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Care team identification in the electronic health record: A critical first step for patient‐centered communication

Author(s)

Jeffrey L. Schnipper, MD, MPH

Patient‐centered communication is a strategy that is used to promote shared understanding of the plan of care among providers and patients.[1, 2, 3] Caring for hospitalized patients is a collaborative effort that requires seamless patient‐centered communication among a rapidly changing care team to safely progress a patient from admission through discharge. Yet, hospitals continue to struggle with improving the complex and increasingly electronic conversation patterns among care team members and patients to achieve effective patient‐centered communication.[4, 5] When members of the care team operate in this environment, patients often receive conflicting information regarding their plan of care, medications, and test results. Ineffective communication can lead to a suboptimal patient experience, additional costs, medical errors, and preventable adverse events.[6, 7, 8, 9, 10]

A critical first step to improving patient‐centered communication is identifying the care team.[11, 12] Accurate and reliable identification of all care team members is a pressing information need; it is fundamental to efficiently conveying information about the plan of care to those who know the patient the best, must make timely decisions, or will assume care once the patient leaves the hospital.[13] Furthermore, it has implications for engaging patients more meaningfully in their care.[14, 15, 16, 17] Ideally, the process of identifying an individual caring for the patient in a specific role is quickly and reliably determined from the electronic health record, the single source of truth where any provider can quickly identify other team members. This source of truth can be updated manually when individual members assign and remove themselves from the care team, or automatically when accessing the patient's record, writing a note, placing an order, or adding a patient to a coverage list. When providers correctly identify other team members in this way, hospital paging directories and secure messaging tools that link to the electronic health record become more effective at supporting care team communication.[18]

In general, the process of identifying care teams is difficult,[19] and maintaining role assignments in the electronic health record is equally challenging. Vawdrey et al. previously reported that care team lists are inaccurate and cannot be used to reliably identify other members at any given moment.[18] The inability to identify team members often leads to incorrectly routed pages, e‐mail messages, and phone calls.[20] Consequently, the potential to reliably manage the care team and improve electronic communication remains untapped, rendering team collaboration and care coordination less effective.[18, 21, 22]

In recent years, the trend toward restructuring inpatient teamsgeographical localization, structured communication interventions, teamwork training, and interdisciplinary roundswould seem to diminish the need for electronic care team identification, as those efforts have already made a positive impact with regard to interprofessional communication and collaboration, team satisfaction, and adverse events.[23, 24, 25, 26] Nonetheless, interdisciplinary teamwork, though critically important for patient‐centered communication, does not completely obviate the need for accurate and reliable care team identification.[26] Although care teams are statically located on units, the plan of care is dynamic; it evolves when the patient's status changes, when new information becomes available, and when key longitudinal providers (eg, primary care physician, subspecialty consultant) make recommendations. Thus, information conveyed as a team on rounds quickly becomes out of date, requiring additional forms of communication. Furthermore, due to frequent ad hoc coverage among team members, the identity of providers covering the patient at any given moment is often not clear.[27] This is particularly problematic for nonunit‐based providers who try to communicate with unit‐based care team members. These providers, in particular, have valuable knowledge and insight that can aid the primary team in decision making.[28, 29] However, they typically do not participate in rounds, often waste time identifying responsible providers,[20] and may communicate their recommendations directly with the patient without discussing with the primary team. These factors in part explain why geographic localization has shown limited improvement in shared understanding of the plan of care.[23]

From the perspective of patients and caregivers, identification of the providers entering and leaving their room is also challenging; only 11% to 51% of patients identify their providers correctly.[30] This adds to confusion regarding who is responsible for which aspects of the patient's care and can negatively affect the perception of the quality of care received.[31] Use of whiteboards has been shown to improve the proportion of patients who could identify key providers,[32] but these are not reliably updated and generally cannot accommodate all team members. When face cards are used, patients and caregivers report that they are more likely to identify their providers correctly.[14, 33, 34] However, potential confusion may ensue when another provider assumes care of the patient in the same role. Finally, use of technology to display team members at the bedside is typically a feature that patients like and can improve identification of care team members.[14, 15, 16] Yet, patient engagement technologies are not readily available in the hospital setting,[35] and ideally should be linked to the electronic health record, which again must be reliably updated.[11, 12, 15, 16]

If care team identification is so critical for delivering effective patient‐centered communication, why is maintaining role assignment problematic? At the individual level, reasons include discontinuity of the care team due to changing clinical rotations and intrateam coverage, shift‐based schedules, and lack of awareness and underutilization of functionality. Additionally, clinicians may have different ways to maintain lists of patients. At the institutional level, functionality to enforce role assignment when accessing patient records may be disabled (to avoid perceived burdens on clinical staff or nonclinical personnel who require access for administrative functions). Finally, electronic health record vendors currently have no incentive to adopt functionality that supports more effective care coordination across settings.[22]

