW); and (B) matched‐patients in the IW (IW‐M) and HW (HW‐M).

More patients in the HW group signed the do‐not‐resuscitate (DNR) consent (P < 0.001) and died during the hospital course, although the difference was not statistically significant (P = 0.068). Among those who expired during hospitalization, DNR consent was signed by 42 (90%) HW and 27 (68%) IW patients (P = 0.014). Among those discharged, 57 (17.2%) HW and 70 (17.6%) IW patients were lost to follow‐up. There was no difference in the 30‐day readmission for any cause between the two groups (P = 0.992).

Due to baseline differences, propensity score‐matching was performed and 101 pairs of patients were selected according to the probability generated from factors significantly different in univariate analysis (ie, age >65 years, pneumonia or urinary tract infections, Charlson score, Barthel's score, and blood urea nitrogen and C‐reactive protein levels on initial admission). The clinical characteristics of the 202 patients were shown in Table 4.

Course and Outcome of the Hospitalized Patients by Their Admission Wards
 Hospitalist‐Run Ward (n = 377)Internist‐Run Wards (n = 433)P‐Value
  • NOTE: Data are no. (%) or mean standard deviation unless otherwise indicated. All costs were at exchange rates of $1.00 = 31.90 yuan (Taiwan dollar) as of July 1, 2010.

  • Abbreviations: NHI, national health insurance (Taiwan).

  • The denominator of the percentage is the number of patients discharged alive.

  • P‐value by the log‐rank test.

Length of hospital stay, days9.3 6.713.1 12.4<0.001
Total admission cost: $ per patient2223.4 3428.23700.8 8010.70.001
Admission cost paid by NHI: $ per patient1640.2 2403.32933.8 7460.70.001
In‐hospital mortality47 (12)37 (9)0.068
Do‐not‐resuscitate consent74 (20)34 (8)<0.001
30‐Day readmission*71 (22)83 (21)0.922

0

Clinical Characteristics, Hospital Course, and Outcome of Propensity Score‐Matched Patients by Their Admission Wards
 Hospitalist‐Run Ward (n = 101)Internist‐Run Wards (n = 101)P‐Value
  • NOTE: Data are no. (%) or mean standard deviation unless otherwise indicated. All costs are at exchange rates of $1.00 = 31.90 yuan (Taiwan dollar) as of July 1, 2010.

  • Abbreviations: COPD, chronic obstructive pulmonary disease; NHI, national health insurance (Taiwan).

  • Other diagnoses included pleural effusion, hemoptysis, arrhythmia, hepatitis, obstruction jaundice, lower gastrointestinal bleeding, severe diarrhea, ileus, and renal failure.

  • The denominator of the percentage is the number of patients discharged alive.

  • P‐value by the log‐rank test.

Age >65 years old59 (58)59 (58)1.000
Gender, Male55 (54)50 (50)0.481
Barthel's score66 3465 350.897
Charlson score3.2 3.23.6 3.50.437
Admission diagnosis   
Pneumonia31 (31)27 (27)0.534
Exacerbation of COPD4 (4)5 (5)0.733
Congestive heart failure2 (2)2 (2)1.000
Gastrointestinal bleeding10 (10)8 (8)0.621
Intra‐abdominal infection18 (18)10 (10)0.103
Urinary tract infection22 (22)21 (21)0.864
Cellulitis6 (6)5 (5)0.757
Ischemic stroke2 (2)00.155
Others*39 (39)30 (30)0.182
Laboratory data in the initial admission   
Leukocyte count, /L12487 628811430 77180.287
Hemoglobin, g/dL12.8 13.712.5 7.50.803
Platelet count, K/L212 102207 1030.710
Blood urea nitrogen, mg/dL25.5 19.724.7 17.50.773
Creatinine, mg/dL1.5 1.21.6 1.50.979
Total bilirubin, mg/dL2.0 7.02.0 6.90.963
C‐reactive protein, mg/dL6.9 7.77.0 6.40.859
Length of hospital stay, days9.2 6.415.2 13.8<0.001
Do‐not‐resuscitate consent18 (18)6 (6)0.009
Total admission cost: $ per patient2019.4 1709.35608.9 14244.80.013
Cost paid by NHI: $ per patient1463.4 1404.64665.8 13553.30.019
In‐hospital mortality9 (9)7 (7)0.602
30‐Day postdischarge readmission17 (18)21 (22)0.492

Both groups had almost the same propensity scores (P = 0.970; see online Supporting Information). Patients in the HW group had significantly lower admission cost, shorter LOS (Figure 1B), and more DNR consent, but similar in‐hospital mortality and readmission rates (Table 4).

DISCUSSION

The hospitalist system, which has been practiced for years in the United States, has not really been reported in Asia.13 Under the universal NHI system, this system has been studied in terms of treating patients in a Taiwan referral center. This study is the first to report on a hospitalist system in an Asian country with an NHI program. The hospitalist system in this study demonstrates efficient performance even though the patients have multiple comorbidities, compared to those in the general medical wards. By propensity score‐matching, admission costs of the hospitalist‐run ward are significantly lower than those of the internist‐run wards despite similar mortality and readmission rates.

The average LOS is reduced by 29% in HW patients and this plays a major role in cost reduction.14, 15 The reason may be the hospitalist's full‐time care, which allows for prompt decision‐making and close interaction with the patients' families.16 These families thus understand the treatment planning and prognosis. Furthermore, the hospitalist system continues working on weekends. As a result, patients are discharged without delay, even on holidays.

The aim of reducing LOS and costs is important because hospital income will decrease under the payment by disease‐related group (DRG) being implemented by the NHI system.17 A shortage of inpatient physicians may also develop due to the high workload but relatively low remuneration.2, 18 In contrast, a hospitalist care system that integrates nurse practitioners demands less human resources and saves on costs. In the future, it may be one of the solutions for hospitals aiming to maintain financial balance.

Another important issue in the NHI coverage is the increasing number of patients in the ED, which seems to be overflowing.19 In a previous Taiwan report, there are 7.1 patients per day who are staying in the ED for more than 72 hours, despite indications for admission.20 The delay is possibly due to the lack of available beds in the inpatient department.21 Amidst increasing demands for admission under the NHI and an aging society,2, 20 experience suggests that a hospitalist care system is a promising alternative to address the high ED patient volumes, especially on holidays. Howell et al. have also demonstrated that hospitalist‐driven bed management enhances the bed utility rate.21, 22 Since the current study also shows reduced LOS in the HW, patients will have a faster turn‐over rate and thereby assist in alleviating ED overcrowding.

Although the LOS of the patients here is comparable to that reported by the Taiwan NHI,2 it is far longer than that reported in the United States (around 4.75.2 days).4, 23 One possible explanation is the social and cultural determinants, including hospital‐ or physician‐dependence.24 In literature from Japan and Taiwan, hospitalization is as long as 13 weeks.25, 26 In addition, the average admission cost is reportedly around $1540 per patient‐day in the US, around 6 times that in this study ($266.6 per patient‐day).4 In the aging society of Taiwan,27 the NHI‐required copayment for admission may be relatively low, such that patients (or their families) may be misled that hospital care is better and hesitate to be discharged.2830

Regarding quality of care and patient safety, the in‐hospital mortality and the 30‐day readmission rates are similar in both groups, although disease severity and underlying comorbidities are worse in the HW at the start. This is consistent with previous reports that hospitalists can manage inpatient as well as internist care systems.4, 23 However, because this study has been performed in a tertiary referral center, patients may be more severely ill, such that the inpatient mortality and 30‐day readmission rates are as high as 10.3% and 21.11%, respectively.31, 32 Nonetheless, generalizing the hospitalist system to regional or district hospitals remains a concern, and this warrants further study.

This study has two other limitations. First, it is an observational study and patients have different demographics even though propensity score‐matching has been performed. Second, the patients were hospitalized without a standardized treatment protocol.

In conclusion, under the NHI system in Taiwan, a hospitalist system can have higher efficiency in shortening LOS and reducing cost than an internist care system, and still have similar hospital mortality and readmission rates. A hospitalist system may address the issue of high patient volume by increasing ward utilization. It can be recommended in a country with NHI that has a shortage of inpatient care staff.

References
  1. Peabody JW,Yu JC,Wang YR,Bickel SR.Health system reform in the Republic of China. Formulating policy in a market‐based health system.JAMA.1995;273:777781.
  2. Wen CP,Tsai SP,Chung WS.A 10‐year experience with universal health insurance in Taiwan: measuring changes in health and health disparity.Ann Intern Med.2008;148:258267.
  3. Davis K,Huang AT.Learning from Taiwan: experience with universal health insurance.Ann Intern Med.2008;148:313314.
  4. Lindenauer PK,Rothberg MB,Pekow PS,Kenwood C,Benjamin EM,Auerbach AD.Outcomes of care by hospitalists, general internists, and family physicians.N Engl J Med.2007;357:25892600.
  5. Cheng SH,Chiang TL.The effect of universal health insurance on health care utilization in Taiwan. Results from a natural experiment.JAMA.1997;278:8993.
  6. Tokuda Y,Hayano K,Ozaki M,Bito S,Yanai H,Koizumi S.The interrelationships between working conditions, job satisfaction, burnout and mental health among hospital physicians in Japan: a path analysis.Ind Health.2009;47:166172.
  7. Wachter RM,Goldman L.The emerging role of “hospitalists” in the American health care system.N Engl J Med.1996;335:514517.
  8. Sharma G,Fletcher KE,Zhang D,Kuo YF,Freeman JL,Goodwin JS.Continuity of outpatient and inpatient care by primary care physicians for hospitalized older adults.JAMA.2009;301:16711680.
  9. Lopez L,Hicks LS,Cohen AP,McKean S,Weissman JS.Hospitalists and the quality of care in hospitals.Arch Intern Med.2009;169:13891394.
  10. Kuo YF,Sharma G,Freeman JL,Goodwin JS.Growth in the care of older patients by hospitalists in the United States.N Engl J Med.2009;360:11021112.
  11. Wang HY,Chew G,Kung CT,Chung KJ,Lee WH.The use of Charlson comorbidity index for patients revisiting the emergency department within 72 hours.Chang Gung Med J.2007;30:437444.
  12. Sainsbury A,Seebass G,Bansal A,Young JB.Reliability of the Barthel Index when used with older people.Age Ageing.2005;34:228232.
  13. Lee KH.The hospitalist movement—a complex adaptive response to fragmentation of care in hospitals.Ann Acad Med Singapore.2008;37:145150.
  14. Dynan L,Stein R,David G,Kenny LC,Eckman M,Short AD.Determinants of hospitalist efficiency: a qualitative and quantitative study.Med Care Res Rev.2009;66:682702.
  15. Mitchell DM.The critical role of hospitalists in controlling healthcare costs.J Hosp Med.2010;5:127132.
  16. Wachter RM.The relationship between hospitalists and primary care physicians.Ann Intern Med.2010;152:474.
  17. Lungen M,Dredge B,Rose A,Roebuck C,Plamper E,Lauterbach K.Using diagnosis‐related groups. The situation in the United Kingdom National Health Service and in Germany.Eur J Health Econ.2004;5:287289.
  18. Chang PY,Hung CY,Wang KI,Huang YH,Chang KJ.Factors influencing medical students' choice of specialty.J Formos Med Assoc.2006;105:489496.
  19. Huang JA,Tsai WC,Chen YC,Hu WH,Yang DY.Factors associated with frequent use of emergency services in a medical center.J Formos Med Assoc.2003;102:222228.
  20. Shih FY,Ma MH,Chen SC, et al.ED overcrowding in Taiwan: facts and strategies.Am J Emerg Med.1999;17:198202.
  21. Howell E,Bessman E,Kravet S,Kolodner K,Marshall R,Wright S.Active bed management by hospitalists and emergency department throughput.Ann Intern Med.2008;149:804811.
  22. Howell E,Bessman E,Marshall R,Wright S.Hospitalist bed management effecting throughput from the emergency department to the intensive care unit.J Crit Care.2010;25:184189.
  23. Halasyamani LK,Valenstein PN,Friedlander MP,Cowen ME.A comparison of two hospitalist models with traditional care in a community teaching hospital.Am J Med.2005;118:536543.
  24. Ma KZ,Norton EC,Tsai EM,Lee SY.Factors associated with tocolytic hospitalizations in Taiwan: evidence from a population‐based and longitudinal study from 1997 to 2004.BMC Pregnancy Childbirth.2009;9:59.
  25. Shen YH,Hwang KP,Niu CK.Complicated parapneumonic effusion and empyema in children.J Microbiol Immunol Infect.2006;39:483488.
  26. Mikami K,Suzuki M,Kitagawa H, et al.Efficacy of corticosteroids in the treatment of community‐acquired pneumonia requiring hospitalization.Lung.2007;185:249255.
  27. Ling Wang Y‐ML,Fan S‐C,Chao W‐T.Analysis of population projections for Taiwan area: 2008 to 2056.Taiwan Economic Forum.2009;7:3669.
  28. Hu WY,Chiu TY,Cheng YR,Chuang RB,Chen CY.Why Taiwanese hospice patients want to stay in hospital: health‐care professionals' beliefs and solutions.Support Care Cancer.2004;12:285292.
  29. Wu CY,Lai HJ,Chen RC.Patient characteristics predict occurrence and outcome of complaints against physicians: a study from a medical center in central Taiwan.J Formos Med Assoc.2009;108:126134.
  30. Lin YH,Chen WY.The demand for healthcare under Taiwan's national health insurance: a count data model approach.Expert Rev Pharmacoecon Outcomes Res.2009;9:1322.
  31. Yang CM,Reinke W.Feasibility and validity of International Classification of Diseases based case mix indices.BMC Health Serv Res.2006;6:125.
  32. Shu CC,Lee LN,Wang JT,Chien YJ,Wang JY,Yu CJ.Non‐tuberculous mycobacterial pleurisy: an 8‐year single‐centre experience in Taiwan.Int J Tuberc Lung Dis.2010;14:635641, p 634 follows 641.
References
  1. Peabody JW,Yu JC,Wang YR,Bickel SR.Health system reform in the Republic of China. Formulating policy in a market‐based health system.JAMA.1995;273:777781.
  2. Wen CP,Tsai SP,Chung WS.A 10‐year experience with universal health insurance in Taiwan: measuring changes in health and health disparity.Ann Intern Med.2008;148:258267.
  3. Davis K,Huang AT.Learning from Taiwan: experience with universal health insurance.Ann Intern Med.2008;148:313314.
  4. Lindenauer PK,Rothberg MB,Pekow PS,Kenwood C,Benjamin EM,Auerbach AD.Outcomes of care by hospitalists, general internists, and family physicians.N Engl J Med.2007;357:25892600.
  5. Cheng SH,Chiang TL.The effect of universal health insurance on health care utilization in Taiwan. Results from a natural experiment.JAMA.1997;278:8993.
  6. Tokuda Y,Hayano K,Ozaki M,Bito S,Yanai H,Koizumi S.The interrelationships between working conditions, job satisfaction, burnout and mental health among hospital physicians in Japan: a path analysis.Ind Health.2009;47:166172.
  7. Wachter RM,Goldman L.The emerging role of “hospitalists” in the American health care system.N Engl J Med.1996;335:514517.
  8. Sharma G,Fletcher KE,Zhang D,Kuo YF,Freeman JL,Goodwin JS.Continuity of outpatient and inpatient care by primary care physicians for hospitalized older adults.JAMA.2009;301:16711680.
  9. Lopez L,Hicks LS,Cohen AP,McKean S,Weissman JS.Hospitalists and the quality of care in hospitals.Arch Intern Med.2009;169:13891394.
  10. Kuo YF,Sharma G,Freeman JL,Goodwin JS.Growth in the care of older patients by hospitalists in the United States.N Engl J Med.2009;360:11021112.
  11. Wang HY,Chew G,Kung CT,Chung KJ,Lee WH.The use of Charlson comorbidity index for patients revisiting the emergency department within 72 hours.Chang Gung Med J.2007;30:437444.
  12. Sainsbury A,Seebass G,Bansal A,Young JB.Reliability of the Barthel Index when used with older people.Age Ageing.2005;34:228232.
  13. Lee KH.The hospitalist movement—a complex adaptive response to fragmentation of care in hospitals.Ann Acad Med Singapore.2008;37:145150.
  14. Dynan L,Stein R,David G,Kenny LC,Eckman M,Short AD.Determinants of hospitalist efficiency: a qualitative and quantitative study.Med Care Res Rev.2009;66:682702.
  15. Mitchell DM.The critical role of hospitalists in controlling healthcare costs.J Hosp Med.2010;5:127132.
  16. Wachter RM.The relationship between hospitalists and primary care physicians.Ann Intern Med.2010;152:474.
  17. Lungen M,Dredge B,Rose A,Roebuck C,Plamper E,Lauterbach K.Using diagnosis‐related groups. The situation in the United Kingdom National Health Service and in Germany.Eur J Health Econ.2004;5:287289.
  18. Chang PY,Hung CY,Wang KI,Huang YH,Chang KJ.Factors influencing medical students' choice of specialty.J Formos Med Assoc.2006;105:489496.
  19. Huang JA,Tsai WC,Chen YC,Hu WH,Yang DY.Factors associated with frequent use of emergency services in a medical center.J Formos Med Assoc.2003;102:222228.
  20. Shih FY,Ma MH,Chen SC, et al.ED overcrowding in Taiwan: facts and strategies.Am J Emerg Med.1999;17:198202.
  21. Howell E,Bessman E,Kravet S,Kolodner K,Marshall R,Wright S.Active bed management by hospitalists and emergency department throughput.Ann Intern Med.2008;149:804811.
  22. Howell E,Bessman E,Marshall R,Wright S.Hospitalist bed management effecting throughput from the emergency department to the intensive care unit.J Crit Care.2010;25:184189.
  23. Halasyamani LK,Valenstein PN,Friedlander MP,Cowen ME.A comparison of two hospitalist models with traditional care in a community teaching hospital.Am J Med.2005;118:536543.
  24. Ma KZ,Norton EC,Tsai EM,Lee SY.Factors associated with tocolytic hospitalizations in Taiwan: evidence from a population‐based and longitudinal study from 1997 to 2004.BMC Pregnancy Childbirth.2009;9:59.
  25. Shen YH,Hwang KP,Niu CK.Complicated parapneumonic effusion and empyema in children.J Microbiol Immunol Infect.2006;39:483488.
  26. Mikami K,Suzuki M,Kitagawa H, et al.Efficacy of corticosteroids in the treatment of community‐acquired pneumonia requiring hospitalization.Lung.2007;185:249255.
  27. Ling Wang Y‐ML,Fan S‐C,Chao W‐T.Analysis of population projections for Taiwan area: 2008 to 2056.Taiwan Economic Forum.2009;7:3669.
  28. Hu WY,Chiu TY,Cheng YR,Chuang RB,Chen CY.Why Taiwanese hospice patients want to stay in hospital: health‐care professionals' beliefs and solutions.Support Care Cancer.2004;12:285292.
  29. Wu CY,Lai HJ,Chen RC.Patient characteristics predict occurrence and outcome of complaints against physicians: a study from a medical center in central Taiwan.J Formos Med Assoc.2009;108:126134.
  30. Lin YH,Chen WY.The demand for healthcare under Taiwan's national health insurance: a count data model approach.Expert Rev Pharmacoecon Outcomes Res.2009;9:1322.
  31. Yang CM,Reinke W.Feasibility and validity of International Classification of Diseases based case mix indices.BMC Health Serv Res.2006;6:125.
  32. Shu CC,Lee LN,Wang JT,Chien YJ,Wang JY,Yu CJ.Non‐tuberculous mycobacterial pleurisy: an 8‐year single‐centre experience in Taiwan.Int J Tuberc Lung Dis.2010;14:635641, p 634 follows 641.
Issue
Journal of Hospital Medicine - 6(7)
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Journal of Hospital Medicine - 6(7)
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Evaluating the performance of a hospitalist system in Taiwan: A pioneer study for nationwide health insurance in Asia
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Evaluating the performance of a hospitalist system in Taiwan: A pioneer study for nationwide health insurance in Asia
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Provider expectations and experiences of comanagement

Comanagement is common in hospital medicine practice. And yet, there is no consensus about how comanagement is different from traditional consultative practice. At its core, hospitalist comanagement is a practice arrangement wherein hospitalists and other specialists manage complex patients collaboratively. Beyond this, Huddleston et al. distinguish comanagement from traditional consultations in the comanaging hospitalists' prerogative to provide direct medical care in addition to consultative advice.1 Siegal focuses on the shared responsibility and authority among partnering providers in the comanagement model.2 Whinney and Michota see comanagement as patient care referral at the onset of a care episode, in contrast to consultations that are activated to address emergent problems.3 In a recent study that found the growing adoption of medical comanagement in Medicare beneficiaries (as much as 40% of surgical hospitalizations in 2006), comanagement was defined as an intensive form of consultation involving a claim for evaluation and management services on greater than 70% of inpatient days.4

In addition to the intensity, frequency, timing, responsibility, and authority of care, comanagement may be described by participating physicians' roles. With recent attention on multidisciplinary teams and an increasing focus on collaborative care, many of the hierarchical relations among healthcare providers are breaking down.5 Several studies of multidisciplinary teams suggest that more egalitarian, rather than hierarchical, problem‐solving and decision‐making among team members are beneficial to patients.67 However, neither the intended nor natural team structure under comanagement is known. We sought to shed some light on provider interactions by characterizing the expectations and experiences of providers of a comanaged service. The findings yielded an opportunity to generate an evolving, but conceptually supported definition of comanagement.

SETTING

We conducted a survey study of providers participating in a comanaged inpatient hepatology service at the University of Chicago Medical Center, a 572‐bed urban teaching hospital. The service was created in 2006, partly to address staffing problems related to housestaff work hour restrictions and partly to improve the care of candidates and recipients of liver transplantation. Nonsurgical floor patients with liver diseases were managed on the service by two collaborating teams of providers. The hepatology team consisted of an attending physician and a fellow, while the hospitalist team consisted of a hospitalist and one or two nonphysician providers (physician assistant or nurse practitioner). The practice model is characterized as comanagement because of the highly interdependent nature of the team's daily tasks and the norms of intensive communication, through formal joint daily rounds and informal direct exchanges of instructions and updates. Hepatologists were mainly responsible for coordinating admissions, managing issues related to liver dysfunction, communicating with transplant surgeons if necessary, and arranging postdischarge care. Hospitalists were responsible for admitting patients, managing routine (eg, ordering daily labs) and urgent issues (eg, responding to critical lab values) during hospitalizations, coordinating with ancillary and consultative staff, and discharging patients. Occasional meetings between the hepatology and hospital medicine groups were used to clarify assignment of responsibilities. Floor nurses received in‐servicing at the commencement of the service. Additional details about the service are described elsewhere.8

DATA COLLECTION AND ANALYSIS

For the purpose of our analysis, we defined interactions between any member of the hospitalist and hepatologist teams as pertinent to comanagement. The hospitalist nonphysician provider (NPP) and hepatologistfellow relationships are governed by the more traditional hierarchical dynamics based on supervision and authority according to laws and regulations. At the beginning of the study period, each participant completed nine items of a Baseline Survey that addressed respondents' expectations and preferences for the management of an ideally comanaged service. Responses were solicited using a 4‐point Likert‐type scale and were dichotomized such that agree and somewhat agree were grouped, while disagree and somewhat disagree were grouped for data analysis. Items were generated to address the salient issues of comanagement after reviewing the pertinent literature.

Subsequently, participants were asked to complete Repeated Surveys immediately before each change in membership of the comanaged team between April and October 2008. The surveys were hand delivered by one of the authors (K.H.) on the last day of each team's rotation and were often completed immediately. The seven items of the Repeated Survey reprised items from the Baseline Survey that were rephrased to allow respondents to report their direct experiences on specific teams. Because all providers rotated on the service more than once during the study period, the average value for each Likert‐type response across multiple surveys completed by a single provider was calculated before being dichotomized at the midpoint (<2.5, agree; 2.5, disagree). We reported proportions of respondents in agreement with survey item statements.

Comparison statistics across providers were generated using the chi‐square test. Differences in proportions between related items of the Baseline and Repeated Surveys were compared using the two‐sample test of proportions. All analyses were conducted using a statistics application (STATA 10.0, College Station, TX) with alpha equal to, or less than, 0.05 considered significant. The Institutional Review Board of the University of Chicago approved this project.

RESULTS

All 43 providers completed the Baseline Survey. During the study period, 32 of these participants rotated on the service and completed 177 of the 233 Repeated Surveys (79%) administered. The responses describe team interactions on the 47 unique combinations of providers comprising the comanaged teams. Details of the response rates are shown in Table 1.

Survey Response Rates by Provider Roles
 Baseline Survey, Completed/ Administered (%)Repeated Surveys, Completed/ Administered (%)Respondents Completing Repeated Surveys, nRepeated Surveys Completed per Respondent, Median (IQR)
  • Abbreviations: NPPs, nonphysician providers; IQR, interquartile range.

Hospitalists18/18 (100)36/43 (84)152 (2, 3)
NPPs5/5 (100)92/97 (95)520 (18, 20)
Hepatologists6/6 (100)26/42 (62)67 (3.75, 8)
Fellows12/12 (100)23/42 (55)67 (5.5, 8.5)
Total43/43 (100)177/223 (79)324.5 (2, 8.25)

As shown in Table 2A, items 13, more members of the hospitalist team preferred to be informed about every management decision compared to members of the hepatologist team. Conversely, more of members of the hepatologist team than the hospitalist team preferred their comanaging partners to participate in every decision. A statistically similar proportion of respondents in each of the professional roles indicated desire for greater influence in directing management decisions (Table 2B, item 1).

Proportion of Respondents Agreeing with Survey Item Statements
A. Baseline SurveyHospitalists, % (n = 18)NPPs, % (n = 5)Hepatologists, % (n = 6)GI Fellows, % (n = 12)P‐value
  • Abbreviations: GI, gastrointestinal; NPP, nonphysician provider.

  • Statistically significant difference between Baseline and Repeated Survey response defined by P 0.05.

1. I prefer to be informed about every decision.831001742<0.01
2. I prefer to participate in every decision.6710033500.11
3. I prefer that my comanager participate in every decision.222050750.02
4. I prefer to have the final say in every decision.508050330.38
5. There should be one physician leader to direct the overall management of the patients' hospital course.89*10067830.43
6. Physician consensus should always be sought in every clinical decision.224050670.11
7. I have a clear understanding of my role on the comanagement service.618083750.66
8. I have as much a sense of ownership of patients on the comanaged service as on a non‐comanaged service.616083500.60
9. Comanagement tends to improve patient care.94100*83100*0.47
B. Repeated SurveysHospitalists, % (n = 15)NPPs, % (n = 5)Hepatologists, % (n = 6)GI Fellows, % (n = 6)P‐value
1. I would have liked greater influence in directing the overall management.40600170.12
2. I was responsible for work in clinical areas I was not comfortable managing.0000NA
3. There was one physician leader to direct the overall management of the patients' hospital course.60*8067830.70
4. Physician consensus was always sought in every clinical decision.404050670.72
5. I (have/had) a clear understanding of my role on the comanagement service.7380100830.57
6. I had as much a sense of ownership of patients on the comanaged service as on a non‐comanaged service.5380100670.20
7. Patients on my service received better care than they would have without comanagement.9340*6750*0.06

For the majority of surveyed areas, there was concordance between expectations and experiences of providers on comanagement. Most providers, regardless of professional role, agreed that there should be a single physician leader to direct the overall management (Table 2A, item 5). The majority perceived that a single physician directed the overall management of the patients' hospital course, although fewer hospitalists did so compared with baseline expectations (Table 2B, item 3). Many respondents felt at baseline that physician consensus should govern every management decision, and a similar proportion actually experienced consensus‐seeking on service.

