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Paperwork consumes docs’ time, erodes morale
A survey of nearly 5000 US physicians showed that the average doctor spent 16.6% of his or her working hours on non-patient-related paperwork.
This includes tasks such as billing, obtaining insurance approvals, financial and personnel management, and negotiating contracts.
The more time doctors spent on such tasks, the less satisfied they were with medicine as a career.
Researchers detailed these findings in the International Journal of Health Services.
“Our crazy health financing system is demoralizing doctors and wasting vast resources,” said study author David Himmelstein, MD, a professor at Hunter College of the City University of New York.
“Turning healthcare into a business means we spend more and more time on billing, insurance paperwork, and the bottom line. We need to move to a simple, nonprofit national health insurance system that lets doctors and hospitals focus on patients, not finances.”
Dr Himmelstein and colleague Steffie Woolhandler, MD, analyzed confidential data from the 2008 Health Tracking Physician Survey (the most recent data available). The survey included information from a nationally representative sample of 4720 physicians who practiced at least 20 hours per week.
The data showed that the average doctor spent 8.7 hours per week, or 16.6% of his or her working time, on non-patient-related administration. This excludes tasks such as writing chart notes, communicating with other doctors, and ordering lab tests.
In total, patient-care physicians spent 168.4 million hours on non-patient-related administrative tasks in 2008. Drs Himmelstein and Woolhandler estimate that the total cost of physician time spent on administration in 2014 will amount to $102 billion.
Career satisfaction was lower for physicians who spent more time on administration. “Very satisfied” doctors spent, on average, 16.1% of their time on administration. “Very dissatisfied” doctors spent 20.6% of their time on such tasks.
Among various specialties, psychiatrists spent the most time on administration (20.3%), followed by internists (17.3%) and family/general practitioners (17.3%). Pediatricians spent the least amount of time (14.1%) on non-patient-related administrative tasks and were the most satisfied group of doctors.
Solo practice was associated with more administrative work, but small group practice was not. Doctors practicing in groups of 100 or more actually spent more time (19.7%) on such tasks than those in small groups (16.3%).
The researchers were surprised to find that physicians who used electronic health records spent more time (17.2% for those using entirely electronic records, 18% for those using a mix of paper and electronic) on administration than those who used only paper records (15.5%).
The pair noted that physicians in Canada spend far less time on administration than US doctors, and they attributed the difference to Canada’s single-payer system, which has greatly simplified billing and reduced bureaucracy.
The researchers pointed out that the only previous nationally representative survey of this kind was carried out in 1995, and that study showed that administration and insurance-related matters accounted for 13.5% of physicians’ total work time. Other, less representative studies also suggest the bureaucratic burden on physicians has grown in the past two decades.
“American doctors are drowning in paperwork,” Dr Woolhandler said. “Our study almost certainly understates physicians’ current administrative burden.”
“Since 2008, when the survey we analyzed was collected, tens of thousands of doctors have moved from small private practices with minimal bureaucracy into giant group practices where bureaucracy is rampant. And under the accountable care organizations favored by insurers, more doctors are facing HMO-type incentives to deny care to their patients, a move that our data shows drives up administrative work.” 
A survey of nearly 5000 US physicians showed that the average doctor spent 16.6% of his or her working hours on non-patient-related paperwork.
This includes tasks such as billing, obtaining insurance approvals, financial and personnel management, and negotiating contracts.
The more time doctors spent on such tasks, the less satisfied they were with medicine as a career.
Researchers detailed these findings in the International Journal of Health Services.
“Our crazy health financing system is demoralizing doctors and wasting vast resources,” said study author David Himmelstein, MD, a professor at Hunter College of the City University of New York.
“Turning healthcare into a business means we spend more and more time on billing, insurance paperwork, and the bottom line. We need to move to a simple, nonprofit national health insurance system that lets doctors and hospitals focus on patients, not finances.”
Dr Himmelstein and colleague Steffie Woolhandler, MD, analyzed confidential data from the 2008 Health Tracking Physician Survey (the most recent data available). The survey included information from a nationally representative sample of 4720 physicians who practiced at least 20 hours per week.
The data showed that the average doctor spent 8.7 hours per week, or 16.6% of his or her working time, on non-patient-related administration. This excludes tasks such as writing chart notes, communicating with other doctors, and ordering lab tests.
In total, patient-care physicians spent 168.4 million hours on non-patient-related administrative tasks in 2008. Drs Himmelstein and Woolhandler estimate that the total cost of physician time spent on administration in 2014 will amount to $102 billion.
Career satisfaction was lower for physicians who spent more time on administration. “Very satisfied” doctors spent, on average, 16.1% of their time on administration. “Very dissatisfied” doctors spent 20.6% of their time on such tasks.
Among various specialties, psychiatrists spent the most time on administration (20.3%), followed by internists (17.3%) and family/general practitioners (17.3%). Pediatricians spent the least amount of time (14.1%) on non-patient-related administrative tasks and were the most satisfied group of doctors.
Solo practice was associated with more administrative work, but small group practice was not. Doctors practicing in groups of 100 or more actually spent more time (19.7%) on such tasks than those in small groups (16.3%).
The researchers were surprised to find that physicians who used electronic health records spent more time (17.2% for those using entirely electronic records, 18% for those using a mix of paper and electronic) on administration than those who used only paper records (15.5%).
The pair noted that physicians in Canada spend far less time on administration than US doctors, and they attributed the difference to Canada’s single-payer system, which has greatly simplified billing and reduced bureaucracy.
The researchers pointed out that the only previous nationally representative survey of this kind was carried out in 1995, and that study showed that administration and insurance-related matters accounted for 13.5% of physicians’ total work time. Other, less representative studies also suggest the bureaucratic burden on physicians has grown in the past two decades.
“American doctors are drowning in paperwork,” Dr Woolhandler said. “Our study almost certainly understates physicians’ current administrative burden.”
“Since 2008, when the survey we analyzed was collected, tens of thousands of doctors have moved from small private practices with minimal bureaucracy into giant group practices where bureaucracy is rampant. And under the accountable care organizations favored by insurers, more doctors are facing HMO-type incentives to deny care to their patients, a move that our data shows drives up administrative work.”
A survey of nearly 5000 US physicians showed that the average doctor spent 16.6% of his or her working hours on non-patient-related paperwork.
This includes tasks such as billing, obtaining insurance approvals, financial and personnel management, and negotiating contracts.
The more time doctors spent on such tasks, the less satisfied they were with medicine as a career.
Researchers detailed these findings in the International Journal of Health Services.
“Our crazy health financing system is demoralizing doctors and wasting vast resources,” said study author David Himmelstein, MD, a professor at Hunter College of the City University of New York.
“Turning healthcare into a business means we spend more and more time on billing, insurance paperwork, and the bottom line. We need to move to a simple, nonprofit national health insurance system that lets doctors and hospitals focus on patients, not finances.”
Dr Himmelstein and colleague Steffie Woolhandler, MD, analyzed confidential data from the 2008 Health Tracking Physician Survey (the most recent data available). The survey included information from a nationally representative sample of 4720 physicians who practiced at least 20 hours per week.
The data showed that the average doctor spent 8.7 hours per week, or 16.6% of his or her working time, on non-patient-related administration. This excludes tasks such as writing chart notes, communicating with other doctors, and ordering lab tests.
In total, patient-care physicians spent 168.4 million hours on non-patient-related administrative tasks in 2008. Drs Himmelstein and Woolhandler estimate that the total cost of physician time spent on administration in 2014 will amount to $102 billion.
Career satisfaction was lower for physicians who spent more time on administration. “Very satisfied” doctors spent, on average, 16.1% of their time on administration. “Very dissatisfied” doctors spent 20.6% of their time on such tasks.
Among various specialties, psychiatrists spent the most time on administration (20.3%), followed by internists (17.3%) and family/general practitioners (17.3%). Pediatricians spent the least amount of time (14.1%) on non-patient-related administrative tasks and were the most satisfied group of doctors.
Solo practice was associated with more administrative work, but small group practice was not. Doctors practicing in groups of 100 or more actually spent more time (19.7%) on such tasks than those in small groups (16.3%).
The researchers were surprised to find that physicians who used electronic health records spent more time (17.2% for those using entirely electronic records, 18% for those using a mix of paper and electronic) on administration than those who used only paper records (15.5%).
The pair noted that physicians in Canada spend far less time on administration than US doctors, and they attributed the difference to Canada’s single-payer system, which has greatly simplified billing and reduced bureaucracy.
The researchers pointed out that the only previous nationally representative survey of this kind was carried out in 1995, and that study showed that administration and insurance-related matters accounted for 13.5% of physicians’ total work time. Other, less representative studies also suggest the bureaucratic burden on physicians has grown in the past two decades.
“American doctors are drowning in paperwork,” Dr Woolhandler said. “Our study almost certainly understates physicians’ current administrative burden.”
“Since 2008, when the survey we analyzed was collected, tens of thousands of doctors have moved from small private practices with minimal bureaucracy into giant group practices where bureaucracy is rampant. And under the accountable care organizations favored by insurers, more doctors are facing HMO-type incentives to deny care to their patients, a move that our data shows drives up administrative work.”
Housestaff Teams and Patient Outcomes
Since the Institute of Medicine Report To Err is Human, increased attention has been paid to improving the care of hospitalized patients.[1] Strategies include utilization of guidelines and pathways, and the application of quality improvement techniques to improve or standardize processes. Despite improvements in focused areas such as prevention of hospital‐acquired infections, evidence suggests that outcomes for hospitalized patients remain suboptimal.[2] Rates of errors and hospital‐related complications such as falls, decubitus ulcers, and infections remain high,[3, 4, 5] and not all patients receive what is known to be appropriate care.[6]
Many attempts to improve inpatient care have used process‐improvement approaches, focusing on impacting individuals' behaviors, or on breaking down processes into component parts. Examples include central line bundles or checklists.[7, 8] These approaches attempt to ensure that providers do things in a standardized way, but are implicitly based on the reductionist assumption that we can break processes down into predictable parts to improve the system. An alternative way to understand clinical systems is based on interdependencies between individuals in the system, or the ways in which parts of the system interact with each other, which may be unpredictable over time.[1, 9] Whereas these interdependencies include care processes, they also encompass the providers who care for patients. Providers working together vary in terms of the kinds of relationships they have with each other. Those relationships are crucial to system function because they are the foundation for the interactions that lead to effective patient care.
The application of several frameworks or approaches for considering healthcare systems in terms of relationships highlights the importance of this way of understanding system function. The include complexity science,[1, 7] relational coordination (which is grounded in complexity science),[10] high reliability,[11] and the Big Five for teamwork.[12]
Research indicates that interactions among healthcare providers can have important influences on outcomes.[13, 14, 15, 16, 17] Additionally, the initial implementation of checklists to prevent central‐line associated infections appeared to change provider relationships in a way that significantly influenced their success.[18] For example, positive primary care clinic member relationships as assessed by the Lanham framework have been associated with better chronic care model implementation, learning, and patient experience of care.[19, 20] This framework, which we apply here, identifies 7 relationship characteristics: (1) trust; (2) diversity; (3) respect; (4) mindfulness, or being open to new ideas from others; (5) heedfulness, or an understanding of how one's roles influence those of others; (6) use of rich in‐person or verbal communication, particularly for potentially ambiguous information open to multiple interpretations; and (7) having a mixture of social and task relatedness among teams, or relatedness outside of only work‐related tasks.[19] Relationships within surgical teams that are characterized by psychological safety and diversity are associated with successful uptake of new techniques and decreased mortality.[13, 14] Relationships are important because the ability of patients and providers to learn and make sense of their patients' illnesses is grounded in relationships.
We sought to better understand and characterize inpatient physician teams' relationships, and assess the association between team relationships as evaluated by Lanham's framework and outcomes for hospitalized patients. Data on relationships among inpatient medical teams are few, despite the fact that these teams provide a great proportion of inpatient care. Additionally, the care of hospitalized medical patients is complex and uncertain, often involving multiple providers, making provider relationships potentially even more important to outcomes than in other settings.
METHODS
Overview
We conducted an observational, convergent mixed‐methods study of inpatient medicine teams.[21, 22, 23] We focused on inpatient physician teams, defining them as the functional work group responsible for medical decision making in academic medical centers. Physician teams in this context have been studied in terms of social hierarchy, authority, and delegation.[24, 25, 26] Focusing on the relationships within these groups could provide insights into strategies to mitigate potential negative effects of hierarchy. We recognize that other providers are closely involved in the care of hospitalized patients, and although we did not have standard interactions between physicians, nurses, case managers, and other providers that we could consistently observe, we did include interactions with these other providers in our observations and assessments of team relationships. Because this work is among the first in inpatient medical teams, we chose to study a small number of teams in great depth, allowing us to make rich assessments of team relationships.
We chose patient outcomes of length of stay (LOS), unnecessary LOS (ULOS), and complication rates, adjusted for patient characteristics and team workload. LOS is an important metric of inpatient care delivery. We feel ULOS is an aspect of LOS that is dependent on the physician team, as it reflects their preparation of the patient for discharge. Finally, we chose complication rates because hospital‐acquired conditions and complications are important contributors to inpatient morbidity, and because recent surgical literature has identified complication rates as a contributor to mortality that could be related to providers' collective ability to recognize complications and act quickly.
This study was approved by the institutional review board at the University of Texas Health Science Center at San Antonio (UTHSCSA), the Research and Development Committee for the South Texas Veterans Health Care System (STVHCS), and the Research Committee at University Health System (UHS). All physicians consented to participate in the study. We obtained a waiver of consent for inclusion of patient data.
Setting and Study Participants
This study was conducted at the 2 UTHSCSA primary teaching affiliates. The Audie L. Murphy Veterans Affairs Hospital is the 220‐bed acute‐care hospital of the STVHCS. University Hospital is the 614‐bed, level‐I trauma, acute‐care facility for UHS, the county system for Bexar County, which includes the San Antonio, Texas major metropolitan area.
The inpatient internal medicine physician team was our unit of study. Inpatient medicine teams consisted of 1 faculty attending physician, 1 postgraduate year (PGY)‐2 or PGY‐3 resident, and 2 PGY‐1 members. In addition, typically 2 to 3 third‐year medical students were part of the team, and a subintern was sometimes present. Doctor of Pharmacy faculty and students were also occasionally part of the team. Social workers and case managers often joined team rounds for portions of the time, and nurses sometimes joined bedside rounds on specific patients. These teams admit all medicine patients with the exception of those with acute coronary syndromes, new onset congestive heart failure, or arrhythmias. Patients are randomly assigned to teams based on time of admission and call schedules.
Between these 2 hospitals, there are 10 inpatient medicine teams caring for patients, with a pool of over 40 potential faculty attendings. Our goal was to observe teams that would be most likely to vary in terms of their relationship characteristics and patient outcomes through observing teams with a range of individual members. We used a purposeful sampling approach to obtain a diverse sample, sampling based on physician attributes and time of year.[16, 17] Three characteristics were most important: attending physician years of experience, attending involvement in educational and administrative leadership, and the presence of struggling resident members, as defined by being on probation or having been discussed in the residency Clinical Competency Committee. We did not set explicit thresholds in terms of attending experience, but instead sought to ensure a range. The attendings we observed were more likely to be involved in education and administrative leadership activities, but were otherwise similar to those we did not observe in terms of years of experience. We included struggling residents to observe individuals with a range of skill sets, and not just high‐performing individuals. We obtained attending information based on our knowledge of the attending faculty pool, and from the internal medicine residency program. We sampled across the year to ensure a diversity of trainee experience, but did not observe teams in either July or August, as these months were early in the academic year. Interns spend approximately 5 months per year on inpatient services, whereas residents spend 2 to 3 months per year. Thus, interns but not residents observed later in the year might have spent significantly more time on an inpatient service. However, in all instances, none of the team members observed had worked together previously.
Data Collection
Data were collected over nine 1‐month periods from September 2008 through June 2011. Teams were observed daily for 2‐ to 4‐week periods during morning rounds, the time when the team discusses each patient and makes clinical decisions. Data collection started on the first day of the month, the first day that all team members worked together, and continued for approximately 27 days, the last day before the resident rotated to a different service. By comprehensively and systematically observing these teams' daily rounds, we obtained rich, in‐depth data with multiple data points, enabling us to assess specific team behaviors and interactions.
During the third and fourth months, we collected data on teams in which the attending changed partway through. We did this to understand the impact of individual attending change on team relationships. Because the team relationships differed with each attending, we analyzed them separately. Thus, we observed 7 teams for approximately 4‐week periods and 4 teams for approximately 2‐week periods.
Observers arrived in the team room prior to rounds to begin observations, staying until after rounds were completed. Detailed free‐text field notes were taken regarding team activities and behaviors, including how the teams made patient care decisions. Field notes included: length of rounds, which team members spoke during each patient discussion, who contributed to management discussions, how information from consultants was incorporated, how communication with others outside of the team occurred, how team members spoke with each other including the types of words used, and team member willingness to perform tasks outside of their usually defined role, among others. Field notes were collected in an open‐ended format to allow for inductive observations. Observers also recorded clinical data daily regarding each patient, including admission and discharge dates, and presenting complaint.
The observation team consisted of the principle investigator (PI) (hospitalist) and 2 research assistants (a graduate‐level medical anthropologist and social psychologist), all of whom were trained by a qualitative research expert to systematically collect data related to topics of interest. Observers were instructed to record what the teams were doing and talking about at all times, noting any behaviors that they felt reflected how team members related to each other and came to decisions about their patients, or that were characteristic of the team. To ensure consistency, the PI and 1 research assistant conducted observations jointly at the start of data collection for each team, checking concordance of observations daily using a percent agreement until general agreement on field note content and patient information reached 90%. Two individuals observed 24 days of data collection, representing 252 patient discussions (13% of observed discussions).
An age‐adjusted Charlson‐Deyo comorbidity score was calculated for each patient admitted to each team, using data from rounds and from each hospital's electronic health records (EHR).[27] We collected data regarding mental health conditions for each patient (substance use, mood disorder, cognitive disorder, or a combination) because these comorbidities could impact LOS or ULOS. Discharge diagnoses were based on the discharge summary in the EHR. We also collected data daily regarding team census and numbers of admissions to and discharges from each team to assess workload.