However, more than technical solutions and policy changes are required; care team identification in the electronic health record requires a change in institutional culture. Maintaining an accurate relationship to each patient requires work without tangible benefitsthe benefits accrue only when everyone else identifies their role on the teama tragedy of the commons. This can be illustrated by our own experience. We conducted a quality improvement initiative (Table 1) as a part of 2 concurrent research initiatives that serve to promote patient‐centered communication:[12] PCORI (Patient‐Centered Outcomes Research Institute Transitions), the goal of which is to improve care transitions within the Partners' Pioneer Accountable Care Organization; and the PROSPECT (Promoting Respect and Ongoing Safety Through Patient‐Centeredness, Engagement, Communication, and Technology) project, an initiative funded by the Gordon and Betty Moore Foundation to eliminate preventable harms in the acute care environment.[29] Our goal was to electronically manage the care team with a high degree of fidelity. We enhanced a home‐grown application, which was developed to improve management of team lists for inpatient providers, accessible from our electronic health record, to facilitate role assignment. Specifically, we leveraged existing care processes (eg, nursing log‐on to the electronic medication administration system) to automatically assign certain providers to the care team at change of shift, added functionality to make it easy to assign a provider to all patients on a list for a defined period of time, and encouraged providers to assign their role by demonstrating benefits including quick access to patient‐specific group e‐mail and secure messaging tools (Table 1, Key Facets). The initiative was well‐received by most disciplines, but uptake was suboptimal. Our research assistants routinely assigned residents and others to the care team because our proactive attempts at advertising and reinforcing use of the application failed to reach a critical mass. Most did not see immediate benefits because it was an added step to their busy day, had other methods of managing team lists, and only saw benefit if everyone else participated. Key facets of our care team identification initiative, successes, and challenges are outlined in Table 1.

Table 1.Key Facets of Electronic Health Record Care Team Identification Initiative, Successes, and Challenges
Key Facets	Successes	Challenges
Linked electronic role assignment to administrative processes and clinical workflows	Leveraged existing processes to identify attending provider by routinely reviewing online schedules Linked role assignment to electronic medication administration system sign‐in process for nurses at the start of their shift	Difficult to generate buy‐in from administrators and specific clinician groups to incorporate routine use of role assignment functionality into existing and/or new workflows No institutional policy mandating role assignments for members of extended care team
Incorporated default functionality to specify length of role assignment (eg, stop date)	Used by trainees (residents, fellows) to automate team list role assignments for a prespecified period of time according to online schedules	Underutilized by subspecialty consultants, many of who were unaware or did not fully appreciate the added value of this functionality Research assistants regularly verified that default role assignments were accurately maintained for trainees
Linked role assignment to patient‐specific group e‐mail and messaging tools	Clinicians acknowledged clear efficiency benefits (eg, automated patient identification within messages, correct routing of e‐mails) Used by specific members of the care team tasked with facilitating coordination of care (eg, nurse practitioner trained as discharge advocate for research study)	Difficult to promote use of patient‐specific messaging, particularly for nonunit‐based providers (eg, consultants, primary care physicians) Required access to an application not typically used for clinical messaging Difficult to change culture of network e‐mail use for clinical messaging
Advertised new functionality and demonstrated potential efficiencies for care team communication	Unit‐based clinicians (hospitalists, nurses, housestaff) typically understood benefits when demonstrated and were easier to engage	Some nonunit‐based clinicians (eg, consulting attendings, primary care physicians) did not see benefits and/or were difficult to engage
	Some nonunit‐based provider groups (eg, social workers, nutritionists, subspecialty fellows) considered the initiative worthwhile, and were open to learning about new functionality to improve communication	Clinicians had several options for managing team lists prior to implementation of new electronic health record
Institutional effort toward implementing new electronic health record detracted from efforts at demonstrating enhanced functionality of existing applications

There were a few glimmers of hope, however. On several PROSPECT units, we displayed team members on a tablet‐based patient portal so that patients would recognize their providers.[11, 17, 36] Similar to recent work by O'Leary et al.,[14] patients on PROSPECT units were able to correctly identify several care team members, but regularly asked why other providers (eg, consulting fellow) were not listed. Those providers asked the same question, and some eventually learned to assign their role via the application. As part of PROSEPCT, we visited other institutions and learned of an effort to display team members on high‐definition televisions in the patient's room. Several providers, wondering why they were not listed, learned to assign their role and their picture then appeared. Social pressure was the driving force.

Coincidently, we recently implemented a new electronic health record at our institution. Anecdotally, although no formal policy was established, many providers (eg, attendings, first responders, nurses, care coordinators, and other unit‐based providers) appear to be assigning their roles. Other providers (eg, dieticians, physical therapists, residents) also assign their role, but often fail to end role assignments upon completing their rotation or when the patient transfers to another service. Finally, even when actively involved, most subspecialists still do not designate their role. Despite these gaps and inconsistencies, we have made progress toward improving care team identification. The reasons for this progress are straightforward; during required training for the new electronic health record, all inpatient providers were taught to assign their role on the treatment team when assuming care of patients and now have 1 option for managing team lists. However, most providers were not trained to end their role assignments, and many have learned that role assignment is not required to access the patient's record; functionality to enforce this was disabled. Based on lessons learned from our experience,[12] we offer several strategies that hospitalists can employ to improve care team identification in the electronic health record (Table 2).