We found that the proportion of providers reporting an understanding of their role increased slightly, though not significantly, from before (Table 2A, item 7) to after rotating on the comanaged service (Table 2B, item 5). Although not statistically significant, there was a trend towards hospitalists and gastrointestinal (GI) fellows reporting a lack of patient ownership, both before and after serving on the comanaged service. Finally, nearly all respondents reported that comanagement should improve care quality, although only the attending hospitalist and hepatologist felt that their experience on the comanaged service actually improved patient care (Table 2B, item 7).

DISCUSSION

In this survey of providers participating on a comanaged medical service, most reported understanding their role in the collaborative arrangement and had an initial perception that comanagement should improve patient care quality. We found that hospitalists preferred and were expected to participate in care globally, while hepatologists themselves preferred and were expected not to focus on every management decision. The prevalence of desire for ultimate authority across the professional roles suggests tensions that exist in this care model around how decisions are made. The majority of providers preferred and experienced a single physician leader under comanagement, but many also experienced consensus‐seeking for every management decision.

From these findings, we conclude that decision‐making processes are not uniform under comanagement and that some role ambiguity is present, but there appears to be a pattern of natural roles. This pattern can be defined by focus (general for hospitalists vs specialty‐specific for hepatologists), rather than by responsibilities for managing particular medical problems. The preference among both generalists and specialists for the broader involvement of hospitalist comanagers suggests an implicit recognition of the need for integrated management to overcome the silo‐effect within the comanagement structure.9 Although details about how such integration was achieved are not available in our data, we found that comanagement may be distinct from traditional consultative practice in that the consultants (hospitalists in this case) manage not only general medical problems, such as diabetes or hypertension, but hospitalizations more generally. From a mission‐based standpoint, comanagement may be seen as a collaborative management of complex patients by two or more clinical experts with distinct knowledge, skills, or focus enacted for the purpose of improving care quality.

The focus of comanagement on improving quality is in line with the founding charge of the hospital medicine specialty to raise hospital care quality.10 In fact, the distinction between comanagement and consultation may be meaningful only if comanagers can work with specialists to implement evidence‐based practice, process improvement, and address quality and cost concerns. But as seen in NPPs and fellows' skepticism of improved quality under comanagement, there is still clearly work to be done to validate this model through measurable improvement in patients' experiences and outcomes. Proving the advantages of comanagement as a platform for practice improvement remains future work.11

Collaborative arrangements create natural tensions related to team function.5 This is seen in the similar proportion of hospitalists and hepatologists indicating desire for final decision‐making authority. Although comanagement evokes assumptions about egalitarian provider interactions involving shared decision‐making and responsibility, it seems to function empirically under hierarchical as well as consensus‐seeking forms of decision‐making. Providers at the top of hierarchical teams typically experience their work as interdependent and collaborative, and report more positive interactions with other care providers.12 Based on the fact that no hepatologists wanted more influence over decision‐making, we assume that hepatologists were the physician leaders for most of the studied comanaged teams. Under situations characterized by high levels of complexity and interdependence, a team governed by a single leader may often be more effective than one governed by shared authority.8 However, even under hierarchical models, a more participatory than supervisory leadership can help avoid alienating partners through a pattern of we decide, you carry it out that is often associated with ineffective leadership styles.1314 In fact, this alienating effect on providers in subordinate roles (ie, NPPs and fellows) may have contributed to the negative perception of the team's function on improving patient care.

This study is limited in the following ways. We did not have 100% participation in the Repeated Surveys. Attitudes and experiences of participants in a single comanagement practice are not representative of all comanaging providers. However, the goal of this studyto collect unique survey data from providers themselves to inform an evolving definition of comanagementis modest enough in scope to not require a generalizable sample. Because this study unearthed differences in expectations and experiences within a single site, they may serve as a lower bound for the extent of differences across and within multiple sites. In addition, comanagement enacted for complex medical patients is not as common as the comanagement of surgical patients. Moreover, comanagement models in academic hospitals may have structural features and priorities not found in community settings. Whether or not these disparate models share enough in common to be categorized under a single rubric is a valid question.

Although the teamwork structure and provider roles within comanagement vary, the practice arrangement's preoccupation with quality can be seen as its defining feature. Limited evidence, to date,1, 1519 and the rapid proliferation of the model, suggest that quality and efficiency advantages can be obtained from an effective implementation of comanagement. As in any team‐based care model, a common understanding of roles and expectations are essential to enhancing teamwork. Our interpretation of the mission of comanagement may further enhance teamwork through an explicit articulation of shared goals.

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References
  1. Huddleston JM,Long KH,Naessens JM, et al.Medical and surgical comanagement after elective hip and knee arthroplasty: A randomized, controlled trial.Ann Intern Med.2004;141(1):2838.
  2. Siegal EM.Just because you can, doesn't mean that you should: A call for the rational application of hospitalist comanagement.J Hosp Med.2008;3(5):398402.
  3. Whinney C,Michota F.Surgical comanagement: A natural evolution of hospitalist practice.J Hosp Med.2008;3(5):394397.
  4. Sharma G,Kuo Y‐F,Freeman J,Zhang DD,Goodwin JS.Comanagement of hospitalized surgical patients by medicine physicians in the United States.Arch Intern Med.2010;170(4):363368.
  5. Cott C.Structure and meaning in multidisciplinary teamwork.Sociol Health Illn.1998;20(6):848873.
  6. de Leval MR,Carthey J,Wright DJ,Farewell VT,Reason JT.Human factors and cardiac surgery: A multicenter study.J Thorac Cardiov Surg.2000;119(4):661670.
  7. Schraeder C,Shelton P,Sager M.The effects of a collaborative model of primary care on the mortality and hospital use of community‐dwelling older adults.J Gerontol A‐Biol.2001;56(2):M106M112.
  8. Hinami K,Whelan CT,Konetzka RT,Edelson DP,Casalino LP,Meltzer DO.Effects of provider characteristics on care coordination under comanagement.J Hosp Med.2010;5:508513.
  9. Corrigan JM,Donaldson MS,Kohn LT.Crossing the Quality Chasm: A New Health System for the Twenty‐First Century.Washington, DC:Institute of Medicine;2001.
  10. Wachter RM,Goldman L.The emerging role of “hospitalists” in the American health care system.N Engl J Med.1996;335(7):514517.
  11. O'Malley PG.Internal medicine comanagement of surgical patients: Can we afford to do this?Arch Intern Med.2010;170(22):19651966.
  12. Makary MA,Sexton JB,Freischlag JA, et al.Operating room teamwork among physicians and nurses: Teamwork in the eye of the beholder.J Am Coll Surg.2006;202(5):746752.
  13. Cott C.“We decide, you carry it out”: A social network analysis of multidisciplinary longterm care teams.Soc Sci Med.1997;45(9):14111421.
  14. Lewin K,Lippitt R,White RK.Patterns of aggressive behavior in experimentally created social climates.J Soc Psychol.1939;10:271301.
  15. Auerbach AD,Wachter RM,Cheng HQ, et al.Comanagement of surgical patients between neurosurgeons and hospitalists.Arch Intern Med.2010;170(22):20042010.
  16. Fisher AA,Davis MW,Rubenach SE,Sivakumaran S,Smith PN,Budge MM.Outcomes for older patients with hip fractures: The impact of orthopedic and geriatric medicine cocare.J Orthop Trauma.2006;20(3):172180.
  17. Phy MP,Vanness DJ,Melton LJ, et al.Effects of a hospitalist model on elderly patients with hip fracture.Arch Intern Med.2005;165(7):796801.
  18. Zuckerman JD,Sakales SR,Fabian DR,Frankel VH.Hip fractures in geriatric patients. Results of an interdisciplinary hospital care program.Clin Orthop Relat Res.1992(274):213225.
  19. Friedman SM,Mendelson DA,Bingham KW,Kates SL.Impact of a comanaged Geriatric Fracture Center on short‐term hip fracture outcomes.Arch Intern Med.2009;169(18):17121717.
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Comanagement is common in hospital medicine practice. And yet, there is no consensus about how comanagement is different from traditional consultative practice. At its core, hospitalist comanagement is a practice arrangement wherein hospitalists and other specialists manage complex patients collaboratively. Beyond this, Huddleston et al. distinguish comanagement from traditional consultations in the comanaging hospitalists' prerogative to provide direct medical care in addition to consultative advice.1 Siegal focuses on the shared responsibility and authority among partnering providers in the comanagement model.2 Whinney and Michota see comanagement as patient care referral at the onset of a care episode, in contrast to consultations that are activated to address emergent problems.3 In a recent study that found the growing adoption of medical comanagement in Medicare beneficiaries (as much as 40% of surgical hospitalizations in 2006), comanagement was defined as an intensive form of consultation involving a claim for evaluation and management services on greater than 70% of inpatient days.4

In addition to the intensity, frequency, timing, responsibility, and authority of care, comanagement may be described by participating physicians' roles. With recent attention on multidisciplinary teams and an increasing focus on collaborative care, many of the hierarchical relations among healthcare providers are breaking down.5 Several studies of multidisciplinary teams suggest that more egalitarian, rather than hierarchical, problem‐solving and decision‐making among team members are beneficial to patients.67 However, neither the intended nor natural team structure under comanagement is known. We sought to shed some light on provider interactions by characterizing the expectations and experiences of providers of a comanaged service. The findings yielded an opportunity to generate an evolving, but conceptually supported definition of comanagement.

SETTING

We conducted a survey study of providers participating in a comanaged inpatient hepatology service at the University of Chicago Medical Center, a 572‐bed urban teaching hospital. The service was created in 2006, partly to address staffing problems related to housestaff work hour restrictions and partly to improve the care of candidates and recipients of liver transplantation. Nonsurgical floor patients with liver diseases were managed on the service by two collaborating teams of providers. The hepatology team consisted of an attending physician and a fellow, while the hospitalist team consisted of a hospitalist and one or two nonphysician providers (physician assistant or nurse practitioner). The practice model is characterized as comanagement because of the highly interdependent nature of the team's daily tasks and the norms of intensive communication, through formal joint daily rounds and informal direct exchanges of instructions and updates. Hepatologists were mainly responsible for coordinating admissions, managing issues related to liver dysfunction, communicating with transplant surgeons if necessary, and arranging postdischarge care. Hospitalists were responsible for admitting patients, managing routine (eg, ordering daily labs) and urgent issues (eg, responding to critical lab values) during hospitalizations, coordinating with ancillary and consultative staff, and discharging patients. Occasional meetings between the hepatology and hospital medicine groups were used to clarify assignment of responsibilities. Floor nurses received in‐servicing at the commencement of the service. Additional details about the service are described elsewhere.8

DATA COLLECTION AND ANALYSIS

For the purpose of our analysis, we defined interactions between any member of the hospitalist and hepatologist teams as pertinent to comanagement. The hospitalist nonphysician provider (NPP) and hepatologistfellow relationships are governed by the more traditional hierarchical dynamics based on supervision and authority according to laws and regulations. At the beginning of the study period, each participant completed nine items of a Baseline Survey that addressed respondents' expectations and preferences for the management of an ideally comanaged service. Responses were solicited using a 4‐point Likert‐type scale and were dichotomized such that agree and somewhat agree were grouped, while disagree and somewhat disagree were grouped for data analysis. Items were generated to address the salient issues of comanagement after reviewing the pertinent literature.

Subsequently, participants were asked to complete Repeated Surveys immediately before each change in membership of the comanaged team between April and October 2008. The surveys were hand delivered by one of the authors (K.H.) on the last day of each team's rotation and were often completed immediately. The seven items of the Repeated Survey reprised items from the Baseline Survey that were rephrased to allow respondents to report their direct experiences on specific teams. Because all providers rotated on the service more than once during the study period, the average value for each Likert‐type response across multiple surveys completed by a single provider was calculated before being dichotomized at the midpoint (<2.5, agree; 2.5, disagree). We reported proportions of respondents in agreement with survey item statements.

Comparison statistics across providers were generated using the chi‐square test. Differences in proportions between related items of the Baseline and Repeated Surveys were compared using the two‐sample test of proportions. All analyses were conducted using a statistics application (STATA 10.0, College Station, TX) with alpha equal to, or less than, 0.05 considered significant. The Institutional Review Board of the University of Chicago approved this project.

RESULTS

All 43 providers completed the Baseline Survey. During the study period, 32 of these participants rotated on the service and completed 177 of the 233 Repeated Surveys (79%) administered. The responses describe team interactions on the 47 unique combinations of providers comprising the comanaged teams. Details of the response rates are shown in Table 1.

Survey Response Rates by Provider Roles
 Baseline Survey, Completed/ Administered (%)Repeated Surveys, Completed/ Administered (%)Respondents Completing Repeated Surveys, nRepeated Surveys Completed per Respondent, Median (IQR)
  • Abbreviations: NPPs, nonphysician providers; IQR, interquartile range.

Hospitalists18/18 (100)36/43 (84)152 (2, 3)
NPPs5/5 (100)92/97 (95)520 (18, 20)
Hepatologists6/6 (100)26/42 (62)67 (3.75, 8)
Fellows12/12 (100)23/42 (55)67 (5.5, 8.5)
Total43/43 (100)177/223 (79)324.5 (2, 8.25)

As shown in Table 2A, items 13, more members of the hospitalist team preferred to be informed about every management decision compared to members of the hepatologist team. Conversely, more of members of the hepatologist team than the hospitalist team preferred their comanaging partners to participate in every decision. A statistically similar proportion of respondents in each of the professional roles indicated desire for greater influence in directing management decisions (Table 2B, item 1).

Proportion of Respondents Agreeing with Survey Item Statements
A. Baseline SurveyHospitalists, % (n = 18)NPPs, % (n = 5)Hepatologists, % (n = 6)GI Fellows, % (n = 12)P‐value
  • Abbreviations: GI, gastrointestinal; NPP, nonphysician provider.

  • Statistically significant difference between Baseline and Repeated Survey response defined by P 0.05.

1. I prefer to be informed about every decision.831001742<0.01
2. I prefer to participate in every decision.6710033500.11
3. I prefer that my comanager participate in every decision.222050750.02
4. I prefer to have the final say in every decision.508050330.38
5. There should be one physician leader to direct the overall management of the patients' hospital course.89*10067830.43
6. Physician consensus should always be sought in every clinical decision.224050670.11
7. I have a clear understanding of my role on the comanagement service.618083750.66
8. I have as much a sense of ownership of patients on the comanaged service as on a non‐comanaged service.616083500.60
9. Comanagement tends to improve patient care.94100*83100*0.47
B. Repeated SurveysHospitalists, % (n = 15)NPPs, % (n = 5)Hepatologists, % (n = 6)GI Fellows, % (n = 6)P‐value
1. I would have liked greater influence in directing the overall management.40600170.12
2. I was responsible for work in clinical areas I was not comfortable managing.0000NA
3. There was one physician leader to direct the overall management of the patients' hospital course.60*8067830.70
4. Physician consensus was always sought in every clinical decision.404050670.72
5. I (have/had) a clear understanding of my role on the comanagement service.7380100830.57
6. I had as much a sense of ownership of patients on the comanaged service as on a non‐comanaged service.5380100670.20
7. Patients on my service received better care than they would have without comanagement.9340*6750*0.06

For the majority of surveyed areas, there was concordance between expectations and experiences of providers on comanagement. Most providers, regardless of professional role, agreed that there should be a single physician leader to direct the overall management (Table 2A, item 5). The majority perceived that a single physician directed the overall management of the patients' hospital course, although fewer hospitalists did so compared with baseline expectations (Table 2B, item 3). Many respondents felt at baseline that physician consensus should govern every management decision, and a similar proportion actually experienced consensus‐seeking on service.

We found that the proportion of providers reporting an understanding of their role increased slightly, though not significantly, from before (Table 2A, item 7) to after rotating on the comanaged service (Table 2B, item 5). Although not statistically significant, there was a trend towards hospitalists and gastrointestinal (GI) fellows reporting a lack of patient ownership, both before and after serving on the comanaged service. Finally, nearly all respondents reported that comanagement should improve care quality, although only the attending hospitalist and hepatologist felt that their experience on the comanaged service actually improved patient care (Table 2B, item 7).

DISCUSSION

In this survey of providers participating on a comanaged medical service, most reported understanding their role in the collaborative arrangement and had an initial perception that comanagement should improve patient care quality. We found that hospitalists preferred and were expected to participate in care globally, while hepatologists themselves preferred and were expected not to focus on every management decision. The prevalence of desire for ultimate authority across the professional roles suggests tensions that exist in this care model around how decisions are made. The majority of providers preferred and experienced a single physician leader under comanagement, but many also experienced consensus‐seeking for every management decision.

From these findings, we conclude that decision‐making processes are not uniform under comanagement and that some role ambiguity is present, but there appears to be a pattern of natural roles. This pattern can be defined by focus (general for hospitalists vs specialty‐specific for hepatologists), rather than by responsibilities for managing particular medical problems. The preference among both generalists and specialists for the broader involvement of hospitalist comanagers suggests an implicit recognition of the need for integrated management to overcome the silo‐effect within the comanagement structure.9 Although details about how such integration was achieved are not available in our data, we found that comanagement may be distinct from traditional consultative practice in that the consultants (hospitalists in this case) manage not only general medical problems, such as diabetes or hypertension, but hospitalizations more generally. From a mission‐based standpoint, comanagement may be seen as a collaborative management of complex patients by two or more clinical experts with distinct knowledge, skills, or focus enacted for the purpose of improving care quality.

The focus of comanagement on improving quality is in line with the founding charge of the hospital medicine specialty to raise hospital care quality.10 In fact, the distinction between comanagement and consultation may be meaningful only if comanagers can work with specialists to implement evidence‐based practice, process improvement, and address quality and cost concerns. But as seen in NPPs and fellows' skepticism of improved quality under comanagement, there is still clearly work to be done to validate this model through measurable improvement in patients' experiences and outcomes. Proving the advantages of comanagement as a platform for practice improvement remains future work.11

Collaborative arrangements create natural tensions related to team function.5 This is seen in the similar proportion of hospitalists and hepatologists indicating desire for final decision‐making authority. Although comanagement evokes assumptions about egalitarian provider interactions involving shared decision‐making and responsibility, it seems to function empirically under hierarchical as well as consensus‐seeking forms of decision‐making. Providers at the top of hierarchical teams typically experience their work as interdependent and collaborative, and report more positive interactions with other care providers.12 Based on the fact that no hepatologists wanted more influence over decision‐making, we assume that hepatologists were the physician leaders for most of the studied comanaged teams. Under situations characterized by high levels of complexity and interdependence, a team governed by a single leader may often be more effective than one governed by shared authority.8 However, even under hierarchical models, a more participatory than supervisory leadership can help avoid alienating partners through a pattern of we decide, you carry it out that is often associated with ineffective leadership styles.1314 In fact, this alienating effect on providers in subordinate roles (ie, NPPs and fellows) may have contributed to the negative perception of the team's function on improving patient care.

This study is limited in the following ways. We did not have 100% participation in the Repeated Surveys. Attitudes and experiences of participants in a single comanagement practice are not representative of all comanaging providers. However, the goal of this studyto collect unique survey data from providers themselves to inform an evolving definition of comanagementis modest enough in scope to not require a generalizable sample. Because this study unearthed differences in expectations and experiences within a single site, they may serve as a lower bound for the extent of differences across and within multiple sites. In addition, comanagement enacted for complex medical patients is not as common as the comanagement of surgical patients. Moreover, comanagement models in academic hospitals may have structural features and priorities not found in community settings. Whether or not these disparate models share enough in common to be categorized under a single rubric is a valid question.

Although the teamwork structure and provider roles within comanagement vary, the practice arrangement's preoccupation with quality can be seen as its defining feature. Limited evidence, to date,1, 1519 and the rapid proliferation of the model, suggest that quality and efficiency advantages can be obtained from an effective implementation of comanagement. As in any team‐based care model, a common understanding of roles and expectations are essential to enhancing teamwork. Our interpretation of the mission of comanagement may further enhance teamwork through an explicit articulation of shared goals.

Comanagement is common in hospital medicine practice. And yet, there is no consensus about how comanagement is different from traditional consultative practice. At its core, hospitalist comanagement is a practice arrangement wherein hospitalists and other specialists manage complex patients collaboratively. Beyond this, Huddleston et al. distinguish comanagement from traditional consultations in the comanaging hospitalists' prerogative to provide direct medical care in addition to consultative advice.1 Siegal focuses on the shared responsibility and authority among partnering providers in the comanagement model.2 Whinney and Michota see comanagement as patient care referral at the onset of a care episode, in contrast to consultations that are activated to address emergent problems.3 In a recent study that found the growing adoption of medical comanagement in Medicare beneficiaries (as much as 40% of surgical hospitalizations in 2006), comanagement was defined as an intensive form of consultation involving a claim for evaluation and management services on greater than 70% of inpatient days.4

In addition to the intensity, frequency, timing, responsibility, and authority of care, comanagement may be described by participating physicians' roles. With recent attention on multidisciplinary teams and an increasing focus on collaborative care, many of the hierarchical relations among healthcare providers are breaking down.5 Several studies of multidisciplinary teams suggest that more egalitarian, rather than hierarchical, problem‐solving and decision‐making among team members are beneficial to patients.67 However, neither the intended nor natural team structure under comanagement is known. We sought to shed some light on provider interactions by characterizing the expectations and experiences of providers of a comanaged service. The findings yielded an opportunity to generate an evolving, but conceptually supported definition of comanagement.

SETTING

We conducted a survey study of providers participating in a comanaged inpatient hepatology service at the University of Chicago Medical Center, a 572‐bed urban teaching hospital. The service was created in 2006, partly to address staffing problems related to housestaff work hour restrictions and partly to improve the care of candidates and recipients of liver transplantation. Nonsurgical floor patients with liver diseases were managed on the service by two collaborating teams of providers. The hepatology team consisted of an attending physician and a fellow, while the hospitalist team consisted of a hospitalist and one or two nonphysician providers (physician assistant or nurse practitioner). The practice model is characterized as comanagement because of the highly interdependent nature of the team's daily tasks and the norms of intensive communication, through formal joint daily rounds and informal direct exchanges of instructions and updates. Hepatologists were mainly responsible for coordinating admissions, managing issues related to liver dysfunction, communicating with transplant surgeons if necessary, and arranging postdischarge care. Hospitalists were responsible for admitting patients, managing routine (eg, ordering daily labs) and urgent issues (eg, responding to critical lab values) during hospitalizations, coordinating with ancillary and consultative staff, and discharging patients. Occasional meetings between the hepatology and hospital medicine groups were used to clarify assignment of responsibilities. Floor nurses received in‐servicing at the commencement of the service. Additional details about the service are described elsewhere.8

DATA COLLECTION AND ANALYSIS

For the purpose of our analysis, we defined interactions between any member of the hospitalist and hepatologist teams as pertinent to comanagement. The hospitalist nonphysician provider (NPP) and hepatologistfellow relationships are governed by the more traditional hierarchical dynamics based on supervision and authority according to laws and regulations. At the beginning of the study period, each participant completed nine items of a Baseline Survey that addressed respondents' expectations and preferences for the management of an ideally comanaged service. Responses were solicited using a 4‐point Likert‐type scale and were dichotomized such that agree and somewhat agree were grouped, while disagree and somewhat disagree were grouped for data analysis. Items were generated to address the salient issues of comanagement after reviewing the pertinent literature.

Subsequently, participants were asked to complete Repeated Surveys immediately before each change in membership of the comanaged team between April and October 2008. The surveys were hand delivered by one of the authors (K.H.) on the last day of each team's rotation and were often completed immediately. The seven items of the Repeated Survey reprised items from the Baseline Survey that were rephrased to allow respondents to report their direct experiences on specific teams. Because all providers rotated on the service more than once during the study period, the average value for each Likert‐type response across multiple surveys completed by a single provider was calculated before being dichotomized at the midpoint (<2.5, agree; 2.5, disagree). We reported proportions of respondents in agreement with survey item statements.

Comparison statistics across providers were generated using the chi‐square test. Differences in proportions between related items of the Baseline and Repeated Surveys were compared using the two‐sample test of proportions. All analyses were conducted using a statistics application (STATA 10.0, College Station, TX) with alpha equal to, or less than, 0.05 considered significant. The Institutional Review Board of the University of Chicago approved this project.

RESULTS

All 43 providers completed the Baseline Survey. During the study period, 32 of these participants rotated on the service and completed 177 of the 233 Repeated Surveys (79%) administered. The responses describe team interactions on the 47 unique combinations of providers comprising the comanaged teams. Details of the response rates are shown in Table 1.

Survey Response Rates by Provider Roles
 Baseline Survey, Completed/ Administered (%)Repeated Surveys, Completed/ Administered (%)Respondents Completing Repeated Surveys, nRepeated Surveys Completed per Respondent, Median (IQR)
  • Abbreviations: NPPs, nonphysician providers; IQR, interquartile range.

Hospitalists18/18 (100)36/43 (84)152 (2, 3)
NPPs5/5 (100)92/97 (95)520 (18, 20)
Hepatologists6/6 (100)26/42 (62)67 (3.75, 8)
Fellows12/12 (100)23/42 (55)67 (5.5, 8.5)
Total43/43 (100)177/223 (79)324.5 (2, 8.25)

As shown in Table 2A, items 13, more members of the hospitalist team preferred to be informed about every management decision compared to members of the hepatologist team. Conversely, more of members of the hepatologist team than the hospitalist team preferred their comanaging partners to participate in every decision. A statistically similar proportion of respondents in each of the professional roles indicated desire for greater influence in directing management decisions (Table 2B, item 1).

Proportion of Respondents Agreeing with Survey Item Statements
A. Baseline SurveyHospitalists, % (n = 18)NPPs, % (n = 5)Hepatologists, % (n = 6)GI Fellows, % (n = 12)P‐value
  • Abbreviations: GI, gastrointestinal; NPP, nonphysician provider.

  • Statistically significant difference between Baseline and Repeated Survey response defined by P 0.05.

1. I prefer to be informed about every decision.831001742<0.01
2. I prefer to participate in every decision.6710033500.11
3. I prefer that my comanager participate in every decision.222050750.02
4. I prefer to have the final say in every decision.508050330.38
5. There should be one physician leader to direct the overall management of the patients' hospital course.89*10067830.43
6. Physician consensus should always be sought in every clinical decision.224050670.11
7. I have a clear understanding of my role on the comanagement service.618083750.66
8. I have as much a sense of ownership of patients on the comanaged service as on a non‐comanaged service.616083500.60
9. Comanagement tends to improve patient care.94100*83100*0.47
B. Repeated SurveysHospitalists, % (n = 15)NPPs, % (n = 5)Hepatologists, % (n = 6)GI Fellows, % (n = 6)P‐value
1. I would have liked greater influence in directing the overall management.40600170.12
2. I was responsible for work in clinical areas I was not comfortable managing.0000NA
3. There was one physician leader to direct the overall management of the patients' hospital course.60*8067830.70
4. Physician consensus was always sought in every clinical decision.404050670.72
5. I (have/had) a clear understanding of my role on the comanagement service.7380100830.57
6. I had as much a sense of ownership of patients on the comanaged service as on a non‐comanaged service.5380100670.20
7. Patients on my service received better care than they would have without comanagement.9340*6750*0.06

For the majority of surveyed areas, there was concordance between expectations and experiences of providers on comanagement. Most providers, regardless of professional role, agreed that there should be a single physician leader to direct the overall management (Table 2A, item 5). The majority perceived that a single physician directed the overall management of the patients' hospital course, although fewer hospitalists did so compared with baseline expectations (Table 2B, item 3). Many respondents felt at baseline that physician consensus should govern every management decision, and a similar proportion actually experienced consensus‐seeking on service.