Three patient outcomes were measured: LOS, ULOS, and complications. LOS was defined as the total number of days the patient was in the hospital. ULOS was defined as the number of days a patient remained in the hospital after the day the team determined the patient was medically ready for discharge (assessed by either discussion on rounds or EHR documentation). ULOS may occur when postdischarge needs have been adequately assessed, or because of delays in care, which may be related to provider communication during the hospitalization. Complications were defined on a per‐patient, per‐day basis in 2 ways: the development of a new problem in the hospital, for example acute kidney injury, a hospital‐acquired infection, or delirium, or by the team noting a clinical deterioration after at least 24 hours of clinical stability, such as the patient requiring transfer to a higher level of care. Complications were determined based on discussions during rounds, with EHR verification if needed.
Analysis Phase I: Assessment of Relationship Characteristics
After the completion of data collection, field notes were reviewed by a research team member not involved in the original study design or primary data collection (senior medical student). We took this approach to guard against biasing the reviewer's view of team behaviors, both in terms of not having conducted observations of the teams and being blinded to patient outcomes.
The reviewer completed a series of 3 readings of all field notes. The first reading provided a summary of the content of the data and the individual teams. Behavioral patterns of each team were used to create an initial team profile. The field notes and profiles were reviewed by the PI and a coauthor not involved in data collection to ensure that the profiles adequately reflected the field notes. No significant changes to the profiles were made based on this review. The profiles were discussed at a meeting with members of the larger research team, including the PI, research assistants, and coinvestigators (with backgrounds in medicine, anthropology, and information and organization management). Behavior characteristics that could be used to distinguish teams were identified in the profiles using a grounded theory approach.
The second review of field notes was conducted to test the applicability of the characteristics identified in the first review. To systematically record the appearance of the behaviors, we created a matrix with a row for each behavior and columns for each team to note whether they exhibited each behavior. If the behavior was exhibited, specific examples were cataloged in the matrix. This matrix was reviewed and refined by the research team. During the final field note review meeting, the research team compared the summary matrix for each team, with the specific behaviors noted during the first reading of the field notes to ensure that all behaviors were recorded.
After cataloging behaviors, the research team assigned each behavior to 1 of the 7 Lanham relationship characteristics. We wanted to assess our observations against a relationship framework to ensure that we were able to systematically assess all aspects of relationships. The Lanham framework was initially developed based on a systematic review of the organizational and educational literatures, making it relevant to the complex environment of an academic medical inpatient team and allowing us to assess relationships at a fine‐grained, richly detailed level. This assignment was done by the author team as a group. Any questions were discussed and different interpretations resolved through consensus. The Lanham framework has 7 characteristics.[19] Based on the presence of behaviors associated with each relationship characteristic, we assigned a point to each team for each relationship characteristic observed. We considered a behavior type to be present if we observed it on at least 3 occasions on separate days. Though we used a threshold of at least 3 occurrences, most teams that did not receive a point for a particular characteristic did not have any instances in which we observed the characteristic. This was particularly true for trust and mindfulness, and least so for social/task relatedness. By summing these points, we calculated a total relationship score for each team, with potential scores ranging from 0 (for teams exhibiting no behaviors reflecting a particular relationship characteristic) to 7.
Analysis Phase II: Factor Analysis
To formally determine which relationship characteristics were most highly related, data were submitted to a principal components factor analysis using oblique rotation. Item separation was determined by visual inspection of the scree plot and eigenvalues over 1.
Analysis Phase III: Assessing the Association between Physician Team Relationship Characteristics and Patient Outcomes
We examined the association between team relationships and patient outcomes using team relationship scores. For the LOS/ULOS analysis, we only included patients whose entire hospitalization occurred under the care of the team we observed. Patients who were on the team at the start of the month, were transferred from another service, or who remained hospitalized after the end of the team's time together were excluded. The longest possible LOS for patients whose entire hospitalization occurred on teams that were observed for half a month was 12 days. To facilitate accurate comparison between teams, we only included patients whose LOS was 12 days.
Complication rates were defined on a per‐patient per‐day basis to normalize for different team volumes and days of observation. For this analysis, we included patients who remained on the team after data collection completion, patients transferred to another team, or patients transferred from another team. However, we only counted complications that occurred at least 24 hours following transfer to minimize the likelihood that the complication was related to the care of other physicians.
Preliminary analysis involved inspection and assessment of the distribution of all variables followed, by a general linear modeling approach to assess the association between patient and workload covariates and outcomes.[28, 29] Because we anticipated that outcome variables would be markedly skewed, we also planned to assess the association between relationship characteristics with outcomes using the Kruskal‐Wallis rank sum test to compare groups with Dunn's test[30] for pairwise comparisons if overall significance occurred.[31] There are no known acceptable methods for covariate adjustments using the Kruskal‐Wallis method. All models were run using SAS software (SAS Institute Inc., Cary, NC).[32]
RESULTS
The research team observed 1941 discussions of 576 individual patients. Observations were conducted over 352 hours and 54 minutes, resulting in 741 pages of notes (see Supporting Table 1 in the online version of this article for data regarding individual team members). Teams observed over half‐months are referred to with a and b designations.
| Relationship Characteristic | Definition | Thirteen Types of Behaviors Observed in Field Notes | Observed Examples |
|---|---|---|---|
| Trust | Willingness to be vulnerable to others | Use of we instead of you or I by the attending | Where are we going with this guy? |
| Attending admitting I don't know | Let's go talk to him, I can't figure this out | ||
| Asking questions to help team members to think through problems | Will the echo change our management? How will it help us? | ||
| Diversity | Including different perspectives and different thinking | Team member participation in conversations about patients that are not theirs | One intern is presenting, another intern asks a question, and the resident joins the discussion |
| Inclusion of perspectives of those outside the team (nursing and family members) | Taking a break to call the nurse, having a family meeting | ||
| Respect | Valuing the opinions of others, honest and tactful interactions | Use of positive reinforcement by the attending | Being encouraging of the medical student's differential, saying excellent |
| How the team talks with patients | Asking if the patient has any concerns, what they can do to make them comfortable | ||
| Heedfulness | Awareness of how each person's roles impact the rest of the team | Team members performing tasks not expected of their role | One intern helping another with changing orders to transfer a patient |
| Summarizing plans and strategizing | Attending recaps the plan for the day, asks what they can do | ||
| Mindfulness | Openness to new ideas/free discussion about what is and is not working | Entire team engaged in discussion | Attending asks the medical student, intern, and resident what they think is going on |
| Social relatedness | Having socially related interactions | Social conversation among team members | Intern talks about their day off |
| Jokes by the attending | Showers and a bowel movement is the key to making people happy | ||
| Appropriate use of rich communication | Use of in‐person communication for sensitive or difficult issues | Using verbal communication with consultants or family | Intern is on the phone with the pharm D because there is a problem with the medication |
Creation of team profiles yielded 13 common behavior characteristics that were inductively identified and that could potentially distinguish teams, including consideration of perspectives outside of the team and team members performing tasks normally outside of their roles. Table 1 provides examples of and summarizes observed behaviors using examples from the field notes, mapping these behavior characteristics onto the Lanham relationship characteristics. The distribution of relationship characteristics and scores for each team are shown in Table 2.
| Relationship Characteristic | Team | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3a | 3b | 4a | 4b | 5 | 6 | 7 | 8 | 9 | |
| Trust | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 |
| Diversity | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 |
| Respect | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 |
| Heedfulness | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 1 |
| Mindfulness | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 |
| Social/task relatedness | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 |
| Rich/lean communication | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 |
| Relationship score (no. of characteristics observed) | 0 | 5 | 7 | 2 | 2 | 3 | 5 | 0 | 7 | 7 | 6 |
Correlation between relationship characteristics ranged from 0.32 to 0.95 (see Supporting Table 2 in the online version of this article). Mindfulness and trust are more highly correlated with each other than with other variables, as are diversity and respect. We performed a principal components factor analysis. Based on scree plot inspection and eigenvalues >1, we kept 3 factors that explained 85% of the total variance (see Supporting Table 3 in the online version of this article).
| No. of Relationship Characteristics | |||
|---|---|---|---|
| 02 | 35 | 67 | |
| |||
| LOS, d, n=293 | |||
| Median | 4 | 5 | 3 |
| IQR | 5 | 4 | 3 |
| Mean | 4.7 (2.72) | 4.7 (2.52) | 4.1 (2.51), P=0.12a |
| ULOS, d, n=293 | |||
| Median | 0 | 0 | 0 |
| IQR | 0 | 0 | 0 |
| Mean | 0.37 (0.99) | 0.33 (0.96) | 0.13 (0.56), P=0.09a |
| Complications (per patient per day), n=398 | |||
| Median | 0 | 0 | 0 |
| IQR | 1 | 1 | 0 |
| Mean | 0.58 (1.06) | 0.45 (0.77) | 0.18 (0.59), P=0.001 compared to teams with 02 or 35 characteristics |
Our analyses of LOS and ULOS included 298 of the 576 patients. Two hundred sixty‐seven patients were excluded because their entire LOS did not occur while under the care of the observed teams. Eleven patients were removed from the analysis because their LOS was >12 days. The analysis of complications included 398 patients. In our preliminary general linear modeling approach, only patient workload was significantly associated with outcomes using a cutoff of P=0.05. Charlson‐Deyo score and mental health comorbidities were not associated with outcomes.
The results of the Kruskal‐Wallis test show the patient average ranking on each of the outcome variables by 3 groups (Table 3). Overall, teams with higher relationship scores had lower rank scores on all outcomes measures. However, the only statistically significant comparisons were for complications. Teams having 6 to 7 characteristics had a significantly lower complication rate ranking than teams with 0 to 2 and 3 to 5 (P=0.001). We did not find consistent differences between individual teams or groups of teams with relationship scores from 0 to 2, 3 to 5, and 6 to 7 with regard to Charlson score, mental health issues, or workload. The only significant differences were between Charlson‐Deyo scores for patients admitted to teams with low relationship scores of 0 to 2 versus high relationship scores of 6 to 7 (6.7 vs 5.1); scores for teams with relationship scores of 3 to 5 were not significantly different from the low or high groups.
Table 4 shows the Kruskal‐Wallace rank test results for each group of relationship characteristics identified in the factor analysis based on whether teams displayed all or none of the characteristics in the factor. There were no differences in these groupings for LOS. Teams that exhibited both mindfulness and trust had lower ranks on ULOS than teams that did not have either. Similarly, teams with heedfulness, social‐task relatedness, and more rich communication demonstrated lower ULOS rankings than teams who did not have all 3 characteristics.
| Mind/Trust | Diversity/Respect | Heed/Relate/Communicate | ||||
|---|---|---|---|---|---|---|
| Patient Outcome | None | Both | None | Both | None | All 3 |
| ||||||
| LOS, d, n=293 | ||||||
| Median | 4 | 4 | 4 | 4 | 4 | 4 |
| IQR | 5 | 3 | 4.5 | 3 | 4 | 4 |
| Mean | 4.7 (2.6) | 4.2 (2.5) | 4.7 (2.6) | 4.3 (2.5) | 4.4 (2.6) | 4.4 (2.6) |
| P value | 0.06a | 0.23a | 0.85a | |||
| ULOS, d, n=293 | ||||||
| Median | 0 | 0 | 0 | 0 | 0 | 0 |
| IQR | 0 | 0 | 0 | 0 | 0 | 0 |
| Mean | 0.39 (1.01) | 0.15 (0.62) | 0.33 (0.92) | 0.18 (0.71) | 0.32 (0.93) | 0.18 (0.69) |
| P value | 0.009 | 0.06 | 0.03 | |||
| Complications (per patient), n=389 | ||||||
| Median | 0 | 0 | 0 | 0 | 0 | 0 |
| IQR | 1 | 0 | 1 | 0 | 1 | 0 |
| Mean | 0.58 (1.01) | 0.19 (0.58) | 0.47 (0.81) | 0.29 (0.82) | 0.26 (0.92) | 0.28 (0.70) |
| P value | <0.0001 | 0.001 | 0.02 | |||
DISCUSSION
Relationships are critical to team function because they are the basis for the social interactions that are central to patient care. These interactions include how providers recognize and make sense of what is happening with patients, and how they learn to care for patients more effectively. Additionally, the high task interdependencies among inpatient providers require effective relationships for optimal care. In our study, inpatient medicine physician teams' relationships varied, and these differences were associated with ULOS and complications. Relationship characteristics are not mutually exclusive, and as our factor analysis demonstrates, are intercorrelated. Trust and mindfulness appear to be particularly important. Trust may foster psychological safety that in turn promotes the willingness of individuals to contribute their thoughts and ideas.[13] In low‐trust teams, providers may fear a negative impact for bringing forward a concern based on limited data. Mindful teams may be more likely to notice nuanced changes, or are more likely to talk when things just do not appear to be going in the right direction with the patient. In the case of acutely ill medical patients, trust and mindfulness may lead to an increased likelihood that clinical changes are recognized and discussed quickly. For example, on a team characterized by trust and mindfulness, the entire team was typically involved in care discussions, and the interns and students frequently asked a lot of questions, even regarding the care of patients they were not directly following. We observed that these questions and discussions often led the team to realize that they needed to make a change in management decisions (eg, discontinuing Bactrim, lowering insulin doses, adjusting antihypertensives, premedicating for intravenous contrast) that they had not caught in the assessment and plan portion of the patient care discussion. In another example, a medical student asked a tentative question after a patient needed to go quickly to the bathroom while they were examining her, leading the team to ask more questions that led to a more rapid evaluation of a potential urinary tract infection. This finding is consistent with the description of failure to rescue among surgical patients, in which mortality has been associated with the failure to recognize complications rapidly and act effectively.[33]
Our findings are limited in several ways. First, these data are from a single academic institution. Although we sought diversity among our teams and collected data across 2 hospitals, there may be local contextual factors that influenced our results. Second, our data demonstrate an association, but not causality. Our findings should be tested in studies that assess causality and potential mechanisms through which relationships influence outcomes. Third, the individuals observing the teams had some knowledge of patient outcomes through hearing patient discussions. However, by involving individuals who did not participate in observations and were blinded to outcomes in assessing team relationships, we addressed this potential bias. Fourth, our observations were largely focused on physician teams, not directly including other providers. Our difficulty in observing regular interactions between physicians and other providers underscores the need to increase contact among those caring for hospitalized patients, such as occurs through multidisciplinary rounds. We did include team communication with other disciplines in our assessment of the relationship characteristics of diversity and rich communication. Finally, our analysis was limited by our sample size. We observed a relatively small number of teams. Although we benefitted from seeing the change in team relationships that occurred with attending changes halfway through some of our data collection months, this did limit the number of patients we could include in our analyses. Though we did not observe obvious differences in relationships between the teams observed across the 2 hospitals, the small number of teams and hospitals precluded our ability to perform multilevel modeling analyses, which would have allowed us to assess or account for the influence of team or organizational factors. However, this small sample size did allow for a richer assessment of team behaviors.
Although preliminary, our findings are an important step in understanding the function of inpatient medical teams not only in terms of processes of care, but also in terms of relationships. Patient care is a social activity, requiring effective communication to develop working diagnoses, recognize changes in patients' clinical courses, and formulate effective treatment plans during and after hospitalization. Future work could follow several directions. One would be to assess the causal mechanisms through which relationships influence patient outcomes. These may include sensemaking, learning, and improved coordination. Positive relationships may facilitate interaction of tacit and explicit information, facilitating the creation of understandings that foster more effective patient care.[34] The dynamic nature of relationships and how patient outcomes in turn feed back into relationships could be an area of exploration. This line of research could build on the idea of teaming.[35] Understanding relationships across multidisciplinary teams or with patients and families would be another direction. Finally, our results could point to potential interventions to improve patient outcomes through improving relationships. Better understanding of the nature of effective relationships among providers should enable us to develop more effective strategies to improve the care of hospitalized patients. In the larger context of payment reforms that require greater coordination and communication among and across providers, a greater understanding of how relationships influence patient outcomes will be important.
Acknowledgements
The authors thank the physicians involved in this study and Ms. Shannon Provost for her involvement in discussions of this work.
Disclosures: The research reported herein was supported by the Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development Service (CDA 07‐022). Investigator salary support was provided through this funding, and through the South Texas Veterans Health Care System. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs. Dr. McDaniel receives support from the IC[2] Institute of the University of Texas at Austin. Dr. Luci Leykum had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The authors report no conflicts of interest.
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Since the Institute of Medicine Report To Err is Human, increased attention has been paid to improving the care of hospitalized patients.[1] Strategies include utilization of guidelines and pathways, and the application of quality improvement techniques to improve or standardize processes. Despite improvements in focused areas such as prevention of hospital‐acquired infections, evidence suggests that outcomes for hospitalized patients remain suboptimal.[2] Rates of errors and hospital‐related complications such as falls, decubitus ulcers, and infections remain high,[3, 4, 5] and not all patients receive what is known to be appropriate care.[6]
Many attempts to improve inpatient care have used process‐improvement approaches, focusing on impacting individuals' behaviors, or on breaking down processes into component parts. Examples include central line bundles or checklists.[7, 8] These approaches attempt to ensure that providers do things in a standardized way, but are implicitly based on the reductionist assumption that we can break processes down into predictable parts to improve the system. An alternative way to understand clinical systems is based on interdependencies between individuals in the system, or the ways in which parts of the system interact with each other, which may be unpredictable over time.[1, 9] Whereas these interdependencies include care processes, they also encompass the providers who care for patients. Providers working together vary in terms of the kinds of relationships they have with each other. Those relationships are crucial to system function because they are the foundation for the interactions that lead to effective patient care.