Table 2.Goals and Strategies Hospitalists Can Employ to Improve Care Team Identification in the Electronic Health Record
Goal	Strategies to Achieve Goal
Identify and/or establish reliable processes that administrative staff can use to ensure accurate care team role assignments	Identify databases that serve as the source of truth for provider schedules and routinely access those databases
	Access resident scheduling application (eg, Amion) that is routinely updated by training program staff
Work with clinical and administrative staff to maintain care team role assignments
Engage affiliated ambulatory practices to ensure patient's primary care physician is updated in the electronic health record
Engage admissions office to improve reliability of attending assignments based on online clinical schedules when patients are admitted
Integrate role assignment into established workflows for specific provider groups when administrative processes not feasible	Link routine care processes to care team role assignment
	Train nurses, interns, physician assistants to assign role on care team when assuming care of patient at shift change
Train residents, fellows to use default functionality to automatically assign their role on care team at the beginning of a clinical rotation
Demonstrate value of maintaining role assignments in the electronic health record to the unit‐based care team	Emphasize how accurate and reliable care team role assignment can facilitate correct routing of information (eg, test results, discharge summaries)
	Helps to maintain patient coverage lists (eg, fellows, consultants, social workers)
	Facilitates patient‐specific communication (eg, via group email and messaging tools linked to the electronic health record's care team functionality)
Align with concurrent institutional initiatives that enforce or incentivize care team role assignment	Mandate role assignment when writing a note, placing an order, or adding a patient to a coverage list in the electronic health record
	Provide patients and caregivers the ability to identify the care team via patient portalcreates social pressure for those providers who do not identify themselves on the care team
Incentivize providers to maintain role assignments during patient's hospitalization in order to receive notifications if patients are readmitted
Automate role assignments for all members of the care team whenever possible	Work with clinical informatics/emnformation system staff to determine feasibility of linking online scheduling systems or log‐in process to other systems routinely accessed by specific providers to automatically assign/unassign specific providers at the beginning/end of a shift (eg, nurses automatically assigned to care team when they access the electronic medication administration record system at beginning of shift)
	Explore availability of default functionality to assign and unassign providers to and from the care team in a specific role by team, service, or unit‐based patient lists
Require a stop time/date for role assignments or set a default if none entered

In the future, care team identification in the electronic health record can be automated by integrating directly with electronic workflows, online scheduling applications, and provider directories. Hospitals could then leverage care team lists to facilitate patient‐centered communication via secure web‐based and mobile messaging applications configured to simultaneously update all team members (eg, group messaging apps, microblogs).[11, 37, 38] By synchronizing with the electronic health record, role assignments can be automatically updated via these applications, further increasing fidelity of care team identification.[12] Finally, as hospitals implement acute care patient portals, team lists can be leveraged to display all care team members correctly so that patients and caregivers can communicate more easily with providers.[17] The potential ramifications for patient‐centered communication are tremendous.

Disclosures

This work was funded by the Patient‐Centered Outcomes Research Institute and the Gordon and Betty Moore Foundation (GBMF3914). The authors report no conflicts of interest.