We found that the proportion of providers reporting an understanding of their role increased slightly, though not significantly, from before (Table 2A, item 7) to after rotating on the comanaged service (Table 2B, item 5). Although not statistically significant, there was a trend towards hospitalists and gastrointestinal (GI) fellows reporting a lack of patient ownership, both before and after serving on the comanaged service. Finally, nearly all respondents reported that comanagement should improve care quality, although only the attending hospitalist and hepatologist felt that their experience on the comanaged service actually improved patient care (Table 2B, item 7).

DISCUSSION

In this survey of providers participating on a comanaged medical service, most reported understanding their role in the collaborative arrangement and had an initial perception that comanagement should improve patient care quality. We found that hospitalists preferred and were expected to participate in care globally, while hepatologists themselves preferred and were expected not to focus on every management decision. The prevalence of desire for ultimate authority across the professional roles suggests tensions that exist in this care model around how decisions are made. The majority of providers preferred and experienced a single physician leader under comanagement, but many also experienced consensus‐seeking for every management decision.

From these findings, we conclude that decision‐making processes are not uniform under comanagement and that some role ambiguity is present, but there appears to be a pattern of natural roles. This pattern can be defined by focus (general for hospitalists vs specialty‐specific for hepatologists), rather than by responsibilities for managing particular medical problems. The preference among both generalists and specialists for the broader involvement of hospitalist comanagers suggests an implicit recognition of the need for integrated management to overcome the silo‐effect within the comanagement structure.9 Although details about how such integration was achieved are not available in our data, we found that comanagement may be distinct from traditional consultative practice in that the consultants (hospitalists in this case) manage not only general medical problems, such as diabetes or hypertension, but hospitalizations more generally. From a mission‐based standpoint, comanagement may be seen as a collaborative management of complex patients by two or more clinical experts with distinct knowledge, skills, or focus enacted for the purpose of improving care quality.

The focus of comanagement on improving quality is in line with the founding charge of the hospital medicine specialty to raise hospital care quality.10 In fact, the distinction between comanagement and consultation may be meaningful only if comanagers can work with specialists to implement evidence‐based practice, process improvement, and address quality and cost concerns. But as seen in NPPs and fellows' skepticism of improved quality under comanagement, there is still clearly work to be done to validate this model through measurable improvement in patients' experiences and outcomes. Proving the advantages of comanagement as a platform for practice improvement remains future work.11

Collaborative arrangements create natural tensions related to team function.5 This is seen in the similar proportion of hospitalists and hepatologists indicating desire for final decision‐making authority. Although comanagement evokes assumptions about egalitarian provider interactions involving shared decision‐making and responsibility, it seems to function empirically under hierarchical as well as consensus‐seeking forms of decision‐making. Providers at the top of hierarchical teams typically experience their work as interdependent and collaborative, and report more positive interactions with other care providers.12 Based on the fact that no hepatologists wanted more influence over decision‐making, we assume that hepatologists were the physician leaders for most of the studied comanaged teams. Under situations characterized by high levels of complexity and interdependence, a team governed by a single leader may often be more effective than one governed by shared authority.8 However, even under hierarchical models, a more participatory than supervisory leadership can help avoid alienating partners through a pattern of we decide, you carry it out that is often associated with ineffective leadership styles.1314 In fact, this alienating effect on providers in subordinate roles (ie, NPPs and fellows) may have contributed to the negative perception of the team's function on improving patient care.

This study is limited in the following ways. We did not have 100% participation in the Repeated Surveys. Attitudes and experiences of participants in a single comanagement practice are not representative of all comanaging providers. However, the goal of this studyto collect unique survey data from providers themselves to inform an evolving definition of comanagementis modest enough in scope to not require a generalizable sample. Because this study unearthed differences in expectations and experiences within a single site, they may serve as a lower bound for the extent of differences across and within multiple sites. In addition, comanagement enacted for complex medical patients is not as common as the comanagement of surgical patients. Moreover, comanagement models in academic hospitals may have structural features and priorities not found in community settings. Whether or not these disparate models share enough in common to be categorized under a single rubric is a valid question.

Although the teamwork structure and provider roles within comanagement vary, the practice arrangement's preoccupation with quality can be seen as its defining feature. Limited evidence, to date,1, 1519 and the rapid proliferation of the model, suggest that quality and efficiency advantages can be obtained from an effective implementation of comanagement. As in any team‐based care model, a common understanding of roles and expectations are essential to enhancing teamwork. Our interpretation of the mission of comanagement may further enhance teamwork through an explicit articulation of shared goals.

References
  1. Huddleston JM,Long KH,Naessens JM, et al.Medical and surgical comanagement after elective hip and knee arthroplasty: A randomized, controlled trial.Ann Intern Med.2004;141(1):2838.
  2. Siegal EM.Just because you can, doesn't mean that you should: A call for the rational application of hospitalist comanagement.J Hosp Med.2008;3(5):398402.
  3. Whinney C,Michota F.Surgical comanagement: A natural evolution of hospitalist practice.J Hosp Med.2008;3(5):394397.
  4. Sharma G,Kuo Y‐F,Freeman J,Zhang DD,Goodwin JS.Comanagement of hospitalized surgical patients by medicine physicians in the United States.Arch Intern Med.2010;170(4):363368.
  5. Cott C.Structure and meaning in multidisciplinary teamwork.Sociol Health Illn.1998;20(6):848873.
  6. de Leval MR,Carthey J,Wright DJ,Farewell VT,Reason JT.Human factors and cardiac surgery: A multicenter study.J Thorac Cardiov Surg.2000;119(4):661670.
  7. Schraeder C,Shelton P,Sager M.The effects of a collaborative model of primary care on the mortality and hospital use of community‐dwelling older adults.J Gerontol A‐Biol.2001;56(2):M106M112.
  8. Hinami K,Whelan CT,Konetzka RT,Edelson DP,Casalino LP,Meltzer DO.Effects of provider characteristics on care coordination under comanagement.J Hosp Med.2010;5:508513.
  9. Corrigan JM,Donaldson MS,Kohn LT.Crossing the Quality Chasm: A New Health System for the Twenty‐First Century.Washington, DC:Institute of Medicine;2001.
  10. Wachter RM,Goldman L.The emerging role of “hospitalists” in the American health care system.N Engl J Med.1996;335(7):514517.
  11. O'Malley PG.Internal medicine comanagement of surgical patients: Can we afford to do this?Arch Intern Med.2010;170(22):19651966.
  12. Makary MA,Sexton JB,Freischlag JA, et al.Operating room teamwork among physicians and nurses: Teamwork in the eye of the beholder.J Am Coll Surg.2006;202(5):746752.
  13. Cott C.“We decide, you carry it out”: A social network analysis of multidisciplinary longterm care teams.Soc Sci Med.1997;45(9):14111421.
  14. Lewin K,Lippitt R,White RK.Patterns of aggressive behavior in experimentally created social climates.J Soc Psychol.1939;10:271301.
  15. Auerbach AD,Wachter RM,Cheng HQ, et al.Comanagement of surgical patients between neurosurgeons and hospitalists.Arch Intern Med.2010;170(22):20042010.
  16. Fisher AA,Davis MW,Rubenach SE,Sivakumaran S,Smith PN,Budge MM.Outcomes for older patients with hip fractures: The impact of orthopedic and geriatric medicine cocare.J Orthop Trauma.2006;20(3):172180.
  17. Phy MP,Vanness DJ,Melton LJ, et al.Effects of a hospitalist model on elderly patients with hip fracture.Arch Intern Med.2005;165(7):796801.
  18. Zuckerman JD,Sakales SR,Fabian DR,Frankel VH.Hip fractures in geriatric patients. Results of an interdisciplinary hospital care program.Clin Orthop Relat Res.1992(274):213225.
  19. Friedman SM,Mendelson DA,Bingham KW,Kates SL.Impact of a comanaged Geriatric Fracture Center on short‐term hip fracture outcomes.Arch Intern Med.2009;169(18):17121717.
References
  1. Huddleston JM,Long KH,Naessens JM, et al.Medical and surgical comanagement after elective hip and knee arthroplasty: A randomized, controlled trial.Ann Intern Med.2004;141(1):2838.
  2. Siegal EM.Just because you can, doesn't mean that you should: A call for the rational application of hospitalist comanagement.J Hosp Med.2008;3(5):398402.
  3. Whinney C,Michota F.Surgical comanagement: A natural evolution of hospitalist practice.J Hosp Med.2008;3(5):394397.
  4. Sharma G,Kuo Y‐F,Freeman J,Zhang DD,Goodwin JS.Comanagement of hospitalized surgical patients by medicine physicians in the United States.Arch Intern Med.2010;170(4):363368.
  5. Cott C.Structure and meaning in multidisciplinary teamwork.Sociol Health Illn.1998;20(6):848873.
  6. de Leval MR,Carthey J,Wright DJ,Farewell VT,Reason JT.Human factors and cardiac surgery: A multicenter study.J Thorac Cardiov Surg.2000;119(4):661670.
  7. Schraeder C,Shelton P,Sager M.The effects of a collaborative model of primary care on the mortality and hospital use of community‐dwelling older adults.J Gerontol A‐Biol.2001;56(2):M106M112.
  8. Hinami K,Whelan CT,Konetzka RT,Edelson DP,Casalino LP,Meltzer DO.Effects of provider characteristics on care coordination under comanagement.J Hosp Med.2010;5:508513.
  9. Corrigan JM,Donaldson MS,Kohn LT.Crossing the Quality Chasm: A New Health System for the Twenty‐First Century.Washington, DC:Institute of Medicine;2001.
  10. Wachter RM,Goldman L.The emerging role of “hospitalists” in the American health care system.N Engl J Med.1996;335(7):514517.
  11. O'Malley PG.Internal medicine comanagement of surgical patients: Can we afford to do this?Arch Intern Med.2010;170(22):19651966.
  12. Makary MA,Sexton JB,Freischlag JA, et al.Operating room teamwork among physicians and nurses: Teamwork in the eye of the beholder.J Am Coll Surg.2006;202(5):746752.
  13. Cott C.“We decide, you carry it out”: A social network analysis of multidisciplinary longterm care teams.Soc Sci Med.1997;45(9):14111421.
  14. Lewin K,Lippitt R,White RK.Patterns of aggressive behavior in experimentally created social climates.J Soc Psychol.1939;10:271301.
  15. Auerbach AD,Wachter RM,Cheng HQ, et al.Comanagement of surgical patients between neurosurgeons and hospitalists.Arch Intern Med.2010;170(22):20042010.
  16. Fisher AA,Davis MW,Rubenach SE,Sivakumaran S,Smith PN,Budge MM.Outcomes for older patients with hip fractures: The impact of orthopedic and geriatric medicine cocare.J Orthop Trauma.2006;20(3):172180.
  17. Phy MP,Vanness DJ,Melton LJ, et al.Effects of a hospitalist model on elderly patients with hip fracture.Arch Intern Med.2005;165(7):796801.
  18. Zuckerman JD,Sakales SR,Fabian DR,Frankel VH.Hip fractures in geriatric patients. Results of an interdisciplinary hospital care program.Clin Orthop Relat Res.1992(274):213225.
  19. Friedman SM,Mendelson DA,Bingham KW,Kates SL.Impact of a comanaged Geriatric Fracture Center on short‐term hip fracture outcomes.Arch Intern Med.2009;169(18):17121717.
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Tried and true: A survey of successfully promoted academic hospitalists

The growth of academic hospital medicine has been driven by multiple factors including expanding clinical needs, housestaff duty hours' limitations, and an increasing focus on quality and patient safety.1 Hospitalists at academic medical centers frequently assume roles that differ substantially from traditional faculty positions. Academic hospitalists may have predominantly clinical positions, and may be involved in quality improvement and patient safety projects.24 Because of these commitments, many academic hospitalists spend less time on research or educational efforts.1, 5 Many have raised concerns that these unique job descriptions might lead to less time to devote to scholarship and academic pursuits, and consequently greater challenges in the promotions process.2, 5

There are little published data on promotion and tenure in academics, and even less specifically focused on the promotion of hospitalists. Theoretically, promotion should recognize an individual's contribution to his or her institution and field. However, each institution has unique criteria though which faculty achieve promotion. Previous articles addressing specific groups, such as part‐time,6 clinical faculty,79 or clinician‐educators10 may be relevant to hospitalists, as hospitalists may be more likely to fall into these categories. These reports suggest general agreement that promotion committees should consider and weigh clinical and educational work (in addition to scholarly publications) in the promotions process, but assessment methods vary across institutions and the contribution of activities, such as quality improvement, remain unclear. The educator's portfolio has gained momentum as a way to document valued teaching in many institutions,11, 12 but academic hospitalist participation in education may be limited.13

Literature related to the development of Divisions of General Internal Medicine is relevant insofar as similar concerns for promotion were expressed with the growth of their faculty.14, 15 However, its applicability may be limited by differences between roles of hospitalists and more traditional general medicine faculty.

To better understand the factors influencing promotion for academic hospitalists, the Society of General Internal Medicine (SGIM) Academic Hospitalist Task Force (AHTF) undertook a survey of promoted hospitalists who had successfully reached the rank of Associate Professor or higher.

Methods

Development of the Survey

The AHTF is a group of 18 academic hospitalists representing 15 institutions. Draft survey questions were developed by the group and sent to its members for refinement based on group consensus. Three cycles of refinement were performed, and the final survey (Appendix) was converted into an electronic format distributed through SurveyMonkey (SurveyMonkey.com, Portland, OR).

Identification of Survey Recipients

We identified a convenience sample of hospitalists who had been promoted to Associate or Full Professor of Medicine by querying members of the AHTF, the Society of Hospital Medicine (SHM) Academic Committee, and colleagues of academic medical centers with established hospitalist programs. We identified 33 promoted hospitalists.

Each recipient received an email from the AHTF cochairs in January 2009 asking them to complete the survey. If a response was not received in three weeks, a second email was sent. If a response was again not received, an AHTF task‐force member who knew the recipient asked him or her to complete the survey. All responses were received by March 2009.

Data Analysis

We examined responses using descriptive measures. Responses were analyzed across all respondents, as well as between these two subgroups. Statistical analysis with Fisher's exact test was performed using Stata 9.0 (StataCorp, College Station, TX).

Results

Of the 33 hospitalists who received the survey, 26 responded (response rate of 79%). Of these, 25 completed the survey in its entirely and were included in our analysis; 1 did not submit details regarding specific promotion‐related activities. General information regarding the respondents and their programs at the time of their promotion is contained in Table 1.

Respondent and Hospitalist Program Characteristics
  • Tenure not defined by survey, but was reported by the respondent.

No. of institutions represented20
Program age5.7 years (range 110)
Size of hospitalist program at the time of promotion10 (range 128)
Size of hospitalist program currently25 (range 745)
Programs that were separate divisions at the time of respondent promotion4 (20%)
Programs that are now separate divisions8 (40%)
Programs with 1‐track* promotion system2 (10%)
Programs with 2‐track promotion system8 (40%)
Programs with 3‐track promotion system9 (45%)
Other type of promotion system1 (5%)
Tenure track*8 (32%)
Institutions with tenure and promotion criteria that explicitly recognized hospitalist work8 (40%)

The seven nonrespondents were from seven different institutions; however two of these institutions were represented by respondents. One nonrespondent had achieved a rank of Professor (through general medicine); the rest had been promoted to Associate Professor. One nonrespondent is known by the authors to hold a research position.

Ten respondents identified themselves as clinician‐educators (40%), ten as clinician‐administrators (40%), and five as clinician‐researchers (20%). Seventeen (68%) of the promoted hospitalists were not on a tenure track (as defined by them); they were more likely to have administrative or educational roles than a research appointment. Though the majority of self‐identified researchers were among the earliest to have been promoted, there were no statistically significant differences in self‐defined job description between more and less recently promoted hospitalists.

Promoted hospitalists were involved in a diverse range of activities which supported their promotion, including service (eg, institutional committees), education, research, and quality improvement. Nearly all hospitalists surveyed listed teaching and educational activities, and almost all had disseminated scholarly output and some degree of grant funding. Table 2 lists the specific activities in which respondents reported being engaged in each of these domains.

Types of Activities Performed by Promoted Hospitalists
ActivityPercent of Respondents Engaged in Activity
Service
School of Medicine56
Department of Medicine84
Hospital80
Professional societies92
Administration67
Education
Medical student72
Housestaff lectures84
Ward/consult attending96
Clinic precepting40
Course director/curriculum development80
Program director (or associate)36
Research
Peer‐reviewed publications92
Abstract/poster presentations80
Invited speaker96
Reviewer/editor80
Study section24
Federal grants32
Nonfederal grants (internal and external)72
Quality improvement/patient safety
Project member36
Project leader52
Institutional leadership32
Curriculum development32

A range of individuals assisted the respondents in the promotion process. Twenty‐three (92%) respondents identified the individuals who supported their promotion, and all listed more than one person. Respondents most commonly credited their Section or Division Chief (43%) with facilitating their promotion, followed by Departmental Chairs or Vice/Associate Chairs (22%). Mentors (13%) or peers (8%) were also named. Four respondents (17%) named themselves as the person providing most guidance through the promotions process.

No consistent themes regarding obstacles emerged from free‐text responses to questions about the promotions process. One respondent felt that high clinical expectations made participation in other academic activities a challenge. The only other barriers noted were not being on the radar screen of the Division Chief of GIM, and difficulty identifying external, senior hospitalists to write letters in support of promotion.

When asked about the most important activities supporting their promotion, 24 respondents listed one to two key activities, detailed in Table 3. The most common response was peer‐reviewed publications (33%). Activities related to education and/or teaching were the next most common response (29%), specifically teaching, educational activities, curriculum design, or program director. Research or research funding represented 26% of responses. Valued activities outside of the respondent's institution included national reputation (21%) and service in professional societies (16%). Service or administrative responsibilities were mentioned by 25% of respondents.

Reported Most Important Activities for Supporting Promotion
Category of ActivityFrequency of Response* (%)
  • Twenty‐four respondents answered this question.

Research14 (58)
Peer‐reviewed publications8 (33)
Research4 (16)
Research funding2 (8)
Activities outside institution8 (33)
National reputation5 (21)
Professional society membership3 (13)
Education7 (29)
Teaching3 (13)
Educational activities2 (8)
Residency Director1 (4)
Curriculum development1 (4)
Service6 (25)
Service3 (13)
Administration/leadership of group3 (13)

Discussion

We conducted a unique and comprehensive survey of academic hospitalists who have been promoted since 1995. We identified the most common and important activities contributing to promotion. Contrary to our expectations, survey respondents generally did not report being a hospitalist was a barrier in the promotions process.

Respondents were engaged in a diverse range of activities, including service, education, and research. Interestingly, no one identified him or herself primarily as a clinician. Teaching appeared to be a core component for all surveyed, regardless of academic appointment. Only one felt that her clinical workload as a hospitalist was an obstacle that prevented her from being engaged in other activities important for promotion. With more programs potentially evolving to separate divisions, the issue of being on the radar screen of a General Internal Medicine Division Chief may become less common over time. We hope that as programs mature and the numbers of associate and full professors increase, there will not be difficulty obtaining outside letters.

Although only 23% self‐identified as clinician‐researchers, nearly all had peer‐reviewed publications and other evidence of disseminated scholarly work. Grant funding, both federal and nonfederal, was also common among this group. This finding is consistent with self‐reported activities of a cohort of junior internal medicine faculty followed over three years who were eventually promoted, though the majority of those participants were classified as having either traditional clinician‐educator or clinician‐researcher positions.16

Despite outlining a seemingly clear pathway to promotion for hospitalists, concerns remain. Most importantly, those surveyed seem to have achieved promotion through relatively traditional academic job descriptions. Obtaining or maintaining these types of positions may be difficult as clinical needs at academic centers increase. According to a recent survey of hospitalist faculty,13 over one‐third spend more than 60% of their time on nonteaching clinical services. In that survey, over half of respondents had little or no protected time for scholarly activities. The contrast between this survey's findings and ours raises the question of whether our promoted sample had positions similar to those of most academic hospitalists. Given that the majority of our respondents noted peer‐reviewed publications and grant funding to be among the most important activities for promotion, there may be a dangerous disconnect for junior academic hospitalists who spend the majority of their time in direct patient care. Moreover, the promoted hospitalists in our survey reported relatively less participation in quality improvement/patient safety activities, in contrast to both anecdotal and survey reports that these activities are a major component of many academic hospitalist positions.5, 17 Most academic medical centers do not yet consider achievements in this area in their promotions criteria, potentially creating a barrier for the ranks of clinician quality improvers.1 Thus, significant obstacles to promotion of academic hospitalists may exist.

Leaders in academic hospital medicine are recognizing these potential barriers. A diverse group from major professional societies recently published a summary of the challenges and opportunities for the field of academic hospital medicine.1 Several needs and areas for intervention were identified, including enhanced faculty development and improved documentation of quality improvement activities. The SGIM, the SHM, and the Association of Chiefs and Leaders of General Internal Medicine (ACLGIM) recently cosponsored an intensive four‐day faculty development course for junior faculty to promote skills necessary for academic hospitalist success. Early reports indicate that this was a success.1820

In addition, the AHTF has developed a Quality Portfolio, paralleling the Educator's Portfolio, that can be used as a tool for documenting quality improvement and patient safety activities in a way that can be useful for career development and promotion.4 Lastly, the Society of Hospital Medicine has hosted the inaugural Academic Hospital Medicine Leadership Summit as part of the national meeting to provide mentorship and professional development opportunities for junior faculty. Our hope is that these opportunities, coupled with the growth of mid‐level and senior leaders in hospital medicine, will provide greater infrastructure for the development and promotion of junior faculty.

Our results may have relevance beyond hospitalist groups. With anticipated further limits on housestaff duty hours, more academic physicians may be asked to fill predominantly clinical roles. In addition, a growing emphasis on quality and patient safety may lead to a more general expansion of academicians who focus on these areas.15

Our survey and methodology have limitations. By including only promoted individuals, we did not survey hospitalists with the most difficulties in the promotions processthose who were not promoted. Thus, we are unable to directly compare successful versus unsuccessful strategies. Identifying nonpromoted academic hospitalists to understand the reasons they were not (or have not yet been) promoted could be a next step in this line of inquiry. Additionally, understanding the attitudes of promotions committees regarding hospitalists, and the clinical and quality improvement roles in which they are engaged, could enhance our current results. Finally, we surveyed a convenience sample of a limited numbers of hospitalists and institutions, and were unable to systematically account for variations in promotions criteria across institutions. However, to our knowledge, this is the most comprehensive study of promotions among academic hospitalists to date. Given the common themes that emerged in terms of activities that supported promotion, mentors, and advice, we believe that our sample was sufficient to identify important themes and advance our understanding of this nascent specialty.

In conclusion, our survey of promoted hospital medicine faculty provides valuable information for junior faculty and hospitalist leaders. Success was found through engaging in a diverse set of activities in the traditional areas of education, service, and scholarship, frequently in conjunction with developing recognition outside of their institutions. While all respondents were clinically active, none described themselves as having purely clinical roles. As academic hospitalist roles evolve, academic leaders will need to provide adequate mentorship, create time for scholarly pursuits, and promote documentation and recognition of nontraditional activities that may nonetheless be worthy of promotion.

Files
References
  1. Flanders SA,Centor B,Weber V,McGinn T,DeSalvo K,Aurebach A.Challenges and opportunities in academic hospital medicine: report from the academic hospital medicine summit.J Gen Intern Med.2009;24(5):636641.
  2. Glasheen JJ,Goldenberg J,Nelson JR.Hospitalist educators: future of inpatient internal medicine training.Mt Sinai J Med.2008;75(5):436451.
  3. Flanders SA,Kaufman SR,Saint S,Parekh VI.Hospitalists as emerging leaders in patient safety: lessons learned and future directions.J Patient Saf.2009;5(1):38.
  4. Quality Portfolio—Template and Instructions. Available at: http://www.sgim.org/userfiles/file/AHTF%20QP%20WEB%20TEMPLATE%20INS TRUCTIONS.pdf. Accessed on April 24,2010.
  5. Howell E.An innovative approach to support hospitalist physicians toward academic success.J Hosp Med.2008;3:314318.
  6. Socolar RR,Kelman LS,Lannon CM,Lohr JA.Institutional policies of U.S. medical schools regarding tenure, promotion, and benefits for part‐time faculty.Acad Med.2000;75(8):846849.
  7. Nora LM,Pomeroy C,Currey TE,Hill NS,Tibbs PA,Wilson EA.Revising appointment, promotion, and tenure procedures to incorporate an expanded definition of scholarship: the University of Kentucky College of Medicine experience.Acad Med.2000;75(9)913924.
  8. Buckley LM,Sanders K,Shih M,Hampton CL.Attitudes of clinical faculty about career progress, career success and recognition, and commitment to academic medicine. Results of a survey.Arch Intern Med.2000;160(17):26252629.
  9. Atasoylu AA,Wright SM,Beasley BW, et al.Promotion criteria for clinician‐educators.J Gen Intern Med.2003;18(9):711716.
  10. Simpson D,Hafler J,Brown D,Wilkerson L.Documentation systems for educators seeking academic promotion in U.S. medical schools.Acad Med.2004;79(8):783790.
  11. Coates WC,Hobgood CD,Birnbaum A,Farrell SE.Faculty development: academic opportunities for emergency medicine faculty on education career tracks.Acad Emerg Med.2003;10(10):11131117.
  12. Gopal R,Glasheen JJ,Miyoshi TJ,Prochazka AV.Burnout and internal medicine resident work hours restrictions.Arch Intern Med.2005;165(22):25952600.
  13. Karpj MD,Levey GS.Development of a Division of General Medicine in a Department of Internal Medicine.J Med Ed.1981;56:390396.
  14. Petersdorf RD.The evolution of departments of medicine.N Engl J Med.1980;303(9):489496.
  15. Shojania KG,Levinson W.Clinicians in quality improvement: a new career pathway in academic medicine.JAMA.2009;301(7):766768.
  16. Beasley BW,Simon SD,Wright SM.A time to be promoted. The prospective study of promotion in academia.J Gen Intern Med.2006;21(2):123129.
  17. Academic Hospitalist Academy. Available at: http://www.sgim.org/index. cfm?pageId=815. Accessed on April 24,2010.
  18. Stubbs D.Reflections on the Academic Hospitalist Academy.SGIM Forum.2010;33(1):5.
  19. Souder J.The Academic Hospitalist Academy: Get anchored, equipped, and energized.SGIM Forum.2010;33(1):56.
  20. Weaver C.Four formative days in the life of an academic hospitalist: the Academic Hospitalist Academy.SGIM Forum.2010;33(1):6.
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The growth of academic hospital medicine has been driven by multiple factors including expanding clinical needs, housestaff duty hours' limitations, and an increasing focus on quality and patient safety.1 Hospitalists at academic medical centers frequently assume roles that differ substantially from traditional faculty positions. Academic hospitalists may have predominantly clinical positions, and may be involved in quality improvement and patient safety projects.24 Because of these commitments, many academic hospitalists spend less time on research or educational efforts.1, 5 Many have raised concerns that these unique job descriptions might lead to less time to devote to scholarship and academic pursuits, and consequently greater challenges in the promotions process.2, 5

There are little published data on promotion and tenure in academics, and even less specifically focused on the promotion of hospitalists. Theoretically, promotion should recognize an individual's contribution to his or her institution and field. However, each institution has unique criteria though which faculty achieve promotion. Previous articles addressing specific groups, such as part‐time,6 clinical faculty,79 or clinician‐educators10 may be relevant to hospitalists, as hospitalists may be more likely to fall into these categories. These reports suggest general agreement that promotion committees should consider and weigh clinical and educational work (in addition to scholarly publications) in the promotions process, but assessment methods vary across institutions and the contribution of activities, such as quality improvement, remain unclear. The educator's portfolio has gained momentum as a way to document valued teaching in many institutions,11, 12 but academic hospitalist participation in education may be limited.13

Literature related to the development of Divisions of General Internal Medicine is relevant insofar as similar concerns for promotion were expressed with the growth of their faculty.14, 15 However, its applicability may be limited by differences between roles of hospitalists and more traditional general medicine faculty.