The application of several frameworks or approaches for considering healthcare systems in terms of relationships highlights the importance of this way of understanding system function. The include complexity science,[1, 7] relational coordination (which is grounded in complexity science),[10] high reliability,[11] and the Big Five for teamwork.[12]
Research indicates that interactions among healthcare providers can have important influences on outcomes.[13, 14, 15, 16, 17] Additionally, the initial implementation of checklists to prevent central‐line associated infections appeared to change provider relationships in a way that significantly influenced their success.[18] For example, positive primary care clinic member relationships as assessed by the Lanham framework have been associated with better chronic care model implementation, learning, and patient experience of care.[19, 20] This framework, which we apply here, identifies 7 relationship characteristics: (1) trust; (2) diversity; (3) respect; (4) mindfulness, or being open to new ideas from others; (5) heedfulness, or an understanding of how one's roles influence those of others; (6) use of rich in‐person or verbal communication, particularly for potentially ambiguous information open to multiple interpretations; and (7) having a mixture of social and task relatedness among teams, or relatedness outside of only work‐related tasks.[19] Relationships within surgical teams that are characterized by psychological safety and diversity are associated with successful uptake of new techniques and decreased mortality.[13, 14] Relationships are important because the ability of patients and providers to learn and make sense of their patients' illnesses is grounded in relationships.
We sought to better understand and characterize inpatient physician teams' relationships, and assess the association between team relationships as evaluated by Lanham's framework and outcomes for hospitalized patients. Data on relationships among inpatient medical teams are few, despite the fact that these teams provide a great proportion of inpatient care. Additionally, the care of hospitalized medical patients is complex and uncertain, often involving multiple providers, making provider relationships potentially even more important to outcomes than in other settings.
METHODS
Overview
We conducted an observational, convergent mixed‐methods study of inpatient medicine teams.[21, 22, 23] We focused on inpatient physician teams, defining them as the functional work group responsible for medical decision making in academic medical centers. Physician teams in this context have been studied in terms of social hierarchy, authority, and delegation.[24, 25, 26] Focusing on the relationships within these groups could provide insights into strategies to mitigate potential negative effects of hierarchy. We recognize that other providers are closely involved in the care of hospitalized patients, and although we did not have standard interactions between physicians, nurses, case managers, and other providers that we could consistently observe, we did include interactions with these other providers in our observations and assessments of team relationships. Because this work is among the first in inpatient medical teams, we chose to study a small number of teams in great depth, allowing us to make rich assessments of team relationships.
We chose patient outcomes of length of stay (LOS), unnecessary LOS (ULOS), and complication rates, adjusted for patient characteristics and team workload. LOS is an important metric of inpatient care delivery. We feel ULOS is an aspect of LOS that is dependent on the physician team, as it reflects their preparation of the patient for discharge. Finally, we chose complication rates because hospital‐acquired conditions and complications are important contributors to inpatient morbidity, and because recent surgical literature has identified complication rates as a contributor to mortality that could be related to providers' collective ability to recognize complications and act quickly.
This study was approved by the institutional review board at the University of Texas Health Science Center at San Antonio (UTHSCSA), the Research and Development Committee for the South Texas Veterans Health Care System (STVHCS), and the Research Committee at University Health System (UHS). All physicians consented to participate in the study. We obtained a waiver of consent for inclusion of patient data.
Setting and Study Participants
This study was conducted at the 2 UTHSCSA primary teaching affiliates. The Audie L. Murphy Veterans Affairs Hospital is the 220‐bed acute‐care hospital of the STVHCS. University Hospital is the 614‐bed, level‐I trauma, acute‐care facility for UHS, the county system for Bexar County, which includes the San Antonio, Texas major metropolitan area.
The inpatient internal medicine physician team was our unit of study. Inpatient medicine teams consisted of 1 faculty attending physician, 1 postgraduate year (PGY)‐2 or PGY‐3 resident, and 2 PGY‐1 members. In addition, typically 2 to 3 third‐year medical students were part of the team, and a subintern was sometimes present. Doctor of Pharmacy faculty and students were also occasionally part of the team. Social workers and case managers often joined team rounds for portions of the time, and nurses sometimes joined bedside rounds on specific patients. These teams admit all medicine patients with the exception of those with acute coronary syndromes, new onset congestive heart failure, or arrhythmias. Patients are randomly assigned to teams based on time of admission and call schedules.
Between these 2 hospitals, there are 10 inpatient medicine teams caring for patients, with a pool of over 40 potential faculty attendings. Our goal was to observe teams that would be most likely to vary in terms of their relationship characteristics and patient outcomes through observing teams with a range of individual members. We used a purposeful sampling approach to obtain a diverse sample, sampling based on physician attributes and time of year.[16, 17] Three characteristics were most important: attending physician years of experience, attending involvement in educational and administrative leadership, and the presence of struggling resident members, as defined by being on probation or having been discussed in the residency Clinical Competency Committee. We did not set explicit thresholds in terms of attending experience, but instead sought to ensure a range. The attendings we observed were more likely to be involved in education and administrative leadership activities, but were otherwise similar to those we did not observe in terms of years of experience. We included struggling residents to observe individuals with a range of skill sets, and not just high‐performing individuals. We obtained attending information based on our knowledge of the attending faculty pool, and from the internal medicine residency program. We sampled across the year to ensure a diversity of trainee experience, but did not observe teams in either July or August, as these months were early in the academic year. Interns spend approximately 5 months per year on inpatient services, whereas residents spend 2 to 3 months per year. Thus, interns but not residents observed later in the year might have spent significantly more time on an inpatient service. However, in all instances, none of the team members observed had worked together previously.
Data Collection
Data were collected over nine 1‐month periods from September 2008 through June 2011. Teams were observed daily for 2‐ to 4‐week periods during morning rounds, the time when the team discusses each patient and makes clinical decisions. Data collection started on the first day of the month, the first day that all team members worked together, and continued for approximately 27 days, the last day before the resident rotated to a different service. By comprehensively and systematically observing these teams' daily rounds, we obtained rich, in‐depth data with multiple data points, enabling us to assess specific team behaviors and interactions.
During the third and fourth months, we collected data on teams in which the attending changed partway through. We did this to understand the impact of individual attending change on team relationships. Because the team relationships differed with each attending, we analyzed them separately. Thus, we observed 7 teams for approximately 4‐week periods and 4 teams for approximately 2‐week periods.
Observers arrived in the team room prior to rounds to begin observations, staying until after rounds were completed. Detailed free‐text field notes were taken regarding team activities and behaviors, including how the teams made patient care decisions. Field notes included: length of rounds, which team members spoke during each patient discussion, who contributed to management discussions, how information from consultants was incorporated, how communication with others outside of the team occurred, how team members spoke with each other including the types of words used, and team member willingness to perform tasks outside of their usually defined role, among others. Field notes were collected in an open‐ended format to allow for inductive observations. Observers also recorded clinical data daily regarding each patient, including admission and discharge dates, and presenting complaint.
The observation team consisted of the principle investigator (PI) (hospitalist) and 2 research assistants (a graduate‐level medical anthropologist and social psychologist), all of whom were trained by a qualitative research expert to systematically collect data related to topics of interest. Observers were instructed to record what the teams were doing and talking about at all times, noting any behaviors that they felt reflected how team members related to each other and came to decisions about their patients, or that were characteristic of the team. To ensure consistency, the PI and 1 research assistant conducted observations jointly at the start of data collection for each team, checking concordance of observations daily using a percent agreement until general agreement on field note content and patient information reached 90%. Two individuals observed 24 days of data collection, representing 252 patient discussions (13% of observed discussions).
An age‐adjusted Charlson‐Deyo comorbidity score was calculated for each patient admitted to each team, using data from rounds and from each hospital's electronic health records (EHR).[27] We collected data regarding mental health conditions for each patient (substance use, mood disorder, cognitive disorder, or a combination) because these comorbidities could impact LOS or ULOS. Discharge diagnoses were based on the discharge summary in the EHR. We also collected data daily regarding team census and numbers of admissions to and discharges from each team to assess workload.
Three patient outcomes were measured: LOS, ULOS, and complications. LOS was defined as the total number of days the patient was in the hospital. ULOS was defined as the number of days a patient remained in the hospital after the day the team determined the patient was medically ready for discharge (assessed by either discussion on rounds or EHR documentation). ULOS may occur when postdischarge needs have been adequately assessed, or because of delays in care, which may be related to provider communication during the hospitalization. Complications were defined on a per‐patient, per‐day basis in 2 ways: the development of a new problem in the hospital, for example acute kidney injury, a hospital‐acquired infection, or delirium, or by the team noting a clinical deterioration after at least 24 hours of clinical stability, such as the patient requiring transfer to a higher level of care. Complications were determined based on discussions during rounds, with EHR verification if needed.
Analysis Phase I: Assessment of Relationship Characteristics
After the completion of data collection, field notes were reviewed by a research team member not involved in the original study design or primary data collection (senior medical student). We took this approach to guard against biasing the reviewer's view of team behaviors, both in terms of not having conducted observations of the teams and being blinded to patient outcomes.
The reviewer completed a series of 3 readings of all field notes. The first reading provided a summary of the content of the data and the individual teams. Behavioral patterns of each team were used to create an initial team profile. The field notes and profiles were reviewed by the PI and a coauthor not involved in data collection to ensure that the profiles adequately reflected the field notes. No significant changes to the profiles were made based on this review. The profiles were discussed at a meeting with members of the larger research team, including the PI, research assistants, and coinvestigators (with backgrounds in medicine, anthropology, and information and organization management). Behavior characteristics that could be used to distinguish teams were identified in the profiles using a grounded theory approach.
The second review of field notes was conducted to test the applicability of the characteristics identified in the first review. To systematically record the appearance of the behaviors, we created a matrix with a row for each behavior and columns for each team to note whether they exhibited each behavior. If the behavior was exhibited, specific examples were cataloged in the matrix. This matrix was reviewed and refined by the research team. During the final field note review meeting, the research team compared the summary matrix for each team, with the specific behaviors noted during the first reading of the field notes to ensure that all behaviors were recorded.
After cataloging behaviors, the research team assigned each behavior to 1 of the 7 Lanham relationship characteristics. We wanted to assess our observations against a relationship framework to ensure that we were able to systematically assess all aspects of relationships. The Lanham framework was initially developed based on a systematic review of the organizational and educational literatures, making it relevant to the complex environment of an academic medical inpatient team and allowing us to assess relationships at a fine‐grained, richly detailed level. This assignment was done by the author team as a group. Any questions were discussed and different interpretations resolved through consensus. The Lanham framework has 7 characteristics.[19] Based on the presence of behaviors associated with each relationship characteristic, we assigned a point to each team for each relationship characteristic observed. We considered a behavior type to be present if we observed it on at least 3 occasions on separate days. Though we used a threshold of at least 3 occurrences, most teams that did not receive a point for a particular characteristic did not have any instances in which we observed the characteristic. This was particularly true for trust and mindfulness, and least so for social/task relatedness. By summing these points, we calculated a total relationship score for each team, with potential scores ranging from 0 (for teams exhibiting no behaviors reflecting a particular relationship characteristic) to 7.
Analysis Phase II: Factor Analysis
To formally determine which relationship characteristics were most highly related, data were submitted to a principal components factor analysis using oblique rotation. Item separation was determined by visual inspection of the scree plot and eigenvalues over 1.
Analysis Phase III: Assessing the Association between Physician Team Relationship Characteristics and Patient Outcomes
We examined the association between team relationships and patient outcomes using team relationship scores. For the LOS/ULOS analysis, we only included patients whose entire hospitalization occurred under the care of the team we observed. Patients who were on the team at the start of the month, were transferred from another service, or who remained hospitalized after the end of the team's time together were excluded. The longest possible LOS for patients whose entire hospitalization occurred on teams that were observed for half a month was 12 days. To facilitate accurate comparison between teams, we only included patients whose LOS was 12 days.
Complication rates were defined on a per‐patient per‐day basis to normalize for different team volumes and days of observation. For this analysis, we included patients who remained on the team after data collection completion, patients transferred to another team, or patients transferred from another team. However, we only counted complications that occurred at least 24 hours following transfer to minimize the likelihood that the complication was related to the care of other physicians.
Preliminary analysis involved inspection and assessment of the distribution of all variables followed, by a general linear modeling approach to assess the association between patient and workload covariates and outcomes.[28, 29] Because we anticipated that outcome variables would be markedly skewed, we also planned to assess the association between relationship characteristics with outcomes using the Kruskal‐Wallis rank sum test to compare groups with Dunn's test[30] for pairwise comparisons if overall significance occurred.[31] There are no known acceptable methods for covariate adjustments using the Kruskal‐Wallis method. All models were run using SAS software (SAS Institute Inc., Cary, NC).[32]
RESULTS
The research team observed 1941 discussions of 576 individual patients. Observations were conducted over 352 hours and 54 minutes, resulting in 741 pages of notes (see Supporting Table 1 in the online version of this article for data regarding individual team members). Teams observed over half‐months are referred to with a and b designations.
| Relationship Characteristic | Definition | Thirteen Types of Behaviors Observed in Field Notes | Observed Examples |
|---|---|---|---|
| Trust | Willingness to be vulnerable to others | Use of we instead of you or I by the attending | Where are we going with this guy? |
| Attending admitting I don't know | Let's go talk to him, I can't figure this out | ||
| Asking questions to help team members to think through problems | Will the echo change our management? How will it help us? | ||
| Diversity | Including different perspectives and different thinking | Team member participation in conversations about patients that are not theirs | One intern is presenting, another intern asks a question, and the resident joins the discussion |
| Inclusion of perspectives of those outside the team (nursing and family members) | Taking a break to call the nurse, having a family meeting | ||
| Respect | Valuing the opinions of others, honest and tactful interactions | Use of positive reinforcement by the attending | Being encouraging of the medical student's differential, saying excellent |
| How the team talks with patients | Asking if the patient has any concerns, what they can do to make them comfortable | ||
| Heedfulness | Awareness of how each person's roles impact the rest of the team | Team members performing tasks not expected of their role | One intern helping another with changing orders to transfer a patient |
| Summarizing plans and strategizing | Attending recaps the plan for the day, asks what they can do | ||
| Mindfulness | Openness to new ideas/free discussion about what is and is not working | Entire team engaged in discussion | Attending asks the medical student, intern, and resident what they think is going on |
| Social relatedness | Having socially related interactions | Social conversation among team members | Intern talks about their day off |
| Jokes by the attending | Showers and a bowel movement is the key to making people happy | ||
| Appropriate use of rich communication | Use of in‐person communication for sensitive or difficult issues | Using verbal communication with consultants or family | Intern is on the phone with the pharm D because there is a problem with the medication |
Creation of team profiles yielded 13 common behavior characteristics that were inductively identified and that could potentially distinguish teams, including consideration of perspectives outside of the team and team members performing tasks normally outside of their roles. Table 1 provides examples of and summarizes observed behaviors using examples from the field notes, mapping these behavior characteristics onto the Lanham relationship characteristics. The distribution of relationship characteristics and scores for each team are shown in Table 2.
| Relationship Characteristic | Team | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3a | 3b | 4a | 4b | 5 | 6 | 7 | 8 | 9 | |
| Trust | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 |
| Diversity | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 |
| Respect | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 |
| Heedfulness | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 1 |
| Mindfulness | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 |
| Social/task relatedness | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 |
| Rich/lean communication | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 |
| Relationship score (no. of characteristics observed) | 0 | 5 | 7 | 2 | 2 | 3 | 5 | 0 | 7 | 7 | 6 |
Correlation between relationship characteristics ranged from 0.32 to 0.95 (see Supporting Table 2 in the online version of this article). Mindfulness and trust are more highly correlated with each other than with other variables, as are diversity and respect. We performed a principal components factor analysis. Based on scree plot inspection and eigenvalues >1, we kept 3 factors that explained 85% of the total variance (see Supporting Table 3 in the online version of this article).
| No. of Relationship Characteristics | |||
|---|---|---|---|
| 02 | 35 | 67 | |
| |||
| LOS, d, n=293 | |||
| Median | 4 | 5 | 3 |
| IQR | 5 | 4 | 3 |
| Mean | 4.7 (2.72) | 4.7 (2.52) | 4.1 (2.51), P=0.12a |
| ULOS, d, n=293 | |||
| Median | 0 | 0 | 0 |
| IQR | 0 | 0 | 0 |
| Mean | 0.37 (0.99) | 0.33 (0.96) | 0.13 (0.56), P=0.09a |
| Complications (per patient per day), n=398 | |||
| Median | 0 | 0 | 0 |
| IQR | 1 | 1 | 0 |
| Mean | 0.58 (1.06) | 0.45 (0.77) | 0.18 (0.59), P=0.001 compared to teams with 02 or 35 characteristics |
Our analyses of LOS and ULOS included 298 of the 576 patients. Two hundred sixty‐seven patients were excluded because their entire LOS did not occur while under the care of the observed teams. Eleven patients were removed from the analysis because their LOS was >12 days. The analysis of complications included 398 patients. In our preliminary general linear modeling approach, only patient workload was significantly associated with outcomes using a cutoff of P=0.05. Charlson‐Deyo score and mental health comorbidities were not associated with outcomes.
The results of the Kruskal‐Wallis test show the patient average ranking on each of the outcome variables by 3 groups (Table 3). Overall, teams with higher relationship scores had lower rank scores on all outcomes measures. However, the only statistically significant comparisons were for complications. Teams having 6 to 7 characteristics had a significantly lower complication rate ranking than teams with 0 to 2 and 3 to 5 (P=0.001). We did not find consistent differences between individual teams or groups of teams with relationship scores from 0 to 2, 3 to 5, and 6 to 7 with regard to Charlson score, mental health issues, or workload. The only significant differences were between Charlson‐Deyo scores for patients admitted to teams with low relationship scores of 0 to 2 versus high relationship scores of 6 to 7 (6.7 vs 5.1); scores for teams with relationship scores of 3 to 5 were not significantly different from the low or high groups.