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References

Brown SJ. Patient‐centered communication. Annu Rev Nurs Res. 1999;17:85–104.
Arora NK, Street RL, Epstein RM, Butow PN. Facilitating patient‐centered cancer communication: a road map. Patient Educ Couns. 2009;77:319–321.
Epstein R, Street RJ. Patient‐Centered Communication in Cancer Care: Promoting Healing and Reducing Suffering. NIH Publication No. 07–6225. Bethesda, MD: National Cancer Institute; 2007.
Coiera E. When conversation is better than computation. J Am Med Inform Assoc. 2000;7:277–286.
Coiera E. Communication systems in healthcare. Clin Biochem Rev. 2006;27:89–98.
Leape LL, Brennan TA, Laird N, et al. The nature of adverse events in hospitalized patients. results of the Harvard Medical Practice Study II. N Engl J Med. 1991;324:377–384.
Donchin Y, Gopher D, Olin M, et al. A look into the nature and causes of human errors in the intensive care unit. Crit Care Med. 1995;23:294–300.
Sutcliffe KM, Lewton E, Rosenthal MM. Communication failures: an insidious contributor to medical mishaps. Acad Med. 2004;79:186–194.
Alvarez G, Coiera E. Interdisciplinary communication: an uncharted source of medical error? J Crit Care. 2006;21:236–242; discussion 242.
Agarwal R, Sands DZ, Schneider JD. Quantifying the economic impact of communication inefficiencies in U.S. hospitals. J Healthc Manag. 2010;55:265–281; discussion 281–282.
Dalal A, Dykes P, Schnipper J, Bates D. Transforming the acute care environment: a web‐based patient‐centered toolkit [abstract]. J Hosp Med. 2014;9(suppl 2):694.
Dalal AK, Gershanik E, Magny‐Normilus C, et al. Creating a culture of patient‐centered care team communication at a large academic medical center [Abstract]. J Hosp Med. 2015;10 (suppl 2). Available at: http://www.shmabstracts.com/abstract/creating‐a‐culture‐of‐patient‐centered‐care‐team‐communication‐at‐a‐large‐academic‐medical‐center. Accessed April 24, 2015.
McKnight L, Stetson PD, Bakken S, Curran C, Cimino JJ. Perceived information needs and communication difficulties of inpatient physicians and nurses. Proc AMIA Symp. 2001:453–457.
O'Leary KJ, Lohman ME, Culver E, Killarney A, Randy Smith G, Liebovitz DM. The effect of tablet computers with a mobile patient portal application on hospitalized patients' knowledge and activation [published online June 15, 2015]. J Am Med Inform Assoc. doi: 10.1093/jamia/ocv058.
Caligtan CA, Carroll DL, Hurley AC, Gersh‐Zaremski R, Dykes PC. Bedside information technology to support patient‐centered care. Int J Med Inform. 2012;81(7):442–451.
Dykes PC, Carroll DL, Hurley AC, et al. Building and testing a patient‐centric electronic bedside communication center. J Gerontol Nurs. 2013;39:15–19.
Dalal AK, Dykes PC, Collins S, et al. A web‐based, patient‐centered toolkit to engage patients and caregivers in the acute care setting: a preliminary evaluation [published online August 2, 2015]. J Am Med Inform Assoc. doi: 10.1093/jamia/ocv093.
Vawdrey D, Wilcox L, Collins S, et al. Awareness of the care team in electronic health records. Appl Clin Inform. 2011;2:395–405.
O'Leary KJ, Ritter CD, Wheeler H, Szekendi MK, Brinton TS, Williams MV. Teamwork on inpatient medical units: assessing attitudes and barriers. Qual Saf Health Care. 2010;19:117–121.
Wong BM, Quan S, Cheung CM, et al. Frequency and clinical importance of pages sent to the wrong physician. Arch Intern Med. 2009;169:1072–1073.
O'Malley AS, Cunningham PJ. Patient experiences with coordination of care: the benefit of continuity and primary care physician as referral source. J Gen Intern Med. 2009;24:170–177.
O'Malley AS, Grossman JM, Cohen GR, Kemper NM, Pham HH. Are electronic medical records helpful for care coordination? Experiences of physician practices. J Gen Intern Med. 2010;25:177–185.
O'Leary KJ, Wayne DB, Landler MP, et al. Impact of localizing physicians to hospital units on nurse‐physician communication and agreement on the plan of care. J Gen Intern Med. 2009;24:1223–1227.
O'Leary KJ, Haviley C, Slade ME, Shah HM, Lee J, Williams MV. Improving teamwork: impact of structured interdisciplinary rounds on a hospitalist unit. J Hosp Med. 2011;6:88–93.
O'Leary KJ, Buck R, Fligiel HM, et al. Structured interdisciplinary rounds in a medical teaching unit: improving patient safety. Arch Intern Med. 2011;171:678–684.
O'Leary KJ, Sehgal NL, Terrell G, Williams MV; High Performance Teams and the Hospital of the Future Project Team. Interdisciplinary teamwork in hospitals: a review and practical recommendations for improvement. J Hosp Med. 2012;7:48–54.
Martin K, Frank M, Fletcher KE. Intrateam coverage is common, intrateam handoffs are not. J Hosp Med. 2014;9:734–736.
Tang N. A primary care physician's ideal transitions of care? Where's the evidence? J Hosp Med. 2013;8:472–477.
Lindquist LA, Yamahiro A, Garrett A, Zei C, Feinglass JM. Primary care physician communication at hospital discharge reduces medication discrepancies. J Hosp Med. 2013;8:672–677.
Arora VM, Press VG. Let's “face” it: time to introduce yourself to patients. J Hosp Med. 2014;9:199–200.
Beaudin CL, Lammers JC, Pedroja AT. Patient perceptions of coordinated care: the importance of organized communication in hospitals. J Healthc Qual. 1999;21:18–23.
Tan M, Hooper Evans K, Braddock CH, Shieh L. Patient whiteboards to improve patient‐centred care in the hospital. Postgrad Med J. 2013;89:604–609.
Unaka NI, White CM, Sucharew HJ, Yau C, Clark SL, Brady PW. Effect of a face sheet tool on medical team provider identification and family satisfaction. J Hosp Med. 2014;9:186–188.
Simons Y, Caprio T, Furiasse N, Kriss M, Williams MV, O'Leary KJ. The impact of facecards on patients' knowledge, satisfaction, trust, and agreement with hospital physicians: a pilot study. J Hosp Med. 2014;9:137–141.
Prey JE, Woollen J, Wilcox L, et al. Patient engagement in the inpatient setting: a systematic review. J Am Med Inform Assoc. 2014;21:742–750.
PROSPECT: Promoting Respect and Ongoing Safety Through Patient‐centeredness, Engagement, Communication, and Technology. Available at: http://www.partners.org/cird/PROSPECT/Index.htm. Accessed May 3, 2015.
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Perspectives in Hospital Medicine

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Journal of Hospital Medicine - 11(5)

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381-385

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Jeffrey L. Schnipper, MD, MPH

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Jeffrey L. Schnipper, MD, MPH

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Table 1.Key Facets of Electronic Health Record Care Team Identification Initiative, Successes, and Challenges
Key Facets	Successes	Challenges
Linked electronic role assignment to administrative processes and clinical workflows	Leveraged existing processes to identify attending provider by routinely reviewing online schedules Linked role assignment to electronic medication administration system sign‐in process for nurses at the start of their shift	Difficult to generate buy‐in from administrators and specific clinician groups to incorporate routine use of role assignment functionality into existing and/or new workflows No institutional policy mandating role assignments for members of extended care team
Incorporated default functionality to specify length of role assignment (eg, stop date)	Used by trainees (residents, fellows) to automate team list role assignments for a prespecified period of time according to online schedules	Underutilized by subspecialty consultants, many of who were unaware or did not fully appreciate the added value of this functionality Research assistants regularly verified that default role assignments were accurately maintained for trainees
Linked role assignment to patient‐specific group e‐mail and messaging tools	Clinicians acknowledged clear efficiency benefits (eg, automated patient identification within messages, correct routing of e‐mails) Used by specific members of the care team tasked with facilitating coordination of care (eg, nurse practitioner trained as discharge advocate for research study)	Difficult to promote use of patient‐specific messaging, particularly for nonunit‐based providers (eg, consultants, primary care physicians) Required access to an application not typically used for clinical messaging Difficult to change culture of network e‐mail use for clinical messaging
Advertised new functionality and demonstrated potential efficiencies for care team communication	Unit‐based clinicians (hospitalists, nurses, housestaff) typically understood benefits when demonstrated and were easier to engage	Some nonunit‐based clinicians (eg, consulting attendings, primary care physicians) did not see benefits and/or were difficult to engage
	Some nonunit‐based provider groups (eg, social workers, nutritionists, subspecialty fellows) considered the initiative worthwhile, and were open to learning about new functionality to improve communication	Clinicians had several options for managing team lists prior to implementation of new electronic health record
Institutional effort toward implementing new electronic health record detracted from efforts at demonstrating enhanced functionality of existing applications