To better understand the factors influencing promotion for academic hospitalists, the Society of General Internal Medicine (SGIM) Academic Hospitalist Task Force (AHTF) undertook a survey of promoted hospitalists who had successfully reached the rank of Associate Professor or higher.

Methods

Development of the Survey

The AHTF is a group of 18 academic hospitalists representing 15 institutions. Draft survey questions were developed by the group and sent to its members for refinement based on group consensus. Three cycles of refinement were performed, and the final survey (Appendix) was converted into an electronic format distributed through SurveyMonkey (SurveyMonkey.com, Portland, OR).

Identification of Survey Recipients

We identified a convenience sample of hospitalists who had been promoted to Associate or Full Professor of Medicine by querying members of the AHTF, the Society of Hospital Medicine (SHM) Academic Committee, and colleagues of academic medical centers with established hospitalist programs. We identified 33 promoted hospitalists.

Each recipient received an email from the AHTF cochairs in January 2009 asking them to complete the survey. If a response was not received in three weeks, a second email was sent. If a response was again not received, an AHTF task‐force member who knew the recipient asked him or her to complete the survey. All responses were received by March 2009.

Data Analysis

We examined responses using descriptive measures. Responses were analyzed across all respondents, as well as between these two subgroups. Statistical analysis with Fisher's exact test was performed using Stata 9.0 (StataCorp, College Station, TX).

Results

Of the 33 hospitalists who received the survey, 26 responded (response rate of 79%). Of these, 25 completed the survey in its entirely and were included in our analysis; 1 did not submit details regarding specific promotion‐related activities. General information regarding the respondents and their programs at the time of their promotion is contained in Table 1.

Respondent and Hospitalist Program Characteristics
  • Tenure not defined by survey, but was reported by the respondent.

No. of institutions represented20
Program age5.7 years (range 110)
Size of hospitalist program at the time of promotion10 (range 128)
Size of hospitalist program currently25 (range 745)
Programs that were separate divisions at the time of respondent promotion4 (20%)
Programs that are now separate divisions8 (40%)
Programs with 1‐track* promotion system2 (10%)
Programs with 2‐track promotion system8 (40%)
Programs with 3‐track promotion system9 (45%)
Other type of promotion system1 (5%)
Tenure track*8 (32%)
Institutions with tenure and promotion criteria that explicitly recognized hospitalist work8 (40%)

The seven nonrespondents were from seven different institutions; however two of these institutions were represented by respondents. One nonrespondent had achieved a rank of Professor (through general medicine); the rest had been promoted to Associate Professor. One nonrespondent is known by the authors to hold a research position.

Ten respondents identified themselves as clinician‐educators (40%), ten as clinician‐administrators (40%), and five as clinician‐researchers (20%). Seventeen (68%) of the promoted hospitalists were not on a tenure track (as defined by them); they were more likely to have administrative or educational roles than a research appointment. Though the majority of self‐identified researchers were among the earliest to have been promoted, there were no statistically significant differences in self‐defined job description between more and less recently promoted hospitalists.

Promoted hospitalists were involved in a diverse range of activities which supported their promotion, including service (eg, institutional committees), education, research, and quality improvement. Nearly all hospitalists surveyed listed teaching and educational activities, and almost all had disseminated scholarly output and some degree of grant funding. Table 2 lists the specific activities in which respondents reported being engaged in each of these domains.

Types of Activities Performed by Promoted Hospitalists
ActivityPercent of Respondents Engaged in Activity
Service
School of Medicine56
Department of Medicine84
Hospital80
Professional societies92
Administration67
Education
Medical student72
Housestaff lectures84
Ward/consult attending96
Clinic precepting40
Course director/curriculum development80
Program director (or associate)36
Research
Peer‐reviewed publications92
Abstract/poster presentations80
Invited speaker96
Reviewer/editor80
Study section24
Federal grants32
Nonfederal grants (internal and external)72
Quality improvement/patient safety
Project member36
Project leader52
Institutional leadership32
Curriculum development32

A range of individuals assisted the respondents in the promotion process. Twenty‐three (92%) respondents identified the individuals who supported their promotion, and all listed more than one person. Respondents most commonly credited their Section or Division Chief (43%) with facilitating their promotion, followed by Departmental Chairs or Vice/Associate Chairs (22%). Mentors (13%) or peers (8%) were also named. Four respondents (17%) named themselves as the person providing most guidance through the promotions process.

No consistent themes regarding obstacles emerged from free‐text responses to questions about the promotions process. One respondent felt that high clinical expectations made participation in other academic activities a challenge. The only other barriers noted were not being on the radar screen of the Division Chief of GIM, and difficulty identifying external, senior hospitalists to write letters in support of promotion.

When asked about the most important activities supporting their promotion, 24 respondents listed one to two key activities, detailed in Table 3. The most common response was peer‐reviewed publications (33%). Activities related to education and/or teaching were the next most common response (29%), specifically teaching, educational activities, curriculum design, or program director. Research or research funding represented 26% of responses. Valued activities outside of the respondent's institution included national reputation (21%) and service in professional societies (16%). Service or administrative responsibilities were mentioned by 25% of respondents.

Reported Most Important Activities for Supporting Promotion
Category of ActivityFrequency of Response* (%)
  • Twenty‐four respondents answered this question.

Research14 (58)
Peer‐reviewed publications8 (33)
Research4 (16)
Research funding2 (8)
Activities outside institution8 (33)
National reputation5 (21)
Professional society membership3 (13)
Education7 (29)
Teaching3 (13)
Educational activities2 (8)
Residency Director1 (4)
Curriculum development1 (4)
Service6 (25)
Service3 (13)
Administration/leadership of group3 (13)

Discussion

We conducted a unique and comprehensive survey of academic hospitalists who have been promoted since 1995. We identified the most common and important activities contributing to promotion. Contrary to our expectations, survey respondents generally did not report being a hospitalist was a barrier in the promotions process.

Respondents were engaged in a diverse range of activities, including service, education, and research. Interestingly, no one identified him or herself primarily as a clinician. Teaching appeared to be a core component for all surveyed, regardless of academic appointment. Only one felt that her clinical workload as a hospitalist was an obstacle that prevented her from being engaged in other activities important for promotion. With more programs potentially evolving to separate divisions, the issue of being on the radar screen of a General Internal Medicine Division Chief may become less common over time. We hope that as programs mature and the numbers of associate and full professors increase, there will not be difficulty obtaining outside letters.

Although only 23% self‐identified as clinician‐researchers, nearly all had peer‐reviewed publications and other evidence of disseminated scholarly work. Grant funding, both federal and nonfederal, was also common among this group. This finding is consistent with self‐reported activities of a cohort of junior internal medicine faculty followed over three years who were eventually promoted, though the majority of those participants were classified as having either traditional clinician‐educator or clinician‐researcher positions.16

Despite outlining a seemingly clear pathway to promotion for hospitalists, concerns remain. Most importantly, those surveyed seem to have achieved promotion through relatively traditional academic job descriptions. Obtaining or maintaining these types of positions may be difficult as clinical needs at academic centers increase. According to a recent survey of hospitalist faculty,13 over one‐third spend more than 60% of their time on nonteaching clinical services. In that survey, over half of respondents had little or no protected time for scholarly activities. The contrast between this survey's findings and ours raises the question of whether our promoted sample had positions similar to those of most academic hospitalists. Given that the majority of our respondents noted peer‐reviewed publications and grant funding to be among the most important activities for promotion, there may be a dangerous disconnect for junior academic hospitalists who spend the majority of their time in direct patient care. Moreover, the promoted hospitalists in our survey reported relatively less participation in quality improvement/patient safety activities, in contrast to both anecdotal and survey reports that these activities are a major component of many academic hospitalist positions.5, 17 Most academic medical centers do not yet consider achievements in this area in their promotions criteria, potentially creating a barrier for the ranks of clinician quality improvers.1 Thus, significant obstacles to promotion of academic hospitalists may exist.

Leaders in academic hospital medicine are recognizing these potential barriers. A diverse group from major professional societies recently published a summary of the challenges and opportunities for the field of academic hospital medicine.1 Several needs and areas for intervention were identified, including enhanced faculty development and improved documentation of quality improvement activities. The SGIM, the SHM, and the Association of Chiefs and Leaders of General Internal Medicine (ACLGIM) recently cosponsored an intensive four‐day faculty development course for junior faculty to promote skills necessary for academic hospitalist success. Early reports indicate that this was a success.1820

In addition, the AHTF has developed a Quality Portfolio, paralleling the Educator's Portfolio, that can be used as a tool for documenting quality improvement and patient safety activities in a way that can be useful for career development and promotion.4 Lastly, the Society of Hospital Medicine has hosted the inaugural Academic Hospital Medicine Leadership Summit as part of the national meeting to provide mentorship and professional development opportunities for junior faculty. Our hope is that these opportunities, coupled with the growth of mid‐level and senior leaders in hospital medicine, will provide greater infrastructure for the development and promotion of junior faculty.

Our results may have relevance beyond hospitalist groups. With anticipated further limits on housestaff duty hours, more academic physicians may be asked to fill predominantly clinical roles. In addition, a growing emphasis on quality and patient safety may lead to a more general expansion of academicians who focus on these areas.15

Our survey and methodology have limitations. By including only promoted individuals, we did not survey hospitalists with the most difficulties in the promotions processthose who were not promoted. Thus, we are unable to directly compare successful versus unsuccessful strategies. Identifying nonpromoted academic hospitalists to understand the reasons they were not (or have not yet been) promoted could be a next step in this line of inquiry. Additionally, understanding the attitudes of promotions committees regarding hospitalists, and the clinical and quality improvement roles in which they are engaged, could enhance our current results. Finally, we surveyed a convenience sample of a limited numbers of hospitalists and institutions, and were unable to systematically account for variations in promotions criteria across institutions. However, to our knowledge, this is the most comprehensive study of promotions among academic hospitalists to date. Given the common themes that emerged in terms of activities that supported promotion, mentors, and advice, we believe that our sample was sufficient to identify important themes and advance our understanding of this nascent specialty.

In conclusion, our survey of promoted hospital medicine faculty provides valuable information for junior faculty and hospitalist leaders. Success was found through engaging in a diverse set of activities in the traditional areas of education, service, and scholarship, frequently in conjunction with developing recognition outside of their institutions. While all respondents were clinically active, none described themselves as having purely clinical roles. As academic hospitalist roles evolve, academic leaders will need to provide adequate mentorship, create time for scholarly pursuits, and promote documentation and recognition of nontraditional activities that may nonetheless be worthy of promotion.

The growth of academic hospital medicine has been driven by multiple factors including expanding clinical needs, housestaff duty hours' limitations, and an increasing focus on quality and patient safety.1 Hospitalists at academic medical centers frequently assume roles that differ substantially from traditional faculty positions. Academic hospitalists may have predominantly clinical positions, and may be involved in quality improvement and patient safety projects.24 Because of these commitments, many academic hospitalists spend less time on research or educational efforts.1, 5 Many have raised concerns that these unique job descriptions might lead to less time to devote to scholarship and academic pursuits, and consequently greater challenges in the promotions process.2, 5

There are little published data on promotion and tenure in academics, and even less specifically focused on the promotion of hospitalists. Theoretically, promotion should recognize an individual's contribution to his or her institution and field. However, each institution has unique criteria though which faculty achieve promotion. Previous articles addressing specific groups, such as part‐time,6 clinical faculty,79 or clinician‐educators10 may be relevant to hospitalists, as hospitalists may be more likely to fall into these categories. These reports suggest general agreement that promotion committees should consider and weigh clinical and educational work (in addition to scholarly publications) in the promotions process, but assessment methods vary across institutions and the contribution of activities, such as quality improvement, remain unclear. The educator's portfolio has gained momentum as a way to document valued teaching in many institutions,11, 12 but academic hospitalist participation in education may be limited.13

Literature related to the development of Divisions of General Internal Medicine is relevant insofar as similar concerns for promotion were expressed with the growth of their faculty.14, 15 However, its applicability may be limited by differences between roles of hospitalists and more traditional general medicine faculty.

To better understand the factors influencing promotion for academic hospitalists, the Society of General Internal Medicine (SGIM) Academic Hospitalist Task Force (AHTF) undertook a survey of promoted hospitalists who had successfully reached the rank of Associate Professor or higher.

Methods

Development of the Survey

The AHTF is a group of 18 academic hospitalists representing 15 institutions. Draft survey questions were developed by the group and sent to its members for refinement based on group consensus. Three cycles of refinement were performed, and the final survey (Appendix) was converted into an electronic format distributed through SurveyMonkey (SurveyMonkey.com, Portland, OR).

Identification of Survey Recipients

We identified a convenience sample of hospitalists who had been promoted to Associate or Full Professor of Medicine by querying members of the AHTF, the Society of Hospital Medicine (SHM) Academic Committee, and colleagues of academic medical centers with established hospitalist programs. We identified 33 promoted hospitalists.

Each recipient received an email from the AHTF cochairs in January 2009 asking them to complete the survey. If a response was not received in three weeks, a second email was sent. If a response was again not received, an AHTF task‐force member who knew the recipient asked him or her to complete the survey. All responses were received by March 2009.

Data Analysis

We examined responses using descriptive measures. Responses were analyzed across all respondents, as well as between these two subgroups. Statistical analysis with Fisher's exact test was performed using Stata 9.0 (StataCorp, College Station, TX).

Results

Of the 33 hospitalists who received the survey, 26 responded (response rate of 79%). Of these, 25 completed the survey in its entirely and were included in our analysis; 1 did not submit details regarding specific promotion‐related activities. General information regarding the respondents and their programs at the time of their promotion is contained in Table 1.

Respondent and Hospitalist Program Characteristics
  • Tenure not defined by survey, but was reported by the respondent.

No. of institutions represented20
Program age5.7 years (range 110)
Size of hospitalist program at the time of promotion10 (range 128)
Size of hospitalist program currently25 (range 745)
Programs that were separate divisions at the time of respondent promotion4 (20%)
Programs that are now separate divisions8 (40%)
Programs with 1‐track* promotion system2 (10%)
Programs with 2‐track promotion system8 (40%)
Programs with 3‐track promotion system9 (45%)
Other type of promotion system1 (5%)
Tenure track*8 (32%)
Institutions with tenure and promotion criteria that explicitly recognized hospitalist work8 (40%)

The seven nonrespondents were from seven different institutions; however two of these institutions were represented by respondents. One nonrespondent had achieved a rank of Professor (through general medicine); the rest had been promoted to Associate Professor. One nonrespondent is known by the authors to hold a research position.

Ten respondents identified themselves as clinician‐educators (40%), ten as clinician‐administrators (40%), and five as clinician‐researchers (20%). Seventeen (68%) of the promoted hospitalists were not on a tenure track (as defined by them); they were more likely to have administrative or educational roles than a research appointment. Though the majority of self‐identified researchers were among the earliest to have been promoted, there were no statistically significant differences in self‐defined job description between more and less recently promoted hospitalists.

Promoted hospitalists were involved in a diverse range of activities which supported their promotion, including service (eg, institutional committees), education, research, and quality improvement. Nearly all hospitalists surveyed listed teaching and educational activities, and almost all had disseminated scholarly output and some degree of grant funding. Table 2 lists the specific activities in which respondents reported being engaged in each of these domains.

Types of Activities Performed by Promoted Hospitalists
ActivityPercent of Respondents Engaged in Activity
Service
School of Medicine56
Department of Medicine84
Hospital80
Professional societies92
Administration67
Education
Medical student72
Housestaff lectures84
Ward/consult attending96
Clinic precepting40
Course director/curriculum development80
Program director (or associate)36
Research
Peer‐reviewed publications92
Abstract/poster presentations80
Invited speaker96
Reviewer/editor80
Study section24
Federal grants32
Nonfederal grants (internal and external)72
Quality improvement/patient safety
Project member36
Project leader52
Institutional leadership32
Curriculum development32

A range of individuals assisted the respondents in the promotion process. Twenty‐three (92%) respondents identified the individuals who supported their promotion, and all listed more than one person. Respondents most commonly credited their Section or Division Chief (43%) with facilitating their promotion, followed by Departmental Chairs or Vice/Associate Chairs (22%). Mentors (13%) or peers (8%) were also named. Four respondents (17%) named themselves as the person providing most guidance through the promotions process.

No consistent themes regarding obstacles emerged from free‐text responses to questions about the promotions process. One respondent felt that high clinical expectations made participation in other academic activities a challenge. The only other barriers noted were not being on the radar screen of the Division Chief of GIM, and difficulty identifying external, senior hospitalists to write letters in support of promotion.

When asked about the most important activities supporting their promotion, 24 respondents listed one to two key activities, detailed in Table 3. The most common response was peer‐reviewed publications (33%). Activities related to education and/or teaching were the next most common response (29%), specifically teaching, educational activities, curriculum design, or program director. Research or research funding represented 26% of responses. Valued activities outside of the respondent's institution included national reputation (21%) and service in professional societies (16%). Service or administrative responsibilities were mentioned by 25% of respondents.

Reported Most Important Activities for Supporting Promotion
Category of ActivityFrequency of Response* (%)
  • Twenty‐four respondents answered this question.

Research14 (58)
Peer‐reviewed publications8 (33)
Research4 (16)
Research funding2 (8)
Activities outside institution8 (33)
National reputation5 (21)
Professional society membership3 (13)
Education7 (29)
Teaching3 (13)
Educational activities2 (8)
Residency Director1 (4)
Curriculum development1 (4)
Service6 (25)
Service3 (13)
Administration/leadership of group3 (13)

Discussion

We conducted a unique and comprehensive survey of academic hospitalists who have been promoted since 1995. We identified the most common and important activities contributing to promotion. Contrary to our expectations, survey respondents generally did not report being a hospitalist was a barrier in the promotions process.

Respondents were engaged in a diverse range of activities, including service, education, and research. Interestingly, no one identified him or herself primarily as a clinician. Teaching appeared to be a core component for all surveyed, regardless of academic appointment. Only one felt that her clinical workload as a hospitalist was an obstacle that prevented her from being engaged in other activities important for promotion. With more programs potentially evolving to separate divisions, the issue of being on the radar screen of a General Internal Medicine Division Chief may become less common over time. We hope that as programs mature and the numbers of associate and full professors increase, there will not be difficulty obtaining outside letters.

Although only 23% self‐identified as clinician‐researchers, nearly all had peer‐reviewed publications and other evidence of disseminated scholarly work. Grant funding, both federal and nonfederal, was also common among this group. This finding is consistent with self‐reported activities of a cohort of junior internal medicine faculty followed over three years who were eventually promoted, though the majority of those participants were classified as having either traditional clinician‐educator or clinician‐researcher positions.16

Despite outlining a seemingly clear pathway to promotion for hospitalists, concerns remain. Most importantly, those surveyed seem to have achieved promotion through relatively traditional academic job descriptions. Obtaining or maintaining these types of positions may be difficult as clinical needs at academic centers increase. According to a recent survey of hospitalist faculty,13 over one‐third spend more than 60% of their time on nonteaching clinical services. In that survey, over half of respondents had little or no protected time for scholarly activities. The contrast between this survey's findings and ours raises the question of whether our promoted sample had positions similar to those of most academic hospitalists. Given that the majority of our respondents noted peer‐reviewed publications and grant funding to be among the most important activities for promotion, there may be a dangerous disconnect for junior academic hospitalists who spend the majority of their time in direct patient care. Moreover, the promoted hospitalists in our survey reported relatively less participation in quality improvement/patient safety activities, in contrast to both anecdotal and survey reports that these activities are a major component of many academic hospitalist positions.5, 17 Most academic medical centers do not yet consider achievements in this area in their promotions criteria, potentially creating a barrier for the ranks of clinician quality improvers.1 Thus, significant obstacles to promotion of academic hospitalists may exist.

Leaders in academic hospital medicine are recognizing these potential barriers. A diverse group from major professional societies recently published a summary of the challenges and opportunities for the field of academic hospital medicine.1 Several needs and areas for intervention were identified, including enhanced faculty development and improved documentation of quality improvement activities. The SGIM, the SHM, and the Association of Chiefs and Leaders of General Internal Medicine (ACLGIM) recently cosponsored an intensive four‐day faculty development course for junior faculty to promote skills necessary for academic hospitalist success. Early reports indicate that this was a success.1820

In addition, the AHTF has developed a Quality Portfolio, paralleling the Educator's Portfolio, that can be used as a tool for documenting quality improvement and patient safety activities in a way that can be useful for career development and promotion.4 Lastly, the Society of Hospital Medicine has hosted the inaugural Academic Hospital Medicine Leadership Summit as part of the national meeting to provide mentorship and professional development opportunities for junior faculty. Our hope is that these opportunities, coupled with the growth of mid‐level and senior leaders in hospital medicine, will provide greater infrastructure for the development and promotion of junior faculty.

Our results may have relevance beyond hospitalist groups. With anticipated further limits on housestaff duty hours, more academic physicians may be asked to fill predominantly clinical roles. In addition, a growing emphasis on quality and patient safety may lead to a more general expansion of academicians who focus on these areas.15

Our survey and methodology have limitations. By including only promoted individuals, we did not survey hospitalists with the most difficulties in the promotions processthose who were not promoted. Thus, we are unable to directly compare successful versus unsuccessful strategies. Identifying nonpromoted academic hospitalists to understand the reasons they were not (or have not yet been) promoted could be a next step in this line of inquiry. Additionally, understanding the attitudes of promotions committees regarding hospitalists, and the clinical and quality improvement roles in which they are engaged, could enhance our current results. Finally, we surveyed a convenience sample of a limited numbers of hospitalists and institutions, and were unable to systematically account for variations in promotions criteria across institutions. However, to our knowledge, this is the most comprehensive study of promotions among academic hospitalists to date. Given the common themes that emerged in terms of activities that supported promotion, mentors, and advice, we believe that our sample was sufficient to identify important themes and advance our understanding of this nascent specialty.

In conclusion, our survey of promoted hospital medicine faculty provides valuable information for junior faculty and hospitalist leaders. Success was found through engaging in a diverse set of activities in the traditional areas of education, service, and scholarship, frequently in conjunction with developing recognition outside of their institutions. While all respondents were clinically active, none described themselves as having purely clinical roles. As academic hospitalist roles evolve, academic leaders will need to provide adequate mentorship, create time for scholarly pursuits, and promote documentation and recognition of nontraditional activities that may nonetheless be worthy of promotion.

References
  1. Flanders SA,Centor B,Weber V,McGinn T,DeSalvo K,Aurebach A.Challenges and opportunities in academic hospital medicine: report from the academic hospital medicine summit.J Gen Intern Med.2009;24(5):636641.
  2. Glasheen JJ,Goldenberg J,Nelson JR.Hospitalist educators: future of inpatient internal medicine training.Mt Sinai J Med.2008;75(5):436451.
  3. Flanders SA,Kaufman SR,Saint S,Parekh VI.Hospitalists as emerging leaders in patient safety: lessons learned and future directions.J Patient Saf.2009;5(1):38.
  4. Quality Portfolio—Template and Instructions. Available at: http://www.sgim.org/userfiles/file/AHTF%20QP%20WEB%20TEMPLATE%20INS TRUCTIONS.pdf. Accessed on April 24,2010.
  5. Howell E.An innovative approach to support hospitalist physicians toward academic success.J Hosp Med.2008;3:314318.
  6. Socolar RR,Kelman LS,Lannon CM,Lohr JA.Institutional policies of U.S. medical schools regarding tenure, promotion, and benefits for part‐time faculty.Acad Med.2000;75(8):846849.
  7. Nora LM,Pomeroy C,Currey TE,Hill NS,Tibbs PA,Wilson EA.Revising appointment, promotion, and tenure procedures to incorporate an expanded definition of scholarship: the University of Kentucky College of Medicine experience.Acad Med.2000;75(9)913924.
  8. Buckley LM,Sanders K,Shih M,Hampton CL.Attitudes of clinical faculty about career progress, career success and recognition, and commitment to academic medicine. Results of a survey.Arch Intern Med.2000;160(17):26252629.
  9. Atasoylu AA,Wright SM,Beasley BW, et al.Promotion criteria for clinician‐educators.J Gen Intern Med.2003;18(9):711716.
  10. Simpson D,Hafler J,Brown D,Wilkerson L.Documentation systems for educators seeking academic promotion in U.S. medical schools.Acad Med.2004;79(8):783790.
  11. Coates WC,Hobgood CD,Birnbaum A,Farrell SE.Faculty development: academic opportunities for emergency medicine faculty on education career tracks.Acad Emerg Med.2003;10(10):11131117.
  12. Gopal R,Glasheen JJ,Miyoshi TJ,Prochazka AV.Burnout and internal medicine resident work hours restrictions.Arch Intern Med.2005;165(22):25952600.
  13. Karpj MD,Levey GS.Development of a Division of General Medicine in a Department of Internal Medicine.J Med Ed.1981;56:390396.
  14. Petersdorf RD.The evolution of departments of medicine.N Engl J Med.1980;303(9):489496.
  15. Shojania KG,Levinson W.Clinicians in quality improvement: a new career pathway in academic medicine.JAMA.2009;301(7):766768.
  16. Beasley BW,Simon SD,Wright SM.A time to be promoted. The prospective study of promotion in academia.J Gen Intern Med.2006;21(2):123129.
  17. Academic Hospitalist Academy. Available at: http://www.sgim.org/index. cfm?pageId=815. Accessed on April 24,2010.
  18. Stubbs D.Reflections on the Academic Hospitalist Academy.SGIM Forum.2010;33(1):5.
  19. Souder J.The Academic Hospitalist Academy: Get anchored, equipped, and energized.SGIM Forum.2010;33(1):56.
  20. Weaver C.Four formative days in the life of an academic hospitalist: the Academic Hospitalist Academy.SGIM Forum.2010;33(1):6.
References
  1. Flanders SA,Centor B,Weber V,McGinn T,DeSalvo K,Aurebach A.Challenges and opportunities in academic hospital medicine: report from the academic hospital medicine summit.J Gen Intern Med.2009;24(5):636641.
  2. Glasheen JJ,Goldenberg J,Nelson JR.Hospitalist educators: future of inpatient internal medicine training.Mt Sinai J Med.2008;75(5):436451.
  3. Flanders SA,Kaufman SR,Saint S,Parekh VI.Hospitalists as emerging leaders in patient safety: lessons learned and future directions.J Patient Saf.2009;5(1):38.
  4. Quality Portfolio—Template and Instructions. Available at: http://www.sgim.org/userfiles/file/AHTF%20QP%20WEB%20TEMPLATE%20INS TRUCTIONS.pdf. Accessed on April 24,2010.
  5. Howell E.An innovative approach to support hospitalist physicians toward academic success.J Hosp Med.2008;3:314318.
  6. Socolar RR,Kelman LS,Lannon CM,Lohr JA.Institutional policies of U.S. medical schools regarding tenure, promotion, and benefits for part‐time faculty.Acad Med.2000;75(8):846849.
  7. Nora LM,Pomeroy C,Currey TE,Hill NS,Tibbs PA,Wilson EA.Revising appointment, promotion, and tenure procedures to incorporate an expanded definition of scholarship: the University of Kentucky College of Medicine experience.Acad Med.2000;75(9)913924.
  8. Buckley LM,Sanders K,Shih M,Hampton CL.Attitudes of clinical faculty about career progress, career success and recognition, and commitment to academic medicine. Results of a survey.Arch Intern Med.2000;160(17):26252629.
  9. Atasoylu AA,Wright SM,Beasley BW, et al.Promotion criteria for clinician‐educators.J Gen Intern Med.2003;18(9):711716.
  10. Simpson D,Hafler J,Brown D,Wilkerson L.Documentation systems for educators seeking academic promotion in U.S. medical schools.Acad Med.2004;79(8):783790.
  11. Coates WC,Hobgood CD,Birnbaum A,Farrell SE.Faculty development: academic opportunities for emergency medicine faculty on education career tracks.Acad Emerg Med.2003;10(10):11131117.
  12. Gopal R,Glasheen JJ,Miyoshi TJ,Prochazka AV.Burnout and internal medicine resident work hours restrictions.Arch Intern Med.2005;165(22):25952600.
  13. Karpj MD,Levey GS.Development of a Division of General Medicine in a Department of Internal Medicine.J Med Ed.1981;56:390396.
  14. Petersdorf RD.The evolution of departments of medicine.N Engl J Med.1980;303(9):489496.
  15. Shojania KG,Levinson W.Clinicians in quality improvement: a new career pathway in academic medicine.JAMA.2009;301(7):766768.
  16. Beasley BW,Simon SD,Wright SM.A time to be promoted. The prospective study of promotion in academia.J Gen Intern Med.2006;21(2):123129.
  17. Academic Hospitalist Academy. Available at: http://www.sgim.org/index. cfm?pageId=815. Accessed on April 24,2010.
  18. Stubbs D.Reflections on the Academic Hospitalist Academy.SGIM Forum.2010;33(1):5.
  19. Souder J.The Academic Hospitalist Academy: Get anchored, equipped, and energized.SGIM Forum.2010;33(1):56.
  20. Weaver C.Four formative days in the life of an academic hospitalist: the Academic Hospitalist Academy.SGIM Forum.2010;33(1):6.
Issue
Journal of Hospital Medicine - 6(7)
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Journal of Hospital Medicine - 6(7)
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Tried and true: A survey of successfully promoted academic hospitalists
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Tried and true: A survey of successfully promoted academic hospitalists
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Department of Medicine, Division of Hospital Medicine, South Texas Veterans Health Care System, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229
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“July Phenomenon” Revisited

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Influence of house‐staff experience on teaching‐hospital mortality: The “July Phenomenon” revisited

The July Phenomenon is a commonly used term referring to poor hospital‐patient outcomes when inexperienced house‐staff start their postgraduate training in July. In addition to being an interesting observation, the validity of July Phenomenon has policy implications for teaching hospitals and residency training programs.