Table 4 shows the Kruskal‐Wallace rank test results for each group of relationship characteristics identified in the factor analysis based on whether teams displayed all or none of the characteristics in the factor. There were no differences in these groupings for LOS. Teams that exhibited both mindfulness and trust had lower ranks on ULOS than teams that did not have either. Similarly, teams with heedfulness, social‐task relatedness, and more rich communication demonstrated lower ULOS rankings than teams who did not have all 3 characteristics.
| Mind/Trust | Diversity/Respect | Heed/Relate/Communicate | ||||
|---|---|---|---|---|---|---|
| Patient Outcome | None | Both | None | Both | None | All 3 |
| ||||||
| LOS, d, n=293 | ||||||
| Median | 4 | 4 | 4 | 4 | 4 | 4 |
| IQR | 5 | 3 | 4.5 | 3 | 4 | 4 |
| Mean | 4.7 (2.6) | 4.2 (2.5) | 4.7 (2.6) | 4.3 (2.5) | 4.4 (2.6) | 4.4 (2.6) |
| P value | 0.06a | 0.23a | 0.85a | |||
| ULOS, d, n=293 | ||||||
| Median | 0 | 0 | 0 | 0 | 0 | 0 |
| IQR | 0 | 0 | 0 | 0 | 0 | 0 |
| Mean | 0.39 (1.01) | 0.15 (0.62) | 0.33 (0.92) | 0.18 (0.71) | 0.32 (0.93) | 0.18 (0.69) |
| P value | 0.009 | 0.06 | 0.03 | |||
| Complications (per patient), n=389 | ||||||
| Median | 0 | 0 | 0 | 0 | 0 | 0 |
| IQR | 1 | 0 | 1 | 0 | 1 | 0 |
| Mean | 0.58 (1.01) | 0.19 (0.58) | 0.47 (0.81) | 0.29 (0.82) | 0.26 (0.92) | 0.28 (0.70) |
| P value | <0.0001 | 0.001 | 0.02 | |||
DISCUSSION
Relationships are critical to team function because they are the basis for the social interactions that are central to patient care. These interactions include how providers recognize and make sense of what is happening with patients, and how they learn to care for patients more effectively. Additionally, the high task interdependencies among inpatient providers require effective relationships for optimal care. In our study, inpatient medicine physician teams' relationships varied, and these differences were associated with ULOS and complications. Relationship characteristics are not mutually exclusive, and as our factor analysis demonstrates, are intercorrelated. Trust and mindfulness appear to be particularly important. Trust may foster psychological safety that in turn promotes the willingness of individuals to contribute their thoughts and ideas.[13] In low‐trust teams, providers may fear a negative impact for bringing forward a concern based on limited data. Mindful teams may be more likely to notice nuanced changes, or are more likely to talk when things just do not appear to be going in the right direction with the patient. In the case of acutely ill medical patients, trust and mindfulness may lead to an increased likelihood that clinical changes are recognized and discussed quickly. For example, on a team characterized by trust and mindfulness, the entire team was typically involved in care discussions, and the interns and students frequently asked a lot of questions, even regarding the care of patients they were not directly following. We observed that these questions and discussions often led the team to realize that they needed to make a change in management decisions (eg, discontinuing Bactrim, lowering insulin doses, adjusting antihypertensives, premedicating for intravenous contrast) that they had not caught in the assessment and plan portion of the patient care discussion. In another example, a medical student asked a tentative question after a patient needed to go quickly to the bathroom while they were examining her, leading the team to ask more questions that led to a more rapid evaluation of a potential urinary tract infection. This finding is consistent with the description of failure to rescue among surgical patients, in which mortality has been associated with the failure to recognize complications rapidly and act effectively.[33]
Our findings are limited in several ways. First, these data are from a single academic institution. Although we sought diversity among our teams and collected data across 2 hospitals, there may be local contextual factors that influenced our results. Second, our data demonstrate an association, but not causality. Our findings should be tested in studies that assess causality and potential mechanisms through which relationships influence outcomes. Third, the individuals observing the teams had some knowledge of patient outcomes through hearing patient discussions. However, by involving individuals who did not participate in observations and were blinded to outcomes in assessing team relationships, we addressed this potential bias. Fourth, our observations were largely focused on physician teams, not directly including other providers. Our difficulty in observing regular interactions between physicians and other providers underscores the need to increase contact among those caring for hospitalized patients, such as occurs through multidisciplinary rounds. We did include team communication with other disciplines in our assessment of the relationship characteristics of diversity and rich communication. Finally, our analysis was limited by our sample size. We observed a relatively small number of teams. Although we benefitted from seeing the change in team relationships that occurred with attending changes halfway through some of our data collection months, this did limit the number of patients we could include in our analyses. Though we did not observe obvious differences in relationships between the teams observed across the 2 hospitals, the small number of teams and hospitals precluded our ability to perform multilevel modeling analyses, which would have allowed us to assess or account for the influence of team or organizational factors. However, this small sample size did allow for a richer assessment of team behaviors.
Although preliminary, our findings are an important step in understanding the function of inpatient medical teams not only in terms of processes of care, but also in terms of relationships. Patient care is a social activity, requiring effective communication to develop working diagnoses, recognize changes in patients' clinical courses, and formulate effective treatment plans during and after hospitalization. Future work could follow several directions. One would be to assess the causal mechanisms through which relationships influence patient outcomes. These may include sensemaking, learning, and improved coordination. Positive relationships may facilitate interaction of tacit and explicit information, facilitating the creation of understandings that foster more effective patient care.[34] The dynamic nature of relationships and how patient outcomes in turn feed back into relationships could be an area of exploration. This line of research could build on the idea of teaming.[35] Understanding relationships across multidisciplinary teams or with patients and families would be another direction. Finally, our results could point to potential interventions to improve patient outcomes through improving relationships. Better understanding of the nature of effective relationships among providers should enable us to develop more effective strategies to improve the care of hospitalized patients. In the larger context of payment reforms that require greater coordination and communication among and across providers, a greater understanding of how relationships influence patient outcomes will be important.
Acknowledgements
The authors thank the physicians involved in this study and Ms. Shannon Provost for her involvement in discussions of this work.
Disclosures: The research reported herein was supported by the Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development Service (CDA 07‐022). Investigator salary support was provided through this funding, and through the South Texas Veterans Health Care System. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs. Dr. McDaniel receives support from the IC[2] Institute of the University of Texas at Austin. Dr. Luci Leykum had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The authors report no conflicts of interest.
Since the Institute of Medicine Report To Err is Human, increased attention has been paid to improving the care of hospitalized patients.[1] Strategies include utilization of guidelines and pathways, and the application of quality improvement techniques to improve or standardize processes. Despite improvements in focused areas such as prevention of hospital‐acquired infections, evidence suggests that outcomes for hospitalized patients remain suboptimal.[2] Rates of errors and hospital‐related complications such as falls, decubitus ulcers, and infections remain high,[3, 4, 5] and not all patients receive what is known to be appropriate care.[6]
Many attempts to improve inpatient care have used process‐improvement approaches, focusing on impacting individuals' behaviors, or on breaking down processes into component parts. Examples include central line bundles or checklists.[7, 8] These approaches attempt to ensure that providers do things in a standardized way, but are implicitly based on the reductionist assumption that we can break processes down into predictable parts to improve the system. An alternative way to understand clinical systems is based on interdependencies between individuals in the system, or the ways in which parts of the system interact with each other, which may be unpredictable over time.[1, 9] Whereas these interdependencies include care processes, they also encompass the providers who care for patients. Providers working together vary in terms of the kinds of relationships they have with each other. Those relationships are crucial to system function because they are the foundation for the interactions that lead to effective patient care.
The application of several frameworks or approaches for considering healthcare systems in terms of relationships highlights the importance of this way of understanding system function. The include complexity science,[1, 7] relational coordination (which is grounded in complexity science),[10] high reliability,[11] and the Big Five for teamwork.[12]
Research indicates that interactions among healthcare providers can have important influences on outcomes.[13, 14, 15, 16, 17] Additionally, the initial implementation of checklists to prevent central‐line associated infections appeared to change provider relationships in a way that significantly influenced their success.[18] For example, positive primary care clinic member relationships as assessed by the Lanham framework have been associated with better chronic care model implementation, learning, and patient experience of care.[19, 20] This framework, which we apply here, identifies 7 relationship characteristics: (1) trust; (2) diversity; (3) respect; (4) mindfulness, or being open to new ideas from others; (5) heedfulness, or an understanding of how one's roles influence those of others; (6) use of rich in‐person or verbal communication, particularly for potentially ambiguous information open to multiple interpretations; and (7) having a mixture of social and task relatedness among teams, or relatedness outside of only work‐related tasks.[19] Relationships within surgical teams that are characterized by psychological safety and diversity are associated with successful uptake of new techniques and decreased mortality.[13, 14] Relationships are important because the ability of patients and providers to learn and make sense of their patients' illnesses is grounded in relationships.
We sought to better understand and characterize inpatient physician teams' relationships, and assess the association between team relationships as evaluated by Lanham's framework and outcomes for hospitalized patients. Data on relationships among inpatient medical teams are few, despite the fact that these teams provide a great proportion of inpatient care. Additionally, the care of hospitalized medical patients is complex and uncertain, often involving multiple providers, making provider relationships potentially even more important to outcomes than in other settings.
METHODS
Overview
We conducted an observational, convergent mixed‐methods study of inpatient medicine teams.[21, 22, 23] We focused on inpatient physician teams, defining them as the functional work group responsible for medical decision making in academic medical centers. Physician teams in this context have been studied in terms of social hierarchy, authority, and delegation.[24, 25, 26] Focusing on the relationships within these groups could provide insights into strategies to mitigate potential negative effects of hierarchy. We recognize that other providers are closely involved in the care of hospitalized patients, and although we did not have standard interactions between physicians, nurses, case managers, and other providers that we could consistently observe, we did include interactions with these other providers in our observations and assessments of team relationships. Because this work is among the first in inpatient medical teams, we chose to study a small number of teams in great depth, allowing us to make rich assessments of team relationships.
We chose patient outcomes of length of stay (LOS), unnecessary LOS (ULOS), and complication rates, adjusted for patient characteristics and team workload. LOS is an important metric of inpatient care delivery. We feel ULOS is an aspect of LOS that is dependent on the physician team, as it reflects their preparation of the patient for discharge. Finally, we chose complication rates because hospital‐acquired conditions and complications are important contributors to inpatient morbidity, and because recent surgical literature has identified complication rates as a contributor to mortality that could be related to providers' collective ability to recognize complications and act quickly.
This study was approved by the institutional review board at the University of Texas Health Science Center at San Antonio (UTHSCSA), the Research and Development Committee for the South Texas Veterans Health Care System (STVHCS), and the Research Committee at University Health System (UHS). All physicians consented to participate in the study. We obtained a waiver of consent for inclusion of patient data.
Setting and Study Participants
This study was conducted at the 2 UTHSCSA primary teaching affiliates. The Audie L. Murphy Veterans Affairs Hospital is the 220‐bed acute‐care hospital of the STVHCS. University Hospital is the 614‐bed, level‐I trauma, acute‐care facility for UHS, the county system for Bexar County, which includes the San Antonio, Texas major metropolitan area.
The inpatient internal medicine physician team was our unit of study. Inpatient medicine teams consisted of 1 faculty attending physician, 1 postgraduate year (PGY)‐2 or PGY‐3 resident, and 2 PGY‐1 members. In addition, typically 2 to 3 third‐year medical students were part of the team, and a subintern was sometimes present. Doctor of Pharmacy faculty and students were also occasionally part of the team. Social workers and case managers often joined team rounds for portions of the time, and nurses sometimes joined bedside rounds on specific patients. These teams admit all medicine patients with the exception of those with acute coronary syndromes, new onset congestive heart failure, or arrhythmias. Patients are randomly assigned to teams based on time of admission and call schedules.
Between these 2 hospitals, there are 10 inpatient medicine teams caring for patients, with a pool of over 40 potential faculty attendings. Our goal was to observe teams that would be most likely to vary in terms of their relationship characteristics and patient outcomes through observing teams with a range of individual members. We used a purposeful sampling approach to obtain a diverse sample, sampling based on physician attributes and time of year.[16, 17] Three characteristics were most important: attending physician years of experience, attending involvement in educational and administrative leadership, and the presence of struggling resident members, as defined by being on probation or having been discussed in the residency Clinical Competency Committee. We did not set explicit thresholds in terms of attending experience, but instead sought to ensure a range. The attendings we observed were more likely to be involved in education and administrative leadership activities, but were otherwise similar to those we did not observe in terms of years of experience. We included struggling residents to observe individuals with a range of skill sets, and not just high‐performing individuals. We obtained attending information based on our knowledge of the attending faculty pool, and from the internal medicine residency program. We sampled across the year to ensure a diversity of trainee experience, but did not observe teams in either July or August, as these months were early in the academic year. Interns spend approximately 5 months per year on inpatient services, whereas residents spend 2 to 3 months per year. Thus, interns but not residents observed later in the year might have spent significantly more time on an inpatient service. However, in all instances, none of the team members observed had worked together previously.
Data Collection
Data were collected over nine 1‐month periods from September 2008 through June 2011. Teams were observed daily for 2‐ to 4‐week periods during morning rounds, the time when the team discusses each patient and makes clinical decisions. Data collection started on the first day of the month, the first day that all team members worked together, and continued for approximately 27 days, the last day before the resident rotated to a different service. By comprehensively and systematically observing these teams' daily rounds, we obtained rich, in‐depth data with multiple data points, enabling us to assess specific team behaviors and interactions.
During the third and fourth months, we collected data on teams in which the attending changed partway through. We did this to understand the impact of individual attending change on team relationships. Because the team relationships differed with each attending, we analyzed them separately. Thus, we observed 7 teams for approximately 4‐week periods and 4 teams for approximately 2‐week periods.
Observers arrived in the team room prior to rounds to begin observations, staying until after rounds were completed. Detailed free‐text field notes were taken regarding team activities and behaviors, including how the teams made patient care decisions. Field notes included: length of rounds, which team members spoke during each patient discussion, who contributed to management discussions, how information from consultants was incorporated, how communication with others outside of the team occurred, how team members spoke with each other including the types of words used, and team member willingness to perform tasks outside of their usually defined role, among others. Field notes were collected in an open‐ended format to allow for inductive observations. Observers also recorded clinical data daily regarding each patient, including admission and discharge dates, and presenting complaint.
The observation team consisted of the principle investigator (PI) (hospitalist) and 2 research assistants (a graduate‐level medical anthropologist and social psychologist), all of whom were trained by a qualitative research expert to systematically collect data related to topics of interest. Observers were instructed to record what the teams were doing and talking about at all times, noting any behaviors that they felt reflected how team members related to each other and came to decisions about their patients, or that were characteristic of the team. To ensure consistency, the PI and 1 research assistant conducted observations jointly at the start of data collection for each team, checking concordance of observations daily using a percent agreement until general agreement on field note content and patient information reached 90%. Two individuals observed 24 days of data collection, representing 252 patient discussions (13% of observed discussions).
An age‐adjusted Charlson‐Deyo comorbidity score was calculated for each patient admitted to each team, using data from rounds and from each hospital's electronic health records (EHR).[27] We collected data regarding mental health conditions for each patient (substance use, mood disorder, cognitive disorder, or a combination) because these comorbidities could impact LOS or ULOS. Discharge diagnoses were based on the discharge summary in the EHR. We also collected data daily regarding team census and numbers of admissions to and discharges from each team to assess workload.
Three patient outcomes were measured: LOS, ULOS, and complications. LOS was defined as the total number of days the patient was in the hospital. ULOS was defined as the number of days a patient remained in the hospital after the day the team determined the patient was medically ready for discharge (assessed by either discussion on rounds or EHR documentation). ULOS may occur when postdischarge needs have been adequately assessed, or because of delays in care, which may be related to provider communication during the hospitalization. Complications were defined on a per‐patient, per‐day basis in 2 ways: the development of a new problem in the hospital, for example acute kidney injury, a hospital‐acquired infection, or delirium, or by the team noting a clinical deterioration after at least 24 hours of clinical stability, such as the patient requiring transfer to a higher level of care. Complications were determined based on discussions during rounds, with EHR verification if needed.
Analysis Phase I: Assessment of Relationship Characteristics
After the completion of data collection, field notes were reviewed by a research team member not involved in the original study design or primary data collection (senior medical student). We took this approach to guard against biasing the reviewer's view of team behaviors, both in terms of not having conducted observations of the teams and being blinded to patient outcomes.
The reviewer completed a series of 3 readings of all field notes. The first reading provided a summary of the content of the data and the individual teams. Behavioral patterns of each team were used to create an initial team profile. The field notes and profiles were reviewed by the PI and a coauthor not involved in data collection to ensure that the profiles adequately reflected the field notes. No significant changes to the profiles were made based on this review. The profiles were discussed at a meeting with members of the larger research team, including the PI, research assistants, and coinvestigators (with backgrounds in medicine, anthropology, and information and organization management). Behavior characteristics that could be used to distinguish teams were identified in the profiles using a grounded theory approach.
The second review of field notes was conducted to test the applicability of the characteristics identified in the first review. To systematically record the appearance of the behaviors, we created a matrix with a row for each behavior and columns for each team to note whether they exhibited each behavior. If the behavior was exhibited, specific examples were cataloged in the matrix. This matrix was reviewed and refined by the research team. During the final field note review meeting, the research team compared the summary matrix for each team, with the specific behaviors noted during the first reading of the field notes to ensure that all behaviors were recorded.
After cataloging behaviors, the research team assigned each behavior to 1 of the 7 Lanham relationship characteristics. We wanted to assess our observations against a relationship framework to ensure that we were able to systematically assess all aspects of relationships. The Lanham framework was initially developed based on a systematic review of the organizational and educational literatures, making it relevant to the complex environment of an academic medical inpatient team and allowing us to assess relationships at a fine‐grained, richly detailed level. This assignment was done by the author team as a group. Any questions were discussed and different interpretations resolved through consensus. The Lanham framework has 7 characteristics.[19] Based on the presence of behaviors associated with each relationship characteristic, we assigned a point to each team for each relationship characteristic observed. We considered a behavior type to be present if we observed it on at least 3 occasions on separate days. Though we used a threshold of at least 3 occurrences, most teams that did not receive a point for a particular characteristic did not have any instances in which we observed the characteristic. This was particularly true for trust and mindfulness, and least so for social/task relatedness. By summing these points, we calculated a total relationship score for each team, with potential scores ranging from 0 (for teams exhibiting no behaviors reflecting a particular relationship characteristic) to 7.