Table 2.Goals and Strategies Hospitalists Can Employ to Improve Care Team Identification in the Electronic Health Record
Goal	Strategies to Achieve Goal
Identify and/or establish reliable processes that administrative staff can use to ensure accurate care team role assignments	Identify databases that serve as the source of truth for provider schedules and routinely access those databases
	Access resident scheduling application (eg, Amion) that is routinely updated by training program staff
Work with clinical and administrative staff to maintain care team role assignments
Engage affiliated ambulatory practices to ensure patient's primary care physician is updated in the electronic health record
Engage admissions office to improve reliability of attending assignments based on online clinical schedules when patients are admitted
Integrate role assignment into established workflows for specific provider groups when administrative processes not feasible	Link routine care processes to care team role assignment
	Train nurses, interns, physician assistants to assign role on care team when assuming care of patient at shift change
Train residents, fellows to use default functionality to automatically assign their role on care team at the beginning of a clinical rotation
Demonstrate value of maintaining role assignments in the electronic health record to the unit‐based care team	Emphasize how accurate and reliable care team role assignment can facilitate correct routing of information (eg, test results, discharge summaries)
	Helps to maintain patient coverage lists (eg, fellows, consultants, social workers)
	Facilitates patient‐specific communication (eg, via group email and messaging tools linked to the electronic health record's care team functionality)
Align with concurrent institutional initiatives that enforce or incentivize care team role assignment	Mandate role assignment when writing a note, placing an order, or adding a patient to a coverage list in the electronic health record
	Provide patients and caregivers the ability to identify the care team via patient portalcreates social pressure for those providers who do not identify themselves on the care team
Incentivize providers to maintain role assignments during patient's hospitalization in order to receive notifications if patients are readmitted
Automate role assignments for all members of the care team whenever possible	Work with clinical informatics/emnformation system staff to determine feasibility of linking online scheduling systems or log‐in process to other systems routinely accessed by specific providers to automatically assign/unassign specific providers at the beginning/end of a shift (eg, nurses automatically assigned to care team when they access the electronic medication administration record system at beginning of shift)
	Explore availability of default functionality to assign and unassign providers to and from the care team in a specific role by team, service, or unit‐based patient lists
Require a stop time/date for role assignments or set a default if none entered

Disclosures

This work was funded by the Patient‐Centered Outcomes Research Institute and the Gordon and Betty Moore Foundation (GBMF3914). The authors report no conflicts of interest.

Table 1.Key Facets of Electronic Health Record Care Team Identification Initiative, Successes, and Challenges
Key Facets	Successes	Challenges
Linked electronic role assignment to administrative processes and clinical workflows	Leveraged existing processes to identify attending provider by routinely reviewing online schedules Linked role assignment to electronic medication administration system sign‐in process for nurses at the start of their shift	Difficult to generate buy‐in from administrators and specific clinician groups to incorporate routine use of role assignment functionality into existing and/or new workflows No institutional policy mandating role assignments for members of extended care team
Incorporated default functionality to specify length of role assignment (eg, stop date)	Used by trainees (residents, fellows) to automate team list role assignments for a prespecified period of time according to online schedules	Underutilized by subspecialty consultants, many of who were unaware or did not fully appreciate the added value of this functionality Research assistants regularly verified that default role assignments were accurately maintained for trainees
Linked role assignment to patient‐specific group e‐mail and messaging tools	Clinicians acknowledged clear efficiency benefits (eg, automated patient identification within messages, correct routing of e‐mails) Used by specific members of the care team tasked with facilitating coordination of care (eg, nurse practitioner trained as discharge advocate for research study)	Difficult to promote use of patient‐specific messaging, particularly for nonunit‐based providers (eg, consultants, primary care physicians) Required access to an application not typically used for clinical messaging Difficult to change culture of network e‐mail use for clinical messaging
Advertised new functionality and demonstrated potential efficiencies for care team communication	Unit‐based clinicians (hospitalists, nurses, housestaff) typically understood benefits when demonstrated and were easier to engage	Some nonunit‐based clinicians (eg, consulting attendings, primary care physicians) did not see benefits and/or were difficult to engage
	Some nonunit‐based provider groups (eg, social workers, nutritionists, subspecialty fellows) considered the initiative worthwhile, and were open to learning about new functionality to improve communication	Clinicians had several options for managing team lists prior to implementation of new electronic health record
Institutional effort toward implementing new electronic health record detracted from efforts at demonstrating enhanced functionality of existing applications