Twenty‐three published studies have tried to determine whether the arrival of new house‐staff is associated with increased patient mortality (see Supporting Appendix A in the online version of this article).123 While those studies make an important attempt to determine the validity of the July Phenomenon, they have some notable limitations. All but four of these studies2, 4, 6, 16 limited their analysis to patients with a specific diagnosis, within a particular hospital unit, or treated by a particular specialty. Many studies limited data to those from a single hospital.1, 3, 4, 10, 11, 14, 15, 20, 22 Nine studies did not include data from the entire year in their analyses,4, 6, 7, 10, 13, 1517, 23 and one did not include data from multiple years.22 One study conducted its analysis on death counts alone and did not account for the number of hospitalized people at risk.6 Finally, the analysis of several studies controlled for no severity of illness markers,6, 10, 21 whereas that from several other studies contained only crude measures of comorbidity and severity of illness.14

In this study, we analyzed data at our teaching hospital to determine if evidence exists for the July Phenomenon at our center. We used a highly discriminative and well‐calibrated multivariate model to calculate the risk of dying in hospital, and quantify the ratio of observed to expected number of hospital deaths. Using this as our outcome statistic, we determined whether or not our hospital experiences a July Phenomenon.

METHODS

This study was approved by The Ottawa Hospital (TOH) Research Ethics Board.

Study Setting

TOH is a tertiary‐care teaching hospital with two inpatient campuses. The hospital operates within a publicly funded health care system, serves a population of approximately 1.5 million people in Ottawa and Eastern Ontario, treats all major trauma patients for the region, and provides most of the oncological care in the region.

TOH is the primary medical teaching hospital at the University of Ottawa. In 2010, there were 197 residents starting their first year of postgraduate training in one of 29 programs.

Inclusion Criteria

The study period extended from April 15, 2004 to December 31, 2008. We used this start time because our hospital switched to new coding systems for procedures and diagnoses in April 2002. Since these new coding systems contributed to our outcome statistic, we used a very long period (ie, two years) for coding patterns to stabilize to ensure that any changes seen were not a function of coding patterns. We ended our study in December 2008 because this was the last date of complete data at the time we started the analysis.

We included all medical, surgical, and obstetrical patients admitted to TOH during this time except those who were: younger than 15 years old; transferred to or from another acute care hospital; or obstetrical patients hospitalized for routine childbirth. These patients were excluded because they were not part of the multivariate model that we used to calculate risk of death in hospital (discussed below).24 These exclusions accounted for 25.4% of all admissions during the study period (36,820less than 15 years old; 12,931transferred to or from the hospital; and 44,220uncomplicated admission for childbirth).

All data used in this study came from The Ottawa Hospital Data Warehouse (TOHDW). This is a repository of clinical, laboratory, and administrative data originating from the hospital's major operational information systems. TOHDW contains information on patient demographics and diagnoses, as well as procedures and patient transfers between different units or hospital services during the admission.

Primary OutcomeRatio of Observed to Expected Number of Deaths per Week

For each study day, we measured the number of hospital deaths from the patient registration table in TOHDW. This statistic was collated for each week to ensure numeric stability, especially in our subgroup analyses.

We calculated the weekly expected number of hospital deaths using an extension of the Escobar model.24 The Escobar is a logistic regression model that estimated the probability of death in hospital that was derived and internally validated on almost 260,000 hospitalizations at 17 hospitals in the Kaiser Permanente Health Plan. It included six covariates that were measurable at admission including: patient age; patient sex; admission urgency (ie, elective or emergent) and service (ie, medical or surgical); admission diagnosis; severity of acute illness as measured by the Laboratory‐based Acute Physiology Score (LAPS); and chronic comorbidities as measured by the COmorbidity Point Score (COPS). Hospitalizations were grouped by admission diagnosis. The final model had excellent discrimination (c‐statistic 0.88) and calibration (P value of Hosmer Lemeshow statistic for entire cohort 0.66). This model was externally validated in our center with a c‐statistic of 0.901.25

We extended the Escobar model in several ways (Wong et al., Derivation and validation of a model to predict the daily risk of death in hospital, 2010, unpublished work). First, we modified it into a survival (rather than a logistic) model so it could estimate a daily probability of death in hospital. Second, we included the same covariates as Escobar except that we expressed LAPS as a time‐dependent covariate (meaning that the model accounted for changes in its value during the hospitalization). Finally, we included other time‐dependent covariates including: admission to intensive care unit; undergoing significant procedures; and awaiting long‐term care. This model had excellent discrimination (concordance probability of 0.895, 95% confidence interval [CI] 0.8890.902) and calibration.

We used this survival model to estimate the daily risk of death for all patients in the hospital each day. Summing these risks over hospital patients on each day returned the daily number of expected hospital deaths. This was collated per week.

The outcome statistic for this study was the ratio of the observed to expected weekly number of hospital deaths. Ratios exceeding 1 indicate that more deaths were observed than were expected (given the distribution of important covariates in those people during that week). This outcome statistic has several advantages. First, it accounts for the number of patients in the hospital each day. This is important because the number of hospital deaths will increase as the number of people in hospital increase. Second, it accounts for the severity of illness in each patient on each hospital day. This accounts for daily changes in risk of patient death, because calculation of the expected number of deaths per day was done using a multivariate survival model that included time‐dependent covariates. Therefore, each individual's predicted hazard of death (which was summed over the entire hospital to calculate the total expected number of deaths in hospital each day) took into account the latest values of these covariates. Previous analyses only accounted for risk of death at admission.

Expressing Physician Experience

The latent measure26 in all July Phenomenon studies is collective house‐staff physician experience. This is quantified by a surrogate date variable in which July 1the date that new house‐staff start their training in North Americarepresents minimal experience and June 30 represents maximal experience. We expressed collective physician experience on a scale from 0 (minimum experience) on July 1 to 1 (maximum experience) on June 30. A similar approach has been used previously13 and has advantages over the other methods used to capture collective house‐staff experience. In the stratified, incomplete approach,47, 911, 13, 1517 periods with inexperienced house‐staff (eg, July and August) are grouped together and compared to times with experienced house‐staff (eg, May and June), while ignoring all other data. The specification of cut‐points for this stratification is arbitrary and the method ignores large amounts of data. In the stratified, complete approach, periods with inexperienced house‐staff (eg, July and August) are grouped together and compared to all other times of the year.8, 12, 14, 1820, 22 This is potentially less biased because there are no lost data. However, the cut‐point for determining when house‐staff transition from inexperienced to experienced is arbitrary, and the model assumes that the transition is sudden. This is suboptimal because acquisition of experience is a gradual, constant process.

The pattern by which collective physician experience changes between July 1st and June 30th is unknown. We therefore expressed this evolution using five different patterns varying from a linear change to a natural logarithmic change (see Supporting Appendix B in the online version of this article).

Analysis

We first examined for autocorrelation in our outcome variable using Ljung‐Box statistics at lag 6 and 12 in PROC ARIMA (SAS 9.2, Cary, NC). If significant autocorrelation was absent in our data, linear regression modeling was used to associate the ratio of the observed to expected number of weekly deaths (the outcome variable) with the collective first year physician experience (the predictor variable). Time‐series methodology was to be used if significant autocorrelation was present.

In our baseline analysis, we included all hospitalizations together. In stratified analyses, we categorized hospitalizations by admission status (emergent vs elective) and admission service (medicine vs surgery).

RESULTS

Between April 15, 2004 and December 31, 2008, The Ottawa Hospital had a total of 152,017 inpatient admissions and 107,731 same day surgeries (an annual rate of 32,222 and 22,835, respectively; an average daily rate of 88 and 63, respectively) that met our study's inclusion criteria. These 259,748 encounters included 164,318 people. Table 1 provides an overall description of the study population.

Description of the Study Cohort
Characteristic 
  • Abbreviations: IQR, interquartile range; LAPS, Laboratory‐based Acute Physiology Score; PIMR, Procedural Independent Mortality Risk (van Walraven et al., The Procedural Independent Mortality Risk [PIMR] score can use administrative data to quantify the independent risk of death in hospital after procedures, 2010, unpublished work).

  • Among admissions where at least one PIMR procedure was performed during the hospitalization.

Patients/hospitalizations, n164,318/259,748
Deaths in‐hospital, n (%)7,679 (3.0)
Length of admission in days, median (IQR)2 (16)
Male, n (%)124,848 (48.1)
Age at admission, median (IQR)60 (4674)
Admission type, n (%) 
Elective surgical136,406 (52.5)
Elective nonsurgical20,104 (7.7)
Emergent surgical32,046 (12.3)
Emergent nonsurgical71,192 (27.4)
Elixhauser score, median (IQR)0 (04)
LAPS at admission, median (IQR)0 (015)
At least one admission to intensive care unit, n (%)7,779 (3.0)
At least one alternative level of care episode, n (%)6,971 (2.7)
At least one PIMR procedure, n (%)47,288 (18.2)
First PIMR score,* median (IQR)2 (52)

Weekly Deaths: Observed, Expected, and Ratio

Figure 1A presents the observed weekly number of deaths during the study period. There was an average of 31 deaths per week (range 1551). Some large fluctuations in the weekly number of deaths were seen; in 2007, for example, the number of observed deaths went from 21 in week 13 up to 46 in week 15. However, no obvious seasonal trends in the observed weekly number of deaths were seen (Figure 1A, heavy line) nor were trends between years obvious.

Figure 1
The weekly number of observed deaths (top plot) and expected deaths (middle plot) for each week of the year (horizontal axis). The bottom plot presents the ratio of weekly observed to expected number of deaths. Each plot presents results for individual study years (light lines) as well as an overall summary for all years (heavy line). The first week of July (when new house‐staff start their training) is represented by the vertical line in the middle of each plot.

Figure 1B presents the expected weekly number of deaths during the study period. The expected weekly number of deaths averaged 29.6 (range 22.238.7). The expected weekly number of deaths was notably less variable than the observed number of deaths. However, important variations in the expected number of deaths were seen; for example, in 2005, the expected number of deaths increased from 24.1 in week 41 to 29.6 in week 44. Again, we saw no obvious seasonal trends in the expected weekly number of deaths (Figure 1B, heavy line).

Figure 1C illustrates the ratio of observed to the expected weekly number of deaths. The average observed to expected ratio slightly exceeded unity (1.05) and ranged from 0.488 (week 24, in 2008) to 1.821 (week 51, in 2008). We saw no obvious seasonal trends in the ratio of the observed to expected number of weekly deaths. In addition, obvious trends in this ratio were absent over the study period.

Association Between House‐Staff Experience and Death in Hospital

We found no evidence of autocorrelation in the ratio of observed to expected weekly number of deaths. The ratio of observed to expected number of hospital deaths was not significantly associated with house‐staff physician experience (Table 2). This conclusion did not change regardless of which house‐staff physician experience pattern was used in the linear model (Table 2). In addition, our analysis found no significant association between physician experience and patient mortality when analyses were stratified by admission service or admission status (Table 2).

Absolute Differences in the Ratio of Observed to Expected Number of Hospital Deaths from Minimal to Maximal Experience
Patient PopulationHouse‐Staff Experience Pattern (95% CI)
LinearSquareSquare RootCubicNatural Logarithm
  • NOTE: This table summarizes the association between collective physician experience and the weekly ratio of observed to expected number of hospital deaths. The first column indicates the patient population included in the analysis. The five patterns of collective house‐staff experience (illustrated in Supporting Appendix B in the online version of this article) are listed across the top. Each entry presents the absolute change in the weekly ratio of observed to expected number of hospital deaths (with its P value in parentheses) when experience changes from the minimal to the maximal value. For example, in the model containing all patients expressing house‐staff experience in a linear pattern (top left), an increase in house‐staff experience from 0 to 1 was associated with an absolute decrease in the ratio of observed to expected numbers of deaths per week of 0.02 (or 2%). Negative values indicate that patient outcomes improve (ie, the ratio of observed to expected number of hospital deaths decreases) with an increase in house‐staff experience.

  • Abbreviations: CI, confidence interval.

All0.03 (0.11, 0.06)0.02 (0.10, 0.07)0.04 (0.15, 0.07)0.01 (0.10, 0.08)0.05 (0.16, 0.07)
Admitting service    
Medicine0.0004 (0.09, 0.10)0.01 (0.08, 0.10)0.01 (0.13, 0.11)0.02 (0.07, 0.11)0.03 (0.15, 0.09)
Surgery0.10 (0.30, 0.10)0.11 (0.30, 0.08)0.12 (0.37, 0.14)0.11 (0.31, 0.08)0.09 (0.35, 0.17)
Admission status    
Elective0.09 (0.53, 0.35)0.10 (0.51, 0.32)0.11 (0.66, 0.44)0.10 (0.53, 0.33)0.11 (0.68, 0.45)
Emergent0.02 (0.11, 0.07)0.01 (0.09, 0.08)0.03 (0.14, 0.08)0.003 (0.09, 0.09)0.04 (0.16, 0.08)

DISCUSSION

It is natural to suspect that physician experience influences patient outcomes. The commonly discussed July Phenomenon explores changes in teaching‐hospital patient outcomes by time of the academic year. This serves as an ecological surrogate for the latent variable of overall house‐staff experience. Our study used a detailed outcomethe ratio of observed to the expected number of weekly hospital deathsthat adjusted for patient severity of illness. We also modeled collective physician experience using a broad range of patterns. We found no significant variation in mortality rates during the academic year; therefore, the risk of death in hospital does not vary by house‐staff experience at our hospital. This is no evidence of a July Phenomenon for mortality at our center.

We were not surprised that the arrival of inexperienced house‐staff did not significantly change patient mortality for several reasons. First year residents are but one group of treating physicians in a teaching hospital. They are surrounded by many other, more experienced physicians who also contribute to patient care and their outcomes. Given these other physicians, the influence that the relatively smaller number of first year residents have on patient outcomes will be minimized. In addition, the role that these more experienced physicians play in patient care will vary by the experience and ability of residents. The influence of new and inexperienced house‐staff in July will be blunted by an increased role played by staff‐people, fellows, and more experienced house‐staff at that time.

Our study was a methodologically rigorous examination of the July Phenomenon. We used a reliable outcome statisticthe ratio of observed to expected weekly number of hospital deathsthat was created with a validated, discriminative, and well‐calibrated model which predicted risk of death in hospital (Wong et al., Derivation and validation of a model to predict the daily risk of death in hospital, 2010, unpublished work). This statistic is inherently understandable and controlled for patient severity of illness. In addition, our study included a very broad and inclusive group of patients over five years at two hospitals.

Twenty‐three other studies have quantitatively sought a July Phenomenon for patient mortality (see Supporting Appendix A in the online version of this article). The studies contained a broad assortment of research methodologies, patient populations, and analytical methodologies. Nineteen of these studies (83%) found no evidence of a July Phenomenon for teaching‐hospital mortality. In contrast, two of these studies found notable adjusted odds ratios for death in hospital (1.41 and 1.34) in patients undergoing either general surgery13 or complex cardiovascular surgery,19 respectively. Blumberg22 also found an increased risk of death in surgical patients in July, but used indirect standardized mortality ratios as the outcome statistic and was based on only 139 cases at Maryland teaching hospitals in 1984. Only Jen et al.16 showed an increased risk of hospital death with new house‐staff in a broad patient population. However, this study was restricted to two arbitrarily chosen days (one before and one after house‐staff change‐over) and showed an increased risk of hospital death (adjusted OR 1.05, 95% CI 1.001.15) whose borderline statistical significance could have been driven by the large sample size of the study (n = 299,741).

Therefore, the vast majority of dataincluding those presented in our analysesshow that the risk of teaching‐hospital death does not significantly increase with the arrival of new house‐staff. This prompts the question as to why the July Phenomenon is commonly presented in popular media as a proven fact.2733 We believe this is likely because the concept of the July Phenomenon is understandable and has a rather morbid attraction to people, both inside and outside of the medical profession. Given the large amount of data refuting the true existence of a July Phenomenon for patient mortality (see Supporting Appendix A in the online version of this article), we believe that this term should only be used only as an example of an interesting idea that is refuted by a proper analysis of the data.

Several limitations of our study are notable. First, our analysis is limited to a single center, albeit with two hospitals. However, ours is one of the largest teaching centers in Canada with many new residents each year. Second, we only examined the association of physician experience on hospital mortality. While it is possible that physician experience significantly influences other patient outcomes, mortality is, obviously, an important and reliably tallied statistic that is used as the primary outcome in most July Phenomenon studies. Third, we excluded approximately a quarter of all hospitalizations from the study. These exclusions were necessary because the Escobar model does not apply to these people and can therefore not be used to predict their risk of death in hospital. However, the vast majority of excluded patients (those less than 15 years old, and women admitted for routine childbirth) have a very low risk of death (the former because they are almost exclusively newborns, and the latter because the risk of maternal death during childbirth is very low). Since these people will contribute very little to either the expected or observed number of deaths, their exclusion will do little to threaten the study's validity. The remaining patients who were transferred to or from other hospitals (n = 12,931) makes a small proportion of the total sampling frame (5% of admissions). Fourth, our study did not identify any significant association between house‐staff experience and patient mortality (Table 2). However, the confidence intervals around our estimates are wide enough, especially in some subgroups such as patients admitted electively, that important changes in patient mortality with house‐staff experience cannot be excluded. For example, whereas our study found that a decrease in the ratio of observed to expected number of deaths exceeding 30% is very unlikely, it is still possible that this decrease is up to 30% (the lower range of the confidence interval in Table 2). However, using this logic, it could also increase by up to 10% (Table 2). Finally, we did not directly measure individual physician experience. New residents can vary extensively in their individual experience and ability. Incorporating individual physician measures of experience and ability would more reliably let us measure the association of new residents with patient outcomes. Without this, we had to rely on an ecological measure of physician experiencenamely calendar date. Again, this method is an industry standard since all studies quantify physician experience ecologically by date (see Supporting Appendix A in the online version of this article).

In summary, our datasimilar to most studies on this topicshow that the risk of death in teaching hospitals does not change with the arrival of new house‐staff.

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References
  1. Rich EC,Gifford G,Dowd B.The effects of scheduled intern rotation on the cost and quality of teaching hospital care.Eval Health Prof.1994;17:259272.
  2. Rich EC,Hillson SD,Dowd B,Morris N.Specialty differences in the “July Phenomenon” for Twin Cities teaching hospitals.Med Care.1993;31:7383.
  3. Rich EC,Gifford G,Luxenberg M,Dowd B.The relationship of house staff experience to the cost and quality of inpatient care.JAMA.1990;263:953957.
  4. Buchwald D,Komaroff AL,Cook EF,Epstein AM.Indirect costs for medical education. Is there a July phenomenon?Arch Intern Med.1989;149:765768.
  5. Alshekhlee A,Walbert T,DeGeorgia M,Preston DC,Furlan AJ.The impact of accreditation council for graduate medical education duty hours, the July phenomenon, and hospital teaching status on stroke outcomes.J Stroke Cerebrovasc Dis.2009;18:232238.
  6. Aylin P,Majeed FA.The killing season—Fact or fiction.BMJ1994;309:1690.
  7. Bakaeen FG,Huh J,LeMaire SA, et al.The July effect: Impact of the beginning of the academic cycle on cardiac surgical outcomes in a cohort of 70,616 patients.Ann Thorac Surg.2009;88:7075.
  8. Barry WA,Rosenthal GE.Is there a July phenomenon? The effect of July admission on intensive care mortality and length of stay in teaching hospitals.J Gen Intern Med.2003;18:639645.
  9. Bruckner TA,Carlo WA,Ambalavanan N,Gould JB.Neonatal mortality among low birth weight infants during the initial months of the academic year.J Perinatol.2008;28:691695.
  10. Claridge JA,Schulman AM,Sawyer RG,Ghezel‐Ayagh A,Young JS.The “July Phenomenon” and the care of the severely injured patient: Fact or fiction?Surgery.2001;130:346353.
  11. Dhaliwal AS,Chu D,Deswal A, et al.The July effect and cardiac surgery: The effect of the beginning of the academic cycle on outcomes.Am J Surg.2008;196:720725.
  12. Englesbe MJ,Fan ZH,Baser O,Birkmeyer JD.Mortality in Medicare patients undergoing surgery in July in teaching hospitals.Ann Surg.2009;249:871876.
  13. Englesbe MJ,Pelletier SJ,Magee JC, et al.Seasonal variation in surgical outcomes as measured by the American College of Surgeons–National Surgical Quality Improvement Program (ACS‐NSQIP).Ann Surg.2007;246:456465.
  14. Finkielman JD,Morales IJ,Peters SG, et al.Mortality rate and length of stay of patients admitted to the intensive care unit in July.Crit Care Med.2004;32:11611165.
  15. Highstead RG,Johnson LC,Street JH,Trankiem CT,Kennedy SO,Sava JA.July—As good a time as any to be injured.J Trauma‐Injury Infect Crit Care.2009;67:10871090.
  16. Jen MH,Bottle A,Majeed A,Bell D,Aylin P.Early in‐hospital mortality following trainee doctors' first day at work.PLoS ONE.2009;4.
  17. Peets AD,Boiteau PJE,Doig CJ.Effect of critical care medicine fellows on patient outcome in the intensive care unit.Acad Med.2006;81:S1S4.
  18. Schroeppel TJ,Fischer PE,Magnotti LJ,Croce MA,Fabian TC.The “July Phenomenon”: Is trauma the exception?J Am Coll Surg.2009;209:378384.
  19. Shuhaiber JH,Goldsmith K,Nashef SAM.Impact of cardiothoracic resident turnover on mortality after cardiac surgery: A dynamic human factor.Ann Thorac Surg.2008;86:123131.
  20. Smith ER,Butler WE,Barker FG.Is there a “July Phenomenon” in pediatric neurosurgery at teaching hospitals?J Neurosurg Pediatr.2006;105:169176.
  21. Soltau TD,Carlo WA,Gee J,Gould J,Ambalavanan N.Mortality and morbidity by month of birth of neonates admitted to an academic neonatal intensive care unit.Pediatrics.2008;122:E1048E1052.
  22. Blumberg MS.Measuring surgical quality in Maryland: A model.Health Aff.1988;7:6278.
  23. Inaba K,Recinos G,Teixeira PG, et al.Complications and death at the start of the new academic year: Is there a July phenomenon?J Trauma‐Injury Infect Crit Care.2010;68(1):1922.
  24. Escobar GJ,Greene JD,Scheirer P,Gardner MN,Draper D,Kipnis P.Risk‐adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases.Med Care.2008;46:232239.
  25. van Walraven C,Escobar GJ,Greene JD,Forster AJ.The Kaiser Permanente inpatient risk adjustment methodology was valid in an external patient population.J Clin Epidemiol.2010;63:798803.
  26. McCutcheon AL.Introduction: The logic of latent variables.Latent Class Analysis.Newbury Park, CA:Sage;1987:510.
  27. July Effect. Wikipedia. Available at: http://en.wikipedia.org/wiki/July_effect. Accessed April 1,2011.
  28. Study proves “killing season” occurs as new doctors start work. September 23,2010. Herald Scotland. Available at: http://www.heraldscotland.com/news/health/study‐proves‐killing‐season‐occurs‐as‐new‐doctors‐start‐work‐1.921632. Accessed April 1, 2011.
  29. The “July effect”: Worst month for fatal hospital errors, study finds. June 3,2010. ABC News. Available at: http://abcnews.go.com/WN/WellnessNews/july‐month‐fatal‐hospital‐errors‐study‐finds/story?id=10819652. Accessed 1 April, 2011.
  30. “Deaths rise” with junior doctors. September 22,2010. BBC News. Available at: http://news.bbc.co.uk/2/hi/health/8269729.stm. Accessed April 1, 2011.
  31. Raloff Janet.July: When not to go to the hospital. June 2,2010. Science News. Available at: http://www.sciencenews.org/view/generic/id/59865/title/July_When_not_to_go_to_the_hospital. Accessed April 1, 2011.
  32. July: A deadly time for hospitals. July 5,2010. National Public Radio. Available at: http://www.npr.org/templates/story/story.php?storyId=128321489. Accessed April 1, 2011.
  33. Brayer Toni.Medical errors and patient safety: Beware the “July effect.” June 4,2010. Better Health. Available at: http://getbetterhealth.com/medical‐errors‐and‐patient‐safety‐beware‐of‐the‐july‐effect/2010.06.04. Accessed April 1, 2011.
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The July Phenomenon is a commonly used term referring to poor hospital‐patient outcomes when inexperienced house‐staff start their postgraduate training in July. In addition to being an interesting observation, the validity of July Phenomenon has policy implications for teaching hospitals and residency training programs.