Analysis Phase II: Factor Analysis
To formally determine which relationship characteristics were most highly related, data were submitted to a principal components factor analysis using oblique rotation. Item separation was determined by visual inspection of the scree plot and eigenvalues over 1.
Analysis Phase III: Assessing the Association between Physician Team Relationship Characteristics and Patient Outcomes
We examined the association between team relationships and patient outcomes using team relationship scores. For the LOS/ULOS analysis, we only included patients whose entire hospitalization occurred under the care of the team we observed. Patients who were on the team at the start of the month, were transferred from another service, or who remained hospitalized after the end of the team's time together were excluded. The longest possible LOS for patients whose entire hospitalization occurred on teams that were observed for half a month was 12 days. To facilitate accurate comparison between teams, we only included patients whose LOS was 12 days.
Complication rates were defined on a per‐patient per‐day basis to normalize for different team volumes and days of observation. For this analysis, we included patients who remained on the team after data collection completion, patients transferred to another team, or patients transferred from another team. However, we only counted complications that occurred at least 24 hours following transfer to minimize the likelihood that the complication was related to the care of other physicians.
Preliminary analysis involved inspection and assessment of the distribution of all variables followed, by a general linear modeling approach to assess the association between patient and workload covariates and outcomes.[28, 29] Because we anticipated that outcome variables would be markedly skewed, we also planned to assess the association between relationship characteristics with outcomes using the Kruskal‐Wallis rank sum test to compare groups with Dunn's test[30] for pairwise comparisons if overall significance occurred.[31] There are no known acceptable methods for covariate adjustments using the Kruskal‐Wallis method. All models were run using SAS software (SAS Institute Inc., Cary, NC).[32]
RESULTS
The research team observed 1941 discussions of 576 individual patients. Observations were conducted over 352 hours and 54 minutes, resulting in 741 pages of notes (see Supporting Table 1 in the online version of this article for data regarding individual team members). Teams observed over half‐months are referred to with a and b designations.
| Relationship Characteristic | Definition | Thirteen Types of Behaviors Observed in Field Notes | Observed Examples |
|---|---|---|---|
| Trust | Willingness to be vulnerable to others | Use of we instead of you or I by the attending | Where are we going with this guy? |
| Attending admitting I don't know | Let's go talk to him, I can't figure this out | ||
| Asking questions to help team members to think through problems | Will the echo change our management? How will it help us? | ||
| Diversity | Including different perspectives and different thinking | Team member participation in conversations about patients that are not theirs | One intern is presenting, another intern asks a question, and the resident joins the discussion |
| Inclusion of perspectives of those outside the team (nursing and family members) | Taking a break to call the nurse, having a family meeting | ||
| Respect | Valuing the opinions of others, honest and tactful interactions | Use of positive reinforcement by the attending | Being encouraging of the medical student's differential, saying excellent |
| How the team talks with patients | Asking if the patient has any concerns, what they can do to make them comfortable | ||
| Heedfulness | Awareness of how each person's roles impact the rest of the team | Team members performing tasks not expected of their role | One intern helping another with changing orders to transfer a patient |
| Summarizing plans and strategizing | Attending recaps the plan for the day, asks what they can do | ||
| Mindfulness | Openness to new ideas/free discussion about what is and is not working | Entire team engaged in discussion | Attending asks the medical student, intern, and resident what they think is going on |
| Social relatedness | Having socially related interactions | Social conversation among team members | Intern talks about their day off |
| Jokes by the attending | Showers and a bowel movement is the key to making people happy | ||
| Appropriate use of rich communication | Use of in‐person communication for sensitive or difficult issues | Using verbal communication with consultants or family | Intern is on the phone with the pharm D because there is a problem with the medication |
Creation of team profiles yielded 13 common behavior characteristics that were inductively identified and that could potentially distinguish teams, including consideration of perspectives outside of the team and team members performing tasks normally outside of their roles. Table 1 provides examples of and summarizes observed behaviors using examples from the field notes, mapping these behavior characteristics onto the Lanham relationship characteristics. The distribution of relationship characteristics and scores for each team are shown in Table 2.
| Relationship Characteristic | Team | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3a | 3b | 4a | 4b | 5 | 6 | 7 | 8 | 9 | |
| Trust | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 |
| Diversity | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 |
| Respect | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 |
| Heedfulness | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 1 |
| Mindfulness | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 |
| Social/task relatedness | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 |
| Rich/lean communication | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 |
| Relationship score (no. of characteristics observed) | 0 | 5 | 7 | 2 | 2 | 3 | 5 | 0 | 7 | 7 | 6 |
Correlation between relationship characteristics ranged from 0.32 to 0.95 (see Supporting Table 2 in the online version of this article). Mindfulness and trust are more highly correlated with each other than with other variables, as are diversity and respect. We performed a principal components factor analysis. Based on scree plot inspection and eigenvalues >1, we kept 3 factors that explained 85% of the total variance (see Supporting Table 3 in the online version of this article).
| No. of Relationship Characteristics | |||
|---|---|---|---|
| 02 | 35 | 67 | |
| |||
| LOS, d, n=293 | |||
| Median | 4 | 5 | 3 |
| IQR | 5 | 4 | 3 |
| Mean | 4.7 (2.72) | 4.7 (2.52) | 4.1 (2.51), P=0.12a |
| ULOS, d, n=293 | |||
| Median | 0 | 0 | 0 |
| IQR | 0 | 0 | 0 |
| Mean | 0.37 (0.99) | 0.33 (0.96) | 0.13 (0.56), P=0.09a |
| Complications (per patient per day), n=398 | |||
| Median | 0 | 0 | 0 |
| IQR | 1 | 1 | 0 |
| Mean | 0.58 (1.06) | 0.45 (0.77) | 0.18 (0.59), P=0.001 compared to teams with 02 or 35 characteristics |
Our analyses of LOS and ULOS included 298 of the 576 patients. Two hundred sixty‐seven patients were excluded because their entire LOS did not occur while under the care of the observed teams. Eleven patients were removed from the analysis because their LOS was >12 days. The analysis of complications included 398 patients. In our preliminary general linear modeling approach, only patient workload was significantly associated with outcomes using a cutoff of P=0.05. Charlson‐Deyo score and mental health comorbidities were not associated with outcomes.
The results of the Kruskal‐Wallis test show the patient average ranking on each of the outcome variables by 3 groups (Table 3). Overall, teams with higher relationship scores had lower rank scores on all outcomes measures. However, the only statistically significant comparisons were for complications. Teams having 6 to 7 characteristics had a significantly lower complication rate ranking than teams with 0 to 2 and 3 to 5 (P=0.001). We did not find consistent differences between individual teams or groups of teams with relationship scores from 0 to 2, 3 to 5, and 6 to 7 with regard to Charlson score, mental health issues, or workload. The only significant differences were between Charlson‐Deyo scores for patients admitted to teams with low relationship scores of 0 to 2 versus high relationship scores of 6 to 7 (6.7 vs 5.1); scores for teams with relationship scores of 3 to 5 were not significantly different from the low or high groups.
Table 4 shows the Kruskal‐Wallace rank test results for each group of relationship characteristics identified in the factor analysis based on whether teams displayed all or none of the characteristics in the factor. There were no differences in these groupings for LOS. Teams that exhibited both mindfulness and trust had lower ranks on ULOS than teams that did not have either. Similarly, teams with heedfulness, social‐task relatedness, and more rich communication demonstrated lower ULOS rankings than teams who did not have all 3 characteristics.
| Mind/Trust | Diversity/Respect | Heed/Relate/Communicate | ||||
|---|---|---|---|---|---|---|
| Patient Outcome | None | Both | None | Both | None | All 3 |
| ||||||
| LOS, d, n=293 | ||||||
| Median | 4 | 4 | 4 | 4 | 4 | 4 |
| IQR | 5 | 3 | 4.5 | 3 | 4 | 4 |
| Mean | 4.7 (2.6) | 4.2 (2.5) | 4.7 (2.6) | 4.3 (2.5) | 4.4 (2.6) | 4.4 (2.6) |
| P value | 0.06a | 0.23a | 0.85a | |||
| ULOS, d, n=293 | ||||||
| Median | 0 | 0 | 0 | 0 | 0 | 0 |
| IQR | 0 | 0 | 0 | 0 | 0 | 0 |
| Mean | 0.39 (1.01) | 0.15 (0.62) | 0.33 (0.92) | 0.18 (0.71) | 0.32 (0.93) | 0.18 (0.69) |
| P value | 0.009 | 0.06 | 0.03 | |||
| Complications (per patient), n=389 | ||||||
| Median | 0 | 0 | 0 | 0 | 0 | 0 |
| IQR | 1 | 0 | 1 | 0 | 1 | 0 |
| Mean | 0.58 (1.01) | 0.19 (0.58) | 0.47 (0.81) | 0.29 (0.82) | 0.26 (0.92) | 0.28 (0.70) |
| P value | <0.0001 | 0.001 | 0.02 | |||
DISCUSSION
Relationships are critical to team function because they are the basis for the social interactions that are central to patient care. These interactions include how providers recognize and make sense of what is happening with patients, and how they learn to care for patients more effectively. Additionally, the high task interdependencies among inpatient providers require effective relationships for optimal care. In our study, inpatient medicine physician teams' relationships varied, and these differences were associated with ULOS and complications. Relationship characteristics are not mutually exclusive, and as our factor analysis demonstrates, are intercorrelated. Trust and mindfulness appear to be particularly important. Trust may foster psychological safety that in turn promotes the willingness of individuals to contribute their thoughts and ideas.[13] In low‐trust teams, providers may fear a negative impact for bringing forward a concern based on limited data. Mindful teams may be more likely to notice nuanced changes, or are more likely to talk when things just do not appear to be going in the right direction with the patient. In the case of acutely ill medical patients, trust and mindfulness may lead to an increased likelihood that clinical changes are recognized and discussed quickly. For example, on a team characterized by trust and mindfulness, the entire team was typically involved in care discussions, and the interns and students frequently asked a lot of questions, even regarding the care of patients they were not directly following. We observed that these questions and discussions often led the team to realize that they needed to make a change in management decisions (eg, discontinuing Bactrim, lowering insulin doses, adjusting antihypertensives, premedicating for intravenous contrast) that they had not caught in the assessment and plan portion of the patient care discussion. In another example, a medical student asked a tentative question after a patient needed to go quickly to the bathroom while they were examining her, leading the team to ask more questions that led to a more rapid evaluation of a potential urinary tract infection. This finding is consistent with the description of failure to rescue among surgical patients, in which mortality has been associated with the failure to recognize complications rapidly and act effectively.[33]
Our findings are limited in several ways. First, these data are from a single academic institution. Although we sought diversity among our teams and collected data across 2 hospitals, there may be local contextual factors that influenced our results. Second, our data demonstrate an association, but not causality. Our findings should be tested in studies that assess causality and potential mechanisms through which relationships influence outcomes. Third, the individuals observing the teams had some knowledge of patient outcomes through hearing patient discussions. However, by involving individuals who did not participate in observations and were blinded to outcomes in assessing team relationships, we addressed this potential bias. Fourth, our observations were largely focused on physician teams, not directly including other providers. Our difficulty in observing regular interactions between physicians and other providers underscores the need to increase contact among those caring for hospitalized patients, such as occurs through multidisciplinary rounds. We did include team communication with other disciplines in our assessment of the relationship characteristics of diversity and rich communication. Finally, our analysis was limited by our sample size. We observed a relatively small number of teams. Although we benefitted from seeing the change in team relationships that occurred with attending changes halfway through some of our data collection months, this did limit the number of patients we could include in our analyses. Though we did not observe obvious differences in relationships between the teams observed across the 2 hospitals, the small number of teams and hospitals precluded our ability to perform multilevel modeling analyses, which would have allowed us to assess or account for the influence of team or organizational factors. However, this small sample size did allow for a richer assessment of team behaviors.
Although preliminary, our findings are an important step in understanding the function of inpatient medical teams not only in terms of processes of care, but also in terms of relationships. Patient care is a social activity, requiring effective communication to develop working diagnoses, recognize changes in patients' clinical courses, and formulate effective treatment plans during and after hospitalization. Future work could follow several directions. One would be to assess the causal mechanisms through which relationships influence patient outcomes. These may include sensemaking, learning, and improved coordination. Positive relationships may facilitate interaction of tacit and explicit information, facilitating the creation of understandings that foster more effective patient care.[34] The dynamic nature of relationships and how patient outcomes in turn feed back into relationships could be an area of exploration. This line of research could build on the idea of teaming.[35] Understanding relationships across multidisciplinary teams or with patients and families would be another direction. Finally, our results could point to potential interventions to improve patient outcomes through improving relationships. Better understanding of the nature of effective relationships among providers should enable us to develop more effective strategies to improve the care of hospitalized patients. In the larger context of payment reforms that require greater coordination and communication among and across providers, a greater understanding of how relationships influence patient outcomes will be important.
Acknowledgements
The authors thank the physicians involved in this study and Ms. Shannon Provost for her involvement in discussions of this work.
Disclosures: The research reported herein was supported by the Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development Service (CDA 07‐022). Investigator salary support was provided through this funding, and through the South Texas Veterans Health Care System. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs. Dr. McDaniel receives support from the IC[2] Institute of the University of Texas at Austin. Dr. Luci Leykum had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The authors report no conflicts of interest.
- . Redesigning health care with insights from the science of complex adaptive systems. In: Crossing the Quality Chasm: A New Heath System for the 21st Century. Washington, DC: National Academy of Sciences; 2000:309–322.
- , , , , , . Temporal trends in rates of patient harm resulting from medical care. N Engl J Med. 2010;323(22):2124–2135.
- , , , et al. Circumstances of patient falls and injuries in 9 hospitals in a mid‐western healthcare system. Infect Control Hosp Epidemiol. 2007;28(5):544–550.
- , . Point prevalence of wounds in a sample of acute hospitals in Canada. Int Wound J. 2009;6(4):287–293.
- , , , , , . Non‐adherence to guidelines: an avoidable cause of failure of empirical antimicrobial therapy in the presence of difficult‐to‐treat bacteria. Intensive Care Med. 2010;36(1):75–82.
- , , , , . Quality of care in U.S. hospitals as reflected by standardized measures, 2002–2004. N Engl J Med. 2005;353(3):255–264.
- Centers for Disease Control and Prevention. National Center for Emerging and Zoonotic Infectious Diseases. Division of Healthcare Quality Promotion. Checklist for prevention of central line associated blood stream infections. Available at: http://www.cdc.gov/HAI/pdfs/bsi/checklist‐for‐CLABSI.pdf. Accessed August 3, 2014.
- Safer Healthcare Partners, LLC. Checklists: a critical patient safety tool. Available at: http://www.saferhealthcare.com/high‐reliability‐topics/checklists. Accessed July 31, 2014.
- . Making Things Work: Solving Complex Problems in a Complex World. Boston, MA: Knowledge Press; 2004:117–160.
- . High Performance Healthcare: Using The Power of Relationships to Achieve Quality, Efficiency, and Resilience. 1st ed. New York, NY: McGraw‐Hill; 2009.
- , . Design of high reliability organizations in health care. Qual Saf Health Care. 2006;15(suppl 1):i4–i9.
- , , , . Does team training work? Principles for health care. Acad Emerg Med. 2008;15(11):1002–1009.
- . Speaking up in the operating room: how team leaders promote learning in interdisciplinary action teams. J Manag Stud. 2003;40(6):1419–1452.
- , , , et al. Association between implementation of a medical team training program and surgical mortality. JAMA. 2010;304(15):1693–1700.
- , , , . Group cognition, membership change, and performance: Investigating the benefits and detriments of collective knowledge. Organ Behav Hum Decis Process. 2007;103(2):159–178.
- , , , , , . Reciprocal learning and chronic care model implementation in primary care: results from a new scale of learning in primary care settings. BMC Health Serv Res. 2011;11:44.
- , , , , . The importance of relational coordination and reciprocal learning for chronic illness care within primary care teams. Health Care Manage Rev. 2012;38(1):20–28.
- , , , , . Explaining Michigan: developing an ex post theory of a quality improvement program. Milbank Q. 2011;89(2):167–205.
- , , , et al. How improving practice relationships among clinicians and nonclinicians can improve quality in primary care. Jt Comm J Qual Patient Saf. 2009;35(9):457–466.
- , , , et al. Relationship quality and patient‐assessed quality of care in VA primary care clinics: development and validation of the work relationships scale. Ann Fam Med. 2013;11(6):543–549.
- , . Designing and Conducting Mixed Methods Research. 2nd ed. Thousand Oaks, CA: Sage; 2011.
- . Qualitative Evaluation Methods. Thousand Oaks, CA: Sage; 2002.
- , , . Qualitative methods in research on health care quality. Qual Saf Health Care. 2002;11:148–152.
- . Managing the negatives of experience in physician teams. Health Care Manage Rev. 2010;35(1):65–76.
- , , , , . Rethinking resident supervision to improve safety: from hierarchical to interprofessional models. J Hosp Med. 2011;6(8):445 b452.
- , , , . Dynamic delegation: shared, hierarchical, and deindividualized leadership in extreme action teams. Adm Sci Q. 2006;51(4):590–621.
- , , . Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases. J Clin Epidemiol. 1992;45(6):613–619.
- . Exploratory Data Analysis. Reading, MA: Addison‐Wesley; 1977.
- . Biostatistical Analysis. 4th ed. Upper Saddle River, NJ: Pearson Prentice‐Hall; 2010.
- . Multiple contrasts using rank sums. Technometrics. 1964;6:241–252.
- , . A SAS macro implementation of a multiple comparison post hoc test for a Kruskal–Wallis analysis. Comput Methods Programs Biomed. 2011;102:75–80.