Table 2.Goals and Strategies Hospitalists Can Employ to Improve Care Team Identification in the Electronic Health Record
Goal	Strategies to Achieve Goal
Identify and/or establish reliable processes that administrative staff can use to ensure accurate care team role assignments	Identify databases that serve as the source of truth for provider schedules and routinely access those databases
	Access resident scheduling application (eg, Amion) that is routinely updated by training program staff
Work with clinical and administrative staff to maintain care team role assignments
Engage affiliated ambulatory practices to ensure patient's primary care physician is updated in the electronic health record
Engage admissions office to improve reliability of attending assignments based on online clinical schedules when patients are admitted
Integrate role assignment into established workflows for specific provider groups when administrative processes not feasible	Link routine care processes to care team role assignment
	Train nurses, interns, physician assistants to assign role on care team when assuming care of patient at shift change
Train residents, fellows to use default functionality to automatically assign their role on care team at the beginning of a clinical rotation
Demonstrate value of maintaining role assignments in the electronic health record to the unit‐based care team	Emphasize how accurate and reliable care team role assignment can facilitate correct routing of information (eg, test results, discharge summaries)
	Helps to maintain patient coverage lists (eg, fellows, consultants, social workers)
	Facilitates patient‐specific communication (eg, via group email and messaging tools linked to the electronic health record's care team functionality)
Align with concurrent institutional initiatives that enforce or incentivize care team role assignment	Mandate role assignment when writing a note, placing an order, or adding a patient to a coverage list in the electronic health record
	Provide patients and caregivers the ability to identify the care team via patient portalcreates social pressure for those providers who do not identify themselves on the care team
Incentivize providers to maintain role assignments during patient's hospitalization in order to receive notifications if patients are readmitted
Automate role assignments for all members of the care team whenever possible	Work with clinical informatics/emnformation system staff to determine feasibility of linking online scheduling systems or log‐in process to other systems routinely accessed by specific providers to automatically assign/unassign specific providers at the beginning/end of a shift (eg, nurses automatically assigned to care team when they access the electronic medication administration record system at beginning of shift)
	Explore availability of default functionality to assign and unassign providers to and from the care team in a specific role by team, service, or unit‐based patient lists
Require a stop time/date for role assignments or set a default if none entered

Disclosures

This work was funded by the Patient‐Centered Outcomes Research Institute and the Gordon and Betty Moore Foundation (GBMF3914). The authors report no conflicts of interest.

References

Brown SJ. Patient‐centered communication. Annu Rev Nurs Res. 1999;17:85–104.
Arora NK, Street RL, Epstein RM, Butow PN. Facilitating patient‐centered cancer communication: a road map. Patient Educ Couns. 2009;77:319–321.
Epstein R, Street RJ. Patient‐Centered Communication in Cancer Care: Promoting Healing and Reducing Suffering. NIH Publication No. 07–6225. Bethesda, MD: National Cancer Institute; 2007.
Coiera E. When conversation is better than computation. J Am Med Inform Assoc. 2000;7:277–286.
Coiera E. Communication systems in healthcare. Clin Biochem Rev. 2006;27:89–98.
Leape LL, Brennan TA, Laird N, et al. The nature of adverse events in hospitalized patients. results of the Harvard Medical Practice Study II. N Engl J Med. 1991;324:377–384.
Donchin Y, Gopher D, Olin M, et al. A look into the nature and causes of human errors in the intensive care unit. Crit Care Med. 1995;23:294–300.
Sutcliffe KM, Lewton E, Rosenthal MM. Communication failures: an insidious contributor to medical mishaps. Acad Med. 2004;79:186–194.
Alvarez G, Coiera E. Interdisciplinary communication: an uncharted source of medical error? J Crit Care. 2006;21:236–242; discussion 242.
Agarwal R, Sands DZ, Schneider JD. Quantifying the economic impact of communication inefficiencies in U.S. hospitals. J Healthc Manag. 2010;55:265–281; discussion 281–282.
Dalal A, Dykes P, Schnipper J, Bates D. Transforming the acute care environment: a web‐based patient‐centered toolkit [abstract]. J Hosp Med. 2014;9(suppl 2):694.