Twenty‐three published studies have tried to determine whether the arrival of new house‐staff is associated with increased patient mortality (see Supporting Appendix A in the online version of this article).123 While those studies make an important attempt to determine the validity of the July Phenomenon, they have some notable limitations. All but four of these studies2, 4, 6, 16 limited their analysis to patients with a specific diagnosis, within a particular hospital unit, or treated by a particular specialty. Many studies limited data to those from a single hospital.1, 3, 4, 10, 11, 14, 15, 20, 22 Nine studies did not include data from the entire year in their analyses,4, 6, 7, 10, 13, 1517, 23 and one did not include data from multiple years.22 One study conducted its analysis on death counts alone and did not account for the number of hospitalized people at risk.6 Finally, the analysis of several studies controlled for no severity of illness markers,6, 10, 21 whereas that from several other studies contained only crude measures of comorbidity and severity of illness.14

In this study, we analyzed data at our teaching hospital to determine if evidence exists for the July Phenomenon at our center. We used a highly discriminative and well‐calibrated multivariate model to calculate the risk of dying in hospital, and quantify the ratio of observed to expected number of hospital deaths. Using this as our outcome statistic, we determined whether or not our hospital experiences a July Phenomenon.

METHODS

This study was approved by The Ottawa Hospital (TOH) Research Ethics Board.

Study Setting

TOH is a tertiary‐care teaching hospital with two inpatient campuses. The hospital operates within a publicly funded health care system, serves a population of approximately 1.5 million people in Ottawa and Eastern Ontario, treats all major trauma patients for the region, and provides most of the oncological care in the region.

TOH is the primary medical teaching hospital at the University of Ottawa. In 2010, there were 197 residents starting their first year of postgraduate training in one of 29 programs.

Inclusion Criteria

The study period extended from April 15, 2004 to December 31, 2008. We used this start time because our hospital switched to new coding systems for procedures and diagnoses in April 2002. Since these new coding systems contributed to our outcome statistic, we used a very long period (ie, two years) for coding patterns to stabilize to ensure that any changes seen were not a function of coding patterns. We ended our study in December 2008 because this was the last date of complete data at the time we started the analysis.

We included all medical, surgical, and obstetrical patients admitted to TOH during this time except those who were: younger than 15 years old; transferred to or from another acute care hospital; or obstetrical patients hospitalized for routine childbirth. These patients were excluded because they were not part of the multivariate model that we used to calculate risk of death in hospital (discussed below).24 These exclusions accounted for 25.4% of all admissions during the study period (36,820less than 15 years old; 12,931transferred to or from the hospital; and 44,220uncomplicated admission for childbirth).

All data used in this study came from The Ottawa Hospital Data Warehouse (TOHDW). This is a repository of clinical, laboratory, and administrative data originating from the hospital's major operational information systems. TOHDW contains information on patient demographics and diagnoses, as well as procedures and patient transfers between different units or hospital services during the admission.

Primary OutcomeRatio of Observed to Expected Number of Deaths per Week

For each study day, we measured the number of hospital deaths from the patient registration table in TOHDW. This statistic was collated for each week to ensure numeric stability, especially in our subgroup analyses.

We calculated the weekly expected number of hospital deaths using an extension of the Escobar model.24 The Escobar is a logistic regression model that estimated the probability of death in hospital that was derived and internally validated on almost 260,000 hospitalizations at 17 hospitals in the Kaiser Permanente Health Plan. It included six covariates that were measurable at admission including: patient age; patient sex; admission urgency (ie, elective or emergent) and service (ie, medical or surgical); admission diagnosis; severity of acute illness as measured by the Laboratory‐based Acute Physiology Score (LAPS); and chronic comorbidities as measured by the COmorbidity Point Score (COPS). Hospitalizations were grouped by admission diagnosis. The final model had excellent discrimination (c‐statistic 0.88) and calibration (P value of Hosmer Lemeshow statistic for entire cohort 0.66). This model was externally validated in our center with a c‐statistic of 0.901.25

We extended the Escobar model in several ways (Wong et al., Derivation and validation of a model to predict the daily risk of death in hospital, 2010, unpublished work). First, we modified it into a survival (rather than a logistic) model so it could estimate a daily probability of death in hospital. Second, we included the same covariates as Escobar except that we expressed LAPS as a time‐dependent covariate (meaning that the model accounted for changes in its value during the hospitalization). Finally, we included other time‐dependent covariates including: admission to intensive care unit; undergoing significant procedures; and awaiting long‐term care. This model had excellent discrimination (concordance probability of 0.895, 95% confidence interval [CI] 0.8890.902) and calibration.

We used this survival model to estimate the daily risk of death for all patients in the hospital each day. Summing these risks over hospital patients on each day returned the daily number of expected hospital deaths. This was collated per week.

The outcome statistic for this study was the ratio of the observed to expected weekly number of hospital deaths. Ratios exceeding 1 indicate that more deaths were observed than were expected (given the distribution of important covariates in those people during that week). This outcome statistic has several advantages. First, it accounts for the number of patients in the hospital each day. This is important because the number of hospital deaths will increase as the number of people in hospital increase. Second, it accounts for the severity of illness in each patient on each hospital day. This accounts for daily changes in risk of patient death, because calculation of the expected number of deaths per day was done using a multivariate survival model that included time‐dependent covariates. Therefore, each individual's predicted hazard of death (which was summed over the entire hospital to calculate the total expected number of deaths in hospital each day) took into account the latest values of these covariates. Previous analyses only accounted for risk of death at admission.

Expressing Physician Experience

The latent measure26 in all July Phenomenon studies is collective house‐staff physician experience. This is quantified by a surrogate date variable in which July 1the date that new house‐staff start their training in North Americarepresents minimal experience and June 30 represents maximal experience. We expressed collective physician experience on a scale from 0 (minimum experience) on July 1 to 1 (maximum experience) on June 30. A similar approach has been used previously13 and has advantages over the other methods used to capture collective house‐staff experience. In the stratified, incomplete approach,47, 911, 13, 1517 periods with inexperienced house‐staff (eg, July and August) are grouped together and compared to times with experienced house‐staff (eg, May and June), while ignoring all other data. The specification of cut‐points for this stratification is arbitrary and the method ignores large amounts of data. In the stratified, complete approach, periods with inexperienced house‐staff (eg, July and August) are grouped together and compared to all other times of the year.8, 12, 14, 1820, 22 This is potentially less biased because there are no lost data. However, the cut‐point for determining when house‐staff transition from inexperienced to experienced is arbitrary, and the model assumes that the transition is sudden. This is suboptimal because acquisition of experience is a gradual, constant process.

The pattern by which collective physician experience changes between July 1st and June 30th is unknown. We therefore expressed this evolution using five different patterns varying from a linear change to a natural logarithmic change (see Supporting Appendix B in the online version of this article).

Analysis

We first examined for autocorrelation in our outcome variable using Ljung‐Box statistics at lag 6 and 12 in PROC ARIMA (SAS 9.2, Cary, NC). If significant autocorrelation was absent in our data, linear regression modeling was used to associate the ratio of the observed to expected number of weekly deaths (the outcome variable) with the collective first year physician experience (the predictor variable). Time‐series methodology was to be used if significant autocorrelation was present.

In our baseline analysis, we included all hospitalizations together. In stratified analyses, we categorized hospitalizations by admission status (emergent vs elective) and admission service (medicine vs surgery).

RESULTS

Between April 15, 2004 and December 31, 2008, The Ottawa Hospital had a total of 152,017 inpatient admissions and 107,731 same day surgeries (an annual rate of 32,222 and 22,835, respectively; an average daily rate of 88 and 63, respectively) that met our study's inclusion criteria. These 259,748 encounters included 164,318 people. Table 1 provides an overall description of the study population.

Description of the Study Cohort
Characteristic 
  • Abbreviations: IQR, interquartile range; LAPS, Laboratory‐based Acute Physiology Score; PIMR, Procedural Independent Mortality Risk (van Walraven et al., The Procedural Independent Mortality Risk [PIMR] score can use administrative data to quantify the independent risk of death in hospital after procedures, 2010, unpublished work).

  • Among admissions where at least one PIMR procedure was performed during the hospitalization.

Patients/hospitalizations, n164,318/259,748
Deaths in‐hospital, n (%)7,679 (3.0)
Length of admission in days, median (IQR)2 (16)
Male, n (%)124,848 (48.1)
Age at admission, median (IQR)60 (4674)
Admission type, n (%) 
Elective surgical136,406 (52.5)
Elective nonsurgical20,104 (7.7)
Emergent surgical32,046 (12.3)
Emergent nonsurgical71,192 (27.4)
Elixhauser score, median (IQR)0 (04)
LAPS at admission, median (IQR)0 (015)
At least one admission to intensive care unit, n (%)7,779 (3.0)
At least one alternative level of care episode, n (%)6,971 (2.7)
At least one PIMR procedure, n (%)47,288 (18.2)
First PIMR score,* median (IQR)2 (52)

Weekly Deaths: Observed, Expected, and Ratio

Figure 1A presents the observed weekly number of deaths during the study period. There was an average of 31 deaths per week (range 1551). Some large fluctuations in the weekly number of deaths were seen; in 2007, for example, the number of observed deaths went from 21 in week 13 up to 46 in week 15. However, no obvious seasonal trends in the observed weekly number of deaths were seen (Figure 1A, heavy line) nor were trends between years obvious.

Figure 1
The weekly number of observed deaths (top plot) and expected deaths (middle plot) for each week of the year (horizontal axis). The bottom plot presents the ratio of weekly observed to expected number of deaths. Each plot presents results for individual study years (light lines) as well as an overall summary for all years (heavy line). The first week of July (when new house‐staff start their training) is represented by the vertical line in the middle of each plot.

Figure 1B presents the expected weekly number of deaths during the study period. The expected weekly number of deaths averaged 29.6 (range 22.238.7). The expected weekly number of deaths was notably less variable than the observed number of deaths. However, important variations in the expected number of deaths were seen; for example, in 2005, the expected number of deaths increased from 24.1 in week 41 to 29.6 in week 44. Again, we saw no obvious seasonal trends in the expected weekly number of deaths (Figure 1B, heavy line).

Figure 1C illustrates the ratio of observed to the expected weekly number of deaths. The average observed to expected ratio slightly exceeded unity (1.05) and ranged from 0.488 (week 24, in 2008) to 1.821 (week 51, in 2008). We saw no obvious seasonal trends in the ratio of the observed to expected number of weekly deaths. In addition, obvious trends in this ratio were absent over the study period.

Association Between House‐Staff Experience and Death in Hospital

We found no evidence of autocorrelation in the ratio of observed to expected weekly number of deaths. The ratio of observed to expected number of hospital deaths was not significantly associated with house‐staff physician experience (Table 2). This conclusion did not change regardless of which house‐staff physician experience pattern was used in the linear model (Table 2). In addition, our analysis found no significant association between physician experience and patient mortality when analyses were stratified by admission service or admission status (Table 2).

Absolute Differences in the Ratio of Observed to Expected Number of Hospital Deaths from Minimal to Maximal Experience
Patient PopulationHouse‐Staff Experience Pattern (95% CI)
LinearSquareSquare RootCubicNatural Logarithm
  • NOTE: This table summarizes the association between collective physician experience and the weekly ratio of observed to expected number of hospital deaths. The first column indicates the patient population included in the analysis. The five patterns of collective house‐staff experience (illustrated in Supporting Appendix B in the online version of this article) are listed across the top. Each entry presents the absolute change in the weekly ratio of observed to expected number of hospital deaths (with its P value in parentheses) when experience changes from the minimal to the maximal value. For example, in the model containing all patients expressing house‐staff experience in a linear pattern (top left), an increase in house‐staff experience from 0 to 1 was associated with an absolute decrease in the ratio of observed to expected numbers of deaths per week of 0.02 (or 2%). Negative values indicate that patient outcomes improve (ie, the ratio of observed to expected number of hospital deaths decreases) with an increase in house‐staff experience.

  • Abbreviations: CI, confidence interval.

All0.03 (0.11, 0.06)0.02 (0.10, 0.07)0.04 (0.15, 0.07)0.01 (0.10, 0.08)0.05 (0.16, 0.07)
Admitting service    
Medicine0.0004 (0.09, 0.10)0.01 (0.08, 0.10)0.01 (0.13, 0.11)0.02 (0.07, 0.11)0.03 (0.15, 0.09)
Surgery0.10 (0.30, 0.10)0.11 (0.30, 0.08)0.12 (0.37, 0.14)0.11 (0.31, 0.08)0.09 (0.35, 0.17)
Admission status    
Elective0.09 (0.53, 0.35)0.10 (0.51, 0.32)0.11 (0.66, 0.44)0.10 (0.53, 0.33)0.11 (0.68, 0.45)
Emergent0.02 (0.11, 0.07)0.01 (0.09, 0.08)0.03 (0.14, 0.08)0.003 (0.09, 0.09)0.04 (0.16, 0.08)

DISCUSSION

It is natural to suspect that physician experience influences patient outcomes. The commonly discussed July Phenomenon explores changes in teaching‐hospital patient outcomes by time of the academic year. This serves as an ecological surrogate for the latent variable of overall house‐staff experience. Our study used a detailed outcomethe ratio of observed to the expected number of weekly hospital deathsthat adjusted for patient severity of illness. We also modeled collective physician experience using a broad range of patterns. We found no significant variation in mortality rates during the academic year; therefore, the risk of death in hospital does not vary by house‐staff experience at our hospital. This is no evidence of a July Phenomenon for mortality at our center.

We were not surprised that the arrival of inexperienced house‐staff did not significantly change patient mortality for several reasons. First year residents are but one group of treating physicians in a teaching hospital. They are surrounded by many other, more experienced physicians who also contribute to patient care and their outcomes. Given these other physicians, the influence that the relatively smaller number of first year residents have on patient outcomes will be minimized. In addition, the role that these more experienced physicians play in patient care will vary by the experience and ability of residents. The influence of new and inexperienced house‐staff in July will be blunted by an increased role played by staff‐people, fellows, and more experienced house‐staff at that time.

Our study was a methodologically rigorous examination of the July Phenomenon. We used a reliable outcome statisticthe ratio of observed to expected weekly number of hospital deathsthat was created with a validated, discriminative, and well‐calibrated model which predicted risk of death in hospital (Wong et al., Derivation and validation of a model to predict the daily risk of death in hospital, 2010, unpublished work). This statistic is inherently understandable and controlled for patient severity of illness. In addition, our study included a very broad and inclusive group of patients over five years at two hospitals.

Twenty‐three other studies have quantitatively sought a July Phenomenon for patient mortality (see Supporting Appendix A in the online version of this article). The studies contained a broad assortment of research methodologies, patient populations, and analytical methodologies. Nineteen of these studies (83%) found no evidence of a July Phenomenon for teaching‐hospital mortality. In contrast, two of these studies found notable adjusted odds ratios for death in hospital (1.41 and 1.34) in patients undergoing either general surgery13 or complex cardiovascular surgery,19 respectively. Blumberg22 also found an increased risk of death in surgical patients in July, but used indirect standardized mortality ratios as the outcome statistic and was based on only 139 cases at Maryland teaching hospitals in 1984. Only Jen et al.16 showed an increased risk of hospital death with new house‐staff in a broad patient population. However, this study was restricted to two arbitrarily chosen days (one before and one after house‐staff change‐over) and showed an increased risk of hospital death (adjusted OR 1.05, 95% CI 1.001.15) whose borderline statistical significance could have been driven by the large sample size of the study (n = 299,741).

Therefore, the vast majority of dataincluding those presented in our analysesshow that the risk of teaching‐hospital death does not significantly increase with the arrival of new house‐staff. This prompts the question as to why the July Phenomenon is commonly presented in popular media as a proven fact.2733 We believe this is likely because the concept of the July Phenomenon is understandable and has a rather morbid attraction to people, both inside and outside of the medical profession. Given the large amount of data refuting the true existence of a July Phenomenon for patient mortality (see Supporting Appendix A in the online version of this article), we believe that this term should only be used only as an example of an interesting idea that is refuted by a proper analysis of the data.

Several limitations of our study are notable. First, our analysis is limited to a single center, albeit with two hospitals. However, ours is one of the largest teaching centers in Canada with many new residents each year. Second, we only examined the association of physician experience on hospital mortality. While it is possible that physician experience significantly influences other patient outcomes, mortality is, obviously, an important and reliably tallied statistic that is used as the primary outcome in most July Phenomenon studies. Third, we excluded approximately a quarter of all hospitalizations from the study. These exclusions were necessary because the Escobar model does not apply to these people and can therefore not be used to predict their risk of death in hospital. However, the vast majority of excluded patients (those less than 15 years old, and women admitted for routine childbirth) have a very low risk of death (the former because they are almost exclusively newborns, and the latter because the risk of maternal death during childbirth is very low). Since these people will contribute very little to either the expected or observed number of deaths, their exclusion will do little to threaten the study's validity. The remaining patients who were transferred to or from other hospitals (n = 12,931) makes a small proportion of the total sampling frame (5% of admissions). Fourth, our study did not identify any significant association between house‐staff experience and patient mortality (Table 2). However, the confidence intervals around our estimates are wide enough, especially in some subgroups such as patients admitted electively, that important changes in patient mortality with house‐staff experience cannot be excluded. For example, whereas our study found that a decrease in the ratio of observed to expected number of deaths exceeding 30% is very unlikely, it is still possible that this decrease is up to 30% (the lower range of the confidence interval in Table 2). However, using this logic, it could also increase by up to 10% (Table 2). Finally, we did not directly measure individual physician experience. New residents can vary extensively in their individual experience and ability. Incorporating individual physician measures of experience and ability would more reliably let us measure the association of new residents with patient outcomes. Without this, we had to rely on an ecological measure of physician experiencenamely calendar date. Again, this method is an industry standard since all studies quantify physician experience ecologically by date (see Supporting Appendix A in the online version of this article).

In summary, our datasimilar to most studies on this topicshow that the risk of death in teaching hospitals does not change with the arrival of new house‐staff.

The July Phenomenon is a commonly used term referring to poor hospital‐patient outcomes when inexperienced house‐staff start their postgraduate training in July. In addition to being an interesting observation, the validity of July Phenomenon has policy implications for teaching hospitals and residency training programs.

Twenty‐three published studies have tried to determine whether the arrival of new house‐staff is associated with increased patient mortality (see Supporting Appendix A in the online version of this article).123 While those studies make an important attempt to determine the validity of the July Phenomenon, they have some notable limitations. All but four of these studies2, 4, 6, 16 limited their analysis to patients with a specific diagnosis, within a particular hospital unit, or treated by a particular specialty. Many studies limited data to those from a single hospital.1, 3, 4, 10, 11, 14, 15, 20, 22 Nine studies did not include data from the entire year in their analyses,4, 6, 7, 10, 13, 1517, 23 and one did not include data from multiple years.22 One study conducted its analysis on death counts alone and did not account for the number of hospitalized people at risk.6 Finally, the analysis of several studies controlled for no severity of illness markers,6, 10, 21 whereas that from several other studies contained only crude measures of comorbidity and severity of illness.14

In this study, we analyzed data at our teaching hospital to determine if evidence exists for the July Phenomenon at our center. We used a highly discriminative and well‐calibrated multivariate model to calculate the risk of dying in hospital, and quantify the ratio of observed to expected number of hospital deaths. Using this as our outcome statistic, we determined whether or not our hospital experiences a July Phenomenon.

METHODS

This study was approved by The Ottawa Hospital (TOH) Research Ethics Board.

Study Setting

TOH is a tertiary‐care teaching hospital with two inpatient campuses. The hospital operates within a publicly funded health care system, serves a population of approximately 1.5 million people in Ottawa and Eastern Ontario, treats all major trauma patients for the region, and provides most of the oncological care in the region.

TOH is the primary medical teaching hospital at the University of Ottawa. In 2010, there were 197 residents starting their first year of postgraduate training in one of 29 programs.

Inclusion Criteria

The study period extended from April 15, 2004 to December 31, 2008. We used this start time because our hospital switched to new coding systems for procedures and diagnoses in April 2002. Since these new coding systems contributed to our outcome statistic, we used a very long period (ie, two years) for coding patterns to stabilize to ensure that any changes seen were not a function of coding patterns. We ended our study in December 2008 because this was the last date of complete data at the time we started the analysis.

We included all medical, surgical, and obstetrical patients admitted to TOH during this time except those who were: younger than 15 years old; transferred to or from another acute care hospital; or obstetrical patients hospitalized for routine childbirth. These patients were excluded because they were not part of the multivariate model that we used to calculate risk of death in hospital (discussed below).24 These exclusions accounted for 25.4% of all admissions during the study period (36,820less than 15 years old; 12,931transferred to or from the hospital; and 44,220uncomplicated admission for childbirth).

All data used in this study came from The Ottawa Hospital Data Warehouse (TOHDW). This is a repository of clinical, laboratory, and administrative data originating from the hospital's major operational information systems. TOHDW contains information on patient demographics and diagnoses, as well as procedures and patient transfers between different units or hospital services during the admission.

Primary OutcomeRatio of Observed to Expected Number of Deaths per Week

For each study day, we measured the number of hospital deaths from the patient registration table in TOHDW. This statistic was collated for each week to ensure numeric stability, especially in our subgroup analyses.

We calculated the weekly expected number of hospital deaths using an extension of the Escobar model.24 The Escobar is a logistic regression model that estimated the probability of death in hospital that was derived and internally validated on almost 260,000 hospitalizations at 17 hospitals in the Kaiser Permanente Health Plan. It included six covariates that were measurable at admission including: patient age; patient sex; admission urgency (ie, elective or emergent) and service (ie, medical or surgical); admission diagnosis; severity of acute illness as measured by the Laboratory‐based Acute Physiology Score (LAPS); and chronic comorbidities as measured by the COmorbidity Point Score (COPS). Hospitalizations were grouped by admission diagnosis. The final model had excellent discrimination (c‐statistic 0.88) and calibration (P value of Hosmer Lemeshow statistic for entire cohort 0.66). This model was externally validated in our center with a c‐statistic of 0.901.25

We extended the Escobar model in several ways (Wong et al., Derivation and validation of a model to predict the daily risk of death in hospital, 2010, unpublished work). First, we modified it into a survival (rather than a logistic) model so it could estimate a daily probability of death in hospital. Second, we included the same covariates as Escobar except that we expressed LAPS as a time‐dependent covariate (meaning that the model accounted for changes in its value during the hospitalization). Finally, we included other time‐dependent covariates including: admission to intensive care unit; undergoing significant procedures; and awaiting long‐term care. This model had excellent discrimination (concordance probability of 0.895, 95% confidence interval [CI] 0.8890.902) and calibration.

We used this survival model to estimate the daily risk of death for all patients in the hospital each day. Summing these risks over hospital patients on each day returned the daily number of expected hospital deaths. This was collated per week.

The outcome statistic for this study was the ratio of the observed to expected weekly number of hospital deaths. Ratios exceeding 1 indicate that more deaths were observed than were expected (given the distribution of important covariates in those people during that week). This outcome statistic has several advantages. First, it accounts for the number of patients in the hospital each day. This is important because the number of hospital deaths will increase as the number of people in hospital increase. Second, it accounts for the severity of illness in each patient on each hospital day. This accounts for daily changes in risk of patient death, because calculation of the expected number of deaths per day was done using a multivariate survival model that included time‐dependent covariates. Therefore, each individual's predicted hazard of death (which was summed over the entire hospital to calculate the total expected number of deaths in hospital each day) took into account the latest values of these covariates. Previous analyses only accounted for risk of death at admission.

Expressing Physician Experience

The latent measure26 in all July Phenomenon studies is collective house‐staff physician experience. This is quantified by a surrogate date variable in which July 1the date that new house‐staff start their training in North Americarepresents minimal experience and June 30 represents maximal experience. We expressed collective physician experience on a scale from 0 (minimum experience) on July 1 to 1 (maximum experience) on June 30. A similar approach has been used previously13 and has advantages over the other methods used to capture collective house‐staff experience. In the stratified, incomplete approach,47, 911, 13, 1517 periods with inexperienced house‐staff (eg, July and August) are grouped together and compared to times with experienced house‐staff (eg, May and June), while ignoring all other data. The specification of cut‐points for this stratification is arbitrary and the method ignores large amounts of data. In the stratified, complete approach, periods with inexperienced house‐staff (eg, July and August) are grouped together and compared to all other times of the year.8, 12, 14, 1820, 22 This is potentially less biased because there are no lost data. However, the cut‐point for determining when house‐staff transition from inexperienced to experienced is arbitrary, and the model assumes that the transition is sudden. This is suboptimal because acquisition of experience is a gradual, constant process.

The pattern by which collective physician experience changes between July 1st and June 30th is unknown. We therefore expressed this evolution using five different patterns varying from a linear change to a natural logarithmic change (see Supporting Appendix B in the online version of this article).

Analysis

We first examined for autocorrelation in our outcome variable using Ljung‐Box statistics at lag 6 and 12 in PROC ARIMA (SAS 9.2, Cary, NC). If significant autocorrelation was absent in our data, linear regression modeling was used to associate the ratio of the observed to expected number of weekly deaths (the outcome variable) with the collective first year physician experience (the predictor variable). Time‐series methodology was to be used if significant autocorrelation was present.

In our baseline analysis, we included all hospitalizations together. In stratified analyses, we categorized hospitalizations by admission status (emergent vs elective) and admission service (medicine vs surgery).

RESULTS

Between April 15, 2004 and December 31, 2008, The Ottawa Hospital had a total of 152,017 inpatient admissions and 107,731 same day surgeries (an annual rate of 32,222 and 22,835, respectively; an average daily rate of 88 and 63, respectively) that met our study's inclusion criteria. These 259,748 encounters included 164,318 people. Table 1 provides an overall description of the study population.

Description of the Study Cohort
Characteristic 
  • Abbreviations: IQR, interquartile range; LAPS, Laboratory‐based Acute Physiology Score; PIMR, Procedural Independent Mortality Risk (van Walraven et al., The Procedural Independent Mortality Risk [PIMR] score can use administrative data to quantify the independent risk of death in hospital after procedures, 2010, unpublished work).

  • Among admissions where at least one PIMR procedure was performed during the hospitalization.

Patients/hospitalizations, n164,318/259,748
Deaths in‐hospital, n (%)7,679 (3.0)
Length of admission in days, median (IQR)2 (16)
Male, n (%)124,848 (48.1)
Age at admission, median (IQR)60 (4674)
Admission type, n (%) 
Elective surgical136,406 (52.5)
Elective nonsurgical20,104 (7.7)
Emergent surgical32,046 (12.3)
Emergent nonsurgical71,192 (27.4)
Elixhauser score, median (IQR)0 (04)
LAPS at admission, median (IQR)0 (015)
At least one admission to intensive care unit, n (%)7,779 (3.0)
At least one alternative level of care episode, n (%)6,971 (2.7)
At least one PIMR procedure, n (%)47,288 (18.2)
First PIMR score,* median (IQR)2 (52)

Weekly Deaths: Observed, Expected, and Ratio

Figure 1A presents the observed weekly number of deaths during the study period. There was an average of 31 deaths per week (range 1551). Some large fluctuations in the weekly number of deaths were seen; in 2007, for example, the number of observed deaths went from 21 in week 13 up to 46 in week 15. However, no obvious seasonal trends in the observed weekly number of deaths were seen (Figure 1A, heavy line) nor were trends between years obvious.