- SAS/STAT Software [computer program]. Version 9.1. Cary, NC: SAS Institute Inc.; 2003.
- , , . Complications, failure to rescue, and mortality with major inpatient surgery in Medicare patients. Ann Surg. 2009;250(6):1029–1034.
- . A dynamic theory of organizational knowledge creation. Org Sci. 1994;5(1):14–37.
- . Teaming: How Organizations Learn, Innovate, and Compete in the Knowledge Economy. 1st ed. Boston, MA: Harvard Business School; 2012.
- . Redesigning health care with insights from the science of complex adaptive systems. In: Crossing the Quality Chasm: A New Heath System for the 21st Century. Washington, DC: National Academy of Sciences; 2000:309–322.
- , , , , , . Temporal trends in rates of patient harm resulting from medical care. N Engl J Med. 2010;323(22):2124–2135.
- , , , et al. Circumstances of patient falls and injuries in 9 hospitals in a mid‐western healthcare system. Infect Control Hosp Epidemiol. 2007;28(5):544–550.
- , . Point prevalence of wounds in a sample of acute hospitals in Canada. Int Wound J. 2009;6(4):287–293.
- , , , , , . Non‐adherence to guidelines: an avoidable cause of failure of empirical antimicrobial therapy in the presence of difficult‐to‐treat bacteria. Intensive Care Med. 2010;36(1):75–82.
- , , , , . Quality of care in U.S. hospitals as reflected by standardized measures, 2002–2004. N Engl J Med. 2005;353(3):255–264.
- Centers for Disease Control and Prevention. National Center for Emerging and Zoonotic Infectious Diseases. Division of Healthcare Quality Promotion. Checklist for prevention of central line associated blood stream infections. Available at: http://www.cdc.gov/HAI/pdfs/bsi/checklist‐for‐CLABSI.pdf. Accessed August 3, 2014.
- Safer Healthcare Partners, LLC. Checklists: a critical patient safety tool. Available at: http://www.saferhealthcare.com/high‐reliability‐topics/checklists. Accessed July 31, 2014.
- . Making Things Work: Solving Complex Problems in a Complex World. Boston, MA: Knowledge Press; 2004:117–160.
- . High Performance Healthcare: Using The Power of Relationships to Achieve Quality, Efficiency, and Resilience. 1st ed. New York, NY: McGraw‐Hill; 2009.
- , . Design of high reliability organizations in health care. Qual Saf Health Care. 2006;15(suppl 1):i4–i9.
- , , , . Does team training work? Principles for health care. Acad Emerg Med. 2008;15(11):1002–1009.
- . Speaking up in the operating room: how team leaders promote learning in interdisciplinary action teams. J Manag Stud. 2003;40(6):1419–1452.
- , , , et al. Association between implementation of a medical team training program and surgical mortality. JAMA. 2010;304(15):1693–1700.
- , , , . Group cognition, membership change, and performance: Investigating the benefits and detriments of collective knowledge. Organ Behav Hum Decis Process. 2007;103(2):159–178.
- , , , , , . Reciprocal learning and chronic care model implementation in primary care: results from a new scale of learning in primary care settings. BMC Health Serv Res. 2011;11:44.
- , , , , . The importance of relational coordination and reciprocal learning for chronic illness care within primary care teams. Health Care Manage Rev. 2012;38(1):20–28.
- , , , , . Explaining Michigan: developing an ex post theory of a quality improvement program. Milbank Q. 2011;89(2):167–205.
- , , , et al. How improving practice relationships among clinicians and nonclinicians can improve quality in primary care. Jt Comm J Qual Patient Saf. 2009;35(9):457–466.
- , , , et al. Relationship quality and patient‐assessed quality of care in VA primary care clinics: development and validation of the work relationships scale. Ann Fam Med. 2013;11(6):543–549.
- , . Designing and Conducting Mixed Methods Research. 2nd ed. Thousand Oaks, CA: Sage; 2011.
- . Qualitative Evaluation Methods. Thousand Oaks, CA: Sage; 2002.
- , , . Qualitative methods in research on health care quality. Qual Saf Health Care. 2002;11:148–152.
- . Managing the negatives of experience in physician teams. Health Care Manage Rev. 2010;35(1):65–76.
- , , , , . Rethinking resident supervision to improve safety: from hierarchical to interprofessional models. J Hosp Med. 2011;6(8):445 b452.
- , , , . Dynamic delegation: shared, hierarchical, and deindividualized leadership in extreme action teams. Adm Sci Q. 2006;51(4):590–621.
- , , . Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases. J Clin Epidemiol. 1992;45(6):613–619.
- . Exploratory Data Analysis. Reading, MA: Addison‐Wesley; 1977.
- . Biostatistical Analysis. 4th ed. Upper Saddle River, NJ: Pearson Prentice‐Hall; 2010.
- . Multiple contrasts using rank sums. Technometrics. 1964;6:241–252.
- , . A SAS macro implementation of a multiple comparison post hoc test for a Kruskal–Wallis analysis. Comput Methods Programs Biomed. 2011;102:75–80.
- SAS/STAT Software [computer program]. Version 9.1. Cary, NC: SAS Institute Inc.; 2003.
- , , . Complications, failure to rescue, and mortality with major inpatient surgery in Medicare patients. Ann Surg. 2009;250(6):1029–1034.
- . A dynamic theory of organizational knowledge creation. Org Sci. 1994;5(1):14–37.
- . Teaming: How Organizations Learn, Innovate, and Compete in the Knowledge Economy. 1st ed. Boston, MA: Harvard Business School; 2012.
© 2014 Society of Hospital Medicine
How to find certified mammography facilities
The Food and Drug Administration has released an online resource to help consumers find FDA-certified mammography facilities by location, the agency announced Oct. 29.
The FDA hopes that women will continue to keep screening and prevention in mind well beyond Breast Cancer Awareness Month, they said. Mammograms are the best way to screen for cancer early, because they can help detect lumps that may be too small for a patient or physician to find during a self-breast exam.
The FDA conducts annual inspections of mammography facilities to ensure they meet standards for equipment and staff training under the Mammography Quality Standards Act. Facilities must be FDA-certified to legally perform mammogram services in the United States.
The agency has also recently approved new 3-D imaging technology that creates cross-sectional images to help doctors evaluate dense tissue and that may even find hidden tumors, they said. The FDA warns that other tools such as thermograms and nipple aspirate tests are not substitutes for mammograms.
The FDA has more information about breast cancer screening at its website, as well as a list of certified mammography facility near you.
The Food and Drug Administration has released an online resource to help consumers find FDA-certified mammography facilities by location, the agency announced Oct. 29.
The FDA hopes that women will continue to keep screening and prevention in mind well beyond Breast Cancer Awareness Month, they said. Mammograms are the best way to screen for cancer early, because they can help detect lumps that may be too small for a patient or physician to find during a self-breast exam.
The FDA conducts annual inspections of mammography facilities to ensure they meet standards for equipment and staff training under the Mammography Quality Standards Act. Facilities must be FDA-certified to legally perform mammogram services in the United States.
The agency has also recently approved new 3-D imaging technology that creates cross-sectional images to help doctors evaluate dense tissue and that may even find hidden tumors, they said. The FDA warns that other tools such as thermograms and nipple aspirate tests are not substitutes for mammograms.
The FDA has more information about breast cancer screening at its website, as well as a list of certified mammography facility near you.
The Food and Drug Administration has released an online resource to help consumers find FDA-certified mammography facilities by location, the agency announced Oct. 29.
The FDA hopes that women will continue to keep screening and prevention in mind well beyond Breast Cancer Awareness Month, they said. Mammograms are the best way to screen for cancer early, because they can help detect lumps that may be too small for a patient or physician to find during a self-breast exam.
The FDA conducts annual inspections of mammography facilities to ensure they meet standards for equipment and staff training under the Mammography Quality Standards Act. Facilities must be FDA-certified to legally perform mammogram services in the United States.
The agency has also recently approved new 3-D imaging technology that creates cross-sectional images to help doctors evaluate dense tissue and that may even find hidden tumors, they said. The FDA warns that other tools such as thermograms and nipple aspirate tests are not substitutes for mammograms.
The FDA has more information about breast cancer screening at its website, as well as a list of certified mammography facility near you.
AHA guidelines recommend Mediterranean diet to prevent stroke
Lifestyle modifications, including eating a Mediterranean or DASH-style diet, should be encouraged to lower an individual’s risk of first-time stroke, according to new guidelines for the primary prevention of stroke from the American Heart Association and American Stroke Association.
Mediterranean and DASH (Dietary Approaches to Stop Hypertension) dietary plans are characterized by their emphasis on fruits, vegetables, whole grains, legumes, nuts, seeds, poultry, and fish, while limiting red meats, sweets, and any foods with saturated fats. This new guidelines – which have been endorsed by the American Academy of Neurology, American Association of Neurological Surgeons, Congress of Neurological Surgeons, and Preventive Cardiovascular Nurses Association – suggest that adopting either of these diets in addition to a few other healthy living habits can dramatically reduce an individual’s odds of suffering a stroke (Stroke 2014;45 [doi: 10.1161/STR.0000000000000046]).
“We have a huge opportunity to improve how we prevent new strokes, because risk factors that can be changed or controlled – especially high blood pressure – account for 90% of strokes,” said Dr. James Meschia, chair of the writing committee and chairman of neurology at the Mayo Clinic in Jacksonville, Fla., in a statement. The last such guidelines were released 3 years ago (Stroke 2011;42:517-84)
The writing committee gathered data pertaining to the age, birth weight, race/ethnicity, and genetic factors, among several others. Studies examined included a U.S. Nationwide Inpatient Sample, which showed that stroke hospitalizations increased between 1998 and 2007 for individuals aged 25-34 years and 35-44 years; the Framingham Heart Study, which estimated that the odds of a middle-aged adult suffering a stroke are 1 in 6; an analysis of South Carolina Medicaid beneficiaries under age 50, which revealed that individuals born weighing less than 2,500 g were twice as likely to have a stroke as those born heavier; and an Atherosclerosis Risk in Communities (ARIC) study that showed Latino and African American populations being at higher risk for stroke due to hypertension, obesity, and diabetes.
Because blood pressure, hypertension, diabetes, and obesity are so commonly linked to stroke risk, the new American Heart Association/American Stroke Association (AHA/ASA) guidelines highly recommend a Mediterranean or DASH-style diet supplemented with nuts. Additionally, the guidelines advise health care professionals to advise patients to cut down on sodium intake, regularly monitor their blood pressure, talk to their physicians immediately if any medication does not do what it is intended to or creates negative side effects, and quit smoking, and, for women, consider an alternative to oral birth control pills.
Hypertension, “the most important, well-documented, modifiable stroke risk factor,” should be treated with antihypertensive medication to a target blood pressure of less than 140/90 mm Hg, the guidelines state.
Furthermore, the ASA/AHA guidelines continue to recommend regular physical exercise and acute monitoring of individuals’ cholesterol levels, such as LDL, HDL, and triglycerides, as failure to keep these numbers in check can easily lead to a serious stroke. The guidelines also state that although heavy alcohol consumption can increase the chance of stroke, “light to moderate” alcohol consumption can actually decrease the odds of suffering a stroke.
The AHA/ASA guidelines also examine factors such as migraines, which are associated with stroke in women under age 55, and hyperhomocysteinemia, which is also associated with an increased risk of stroke. Other factors like hypercoagulability and sleep apnea were not shown to have any identifiable relationship with an increased risk of stroke.
“As health professionals, we must ensure that progress in preventing stroke does not lead to complacency,” say the guidelines. “We must acknowledge that several recommendations remain vague because of suboptimal clinical trial evidence or, even more concerning, may be out of date and therefore irrelevant.”
Dr. Meschia and his associates warn that although medications are helpful, the best way to safeguard against a stroke is to change a person’s lifestyle into one of healthy eating and exercise habits. Unfortunately, say the authors, “it is easier to convince a patient to take a pill than to radically change his or her lifestyle, [but] we must expect the same standards of evidence for lifestyle interventions.”
Dr. Meschia disclosed that his research grant comes from the National Institute of Neurological Disorders and Stroke. He had no other relevant financial disclosures of interest. Several of the guidelines’ coauthors had disclosures of their own, which are listed in the statement.
Lifestyle modifications, including eating a Mediterranean or DASH-style diet, should be encouraged to lower an individual’s risk of first-time stroke, according to new guidelines for the primary prevention of stroke from the American Heart Association and American Stroke Association.
Mediterranean and DASH (Dietary Approaches to Stop Hypertension) dietary plans are characterized by their emphasis on fruits, vegetables, whole grains, legumes, nuts, seeds, poultry, and fish, while limiting red meats, sweets, and any foods with saturated fats. This new guidelines – which have been endorsed by the American Academy of Neurology, American Association of Neurological Surgeons, Congress of Neurological Surgeons, and Preventive Cardiovascular Nurses Association – suggest that adopting either of these diets in addition to a few other healthy living habits can dramatically reduce an individual’s odds of suffering a stroke (Stroke 2014;45 [doi: 10.1161/STR.0000000000000046]).
“We have a huge opportunity to improve how we prevent new strokes, because risk factors that can be changed or controlled – especially high blood pressure – account for 90% of strokes,” said Dr. James Meschia, chair of the writing committee and chairman of neurology at the Mayo Clinic in Jacksonville, Fla., in a statement. The last such guidelines were released 3 years ago (Stroke 2011;42:517-84)
The writing committee gathered data pertaining to the age, birth weight, race/ethnicity, and genetic factors, among several others. Studies examined included a U.S. Nationwide Inpatient Sample, which showed that stroke hospitalizations increased between 1998 and 2007 for individuals aged 25-34 years and 35-44 years; the Framingham Heart Study, which estimated that the odds of a middle-aged adult suffering a stroke are 1 in 6; an analysis of South Carolina Medicaid beneficiaries under age 50, which revealed that individuals born weighing less than 2,500 g were twice as likely to have a stroke as those born heavier; and an Atherosclerosis Risk in Communities (ARIC) study that showed Latino and African American populations being at higher risk for stroke due to hypertension, obesity, and diabetes.
Because blood pressure, hypertension, diabetes, and obesity are so commonly linked to stroke risk, the new American Heart Association/American Stroke Association (AHA/ASA) guidelines highly recommend a Mediterranean or DASH-style diet supplemented with nuts. Additionally, the guidelines advise health care professionals to advise patients to cut down on sodium intake, regularly monitor their blood pressure, talk to their physicians immediately if any medication does not do what it is intended to or creates negative side effects, and quit smoking, and, for women, consider an alternative to oral birth control pills.
Hypertension, “the most important, well-documented, modifiable stroke risk factor,” should be treated with antihypertensive medication to a target blood pressure of less than 140/90 mm Hg, the guidelines state.
Furthermore, the ASA/AHA guidelines continue to recommend regular physical exercise and acute monitoring of individuals’ cholesterol levels, such as LDL, HDL, and triglycerides, as failure to keep these numbers in check can easily lead to a serious stroke. The guidelines also state that although heavy alcohol consumption can increase the chance of stroke, “light to moderate” alcohol consumption can actually decrease the odds of suffering a stroke.
The AHA/ASA guidelines also examine factors such as migraines, which are associated with stroke in women under age 55, and hyperhomocysteinemia, which is also associated with an increased risk of stroke. Other factors like hypercoagulability and sleep apnea were not shown to have any identifiable relationship with an increased risk of stroke.
“As health professionals, we must ensure that progress in preventing stroke does not lead to complacency,” say the guidelines. “We must acknowledge that several recommendations remain vague because of suboptimal clinical trial evidence or, even more concerning, may be out of date and therefore irrelevant.”
Dr. Meschia and his associates warn that although medications are helpful, the best way to safeguard against a stroke is to change a person’s lifestyle into one of healthy eating and exercise habits. Unfortunately, say the authors, “it is easier to convince a patient to take a pill than to radically change his or her lifestyle, [but] we must expect the same standards of evidence for lifestyle interventions.”
Dr. Meschia disclosed that his research grant comes from the National Institute of Neurological Disorders and Stroke. He had no other relevant financial disclosures of interest. Several of the guidelines’ coauthors had disclosures of their own, which are listed in the statement.
Lifestyle modifications, including eating a Mediterranean or DASH-style diet, should be encouraged to lower an individual’s risk of first-time stroke, according to new guidelines for the primary prevention of stroke from the American Heart Association and American Stroke Association.
Mediterranean and DASH (Dietary Approaches to Stop Hypertension) dietary plans are characterized by their emphasis on fruits, vegetables, whole grains, legumes, nuts, seeds, poultry, and fish, while limiting red meats, sweets, and any foods with saturated fats. This new guidelines – which have been endorsed by the American Academy of Neurology, American Association of Neurological Surgeons, Congress of Neurological Surgeons, and Preventive Cardiovascular Nurses Association – suggest that adopting either of these diets in addition to a few other healthy living habits can dramatically reduce an individual’s odds of suffering a stroke (Stroke 2014;45 [doi: 10.1161/STR.0000000000000046]).
“We have a huge opportunity to improve how we prevent new strokes, because risk factors that can be changed or controlled – especially high blood pressure – account for 90% of strokes,” said Dr. James Meschia, chair of the writing committee and chairman of neurology at the Mayo Clinic in Jacksonville, Fla., in a statement. The last such guidelines were released 3 years ago (Stroke 2011;42:517-84)
The writing committee gathered data pertaining to the age, birth weight, race/ethnicity, and genetic factors, among several others. Studies examined included a U.S. Nationwide Inpatient Sample, which showed that stroke hospitalizations increased between 1998 and 2007 for individuals aged 25-34 years and 35-44 years; the Framingham Heart Study, which estimated that the odds of a middle-aged adult suffering a stroke are 1 in 6; an analysis of South Carolina Medicaid beneficiaries under age 50, which revealed that individuals born weighing less than 2,500 g were twice as likely to have a stroke as those born heavier; and an Atherosclerosis Risk in Communities (ARIC) study that showed Latino and African American populations being at higher risk for stroke due to hypertension, obesity, and diabetes.