Dalal AK, Gershanik E, Magny‐Normilus C, et al. Creating a culture of patient‐centered care team communication at a large academic medical center [Abstract]. J Hosp Med. 2015;10 (suppl 2). Available at: http://www.shmabstracts.com/abstract/creating‐a‐culture‐of‐patient‐centered‐care‐team‐communication‐at‐a‐large‐academic‐medical‐center. Accessed April 24, 2015.
McKnight L, Stetson PD, Bakken S, Curran C, Cimino JJ. Perceived information needs and communication difficulties of inpatient physicians and nurses. Proc AMIA Symp. 2001:453–457.
O'Leary KJ, Lohman ME, Culver E, Killarney A, Randy Smith G, Liebovitz DM. The effect of tablet computers with a mobile patient portal application on hospitalized patients' knowledge and activation [published online June 15, 2015]. J Am Med Inform Assoc. doi: 10.1093/jamia/ocv058.
Caligtan CA, Carroll DL, Hurley AC, Gersh‐Zaremski R, Dykes PC. Bedside information technology to support patient‐centered care. Int J Med Inform. 2012;81(7):442–451.
Dykes PC, Carroll DL, Hurley AC, et al. Building and testing a patient‐centric electronic bedside communication center. J Gerontol Nurs. 2013;39:15–19.
Dalal AK, Dykes PC, Collins S, et al. A web‐based, patient‐centered toolkit to engage patients and caregivers in the acute care setting: a preliminary evaluation [published online August 2, 2015]. J Am Med Inform Assoc. doi: 10.1093/jamia/ocv093.
Vawdrey D, Wilcox L, Collins S, et al. Awareness of the care team in electronic health records. Appl Clin Inform. 2011;2:395–405.
O'Leary KJ, Ritter CD, Wheeler H, Szekendi MK, Brinton TS, Williams MV. Teamwork on inpatient medical units: assessing attitudes and barriers. Qual Saf Health Care. 2010;19:117–121.
Wong BM, Quan S, Cheung CM, et al. Frequency and clinical importance of pages sent to the wrong physician. Arch Intern Med. 2009;169:1072–1073.
O'Malley AS, Cunningham PJ. Patient experiences with coordination of care: the benefit of continuity and primary care physician as referral source. J Gen Intern Med. 2009;24:170–177.
O'Malley AS, Grossman JM, Cohen GR, Kemper NM, Pham HH. Are electronic medical records helpful for care coordination? Experiences of physician practices. J Gen Intern Med. 2010;25:177–185.
O'Leary KJ, Wayne DB, Landler MP, et al. Impact of localizing physicians to hospital units on nurse‐physician communication and agreement on the plan of care. J Gen Intern Med. 2009;24:1223–1227.
O'Leary KJ, Haviley C, Slade ME, Shah HM, Lee J, Williams MV. Improving teamwork: impact of structured interdisciplinary rounds on a hospitalist unit. J Hosp Med. 2011;6:88–93.
O'Leary KJ, Buck R, Fligiel HM, et al. Structured interdisciplinary rounds in a medical teaching unit: improving patient safety. Arch Intern Med. 2011;171:678–684.
O'Leary KJ, Sehgal NL, Terrell G, Williams MV; High Performance Teams and the Hospital of the Future Project Team. Interdisciplinary teamwork in hospitals: a review and practical recommendations for improvement. J Hosp Med. 2012;7:48–54.
Martin K, Frank M, Fletcher KE. Intrateam coverage is common, intrateam handoffs are not. J Hosp Med. 2014;9:734–736.
Tang N. A primary care physician's ideal transitions of care? Where's the evidence? J Hosp Med. 2013;8:472–477.
Lindquist LA, Yamahiro A, Garrett A, Zei C, Feinglass JM. Primary care physician communication at hospital discharge reduces medication discrepancies. J Hosp Med. 2013;8:672–677.
Arora VM, Press VG. Let's “face” it: time to introduce yourself to patients. J Hosp Med. 2014;9:199–200.
Beaudin CL, Lammers JC, Pedroja AT. Patient perceptions of coordinated care: the importance of organized communication in hospitals. J Healthc Qual. 1999;21:18–23.
Tan M, Hooper Evans K, Braddock CH, Shieh L. Patient whiteboards to improve patient‐centred care in the hospital. Postgrad Med J. 2013;89:604–609.
Unaka NI, White CM, Sucharew HJ, Yau C, Clark SL, Brady PW. Effect of a face sheet tool on medical team provider identification and family satisfaction. J Hosp Med. 2014;9:186–188.
Simons Y, Caprio T, Furiasse N, Kriss M, Williams MV, O'Leary KJ. The impact of facecards on patients' knowledge, satisfaction, trust, and agreement with hospital physicians: a pilot study. J Hosp Med. 2014;9:137–141.
Prey JE, Woollen J, Wilcox L, et al. Patient engagement in the inpatient setting: a systematic review. J Am Med Inform Assoc. 2014;21:742–750.
PROSPECT: Promoting Respect and Ongoing Safety Through Patient‐centeredness, Engagement, Communication, and Technology. Available at: http://www.partners.org/cird/PROSPECT/Index.htm. Accessed May 3, 2015.
Przybylo JA, Wang A, Loftus P, Evans KH, Chu I, Shieh L. Smarter hospital communication: secure smartphone text messaging improves provider satisfaction and perception of efficacy, workflow. J Hosp Med. 2014;9:573–578.
Dalal A, Dykes P, McNally K, et al. Engaging patients, providers, and institutional stakeholders in developing a patient‐centered microblog. Paper presented at: Proceeding of the American Medical Informatics Association Annual Fall Symposium; November 16–19, 2014; Washington, DC.