Figure 1
The weekly number of observed deaths (top plot) and expected deaths (middle plot) for each week of the year (horizontal axis). The bottom plot presents the ratio of weekly observed to expected number of deaths. Each plot presents results for individual study years (light lines) as well as an overall summary for all years (heavy line). The first week of July (when new house‐staff start their training) is represented by the vertical line in the middle of each plot.

Figure 1B presents the expected weekly number of deaths during the study period. The expected weekly number of deaths averaged 29.6 (range 22.238.7). The expected weekly number of deaths was notably less variable than the observed number of deaths. However, important variations in the expected number of deaths were seen; for example, in 2005, the expected number of deaths increased from 24.1 in week 41 to 29.6 in week 44. Again, we saw no obvious seasonal trends in the expected weekly number of deaths (Figure 1B, heavy line).

Figure 1C illustrates the ratio of observed to the expected weekly number of deaths. The average observed to expected ratio slightly exceeded unity (1.05) and ranged from 0.488 (week 24, in 2008) to 1.821 (week 51, in 2008). We saw no obvious seasonal trends in the ratio of the observed to expected number of weekly deaths. In addition, obvious trends in this ratio were absent over the study period.

Association Between House‐Staff Experience and Death in Hospital

We found no evidence of autocorrelation in the ratio of observed to expected weekly number of deaths. The ratio of observed to expected number of hospital deaths was not significantly associated with house‐staff physician experience (Table 2). This conclusion did not change regardless of which house‐staff physician experience pattern was used in the linear model (Table 2). In addition, our analysis found no significant association between physician experience and patient mortality when analyses were stratified by admission service or admission status (Table 2).

Absolute Differences in the Ratio of Observed to Expected Number of Hospital Deaths from Minimal to Maximal Experience
Patient PopulationHouse‐Staff Experience Pattern (95% CI)
LinearSquareSquare RootCubicNatural Logarithm
  • NOTE: This table summarizes the association between collective physician experience and the weekly ratio of observed to expected number of hospital deaths. The first column indicates the patient population included in the analysis. The five patterns of collective house‐staff experience (illustrated in Supporting Appendix B in the online version of this article) are listed across the top. Each entry presents the absolute change in the weekly ratio of observed to expected number of hospital deaths (with its P value in parentheses) when experience changes from the minimal to the maximal value. For example, in the model containing all patients expressing house‐staff experience in a linear pattern (top left), an increase in house‐staff experience from 0 to 1 was associated with an absolute decrease in the ratio of observed to expected numbers of deaths per week of 0.02 (or 2%). Negative values indicate that patient outcomes improve (ie, the ratio of observed to expected number of hospital deaths decreases) with an increase in house‐staff experience.

  • Abbreviations: CI, confidence interval.

All0.03 (0.11, 0.06)0.02 (0.10, 0.07)0.04 (0.15, 0.07)0.01 (0.10, 0.08)0.05 (0.16, 0.07)
Admitting service    
Medicine0.0004 (0.09, 0.10)0.01 (0.08, 0.10)0.01 (0.13, 0.11)0.02 (0.07, 0.11)0.03 (0.15, 0.09)
Surgery0.10 (0.30, 0.10)0.11 (0.30, 0.08)0.12 (0.37, 0.14)0.11 (0.31, 0.08)0.09 (0.35, 0.17)
Admission status    
Elective0.09 (0.53, 0.35)0.10 (0.51, 0.32)0.11 (0.66, 0.44)0.10 (0.53, 0.33)0.11 (0.68, 0.45)
Emergent0.02 (0.11, 0.07)0.01 (0.09, 0.08)0.03 (0.14, 0.08)0.003 (0.09, 0.09)0.04 (0.16, 0.08)

DISCUSSION

It is natural to suspect that physician experience influences patient outcomes. The commonly discussed July Phenomenon explores changes in teaching‐hospital patient outcomes by time of the academic year. This serves as an ecological surrogate for the latent variable of overall house‐staff experience. Our study used a detailed outcomethe ratio of observed to the expected number of weekly hospital deathsthat adjusted for patient severity of illness. We also modeled collective physician experience using a broad range of patterns. We found no significant variation in mortality rates during the academic year; therefore, the risk of death in hospital does not vary by house‐staff experience at our hospital. This is no evidence of a July Phenomenon for mortality at our center.

We were not surprised that the arrival of inexperienced house‐staff did not significantly change patient mortality for several reasons. First year residents are but one group of treating physicians in a teaching hospital. They are surrounded by many other, more experienced physicians who also contribute to patient care and their outcomes. Given these other physicians, the influence that the relatively smaller number of first year residents have on patient outcomes will be minimized. In addition, the role that these more experienced physicians play in patient care will vary by the experience and ability of residents. The influence of new and inexperienced house‐staff in July will be blunted by an increased role played by staff‐people, fellows, and more experienced house‐staff at that time.

Our study was a methodologically rigorous examination of the July Phenomenon. We used a reliable outcome statisticthe ratio of observed to expected weekly number of hospital deathsthat was created with a validated, discriminative, and well‐calibrated model which predicted risk of death in hospital (Wong et al., Derivation and validation of a model to predict the daily risk of death in hospital, 2010, unpublished work). This statistic is inherently understandable and controlled for patient severity of illness. In addition, our study included a very broad and inclusive group of patients over five years at two hospitals.

Twenty‐three other studies have quantitatively sought a July Phenomenon for patient mortality (see Supporting Appendix A in the online version of this article). The studies contained a broad assortment of research methodologies, patient populations, and analytical methodologies. Nineteen of these studies (83%) found no evidence of a July Phenomenon for teaching‐hospital mortality. In contrast, two of these studies found notable adjusted odds ratios for death in hospital (1.41 and 1.34) in patients undergoing either general surgery13 or complex cardiovascular surgery,19 respectively. Blumberg22 also found an increased risk of death in surgical patients in July, but used indirect standardized mortality ratios as the outcome statistic and was based on only 139 cases at Maryland teaching hospitals in 1984. Only Jen et al.16 showed an increased risk of hospital death with new house‐staff in a broad patient population. However, this study was restricted to two arbitrarily chosen days (one before and one after house‐staff change‐over) and showed an increased risk of hospital death (adjusted OR 1.05, 95% CI 1.001.15) whose borderline statistical significance could have been driven by the large sample size of the study (n = 299,741).

Therefore, the vast majority of dataincluding those presented in our analysesshow that the risk of teaching‐hospital death does not significantly increase with the arrival of new house‐staff. This prompts the question as to why the July Phenomenon is commonly presented in popular media as a proven fact.2733 We believe this is likely because the concept of the July Phenomenon is understandable and has a rather morbid attraction to people, both inside and outside of the medical profession. Given the large amount of data refuting the true existence of a July Phenomenon for patient mortality (see Supporting Appendix A in the online version of this article), we believe that this term should only be used only as an example of an interesting idea that is refuted by a proper analysis of the data.

Several limitations of our study are notable. First, our analysis is limited to a single center, albeit with two hospitals. However, ours is one of the largest teaching centers in Canada with many new residents each year. Second, we only examined the association of physician experience on hospital mortality. While it is possible that physician experience significantly influences other patient outcomes, mortality is, obviously, an important and reliably tallied statistic that is used as the primary outcome in most July Phenomenon studies. Third, we excluded approximately a quarter of all hospitalizations from the study. These exclusions were necessary because the Escobar model does not apply to these people and can therefore not be used to predict their risk of death in hospital. However, the vast majority of excluded patients (those less than 15 years old, and women admitted for routine childbirth) have a very low risk of death (the former because they are almost exclusively newborns, and the latter because the risk of maternal death during childbirth is very low). Since these people will contribute very little to either the expected or observed number of deaths, their exclusion will do little to threaten the study's validity. The remaining patients who were transferred to or from other hospitals (n = 12,931) makes a small proportion of the total sampling frame (5% of admissions). Fourth, our study did not identify any significant association between house‐staff experience and patient mortality (Table 2). However, the confidence intervals around our estimates are wide enough, especially in some subgroups such as patients admitted electively, that important changes in patient mortality with house‐staff experience cannot be excluded. For example, whereas our study found that a decrease in the ratio of observed to expected number of deaths exceeding 30% is very unlikely, it is still possible that this decrease is up to 30% (the lower range of the confidence interval in Table 2). However, using this logic, it could also increase by up to 10% (Table 2). Finally, we did not directly measure individual physician experience. New residents can vary extensively in their individual experience and ability. Incorporating individual physician measures of experience and ability would more reliably let us measure the association of new residents with patient outcomes. Without this, we had to rely on an ecological measure of physician experiencenamely calendar date. Again, this method is an industry standard since all studies quantify physician experience ecologically by date (see Supporting Appendix A in the online version of this article).

In summary, our datasimilar to most studies on this topicshow that the risk of death in teaching hospitals does not change with the arrival of new house‐staff.

References
  1. Rich EC,Gifford G,Dowd B.The effects of scheduled intern rotation on the cost and quality of teaching hospital care.Eval Health Prof.1994;17:259272.
  2. Rich EC,Hillson SD,Dowd B,Morris N.Specialty differences in the “July Phenomenon” for Twin Cities teaching hospitals.Med Care.1993;31:7383.
  3. Rich EC,Gifford G,Luxenberg M,Dowd B.The relationship of house staff experience to the cost and quality of inpatient care.JAMA.1990;263:953957.
  4. Buchwald D,Komaroff AL,Cook EF,Epstein AM.Indirect costs for medical education. Is there a July phenomenon?Arch Intern Med.1989;149:765768.
  5. Alshekhlee A,Walbert T,DeGeorgia M,Preston DC,Furlan AJ.The impact of accreditation council for graduate medical education duty hours, the July phenomenon, and hospital teaching status on stroke outcomes.J Stroke Cerebrovasc Dis.2009;18:232238.
  6. Aylin P,Majeed FA.The killing season—Fact or fiction.BMJ1994;309:1690.
  7. Bakaeen FG,Huh J,LeMaire SA, et al.The July effect: Impact of the beginning of the academic cycle on cardiac surgical outcomes in a cohort of 70,616 patients.Ann Thorac Surg.2009;88:7075.
  8. Barry WA,Rosenthal GE.Is there a July phenomenon? The effect of July admission on intensive care mortality and length of stay in teaching hospitals.J Gen Intern Med.2003;18:639645.
  9. Bruckner TA,Carlo WA,Ambalavanan N,Gould JB.Neonatal mortality among low birth weight infants during the initial months of the academic year.J Perinatol.2008;28:691695.
  10. Claridge JA,Schulman AM,Sawyer RG,Ghezel‐Ayagh A,Young JS.The “July Phenomenon” and the care of the severely injured patient: Fact or fiction?Surgery.2001;130:346353.
  11. Dhaliwal AS,Chu D,Deswal A, et al.The July effect and cardiac surgery: The effect of the beginning of the academic cycle on outcomes.Am J Surg.2008;196:720725.
  12. Englesbe MJ,Fan ZH,Baser O,Birkmeyer JD.Mortality in Medicare patients undergoing surgery in July in teaching hospitals.Ann Surg.2009;249:871876.
  13. Englesbe MJ,Pelletier SJ,Magee JC, et al.Seasonal variation in surgical outcomes as measured by the American College of Surgeons–National Surgical Quality Improvement Program (ACS‐NSQIP).Ann Surg.2007;246:456465.
  14. Finkielman JD,Morales IJ,Peters SG, et al.Mortality rate and length of stay of patients admitted to the intensive care unit in July.Crit Care Med.2004;32:11611165.
  15. Highstead RG,Johnson LC,Street JH,Trankiem CT,Kennedy SO,Sava JA.July—As good a time as any to be injured.J Trauma‐Injury Infect Crit Care.2009;67:10871090.
  16. Jen MH,Bottle A,Majeed A,Bell D,Aylin P.Early in‐hospital mortality following trainee doctors' first day at work.PLoS ONE.2009;4.
  17. Peets AD,Boiteau PJE,Doig CJ.Effect of critical care medicine fellows on patient outcome in the intensive care unit.Acad Med.2006;81:S1S4.
  18. Schroeppel TJ,Fischer PE,Magnotti LJ,Croce MA,Fabian TC.The “July Phenomenon”: Is trauma the exception?J Am Coll Surg.2009;209:378384.
  19. Shuhaiber JH,Goldsmith K,Nashef SAM.Impact of cardiothoracic resident turnover on mortality after cardiac surgery: A dynamic human factor.Ann Thorac Surg.2008;86:123131.
  20. Smith ER,Butler WE,Barker FG.Is there a “July Phenomenon” in pediatric neurosurgery at teaching hospitals?J Neurosurg Pediatr.2006;105:169176.
  21. Soltau TD,Carlo WA,Gee J,Gould J,Ambalavanan N.Mortality and morbidity by month of birth of neonates admitted to an academic neonatal intensive care unit.Pediatrics.2008;122:E1048E1052.
  22. Blumberg MS.Measuring surgical quality in Maryland: A model.Health Aff.1988;7:6278.
  23. Inaba K,Recinos G,Teixeira PG, et al.Complications and death at the start of the new academic year: Is there a July phenomenon?J Trauma‐Injury Infect Crit Care.2010;68(1):1922.
  24. Escobar GJ,Greene JD,Scheirer P,Gardner MN,Draper D,Kipnis P.Risk‐adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases.Med Care.2008;46:232239.
  25. van Walraven C,Escobar GJ,Greene JD,Forster AJ.The Kaiser Permanente inpatient risk adjustment methodology was valid in an external patient population.J Clin Epidemiol.2010;63:798803.
  26. McCutcheon AL.Introduction: The logic of latent variables.Latent Class Analysis.Newbury Park, CA:Sage;1987:510.
  27. July Effect. Wikipedia. Available at: http://en.wikipedia.org/wiki/July_effect. Accessed April 1,2011.
  28. Study proves “killing season” occurs as new doctors start work. September 23,2010. Herald Scotland. Available at: http://www.heraldscotland.com/news/health/study‐proves‐killing‐season‐occurs‐as‐new‐doctors‐start‐work‐1.921632. Accessed April 1, 2011.
  29. The “July effect”: Worst month for fatal hospital errors, study finds. June 3,2010. ABC News. Available at: http://abcnews.go.com/WN/WellnessNews/july‐month‐fatal‐hospital‐errors‐study‐finds/story?id=10819652. Accessed 1 April, 2011.
  30. “Deaths rise” with junior doctors. September 22,2010. BBC News. Available at: http://news.bbc.co.uk/2/hi/health/8269729.stm. Accessed April 1, 2011.
  31. Raloff Janet.July: When not to go to the hospital. June 2,2010. Science News. Available at: http://www.sciencenews.org/view/generic/id/59865/title/July_When_not_to_go_to_the_hospital. Accessed April 1, 2011.
  32. July: A deadly time for hospitals. July 5,2010. National Public Radio. Available at: http://www.npr.org/templates/story/story.php?storyId=128321489. Accessed April 1, 2011.
  33. Brayer Toni.Medical errors and patient safety: Beware the “July effect.” June 4,2010. Better Health. Available at: http://getbetterhealth.com/medical‐errors‐and‐patient‐safety‐beware‐of‐the‐july‐effect/2010.06.04. Accessed April 1, 2011.
References
  1. Rich EC,Gifford G,Dowd B.The effects of scheduled intern rotation on the cost and quality of teaching hospital care.Eval Health Prof.1994;17:259272.
  2. Rich EC,Hillson SD,Dowd B,Morris N.Specialty differences in the “July Phenomenon” for Twin Cities teaching hospitals.Med Care.1993;31:7383.
  3. Rich EC,Gifford G,Luxenberg M,Dowd B.The relationship of house staff experience to the cost and quality of inpatient care.JAMA.1990;263:953957.
  4. Buchwald D,Komaroff AL,Cook EF,Epstein AM.Indirect costs for medical education. Is there a July phenomenon?Arch Intern Med.1989;149:765768.
  5. Alshekhlee A,Walbert T,DeGeorgia M,Preston DC,Furlan AJ.The impact of accreditation council for graduate medical education duty hours, the July phenomenon, and hospital teaching status on stroke outcomes.J Stroke Cerebrovasc Dis.2009;18:232238.
  6. Aylin P,Majeed FA.The killing season—Fact or fiction.BMJ1994;309:1690.
  7. Bakaeen FG,Huh J,LeMaire SA, et al.The July effect: Impact of the beginning of the academic cycle on cardiac surgical outcomes in a cohort of 70,616 patients.Ann Thorac Surg.2009;88:7075.
  8. Barry WA,Rosenthal GE.Is there a July phenomenon? The effect of July admission on intensive care mortality and length of stay in teaching hospitals.J Gen Intern Med.2003;18:639645.
  9. Bruckner TA,Carlo WA,Ambalavanan N,Gould JB.Neonatal mortality among low birth weight infants during the initial months of the academic year.J Perinatol.2008;28:691695.
  10. Claridge JA,Schulman AM,Sawyer RG,Ghezel‐Ayagh A,Young JS.The “July Phenomenon” and the care of the severely injured patient: Fact or fiction?Surgery.2001;130:346353.
  11. Dhaliwal AS,Chu D,Deswal A, et al.The July effect and cardiac surgery: The effect of the beginning of the academic cycle on outcomes.Am J Surg.2008;196:720725.
  12. Englesbe MJ,Fan ZH,Baser O,Birkmeyer JD.Mortality in Medicare patients undergoing surgery in July in teaching hospitals.Ann Surg.2009;249:871876.
  13. Englesbe MJ,Pelletier SJ,Magee JC, et al.Seasonal variation in surgical outcomes as measured by the American College of Surgeons–National Surgical Quality Improvement Program (ACS‐NSQIP).Ann Surg.2007;246:456465.
  14. Finkielman JD,Morales IJ,Peters SG, et al.Mortality rate and length of stay of patients admitted to the intensive care unit in July.Crit Care Med.2004;32:11611165.
  15. Highstead RG,Johnson LC,Street JH,Trankiem CT,Kennedy SO,Sava JA.July—As good a time as any to be injured.J Trauma‐Injury Infect Crit Care.2009;67:10871090.
  16. Jen MH,Bottle A,Majeed A,Bell D,Aylin P.Early in‐hospital mortality following trainee doctors' first day at work.PLoS ONE.2009;4.
  17. Peets AD,Boiteau PJE,Doig CJ.Effect of critical care medicine fellows on patient outcome in the intensive care unit.Acad Med.2006;81:S1S4.
  18. Schroeppel TJ,Fischer PE,Magnotti LJ,Croce MA,Fabian TC.The “July Phenomenon”: Is trauma the exception?J Am Coll Surg.2009;209:378384.
  19. Shuhaiber JH,Goldsmith K,Nashef SAM.Impact of cardiothoracic resident turnover on mortality after cardiac surgery: A dynamic human factor.Ann Thorac Surg.2008;86:123131.
  20. Smith ER,Butler WE,Barker FG.Is there a “July Phenomenon” in pediatric neurosurgery at teaching hospitals?J Neurosurg Pediatr.2006;105:169176.
  21. Soltau TD,Carlo WA,Gee J,Gould J,Ambalavanan N.Mortality and morbidity by month of birth of neonates admitted to an academic neonatal intensive care unit.Pediatrics.2008;122:E1048E1052.
  22. Blumberg MS.Measuring surgical quality in Maryland: A model.Health Aff.1988;7:6278.
  23. Inaba K,Recinos G,Teixeira PG, et al.Complications and death at the start of the new academic year: Is there a July phenomenon?J Trauma‐Injury Infect Crit Care.2010;68(1):1922.
  24. Escobar GJ,Greene JD,Scheirer P,Gardner MN,Draper D,Kipnis P.Risk‐adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases.Med Care.2008;46:232239.
  25. van Walraven C,Escobar GJ,Greene JD,Forster AJ.The Kaiser Permanente inpatient risk adjustment methodology was valid in an external patient population.J Clin Epidemiol.2010;63:798803.
  26. McCutcheon AL.Introduction: The logic of latent variables.Latent Class Analysis.Newbury Park, CA:Sage;1987:510.
  27. July Effect. Wikipedia. Available at: http://en.wikipedia.org/wiki/July_effect. Accessed April 1,2011.
  28. Study proves “killing season” occurs as new doctors start work. September 23,2010. Herald Scotland. Available at: http://www.heraldscotland.com/news/health/study‐proves‐killing‐season‐occurs‐as‐new‐doctors‐start‐work‐1.921632. Accessed April 1, 2011.
  29. The “July effect”: Worst month for fatal hospital errors, study finds. June 3,2010. ABC News. Available at: http://abcnews.go.com/WN/WellnessNews/july‐month‐fatal‐hospital‐errors‐study‐finds/story?id=10819652. Accessed 1 April, 2011.
  30. “Deaths rise” with junior doctors. September 22,2010. BBC News. Available at: http://news.bbc.co.uk/2/hi/health/8269729.stm. Accessed April 1, 2011.
  31. Raloff Janet.July: When not to go to the hospital. June 2,2010. Science News. Available at: http://www.sciencenews.org/view/generic/id/59865/title/July_When_not_to_go_to_the_hospital. Accessed April 1, 2011.
  32. July: A deadly time for hospitals. July 5,2010. National Public Radio. Available at: http://www.npr.org/templates/story/story.php?storyId=128321489. Accessed April 1, 2011.
  33. Brayer Toni.Medical errors and patient safety: Beware the “July effect.” June 4,2010. Better Health. Available at: http://getbetterhealth.com/medical‐errors‐and‐patient‐safety‐beware‐of‐the‐july‐effect/2010.06.04. Accessed April 1, 2011.
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AMSTERDAM – Management of advanced non–small cell lung cancer now demands molecular profiling and personalized treatment. This new era has just begun, but it will quickly transform the field over the next 4 years, Dr. David R. Gandara said in a talk on the state of lung cancer medical oncology.

Increased molecular profiling – Dr. Gandara called for routine molecular profiling for every patient with advanced NSCLC – will mean a "culture change" for the field, and a sharp turn toward "ungrouping" the universe of NSCLC patients into individuals, he told attendees at the World Conference on Lung Cancer, which was sponsored by the International Association for the Study of Lung Cancer.

Dr. David R. Gandara

"We shouldn’t even talk about non–small cell lung cancer" as though it were a single entity, said Dr. Gandara, professor and director of the thoracic oncology program at the University of California, Davis, Cancer Center in Sacramento.

He also recommended new paradigms of drug development to reflect the complex, underlying biology and the inter- and intrapatient heterogeneity of lung cancer. "Transition from empiric to rationally selected and personalized therapy is challenging," Dr. Gandara conceded. But the transition is underway and accelerating.

Until about a year ago, the only lung cancer genes undergoing routine profiling at cancer centers were those for the epidermal growth factor receptor (EGFR) and, at fewer locations, for the oncogene KRAS. Dr. Bruce Johnson, a professor of medicine at Harvard Medical School and the Dana Farber Cancer Institute, both in Boston, takes credit for starting both; his EGFR program began 7 years ago and KRAS testing has been going for 5 years, he said.

Dr. Marileila Varella Garcia

Today, testing at several major U.S. cancer centers has added investigational tests for genes such as HER2, PIK3CA, ALK, MET, MEK, BRAF, AKTI, and NRAS. The Lung Cancer Mutation Consortium is in the midst of profiling 10 genes in 1,000 patients.

"Progress has been so dramatic. All but EGFR and KRAS came in the past year," said Marileila Varella Garcia, Ph.D., professor of medical oncology at the University of Colorado in Denver and a leader of the consortium. "At the University of Colorado, it took us 18 months to optimize the test [for 10 mutations], but now we have it and it can only improve. We test for 10 mutations for the same cost as testing for one."

At the Yale Cancer Center, the routine-profiling list stands at 13 genes, said Dr. Roy S. Herbst, chief of medical oncology at Yale in New Haven, Conn. "Right now, the only test that [insurers] pay for is the EGFR mutation test. Once the ALK story is more validated, they will probably pay to find ALK translocations, but with a chip, for the same money you can also test for other mutations for research," Dr. Herbst said.

"In the United States, testing for EGFR mutations is standard of care at most top cancer centers. EGFR is an actionable mutation, with patients considered for erlotinib treatment."

Dr. Varella Garcia, Dr. Johnson, and their collaborators from the consortium reported on the first 516 patients with advanced lung cancer who were tested with the 10-gene panel. The results showed that 280 of the tumors (54%) carried at least one mutation in at least one of the 10 genes that the consortium tested.

KRAS mutations were most prevalent, in 22% of the patients, followed by EGFR mutations in 17%, ALK rearrangement in 10%, MET amplifications in 4%, and a smaller number of genetic changes in each of the other six genes tested. Most mutations were mutually exclusive, with only 3% of tumors having mutations in two different genes, and no tumors with mutations in three or more genes.

"It was surprising that they found actionable mutations in more than half of the tumors they have tested so far. That is very promising," Dr. Gandara said.

Over the next 3-4 years, further studies will likely validate additional genes and mutations, perhaps encompassing about 90% of patients with advanced-stage lung cancer by 2015, Dr. Varella Garcia said in an interview. It’s also likely that a small percentage of these cancers won’t link with any single, identifiable gene mutation and will instead depend on changes in several pathways, something much harder to sort out.

The number of "actionable" gene mutations (mutations that, once found, can receive a targeted treatment) also remains limited but growing. Until recently, the list had a single gene, EGFR. Patients with EGFR mutations are candidates for treatment with erlotinib (Tarceva) or gefitinib (Iressa, which was not approved for routine U.S. use), both drugs from the tyrosine kinase inhibitor class.

 

 

A second, recent success story for targeted treatment involves the ALK fusion mutation, a genetic profile of tumors responsive to crizotinib. Results from phase I and II studies showed that crizotinib improved progression-free survival and overall survival in patients with tumors that had an ALK mutation; phase III studies are in progress.

"Patients with tumors that depend on these drivers have significantly better clinical outcomes when treated with specific inhibitors," Dr. Varella Garcia said.

This year’s meeting featured three main themes for patient management, but ultimately all three boil down to molecular biomarkers and molecularly directed treatment, Dr. Gandara said. One main theme – histologic profiles of advanced lung cancer – has been an important focus, but "histology is a transient selection method," he said. "At best, histology is a crude molecular selection device" superseded by molecular profiling itself.

Another important, recent focus has been maintenance therapy, but "the real questions are who gets further treatment after platinum-based induction, and when should they get it," questions best answerable by molecular profiling, he added.

"We have many ‘druggable’ molecular targets," Dr. Gandara noted.

"For almost every mutation [of the 10 genes that] the consortium is testing, we have phase I treatment trials underway," said Dr. Varella Garcia. Patients with tumors that carry KRAS and MEK mutations receive an investigational MEK inhibitor. Patients with tumors that contain HER2 mutations receive trastuzumab (Herceptin) as an investigational agent. Patients with MET mutations receive a MET monoclonal antibody.

Despite success so far, and pervasive optimism that current studies will validate new treatments, researchers cautioned that the management of advanced lung cancer also has some critical, unavoidable limitations.