Because blood pressure, hypertension, diabetes, and obesity are so commonly linked to stroke risk, the new American Heart Association/American Stroke Association (AHA/ASA) guidelines highly recommend a Mediterranean or DASH-style diet supplemented with nuts. Additionally, the guidelines advise health care professionals to advise patients to cut down on sodium intake, regularly monitor their blood pressure, talk to their physicians immediately if any medication does not do what it is intended to or creates negative side effects, and quit smoking, and, for women, consider an alternative to oral birth control pills.
Hypertension, “the most important, well-documented, modifiable stroke risk factor,” should be treated with antihypertensive medication to a target blood pressure of less than 140/90 mm Hg, the guidelines state.
Furthermore, the ASA/AHA guidelines continue to recommend regular physical exercise and acute monitoring of individuals’ cholesterol levels, such as LDL, HDL, and triglycerides, as failure to keep these numbers in check can easily lead to a serious stroke. The guidelines also state that although heavy alcohol consumption can increase the chance of stroke, “light to moderate” alcohol consumption can actually decrease the odds of suffering a stroke.
The AHA/ASA guidelines also examine factors such as migraines, which are associated with stroke in women under age 55, and hyperhomocysteinemia, which is also associated with an increased risk of stroke. Other factors like hypercoagulability and sleep apnea were not shown to have any identifiable relationship with an increased risk of stroke.
“As health professionals, we must ensure that progress in preventing stroke does not lead to complacency,” say the guidelines. “We must acknowledge that several recommendations remain vague because of suboptimal clinical trial evidence or, even more concerning, may be out of date and therefore irrelevant.”
Dr. Meschia and his associates warn that although medications are helpful, the best way to safeguard against a stroke is to change a person’s lifestyle into one of healthy eating and exercise habits. Unfortunately, say the authors, “it is easier to convince a patient to take a pill than to radically change his or her lifestyle, [but] we must expect the same standards of evidence for lifestyle interventions.”
Dr. Meschia disclosed that his research grant comes from the National Institute of Neurological Disorders and Stroke. He had no other relevant financial disclosures of interest. Several of the guidelines’ coauthors had disclosures of their own, which are listed in the statement.
FROM THE AMERICAN HEART ASSOCIATION AND THE AMERICAN STROKE ASSOCIATION
ACC project seeks to reduce heart failure, MI readmissions
A program that’s designed to help hospitals reduce readmissions after inpatient treatment for a heart attack or heart failure is being launched in 35 selected hospitals.
The Patient Navigator Program is sponsored by the American College of Cardiology and AstraZeneca, which provided $10 million in funding for the 2-year pilot program, but was not involved in selection of facilities or any other aspect.
It’s “a unique collaboration between the cardiovascular care team, patients, and families to manage the stress of hospitalization for complex conditions in a way that allows patients to return home, remain healthy, and avoid the need for readmission whenever possible,” said ACC President Patrick T. O’Gara, in a statement.
Hospitals have been under pressure to reduce readmissions since the fall of 2012. That’s when Medicare began penalizing facilities up to 1% of their inpatient admissions for excess readmissions within 30 days of patients with acute myocardial infarctions, heart failure, and pneumonia. The penalty increased to 2% in fiscal year 2014, and went up to 3% in the fiscal year that started Oct. 1. For this year, chronic obstructive pulmonary disease and hip/knee arthroplasty were added to the list of conditions being monitored for readmissions.
The penalties have already been assessed for fiscal year 2015.
Medicare’s Readmissions Reduction Program bases penalties on a prior 3-year period. Fiscal 2015 penalties were based on readmissions from 2010 to 2013.
The Medicare penalties were the driving force behind the creation of the program a few years ago, said Dr. Mary Norine Walsh, chair of the ACC’s Care Transition Oversight Program. But it also represented a chance “to pursue excellence,” said Dr. Walsh in an interview.
The 35 hospitals that are participating were selected by ACC senior staff and cardiologists like Dr. Walsh who are involved in the ACC’s quality improvement efforts. To be eligible, they had to be participants in the ACC’s National Cardiovascular Data Registry ACTION Registry–GWTG, which, according to the ACC, “is a risk-adjusted, outcomes-based quality improvement program that focuses exclusively on high-risk STEMI/NSTEMI myocardial infarction patients.” The registry helps hospitals apply ACC and American Heart Association clinical guideline recommendations and provides quality improvement tools.
They also had to be part of the ACC’s Hospital to Home Initiative, which helps hospitals and cardiovascular care providers improve transitions from hospital to homes.
All 35 hospitals are eligible to receive $80,000 a year for 2 years. Most likely, the facilities will use that money to hire an individual or individuals who can act as a navigator for heart failure and MI patients, said Dr. Walsh, who is the medical director of the heart failure and cardiac transplant program at St. Vincent Heart Center, Indianapolis, Ind.
While there are few randomized, controlled trials that examine what works to reduce readmission rates, there are several interventions that have been shown to help, she said. Patient eduction and getting patients in for follow-up care within 7 days are two key components that can make a difference, said Dr. Walsh. Multidisciplinary heart failure programs also have an impact.
The participating hospitals will share their processes and models, and at the end of the 2 years, the hope is that the facilities will continue to fund the program, said Dr. Walsh.
The ACC will also “be interested to find out what success looks like,” she said.
The Patient Navigator Program probably won’t help hospitals avoid penalties until fiscal year 2017 at the earliest, Dr. Walsh noted. However, the model will still be important, she said.
“We know that value-based purchasing is moving on, and the penalties will almost certainly extend to other diagnoses each sequential year, so hospitals are interested in preparing for the future,” said Dr. Walsh.
On Twitter @aliciaault
A program that’s designed to help hospitals reduce readmissions after inpatient treatment for a heart attack or heart failure is being launched in 35 selected hospitals.
The Patient Navigator Program is sponsored by the American College of Cardiology and AstraZeneca, which provided $10 million in funding for the 2-year pilot program, but was not involved in selection of facilities or any other aspect.
It’s “a unique collaboration between the cardiovascular care team, patients, and families to manage the stress of hospitalization for complex conditions in a way that allows patients to return home, remain healthy, and avoid the need for readmission whenever possible,” said ACC President Patrick T. O’Gara, in a statement.
Hospitals have been under pressure to reduce readmissions since the fall of 2012. That’s when Medicare began penalizing facilities up to 1% of their inpatient admissions for excess readmissions within 30 days of patients with acute myocardial infarctions, heart failure, and pneumonia. The penalty increased to 2% in fiscal year 2014, and went up to 3% in the fiscal year that started Oct. 1. For this year, chronic obstructive pulmonary disease and hip/knee arthroplasty were added to the list of conditions being monitored for readmissions.
The penalties have already been assessed for fiscal year 2015.
Medicare’s Readmissions Reduction Program bases penalties on a prior 3-year period. Fiscal 2015 penalties were based on readmissions from 2010 to 2013.
The Medicare penalties were the driving force behind the creation of the program a few years ago, said Dr. Mary Norine Walsh, chair of the ACC’s Care Transition Oversight Program. But it also represented a chance “to pursue excellence,” said Dr. Walsh in an interview.
The 35 hospitals that are participating were selected by ACC senior staff and cardiologists like Dr. Walsh who are involved in the ACC’s quality improvement efforts. To be eligible, they had to be participants in the ACC’s National Cardiovascular Data Registry ACTION Registry–GWTG, which, according to the ACC, “is a risk-adjusted, outcomes-based quality improvement program that focuses exclusively on high-risk STEMI/NSTEMI myocardial infarction patients.” The registry helps hospitals apply ACC and American Heart Association clinical guideline recommendations and provides quality improvement tools.
They also had to be part of the ACC’s Hospital to Home Initiative, which helps hospitals and cardiovascular care providers improve transitions from hospital to homes.
All 35 hospitals are eligible to receive $80,000 a year for 2 years. Most likely, the facilities will use that money to hire an individual or individuals who can act as a navigator for heart failure and MI patients, said Dr. Walsh, who is the medical director of the heart failure and cardiac transplant program at St. Vincent Heart Center, Indianapolis, Ind.
While there are few randomized, controlled trials that examine what works to reduce readmission rates, there are several interventions that have been shown to help, she said. Patient eduction and getting patients in for follow-up care within 7 days are two key components that can make a difference, said Dr. Walsh. Multidisciplinary heart failure programs also have an impact.
The participating hospitals will share their processes and models, and at the end of the 2 years, the hope is that the facilities will continue to fund the program, said Dr. Walsh.
The ACC will also “be interested to find out what success looks like,” she said.
The Patient Navigator Program probably won’t help hospitals avoid penalties until fiscal year 2017 at the earliest, Dr. Walsh noted. However, the model will still be important, she said.
“We know that value-based purchasing is moving on, and the penalties will almost certainly extend to other diagnoses each sequential year, so hospitals are interested in preparing for the future,” said Dr. Walsh.
On Twitter @aliciaault
A program that’s designed to help hospitals reduce readmissions after inpatient treatment for a heart attack or heart failure is being launched in 35 selected hospitals.
The Patient Navigator Program is sponsored by the American College of Cardiology and AstraZeneca, which provided $10 million in funding for the 2-year pilot program, but was not involved in selection of facilities or any other aspect.
It’s “a unique collaboration between the cardiovascular care team, patients, and families to manage the stress of hospitalization for complex conditions in a way that allows patients to return home, remain healthy, and avoid the need for readmission whenever possible,” said ACC President Patrick T. O’Gara, in a statement.
Hospitals have been under pressure to reduce readmissions since the fall of 2012. That’s when Medicare began penalizing facilities up to 1% of their inpatient admissions for excess readmissions within 30 days of patients with acute myocardial infarctions, heart failure, and pneumonia. The penalty increased to 2% in fiscal year 2014, and went up to 3% in the fiscal year that started Oct. 1. For this year, chronic obstructive pulmonary disease and hip/knee arthroplasty were added to the list of conditions being monitored for readmissions.
The penalties have already been assessed for fiscal year 2015.
Medicare’s Readmissions Reduction Program bases penalties on a prior 3-year period. Fiscal 2015 penalties were based on readmissions from 2010 to 2013.
The Medicare penalties were the driving force behind the creation of the program a few years ago, said Dr. Mary Norine Walsh, chair of the ACC’s Care Transition Oversight Program. But it also represented a chance “to pursue excellence,” said Dr. Walsh in an interview.
The 35 hospitals that are participating were selected by ACC senior staff and cardiologists like Dr. Walsh who are involved in the ACC’s quality improvement efforts. To be eligible, they had to be participants in the ACC’s National Cardiovascular Data Registry ACTION Registry–GWTG, which, according to the ACC, “is a risk-adjusted, outcomes-based quality improvement program that focuses exclusively on high-risk STEMI/NSTEMI myocardial infarction patients.” The registry helps hospitals apply ACC and American Heart Association clinical guideline recommendations and provides quality improvement tools.
They also had to be part of the ACC’s Hospital to Home Initiative, which helps hospitals and cardiovascular care providers improve transitions from hospital to homes.
All 35 hospitals are eligible to receive $80,000 a year for 2 years. Most likely, the facilities will use that money to hire an individual or individuals who can act as a navigator for heart failure and MI patients, said Dr. Walsh, who is the medical director of the heart failure and cardiac transplant program at St. Vincent Heart Center, Indianapolis, Ind.
While there are few randomized, controlled trials that examine what works to reduce readmission rates, there are several interventions that have been shown to help, she said. Patient eduction and getting patients in for follow-up care within 7 days are two key components that can make a difference, said Dr. Walsh. Multidisciplinary heart failure programs also have an impact.
The participating hospitals will share their processes and models, and at the end of the 2 years, the hope is that the facilities will continue to fund the program, said Dr. Walsh.
The ACC will also “be interested to find out what success looks like,” she said.
The Patient Navigator Program probably won’t help hospitals avoid penalties until fiscal year 2017 at the earliest, Dr. Walsh noted. However, the model will still be important, she said.
“We know that value-based purchasing is moving on, and the penalties will almost certainly extend to other diagnoses each sequential year, so hospitals are interested in preparing for the future,” said Dr. Walsh.
On Twitter @aliciaault
VIDEO: Should physicians reevaluate the role of clopidogrel?
AUSTIN, TEX.– Genetic testing holds promise for guiding the prescribing of antiplatelet therapies, particularly clopidogrel, but “we’re not there yet,” according to Dr. Steven Hollenberg, director of the coronary care unit at Cooper University Hospital in Camden, N.J.Genetic testing for clopidogrel responsiveness “certainly makes good sense, but I think we’re going to have to wait for good data” that better informs clinical decision making.Dr. Hollenberg discussed the implications of the negative results of the ARCTIC trial, which showed platelet function testing with antiplatelet therapy adjustment failed to improve clinical outcomes compared with standard unmonitored thienopyridine therapy in elective PCI. He also analyzed the results of other studies relevant to optimal antiplatelet and anticoagulant therapy, including the surprising outcomes of the WOEST trial.
The video associated with this article is no longer available on this site. Please view all of our videos on the MDedge YouTube channel
On Twitter @whitneymcknight
AUSTIN, TEX.– Genetic testing holds promise for guiding the prescribing of antiplatelet therapies, particularly clopidogrel, but “we’re not there yet,” according to Dr. Steven Hollenberg, director of the coronary care unit at Cooper University Hospital in Camden, N.J.Genetic testing for clopidogrel responsiveness “certainly makes good sense, but I think we’re going to have to wait for good data” that better informs clinical decision making.Dr. Hollenberg discussed the implications of the negative results of the ARCTIC trial, which showed platelet function testing with antiplatelet therapy adjustment failed to improve clinical outcomes compared with standard unmonitored thienopyridine therapy in elective PCI. He also analyzed the results of other studies relevant to optimal antiplatelet and anticoagulant therapy, including the surprising outcomes of the WOEST trial.
The video associated with this article is no longer available on this site. Please view all of our videos on the MDedge YouTube channel
On Twitter @whitneymcknight
AUSTIN, TEX.– Genetic testing holds promise for guiding the prescribing of antiplatelet therapies, particularly clopidogrel, but “we’re not there yet,” according to Dr. Steven Hollenberg, director of the coronary care unit at Cooper University Hospital in Camden, N.J.Genetic testing for clopidogrel responsiveness “certainly makes good sense, but I think we’re going to have to wait for good data” that better informs clinical decision making.Dr. Hollenberg discussed the implications of the negative results of the ARCTIC trial, which showed platelet function testing with antiplatelet therapy adjustment failed to improve clinical outcomes compared with standard unmonitored thienopyridine therapy in elective PCI. He also analyzed the results of other studies relevant to optimal antiplatelet and anticoagulant therapy, including the surprising outcomes of the WOEST trial.
The video associated with this article is no longer available on this site. Please view all of our videos on the MDedge YouTube channel
On Twitter @whitneymcknight
AT CHEST 2014
Antibiotic Prophylaxis Might Prevent Recurrent UTIs
Clinical question: Does antibiotic prophylaxis prevent future episodes of urinary tract infections?
Background: Recurrent urinary tract infections (UTI) in children might be associated with renal scarring and subsequent clinical consequences associated with long-term morbidity. Historically, antibiotic prophylaxis has been recommended for children who might have risk factors for recurrent infection, most commonly vesicoureteral reflux. However, scars may be present in the absence of known risk factors and upon first UTI. The efficacy of antibiotic prophylaxis in preventing recurrent UTIs is unclear.
Study design: Randomized, double-blind, placebo-controlled trial.
Setting: Four centers in Australia.
Synopsis: The study looked at 576 children under the age of 18 with a history of at least one symptomatic UTI. The patients were randomized to receive trimethoprim-sulfamethoxazole (TMP-SMX) or placebo for 12 months. Children with vesicoureteral reflux were included, but those with known neurologic, skeletal, or urologic predispositions were excluded.
Thirteen percent of patients in the antibiotic group developed a UTI compared with 19% of patients in the placebo group (P=0.02). The authors calculate that at 12 months, 14 patients would need to be treated to prevent one UTI.
This study was unable to enroll the planned number of children but remained adequately powered to show a reduction in the primary outcome (rate of symptomatic UTI). However, a significant number of patients (approximately 28%) in each arm stopped taking the medication, the majority for undisclosed reasons. Despite an intention-to-treat analysis, this degree of dropout raises questions about the true effect size. Additionally, this study does not answer the more important clinical question regarding the effect of prophylaxis on potential future renal damage, specifically in children with vesicoureteral reflux.
Bottom line: Antibiotic prophylaxis might be modestly effective in preventing recurrent UTIs.
Citation: Craig JC, Simpson JM, Williams GJ, et al. Antibiotic prophylaxis and recurrent urinary tract infection in children. N Engl J Med. 2009;361(18):1748-1759.
Clinical question: Does antibiotic prophylaxis prevent future episodes of urinary tract infections?
Background: Recurrent urinary tract infections (UTI) in children might be associated with renal scarring and subsequent clinical consequences associated with long-term morbidity. Historically, antibiotic prophylaxis has been recommended for children who might have risk factors for recurrent infection, most commonly vesicoureteral reflux. However, scars may be present in the absence of known risk factors and upon first UTI. The efficacy of antibiotic prophylaxis in preventing recurrent UTIs is unclear.
Study design: Randomized, double-blind, placebo-controlled trial.
Setting: Four centers in Australia.
Synopsis: The study looked at 576 children under the age of 18 with a history of at least one symptomatic UTI. The patients were randomized to receive trimethoprim-sulfamethoxazole (TMP-SMX) or placebo for 12 months. Children with vesicoureteral reflux were included, but those with known neurologic, skeletal, or urologic predispositions were excluded.
Thirteen percent of patients in the antibiotic group developed a UTI compared with 19% of patients in the placebo group (P=0.02). The authors calculate that at 12 months, 14 patients would need to be treated to prevent one UTI.
This study was unable to enroll the planned number of children but remained adequately powered to show a reduction in the primary outcome (rate of symptomatic UTI). However, a significant number of patients (approximately 28%) in each arm stopped taking the medication, the majority for undisclosed reasons. Despite an intention-to-treat analysis, this degree of dropout raises questions about the true effect size. Additionally, this study does not answer the more important clinical question regarding the effect of prophylaxis on potential future renal damage, specifically in children with vesicoureteral reflux.