"We will never cure advanced lung cancer; we can make it a chronic disease," Dr. Varella Garcia said. Effective treatment means that patients’ quality of life improves, and their disease comes under control for several years. But "it is almost universal that these patients with eventually progress again. We cannot cure advanced lung cancer. We can control it with new, targeted treatments that use oral drugs with low toxicity."

Dr. Gandara, Dr. Johnson, and Dr. Herbst disclosed relationships with numerous pharmaceutical and biotechnology companies. Among these, Dr. Johnson listed stock in Celgene and a patent for EGFR testing by Genzyme. Dr. Varella Garcia said that she has been a consultant to Abbott.

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AMSTERDAM – Management of advanced non–small cell lung cancer now demands molecular profiling and personalized treatment. This new era has just begun, but it will quickly transform the field over the next 4 years, Dr. David R. Gandara said in a talk on the state of lung cancer medical oncology.

Increased molecular profiling – Dr. Gandara called for routine molecular profiling for every patient with advanced NSCLC – will mean a "culture change" for the field, and a sharp turn toward "ungrouping" the universe of NSCLC patients into individuals, he told attendees at the World Conference on Lung Cancer, which was sponsored by the International Association for the Study of Lung Cancer.

Dr. David R. Gandara

"We shouldn’t even talk about non–small cell lung cancer" as though it were a single entity, said Dr. Gandara, professor and director of the thoracic oncology program at the University of California, Davis, Cancer Center in Sacramento.

He also recommended new paradigms of drug development to reflect the complex, underlying biology and the inter- and intrapatient heterogeneity of lung cancer. "Transition from empiric to rationally selected and personalized therapy is challenging," Dr. Gandara conceded. But the transition is underway and accelerating.

Until about a year ago, the only lung cancer genes undergoing routine profiling at cancer centers were those for the epidermal growth factor receptor (EGFR) and, at fewer locations, for the oncogene KRAS. Dr. Bruce Johnson, a professor of medicine at Harvard Medical School and the Dana Farber Cancer Institute, both in Boston, takes credit for starting both; his EGFR program began 7 years ago and KRAS testing has been going for 5 years, he said.

Dr. Marileila Varella Garcia

Today, testing at several major U.S. cancer centers has added investigational tests for genes such as HER2, PIK3CA, ALK, MET, MEK, BRAF, AKTI, and NRAS. The Lung Cancer Mutation Consortium is in the midst of profiling 10 genes in 1,000 patients.

"Progress has been so dramatic. All but EGFR and KRAS came in the past year," said Marileila Varella Garcia, Ph.D., professor of medical oncology at the University of Colorado in Denver and a leader of the consortium. "At the University of Colorado, it took us 18 months to optimize the test [for 10 mutations], but now we have it and it can only improve. We test for 10 mutations for the same cost as testing for one."

At the Yale Cancer Center, the routine-profiling list stands at 13 genes, said Dr. Roy S. Herbst, chief of medical oncology at Yale in New Haven, Conn. "Right now, the only test that [insurers] pay for is the EGFR mutation test. Once the ALK story is more validated, they will probably pay to find ALK translocations, but with a chip, for the same money you can also test for other mutations for research," Dr. Herbst said.

"In the United States, testing for EGFR mutations is standard of care at most top cancer centers. EGFR is an actionable mutation, with patients considered for erlotinib treatment."

Dr. Varella Garcia, Dr. Johnson, and their collaborators from the consortium reported on the first 516 patients with advanced lung cancer who were tested with the 10-gene panel. The results showed that 280 of the tumors (54%) carried at least one mutation in at least one of the 10 genes that the consortium tested.

KRAS mutations were most prevalent, in 22% of the patients, followed by EGFR mutations in 17%, ALK rearrangement in 10%, MET amplifications in 4%, and a smaller number of genetic changes in each of the other six genes tested. Most mutations were mutually exclusive, with only 3% of tumors having mutations in two different genes, and no tumors with mutations in three or more genes.

"It was surprising that they found actionable mutations in more than half of the tumors they have tested so far. That is very promising," Dr. Gandara said.

Over the next 3-4 years, further studies will likely validate additional genes and mutations, perhaps encompassing about 90% of patients with advanced-stage lung cancer by 2015, Dr. Varella Garcia said in an interview. It’s also likely that a small percentage of these cancers won’t link with any single, identifiable gene mutation and will instead depend on changes in several pathways, something much harder to sort out.

The number of "actionable" gene mutations (mutations that, once found, can receive a targeted treatment) also remains limited but growing. Until recently, the list had a single gene, EGFR. Patients with EGFR mutations are candidates for treatment with erlotinib (Tarceva) or gefitinib (Iressa, which was not approved for routine U.S. use), both drugs from the tyrosine kinase inhibitor class.

 

 

A second, recent success story for targeted treatment involves the ALK fusion mutation, a genetic profile of tumors responsive to crizotinib. Results from phase I and II studies showed that crizotinib improved progression-free survival and overall survival in patients with tumors that had an ALK mutation; phase III studies are in progress.

"Patients with tumors that depend on these drivers have significantly better clinical outcomes when treated with specific inhibitors," Dr. Varella Garcia said.

This year’s meeting featured three main themes for patient management, but ultimately all three boil down to molecular biomarkers and molecularly directed treatment, Dr. Gandara said. One main theme – histologic profiles of advanced lung cancer – has been an important focus, but "histology is a transient selection method," he said. "At best, histology is a crude molecular selection device" superseded by molecular profiling itself.

Another important, recent focus has been maintenance therapy, but "the real questions are who gets further treatment after platinum-based induction, and when should they get it," questions best answerable by molecular profiling, he added.

"We have many ‘druggable’ molecular targets," Dr. Gandara noted.

"For almost every mutation [of the 10 genes that] the consortium is testing, we have phase I treatment trials underway," said Dr. Varella Garcia. Patients with tumors that carry KRAS and MEK mutations receive an investigational MEK inhibitor. Patients with tumors that contain HER2 mutations receive trastuzumab (Herceptin) as an investigational agent. Patients with MET mutations receive a MET monoclonal antibody.

Despite success so far, and pervasive optimism that current studies will validate new treatments, researchers cautioned that the management of advanced lung cancer also has some critical, unavoidable limitations.

"We will never cure advanced lung cancer; we can make it a chronic disease," Dr. Varella Garcia said. Effective treatment means that patients’ quality of life improves, and their disease comes under control for several years. But "it is almost universal that these patients with eventually progress again. We cannot cure advanced lung cancer. We can control it with new, targeted treatments that use oral drugs with low toxicity."

Dr. Gandara, Dr. Johnson, and Dr. Herbst disclosed relationships with numerous pharmaceutical and biotechnology companies. Among these, Dr. Johnson listed stock in Celgene and a patent for EGFR testing by Genzyme. Dr. Varella Garcia said that she has been a consultant to Abbott.

AMSTERDAM – Management of advanced non–small cell lung cancer now demands molecular profiling and personalized treatment. This new era has just begun, but it will quickly transform the field over the next 4 years, Dr. David R. Gandara said in a talk on the state of lung cancer medical oncology.

Increased molecular profiling – Dr. Gandara called for routine molecular profiling for every patient with advanced NSCLC – will mean a "culture change" for the field, and a sharp turn toward "ungrouping" the universe of NSCLC patients into individuals, he told attendees at the World Conference on Lung Cancer, which was sponsored by the International Association for the Study of Lung Cancer.

Dr. David R. Gandara

"We shouldn’t even talk about non–small cell lung cancer" as though it were a single entity, said Dr. Gandara, professor and director of the thoracic oncology program at the University of California, Davis, Cancer Center in Sacramento.

He also recommended new paradigms of drug development to reflect the complex, underlying biology and the inter- and intrapatient heterogeneity of lung cancer. "Transition from empiric to rationally selected and personalized therapy is challenging," Dr. Gandara conceded. But the transition is underway and accelerating.

Until about a year ago, the only lung cancer genes undergoing routine profiling at cancer centers were those for the epidermal growth factor receptor (EGFR) and, at fewer locations, for the oncogene KRAS. Dr. Bruce Johnson, a professor of medicine at Harvard Medical School and the Dana Farber Cancer Institute, both in Boston, takes credit for starting both; his EGFR program began 7 years ago and KRAS testing has been going for 5 years, he said.

Dr. Marileila Varella Garcia

Today, testing at several major U.S. cancer centers has added investigational tests for genes such as HER2, PIK3CA, ALK, MET, MEK, BRAF, AKTI, and NRAS. The Lung Cancer Mutation Consortium is in the midst of profiling 10 genes in 1,000 patients.

"Progress has been so dramatic. All but EGFR and KRAS came in the past year," said Marileila Varella Garcia, Ph.D., professor of medical oncology at the University of Colorado in Denver and a leader of the consortium. "At the University of Colorado, it took us 18 months to optimize the test [for 10 mutations], but now we have it and it can only improve. We test for 10 mutations for the same cost as testing for one."

At the Yale Cancer Center, the routine-profiling list stands at 13 genes, said Dr. Roy S. Herbst, chief of medical oncology at Yale in New Haven, Conn. "Right now, the only test that [insurers] pay for is the EGFR mutation test. Once the ALK story is more validated, they will probably pay to find ALK translocations, but with a chip, for the same money you can also test for other mutations for research," Dr. Herbst said.

"In the United States, testing for EGFR mutations is standard of care at most top cancer centers. EGFR is an actionable mutation, with patients considered for erlotinib treatment."

Dr. Varella Garcia, Dr. Johnson, and their collaborators from the consortium reported on the first 516 patients with advanced lung cancer who were tested with the 10-gene panel. The results showed that 280 of the tumors (54%) carried at least one mutation in at least one of the 10 genes that the consortium tested.

KRAS mutations were most prevalent, in 22% of the patients, followed by EGFR mutations in 17%, ALK rearrangement in 10%, MET amplifications in 4%, and a smaller number of genetic changes in each of the other six genes tested. Most mutations were mutually exclusive, with only 3% of tumors having mutations in two different genes, and no tumors with mutations in three or more genes.

"It was surprising that they found actionable mutations in more than half of the tumors they have tested so far. That is very promising," Dr. Gandara said.

Over the next 3-4 years, further studies will likely validate additional genes and mutations, perhaps encompassing about 90% of patients with advanced-stage lung cancer by 2015, Dr. Varella Garcia said in an interview. It’s also likely that a small percentage of these cancers won’t link with any single, identifiable gene mutation and will instead depend on changes in several pathways, something much harder to sort out.

The number of "actionable" gene mutations (mutations that, once found, can receive a targeted treatment) also remains limited but growing. Until recently, the list had a single gene, EGFR. Patients with EGFR mutations are candidates for treatment with erlotinib (Tarceva) or gefitinib (Iressa, which was not approved for routine U.S. use), both drugs from the tyrosine kinase inhibitor class.

 

 

A second, recent success story for targeted treatment involves the ALK fusion mutation, a genetic profile of tumors responsive to crizotinib. Results from phase I and II studies showed that crizotinib improved progression-free survival and overall survival in patients with tumors that had an ALK mutation; phase III studies are in progress.

"Patients with tumors that depend on these drivers have significantly better clinical outcomes when treated with specific inhibitors," Dr. Varella Garcia said.

This year’s meeting featured three main themes for patient management, but ultimately all three boil down to molecular biomarkers and molecularly directed treatment, Dr. Gandara said. One main theme – histologic profiles of advanced lung cancer – has been an important focus, but "histology is a transient selection method," he said. "At best, histology is a crude molecular selection device" superseded by molecular profiling itself.

Another important, recent focus has been maintenance therapy, but "the real questions are who gets further treatment after platinum-based induction, and when should they get it," questions best answerable by molecular profiling, he added.

"We have many ‘druggable’ molecular targets," Dr. Gandara noted.

"For almost every mutation [of the 10 genes that] the consortium is testing, we have phase I treatment trials underway," said Dr. Varella Garcia. Patients with tumors that carry KRAS and MEK mutations receive an investigational MEK inhibitor. Patients with tumors that contain HER2 mutations receive trastuzumab (Herceptin) as an investigational agent. Patients with MET mutations receive a MET monoclonal antibody.

Despite success so far, and pervasive optimism that current studies will validate new treatments, researchers cautioned that the management of advanced lung cancer also has some critical, unavoidable limitations.

"We will never cure advanced lung cancer; we can make it a chronic disease," Dr. Varella Garcia said. Effective treatment means that patients’ quality of life improves, and their disease comes under control for several years. But "it is almost universal that these patients with eventually progress again. We cannot cure advanced lung cancer. We can control it with new, targeted treatments that use oral drugs with low toxicity."

Dr. Gandara, Dr. Johnson, and Dr. Herbst disclosed relationships with numerous pharmaceutical and biotechnology companies. Among these, Dr. Johnson listed stock in Celgene and a patent for EGFR testing by Genzyme. Dr. Varella Garcia said that she has been a consultant to Abbott.

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Study supports lifting lifetime ban on MSM

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Blood donation
Photo by Juan D. Alfonso

The lifetime ban on blood donation from men who have sex with men (MSM) has been lifted in England, Wales, and Scotland.

Beginning in November, MSM in these countries can donate blood if they have not engaged in sexual activity within the past 12 months. A study published September 8 on bmj.com helped inform this decision. 

Several other countries previously lifted the lifetime ban instituted in the 1980s and introduced deferment periods instead. For example, MSM in South Africa must defer blood donation 6 months after sexual activity. And MSM in Australia, Sweden, and Japan must wait 12 months.

The changes to policy in these countries—along with advances in blood screening techniques and knowledge of HIV—prompted calls for Great Britain to revise its blood donor policy.

So Kaye Wellings, of London School of Hygiene and Tropical Medicine, and her colleagues decided to assess the possible effects of revising the policy, as well as past compliance with the lifetime ban. The results of their study were used to inform the policy review conducted by the Advisory Committee on the Safety of Blood, Tissues, and Organs.

Between April 2009 and June 2010, Wellings’s team surveyed 1028 men in Britain who reported having any sexual contact with other men. Of those surveyed, 10.6% reported donating blood since having penetrative sex with a man, and 2.5% had done so in the past 12 months.

The men cited various reasons for not complying with the ban. They believed themselves to be at low risk of having HIV, had confidentiality concerns, did not understand the ban, or thought the ban unfair.

To gain more insight, Wellings and her colleagues conducted interviews with 30 MSMs—19 who had complied with the lifetime ban on blood donation and 11 who had not.

Many of these men considered the lifetime ban to be unfair, discriminatory, and lacking a clear rationale. However, they generally viewed a 1-year deferral rule as feasible and acceptable.

This prompted Wellings and her colleagues to conclude that MSM might be more likely to comply with a 1-year deferral rule than a lifetime ban. And compliance might improve further with better communication, improved confidentiality measures, and clear explanations of the rationale behind the rule.

Publications
Topics

Blood donation
Photo by Juan D. Alfonso

The lifetime ban on blood donation from men who have sex with men (MSM) has been lifted in England, Wales, and Scotland.

Beginning in November, MSM in these countries can donate blood if they have not engaged in sexual activity within the past 12 months. A study published September 8 on bmj.com helped inform this decision. 

Several other countries previously lifted the lifetime ban instituted in the 1980s and introduced deferment periods instead. For example, MSM in South Africa must defer blood donation 6 months after sexual activity. And MSM in Australia, Sweden, and Japan must wait 12 months.

The changes to policy in these countries—along with advances in blood screening techniques and knowledge of HIV—prompted calls for Great Britain to revise its blood donor policy.

So Kaye Wellings, of London School of Hygiene and Tropical Medicine, and her colleagues decided to assess the possible effects of revising the policy, as well as past compliance with the lifetime ban. The results of their study were used to inform the policy review conducted by the Advisory Committee on the Safety of Blood, Tissues, and Organs.

Between April 2009 and June 2010, Wellings’s team surveyed 1028 men in Britain who reported having any sexual contact with other men. Of those surveyed, 10.6% reported donating blood since having penetrative sex with a man, and 2.5% had done so in the past 12 months.

The men cited various reasons for not complying with the ban. They believed themselves to be at low risk of having HIV, had confidentiality concerns, did not understand the ban, or thought the ban unfair.

To gain more insight, Wellings and her colleagues conducted interviews with 30 MSMs—19 who had complied with the lifetime ban on blood donation and 11 who had not.

Many of these men considered the lifetime ban to be unfair, discriminatory, and lacking a clear rationale. However, they generally viewed a 1-year deferral rule as feasible and acceptable.

This prompted Wellings and her colleagues to conclude that MSM might be more likely to comply with a 1-year deferral rule than a lifetime ban. And compliance might improve further with better communication, improved confidentiality measures, and clear explanations of the rationale behind the rule.

Blood donation
Photo by Juan D. Alfonso

The lifetime ban on blood donation from men who have sex with men (MSM) has been lifted in England, Wales, and Scotland.

Beginning in November, MSM in these countries can donate blood if they have not engaged in sexual activity within the past 12 months. A study published September 8 on bmj.com helped inform this decision. 

Several other countries previously lifted the lifetime ban instituted in the 1980s and introduced deferment periods instead. For example, MSM in South Africa must defer blood donation 6 months after sexual activity. And MSM in Australia, Sweden, and Japan must wait 12 months.

The changes to policy in these countries—along with advances in blood screening techniques and knowledge of HIV—prompted calls for Great Britain to revise its blood donor policy.

So Kaye Wellings, of London School of Hygiene and Tropical Medicine, and her colleagues decided to assess the possible effects of revising the policy, as well as past compliance with the lifetime ban. The results of their study were used to inform the policy review conducted by the Advisory Committee on the Safety of Blood, Tissues, and Organs.

Between April 2009 and June 2010, Wellings’s team surveyed 1028 men in Britain who reported having any sexual contact with other men. Of those surveyed, 10.6% reported donating blood since having penetrative sex with a man, and 2.5% had done so in the past 12 months.

The men cited various reasons for not complying with the ban. They believed themselves to be at low risk of having HIV, had confidentiality concerns, did not understand the ban, or thought the ban unfair.

To gain more insight, Wellings and her colleagues conducted interviews with 30 MSMs—19 who had complied with the lifetime ban on blood donation and 11 who had not.

Many of these men considered the lifetime ban to be unfair, discriminatory, and lacking a clear rationale. However, they generally viewed a 1-year deferral rule as feasible and acceptable.

This prompted Wellings and her colleagues to conclude that MSM might be more likely to comply with a 1-year deferral rule than a lifetime ban. And compliance might improve further with better communication, improved confidentiality measures, and clear explanations of the rationale behind the rule.

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Hospitalists See Value in Palliative Care

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HM groups looking for a new revenue stream would be well served to keep an eye on the explosive growth of palliative care, according to a former SHM president who also runs a palliative service.

Steven Pantilat, MD, FACP, SFHM, director of the Palliative Care Leadership Center at the University of California at San Francisco, says data released this summer by the Center to Advance Palliative Care (CAPC) show that 63% of hospitals have palliative-care teams, up from 24.5% in 2000. But growth is lagging in both smaller hospitals and hospitals in the South.

"Hospitals that are looking to improve the systems of care, hospitals that are looking to be more cutting-edge, looking to be adopters of new models of care are going to pursue both hospital medicine and palliative care," Dr. Pantilat says. "That is another way that hospitalists can demonstrate added value."

Dr. Pantilat, who helped create SHM's Palliative-Care Task Force, says hospitalists can provide primary palliative care and should be mindful to identify patients who should be referred to palliative teams. Hospitalists interested in learning more about palliative skills can pursue training programs through CAPC or the American Academy of Hospice and Palliative Medicine.

The growth of HM and palliative care have followed similar tracks in the past decade, and the business case for both services is similar, Dr. Pantilat says. Because demand still outweighs supply in both specialties, many institutions looking for palliative expertise would be pleased to have their HM group take that mantle, particularly as hospitalists are now caring for the majority of inpatients that would benefit from those services, he adds.

"Hospitalists are the ones taking care of those people with advanced, serious, and life-threatening illnesses," Dr. Pantilat says. "De facto, they are already doing this work."

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HM groups looking for a new revenue stream would be well served to keep an eye on the explosive growth of palliative care, according to a former SHM president who also runs a palliative service.

Steven Pantilat, MD, FACP, SFHM, director of the Palliative Care Leadership Center at the University of California at San Francisco, says data released this summer by the Center to Advance Palliative Care (CAPC) show that 63% of hospitals have palliative-care teams, up from 24.5% in 2000. But growth is lagging in both smaller hospitals and hospitals in the South.

"Hospitals that are looking to improve the systems of care, hospitals that are looking to be more cutting-edge, looking to be adopters of new models of care are going to pursue both hospital medicine and palliative care," Dr. Pantilat says. "That is another way that hospitalists can demonstrate added value."

Dr. Pantilat, who helped create SHM's Palliative-Care Task Force, says hospitalists can provide primary palliative care and should be mindful to identify patients who should be referred to palliative teams. Hospitalists interested in learning more about palliative skills can pursue training programs through CAPC or the American Academy of Hospice and Palliative Medicine.

The growth of HM and palliative care have followed similar tracks in the past decade, and the business case for both services is similar, Dr. Pantilat says. Because demand still outweighs supply in both specialties, many institutions looking for palliative expertise would be pleased to have their HM group take that mantle, particularly as hospitalists are now caring for the majority of inpatients that would benefit from those services, he adds.

"Hospitalists are the ones taking care of those people with advanced, serious, and life-threatening illnesses," Dr. Pantilat says. "De facto, they are already doing this work."

HM groups looking for a new revenue stream would be well served to keep an eye on the explosive growth of palliative care, according to a former SHM president who also runs a palliative service.

Steven Pantilat, MD, FACP, SFHM, director of the Palliative Care Leadership Center at the University of California at San Francisco, says data released this summer by the Center to Advance Palliative Care (CAPC) show that 63% of hospitals have palliative-care teams, up from 24.5% in 2000. But growth is lagging in both smaller hospitals and hospitals in the South.

"Hospitals that are looking to improve the systems of care, hospitals that are looking to be more cutting-edge, looking to be adopters of new models of care are going to pursue both hospital medicine and palliative care," Dr. Pantilat says. "That is another way that hospitalists can demonstrate added value."

Dr. Pantilat, who helped create SHM's Palliative-Care Task Force, says hospitalists can provide primary palliative care and should be mindful to identify patients who should be referred to palliative teams. Hospitalists interested in learning more about palliative skills can pursue training programs through CAPC or the American Academy of Hospice and Palliative Medicine.

The growth of HM and palliative care have followed similar tracks in the past decade, and the business case for both services is similar, Dr. Pantilat says. Because demand still outweighs supply in both specialties, many institutions looking for palliative expertise would be pleased to have their HM group take that mantle, particularly as hospitalists are now caring for the majority of inpatients that would benefit from those services, he adds.

"Hospitalists are the ones taking care of those people with advanced, serious, and life-threatening illnesses," Dr. Pantilat says. "De facto, they are already doing this work."

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To Friend or Not to Friend?

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Social networking is nothing new, but with more doctors logging on, it is important to recognize the inherent professional risks.

To help their physicians manage their online reputations, the British Medical Association recently issued social media guidelines reminding their doctors that "ethical and legal duties apply just as much on the Internet as when they are offline." U.S.-based physicians are also encouraged to take precautions.

As a mentor for young hospitalists, Paul Grant, MD, assistant professor at University of Michigan Health System and chair of the SHM Early Career Hospitalist committee, agrees that while there is value in using sites like Facebook and Twitter, it's important to keep the conversation professional.

"While the issue of patient friend requests is probably more common with long-term-care physicians, our group has been encouraged to be aware of our Internet profiles, and to Google ourselves periodically to see what’s out there," he says. Dr. Grant admits he occasionally receives requests from colleagues, but he declines.

"That's the nice thing about Facebook: You treat it like you would any other relationship," says Glenn Lombardi, president of Officite, a Downers Grove, Ill.-based medical website and web-marketing firm that manages more than 1,000 Facebook accounts for medical practices. "You share certain things with certain people, and it's not anything bigger that that."

Lombardi and Dr. Grant offer the following social-networking tips:

 

     

     

  1. Maintain privacy. Don't accept personal friend requests from patients or colleagues.
  2.  

     

  3. Be proactive. Have a search-engine-optimized website. Make sure your patients know it's the best place to go for information.
  4.  

     

  5. Wait on trends. New social-networking sites, such as Google+, have great potential, Lombardi says, but it is better to see what experts identify as their best and safest purposes before creating a profile.
  6.  

     

 

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Social networking is nothing new, but with more doctors logging on, it is important to recognize the inherent professional risks.

To help their physicians manage their online reputations, the British Medical Association recently issued social media guidelines reminding their doctors that "ethical and legal duties apply just as much on the Internet as when they are offline." U.S.-based physicians are also encouraged to take precautions.

As a mentor for young hospitalists, Paul Grant, MD, assistant professor at University of Michigan Health System and chair of the SHM Early Career Hospitalist committee, agrees that while there is value in using sites like Facebook and Twitter, it's important to keep the conversation professional.

"While the issue of patient friend requests is probably more common with long-term-care physicians, our group has been encouraged to be aware of our Internet profiles, and to Google ourselves periodically to see what’s out there," he says. Dr. Grant admits he occasionally receives requests from colleagues, but he declines.

"That's the nice thing about Facebook: You treat it like you would any other relationship," says Glenn Lombardi, president of Officite, a Downers Grove, Ill.-based medical website and web-marketing firm that manages more than 1,000 Facebook accounts for medical practices. "You share certain things with certain people, and it's not anything bigger that that."

Lombardi and Dr. Grant offer the following social-networking tips:

 

     

     

  1. Maintain privacy. Don't accept personal friend requests from patients or colleagues.
  2.  

     

  3. Be proactive. Have a search-engine-optimized website. Make sure your patients know it's the best place to go for information.
  4.  

     

  5. Wait on trends. New social-networking sites, such as Google+, have great potential, Lombardi says, but it is better to see what experts identify as their best and safest purposes before creating a profile.
  6.  

     

 

Social networking is nothing new, but with more doctors logging on, it is important to recognize the inherent professional risks.

To help their physicians manage their online reputations, the British Medical Association recently issued social media guidelines reminding their doctors that "ethical and legal duties apply just as much on the Internet as when they are offline." U.S.-based physicians are also encouraged to take precautions.

As a mentor for young hospitalists, Paul Grant, MD, assistant professor at University of Michigan Health System and chair of the SHM Early Career Hospitalist committee, agrees that while there is value in using sites like Facebook and Twitter, it's important to keep the conversation professional.

"While the issue of patient friend requests is probably more common with long-term-care physicians, our group has been encouraged to be aware of our Internet profiles, and to Google ourselves periodically to see what’s out there," he says. Dr. Grant admits he occasionally receives requests from colleagues, but he declines.

"That's the nice thing about Facebook: You treat it like you would any other relationship," says Glenn Lombardi, president of Officite, a Downers Grove, Ill.-based medical website and web-marketing firm that manages more than 1,000 Facebook accounts for medical practices. "You share certain things with certain people, and it's not anything bigger that that."

Lombardi and Dr. Grant offer the following social-networking tips:

 

     

     

  1. Maintain privacy. Don't accept personal friend requests from patients or colleagues.
  2.  

     

  3. Be proactive. Have a search-engine-optimized website. Make sure your patients know it's the best place to go for information.
  4.  

     

  5. Wait on trends. New social-networking sites, such as Google+, have great potential, Lombardi says, but it is better to see what experts identify as their best and safest purposes before creating a profile.
  6.  

     

 

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