Bottom line: Antibiotic prophylaxis might be modestly effective in preventing recurrent UTIs.
Citation: Craig JC, Simpson JM, Williams GJ, et al. Antibiotic prophylaxis and recurrent urinary tract infection in children. N Engl J Med. 2009;361(18):1748-1759.
Clinical question: Does antibiotic prophylaxis prevent future episodes of urinary tract infections?
Background: Recurrent urinary tract infections (UTI) in children might be associated with renal scarring and subsequent clinical consequences associated with long-term morbidity. Historically, antibiotic prophylaxis has been recommended for children who might have risk factors for recurrent infection, most commonly vesicoureteral reflux. However, scars may be present in the absence of known risk factors and upon first UTI. The efficacy of antibiotic prophylaxis in preventing recurrent UTIs is unclear.
Study design: Randomized, double-blind, placebo-controlled trial.
Setting: Four centers in Australia.
Synopsis: The study looked at 576 children under the age of 18 with a history of at least one symptomatic UTI. The patients were randomized to receive trimethoprim-sulfamethoxazole (TMP-SMX) or placebo for 12 months. Children with vesicoureteral reflux were included, but those with known neurologic, skeletal, or urologic predispositions were excluded.
Thirteen percent of patients in the antibiotic group developed a UTI compared with 19% of patients in the placebo group (P=0.02). The authors calculate that at 12 months, 14 patients would need to be treated to prevent one UTI.
This study was unable to enroll the planned number of children but remained adequately powered to show a reduction in the primary outcome (rate of symptomatic UTI). However, a significant number of patients (approximately 28%) in each arm stopped taking the medication, the majority for undisclosed reasons. Despite an intention-to-treat analysis, this degree of dropout raises questions about the true effect size. Additionally, this study does not answer the more important clinical question regarding the effect of prophylaxis on potential future renal damage, specifically in children with vesicoureteral reflux.
Bottom line: Antibiotic prophylaxis might be modestly effective in preventing recurrent UTIs.
Citation: Craig JC, Simpson JM, Williams GJ, et al. Antibiotic prophylaxis and recurrent urinary tract infection in children. N Engl J Med. 2009;361(18):1748-1759.
Advanced Dementia Is a Terminal Illness with High Morbidity and Mortality
Clinical question: Does understanding the expected clinical course of advanced dementia influence end-of-life decisions by proxy decision-makers?
Background: Advanced dementia is a leading cause of death in the United States, but the clinical course of advanced dementia has not been described in a rigorous, prospective manner. The lack of information might cause risk to be underestimated, and patients might receive suboptimal palliative care.
Study design: Multicenter prospective cohort study.
Setting: Twenty-two nursing homes in a single U.S. city.
Synopsis: The survey examined 323 nursing home residents with advanced dementia. The patients were clinically assessed at baseline and quarterly for 18 months through chart reviews, nursing interviews, and physical examinations. Additionally, their proxies were surveyed regarding their understanding of the subjects’ prognoses.
During the survey period, 41.1% of patients developed pneumonia, 52.6% of patients experienced a febrile episode, and 85.8% of patients developed an eating problem; cumulative all-cause mortality was 54.8%. Adjusted for age, sex, and disease duration, the six-month mortality rate for subjects who had pneumonia was 46.7%; a febrile episode, 44.5%; and an eating problem, 38.6%.
Distressing symptoms, including dyspnea (46.0%) and pain (39.1%), were common. In the last three months of life, 40.7% of subjects underwent at least one burdensome intervention (defined as hospitalization, ED visit, parenteral therapy, or tube feeding).
Subjects whose proxies reported an understanding of the poor prognosis and expected clinical complications of advanced dementia underwent significantly fewer burdensome interventions (adjusted odds ratio 0.12).
Bottom line: Advanced dementia is associated with frequent complications, including infections and eating problems, with high six-month mortality and significant associated morbidity. Patients whose healthcare proxies have a good understanding of the expected clinical course and prognosis receive less-aggressive end-of-life care.
Citation: Mitchell SL, Teno JM, Kiely DK, et al. The clinical course of advanced dementia. N Engl J Med. 2009;361(16):1529-1538. TH
Clinical question: Does understanding the expected clinical course of advanced dementia influence end-of-life decisions by proxy decision-makers?
Background: Advanced dementia is a leading cause of death in the United States, but the clinical course of advanced dementia has not been described in a rigorous, prospective manner. The lack of information might cause risk to be underestimated, and patients might receive suboptimal palliative care.
Study design: Multicenter prospective cohort study.
Setting: Twenty-two nursing homes in a single U.S. city.
Synopsis: The survey examined 323 nursing home residents with advanced dementia. The patients were clinically assessed at baseline and quarterly for 18 months through chart reviews, nursing interviews, and physical examinations. Additionally, their proxies were surveyed regarding their understanding of the subjects’ prognoses.
During the survey period, 41.1% of patients developed pneumonia, 52.6% of patients experienced a febrile episode, and 85.8% of patients developed an eating problem; cumulative all-cause mortality was 54.8%. Adjusted for age, sex, and disease duration, the six-month mortality rate for subjects who had pneumonia was 46.7%; a febrile episode, 44.5%; and an eating problem, 38.6%.
Distressing symptoms, including dyspnea (46.0%) and pain (39.1%), were common. In the last three months of life, 40.7% of subjects underwent at least one burdensome intervention (defined as hospitalization, ED visit, parenteral therapy, or tube feeding).
Subjects whose proxies reported an understanding of the poor prognosis and expected clinical complications of advanced dementia underwent significantly fewer burdensome interventions (adjusted odds ratio 0.12).
Bottom line: Advanced dementia is associated with frequent complications, including infections and eating problems, with high six-month mortality and significant associated morbidity. Patients whose healthcare proxies have a good understanding of the expected clinical course and prognosis receive less-aggressive end-of-life care.
Citation: Mitchell SL, Teno JM, Kiely DK, et al. The clinical course of advanced dementia. N Engl J Med. 2009;361(16):1529-1538. TH
Clinical question: Does understanding the expected clinical course of advanced dementia influence end-of-life decisions by proxy decision-makers?
Background: Advanced dementia is a leading cause of death in the United States, but the clinical course of advanced dementia has not been described in a rigorous, prospective manner. The lack of information might cause risk to be underestimated, and patients might receive suboptimal palliative care.
Study design: Multicenter prospective cohort study.
Setting: Twenty-two nursing homes in a single U.S. city.
Synopsis: The survey examined 323 nursing home residents with advanced dementia. The patients were clinically assessed at baseline and quarterly for 18 months through chart reviews, nursing interviews, and physical examinations. Additionally, their proxies were surveyed regarding their understanding of the subjects’ prognoses.
During the survey period, 41.1% of patients developed pneumonia, 52.6% of patients experienced a febrile episode, and 85.8% of patients developed an eating problem; cumulative all-cause mortality was 54.8%. Adjusted for age, sex, and disease duration, the six-month mortality rate for subjects who had pneumonia was 46.7%; a febrile episode, 44.5%; and an eating problem, 38.6%.
Distressing symptoms, including dyspnea (46.0%) and pain (39.1%), were common. In the last three months of life, 40.7% of subjects underwent at least one burdensome intervention (defined as hospitalization, ED visit, parenteral therapy, or tube feeding).
Subjects whose proxies reported an understanding of the poor prognosis and expected clinical complications of advanced dementia underwent significantly fewer burdensome interventions (adjusted odds ratio 0.12).
Bottom line: Advanced dementia is associated with frequent complications, including infections and eating problems, with high six-month mortality and significant associated morbidity. Patients whose healthcare proxies have a good understanding of the expected clinical course and prognosis receive less-aggressive end-of-life care.
Citation: Mitchell SL, Teno JM, Kiely DK, et al. The clinical course of advanced dementia. N Engl J Med. 2009;361(16):1529-1538. TH
Adding Basal Insulin to Oral Agents in Type 2 Diabetes Might Offer Best Glycemic Control
Clinical question: When added to oral diabetic agents, which insulin regimen (biphasic, prandial or basal) best achieves glycemic control in patients with Type 2 diabetes?
Background: Most patients with Type 2 diabetes mellitus (DM2) require insulin when oral agents provide suboptimal glycemic control. Little is known about which insulin regimen is most effective.
Study design: Three-year, open-label, multicenter trial.
Setting: Fifty-eight clinical centers in the United Kingdom and Ireland.
Synopsis: The authors randomized 708 insulin-naïve DM2 patients (median age 62 years) with HgbA1c 7% to 10% on maximum-dose metformin or sulfonylurea to one of three regimens: biphasic insulin twice daily; prandial insulin three times daily; or basal insulin once daily. Outcomes were HgbA1c, hypoglycemia rates, and weight gain. Sulfonylureas were replaced by another insulin if glycemic control was unacceptable.
The patients were mostly Caucasian and overweight. At three years of followup, median HgbA1c was similar in all groups (7.1% biphasic, 6.8% prandial, 6.9% basal); however, more patients who received prandial or basal insulin achieved HgbA1c less than 6.5% (45% and 43%, respectively) than in the biphasic group (32%).
Hypoglycemia was significantly less frequent in the basal insulin group (1.7 per patient per year versus 3.0 and 5.5 with biphasic and prandial, respectively). Patients gained weight in all groups; the greatest gain was with prandial insulin. At three years, there were no significant between-group differences in blood pressure, cholesterol, albuminuria, or quality of life.
Bottom line: Adding insulin to oral diabetic regimens improves glycemic control. Basal or prandial insulin regimens achieve glycemic targets more frequently than biphasic dosing.
Citation: Holman RR, Farmer AJ, Davies MJ, et al. Three-year efficacy of complex insulin regimens in type 2 diabetes. N Engl J Med. 2009;361(18):1736-1747.
Clinical question: When added to oral diabetic agents, which insulin regimen (biphasic, prandial or basal) best achieves glycemic control in patients with Type 2 diabetes?
Background: Most patients with Type 2 diabetes mellitus (DM2) require insulin when oral agents provide suboptimal glycemic control. Little is known about which insulin regimen is most effective.
Study design: Three-year, open-label, multicenter trial.
Setting: Fifty-eight clinical centers in the United Kingdom and Ireland.
Synopsis: The authors randomized 708 insulin-naïve DM2 patients (median age 62 years) with HgbA1c 7% to 10% on maximum-dose metformin or sulfonylurea to one of three regimens: biphasic insulin twice daily; prandial insulin three times daily; or basal insulin once daily. Outcomes were HgbA1c, hypoglycemia rates, and weight gain. Sulfonylureas were replaced by another insulin if glycemic control was unacceptable.
The patients were mostly Caucasian and overweight. At three years of followup, median HgbA1c was similar in all groups (7.1% biphasic, 6.8% prandial, 6.9% basal); however, more patients who received prandial or basal insulin achieved HgbA1c less than 6.5% (45% and 43%, respectively) than in the biphasic group (32%).
Hypoglycemia was significantly less frequent in the basal insulin group (1.7 per patient per year versus 3.0 and 5.5 with biphasic and prandial, respectively). Patients gained weight in all groups; the greatest gain was with prandial insulin. At three years, there were no significant between-group differences in blood pressure, cholesterol, albuminuria, or quality of life.
Bottom line: Adding insulin to oral diabetic regimens improves glycemic control. Basal or prandial insulin regimens achieve glycemic targets more frequently than biphasic dosing.
Citation: Holman RR, Farmer AJ, Davies MJ, et al. Three-year efficacy of complex insulin regimens in type 2 diabetes. N Engl J Med. 2009;361(18):1736-1747.
Clinical question: When added to oral diabetic agents, which insulin regimen (biphasic, prandial or basal) best achieves glycemic control in patients with Type 2 diabetes?
Background: Most patients with Type 2 diabetes mellitus (DM2) require insulin when oral agents provide suboptimal glycemic control. Little is known about which insulin regimen is most effective.
Study design: Three-year, open-label, multicenter trial.
Setting: Fifty-eight clinical centers in the United Kingdom and Ireland.
Synopsis: The authors randomized 708 insulin-naïve DM2 patients (median age 62 years) with HgbA1c 7% to 10% on maximum-dose metformin or sulfonylurea to one of three regimens: biphasic insulin twice daily; prandial insulin three times daily; or basal insulin once daily. Outcomes were HgbA1c, hypoglycemia rates, and weight gain. Sulfonylureas were replaced by another insulin if glycemic control was unacceptable.
The patients were mostly Caucasian and overweight. At three years of followup, median HgbA1c was similar in all groups (7.1% biphasic, 6.8% prandial, 6.9% basal); however, more patients who received prandial or basal insulin achieved HgbA1c less than 6.5% (45% and 43%, respectively) than in the biphasic group (32%).
Hypoglycemia was significantly less frequent in the basal insulin group (1.7 per patient per year versus 3.0 and 5.5 with biphasic and prandial, respectively). Patients gained weight in all groups; the greatest gain was with prandial insulin. At three years, there were no significant between-group differences in blood pressure, cholesterol, albuminuria, or quality of life.
Bottom line: Adding insulin to oral diabetic regimens improves glycemic control. Basal or prandial insulin regimens achieve glycemic targets more frequently than biphasic dosing.
Citation: Holman RR, Farmer AJ, Davies MJ, et al. Three-year efficacy of complex insulin regimens in type 2 diabetes. N Engl J Med. 2009;361(18):1736-1747.
Initiation of Dialysis Does Not Help Maintain Functional Status in Elderly
Clinical question: Is functional status in the elderly maintained over time after initiating long-term dialysis?
Background: Quality-of-life maintenance often is used as a goal when initiating long-term dialysis in elderly patients with end-stage renal disease. More elderly patients are being offered long-term dialysis treatment. Little is known about the functional status of elderly patients on long-term dialysis.
Study design: Retrospective cohort study.
Setting: U.S. nursing homes.
Synopsis: By cross-linking data from two population-based administrative datasets, this study identified 3,702 nursing home patients (mean 73.4 years) who had started long-term dialysis and whose functional status had been assessed. Activities of daily living assessments before and at three-month intervals after dialysis initiation were compared to see if functional status was maintained.
Within three months of starting dialysis, 61% of patients had a decline in functional status or had died. By one year, only 1 in 8 patients had maintained their pre-dialysis functional status.
Decline in functional status cannot be attributed solely to dialysis because study patients were not compared to patients with chronic kidney disease who were not dialyzed. In addition, these results might not apply to all elderly patients on dialysis, as the functional status of elderly nursing home patients might differ significantly from those living at home.
Bottom line: Functional status is not maintained in most elderly nursing home patients in the first 12 months after long-term dialysis is initiated. Elderly patients considering dialysis treatment should be aware that dialysis might not help maintain functional status and quality of life.
Citation: Kurella Tamura MK, Covinsky KE, Chertow GM, Yaffe C, Landefeld CS, McCulloch CE. Functional status of elderly adults before and after initiation of dialysis. N Engl J Med. 2009;361(16):1539-1547.
Clinical question: Is functional status in the elderly maintained over time after initiating long-term dialysis?
Background: Quality-of-life maintenance often is used as a goal when initiating long-term dialysis in elderly patients with end-stage renal disease. More elderly patients are being offered long-term dialysis treatment. Little is known about the functional status of elderly patients on long-term dialysis.
Study design: Retrospective cohort study.
Setting: U.S. nursing homes.
Synopsis: By cross-linking data from two population-based administrative datasets, this study identified 3,702 nursing home patients (mean 73.4 years) who had started long-term dialysis and whose functional status had been assessed. Activities of daily living assessments before and at three-month intervals after dialysis initiation were compared to see if functional status was maintained.
Within three months of starting dialysis, 61% of patients had a decline in functional status or had died. By one year, only 1 in 8 patients had maintained their pre-dialysis functional status.
Decline in functional status cannot be attributed solely to dialysis because study patients were not compared to patients with chronic kidney disease who were not dialyzed. In addition, these results might not apply to all elderly patients on dialysis, as the functional status of elderly nursing home patients might differ significantly from those living at home.
Bottom line: Functional status is not maintained in most elderly nursing home patients in the first 12 months after long-term dialysis is initiated. Elderly patients considering dialysis treatment should be aware that dialysis might not help maintain functional status and quality of life.
Citation: Kurella Tamura MK, Covinsky KE, Chertow GM, Yaffe C, Landefeld CS, McCulloch CE. Functional status of elderly adults before and after initiation of dialysis. N Engl J Med. 2009;361(16):1539-1547.
Clinical question: Is functional status in the elderly maintained over time after initiating long-term dialysis?
Background: Quality-of-life maintenance often is used as a goal when initiating long-term dialysis in elderly patients with end-stage renal disease. More elderly patients are being offered long-term dialysis treatment. Little is known about the functional status of elderly patients on long-term dialysis.
Study design: Retrospective cohort study.
Setting: U.S. nursing homes.
Synopsis: By cross-linking data from two population-based administrative datasets, this study identified 3,702 nursing home patients (mean 73.4 years) who had started long-term dialysis and whose functional status had been assessed. Activities of daily living assessments before and at three-month intervals after dialysis initiation were compared to see if functional status was maintained.
Within three months of starting dialysis, 61% of patients had a decline in functional status or had died. By one year, only 1 in 8 patients had maintained their pre-dialysis functional status.
Decline in functional status cannot be attributed solely to dialysis because study patients were not compared to patients with chronic kidney disease who were not dialyzed. In addition, these results might not apply to all elderly patients on dialysis, as the functional status of elderly nursing home patients might differ significantly from those living at home.
Bottom line: Functional status is not maintained in most elderly nursing home patients in the first 12 months after long-term dialysis is initiated. Elderly patients considering dialysis treatment should be aware that dialysis might not help maintain functional status and quality of life.
Citation: Kurella Tamura MK, Covinsky KE, Chertow GM, Yaffe C, Landefeld CS, McCulloch CE. Functional status of elderly adults before and after initiation of dialysis. N Engl J Med. 2009;361(16):1539-1547.