References

Brown SJ. Patient‐centered communication. Annu Rev Nurs Res. 1999;17:85–104.
Arora NK, Street RL, Epstein RM, Butow PN. Facilitating patient‐centered cancer communication: a road map. Patient Educ Couns. 2009;77:319–321.
Epstein R, Street RJ. Patient‐Centered Communication in Cancer Care: Promoting Healing and Reducing Suffering. NIH Publication No. 07–6225. Bethesda, MD: National Cancer Institute; 2007.
Coiera E. When conversation is better than computation. J Am Med Inform Assoc. 2000;7:277–286.
Coiera E. Communication systems in healthcare. Clin Biochem Rev. 2006;27:89–98.
Leape LL, Brennan TA, Laird N, et al. The nature of adverse events in hospitalized patients. results of the Harvard Medical Practice Study II. N Engl J Med. 1991;324:377–384.
Donchin Y, Gopher D, Olin M, et al. A look into the nature and causes of human errors in the intensive care unit. Crit Care Med. 1995;23:294–300.
Sutcliffe KM, Lewton E, Rosenthal MM. Communication failures: an insidious contributor to medical mishaps. Acad Med. 2004;79:186–194.
Alvarez G, Coiera E. Interdisciplinary communication: an uncharted source of medical error? J Crit Care. 2006;21:236–242; discussion 242.
Agarwal R, Sands DZ, Schneider JD. Quantifying the economic impact of communication inefficiencies in U.S. hospitals. J Healthc Manag. 2010;55:265–281; discussion 281–282.
Dalal A, Dykes P, Schnipper J, Bates D. Transforming the acute care environment: a web‐based patient‐centered toolkit [abstract]. J Hosp Med. 2014;9(suppl 2):694.
Dalal AK, Gershanik E, Magny‐Normilus C, et al. Creating a culture of patient‐centered care team communication at a large academic medical center [Abstract]. J Hosp Med. 2015;10 (suppl 2). Available at: http://www.shmabstracts.com/abstract/creating‐a‐culture‐of‐patient‐centered‐care‐team‐communication‐at‐a‐large‐academic‐medical‐center. Accessed April 24, 2015.
McKnight L, Stetson PD, Bakken S, Curran C, Cimino JJ. Perceived information needs and communication difficulties of inpatient physicians and nurses. Proc AMIA Symp. 2001:453–457.
O'Leary KJ, Lohman ME, Culver E, Killarney A, Randy Smith G, Liebovitz DM. The effect of tablet computers with a mobile patient portal application on hospitalized patients' knowledge and activation [published online June 15, 2015]. J Am Med Inform Assoc. doi: 10.1093/jamia/ocv058.
Caligtan CA, Carroll DL, Hurley AC, Gersh‐Zaremski R, Dykes PC. Bedside information technology to support patient‐centered care. Int J Med Inform. 2012;81(7):442–451.
Dykes PC, Carroll DL, Hurley AC, et al. Building and testing a patient‐centric electronic bedside communication center. J Gerontol Nurs. 2013;39:15–19.
Dalal AK, Dykes PC, Collins S, et al. A web‐based, patient‐centered toolkit to engage patients and caregivers in the acute care setting: a preliminary evaluation [published online August 2, 2015]. J Am Med Inform Assoc. doi: 10.1093/jamia/ocv093.
Vawdrey D, Wilcox L, Collins S, et al. Awareness of the care team in electronic health records. Appl Clin Inform. 2011;2:395–405.
O'Leary KJ, Ritter CD, Wheeler H, Szekendi MK, Brinton TS, Williams MV. Teamwork on inpatient medical units: assessing attitudes and barriers. Qual Saf Health Care. 2010;19:117–121.
Wong BM, Quan S, Cheung CM, et al. Frequency and clinical importance of pages sent to the wrong physician. Arch Intern Med. 2009;169:1072–1073.
O'Malley AS, Cunningham PJ. Patient experiences with coordination of care: the benefit of continuity and primary care physician as referral source. J Gen Intern Med. 2009;24:170–177.
O'Malley AS, Grossman JM, Cohen GR, Kemper NM, Pham HH. Are electronic medical records helpful for care coordination? Experiences of physician practices. J Gen Intern Med. 2010;25:177–185.
O'Leary KJ, Wayne DB, Landler MP, et al. Impact of localizing physicians to hospital units on nurse‐physician communication and agreement on the plan of care. J Gen Intern Med. 2009;24:1223–1227.
O'Leary KJ, Haviley C, Slade ME, Shah HM, Lee J, Williams MV. Improving teamwork: impact of structured interdisciplinary rounds on a hospitalist unit. J Hosp Med. 2011;6:88–93.
O'Leary KJ, Buck R, Fligiel HM, et al. Structured interdisciplinary rounds in a medical teaching unit: improving patient safety. Arch Intern Med. 2011;171:678–684.
O'Leary KJ, Sehgal NL, Terrell G, Williams MV; High Performance Teams and the Hospital of the Future Project Team. Interdisciplinary teamwork in hospitals: a review and practical recommendations for improvement. J Hosp Med. 2012;7:48–54.
Martin K, Frank M, Fletcher KE. Intrateam coverage is common, intrateam handoffs are not. J Hosp Med. 2014;9:734–736.
Tang N. A primary care physician's ideal transitions of care? Where's the evidence? J Hosp Med. 2013;8:472–477.
Lindquist LA, Yamahiro A, Garrett A, Zei C, Feinglass JM. Primary care physician communication at hospital discharge reduces medication discrepancies. J Hosp Med. 2013;8:672–677.
Arora VM, Press VG. Let's “face” it: time to introduce yourself to patients. J Hosp Med. 2014;9:199–200.
Beaudin CL, Lammers JC, Pedroja AT. Patient perceptions of coordinated care: the importance of organized communication in hospitals. J Healthc Qual. 1999;21:18–23.
Tan M, Hooper Evans K, Braddock CH, Shieh L. Patient whiteboards to improve patient‐centred care in the hospital. Postgrad Med J. 2013;89:604–609.
Unaka NI, White CM, Sucharew HJ, Yau C, Clark SL, Brady PW. Effect of a face sheet tool on medical team provider identification and family satisfaction. J Hosp Med. 2014;9:186–188.
Simons Y, Caprio T, Furiasse N, Kriss M, Williams MV, O'Leary KJ. The impact of facecards on patients' knowledge, satisfaction, trust, and agreement with hospital physicians: a pilot study. J Hosp Med. 2014;9:137–141.
Prey JE, Woollen J, Wilcox L, et al. Patient engagement in the inpatient setting: a systematic review. J Am Med Inform Assoc. 2014;21:742–750.
PROSPECT: Promoting Respect and Ongoing Safety Through Patient‐centeredness, Engagement, Communication, and Technology. Available at: http://www.partners.org/cird/PROSPECT/Index.htm. Accessed May 3, 2015.
Przybylo JA, Wang A, Loftus P, Evans KH, Chu I, Shieh L. Smarter hospital communication: secure smartphone text messaging improves provider satisfaction and perception of efficacy, workflow. J Hosp Med. 2014;9:573–578.
Dalal A, Dykes P, McNally K, et al. Engaging patients, providers, and institutional stakeholders in developing a patient‐centered microblog. Paper presented at: Proceeding of the American Medical Informatics Association Annual Fall Symposium; November 16–19, 2014; Washington, DC.

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Journal of Hospital Medicine - 11(5)

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Care team identification in the electronic health record: A critical first step for patient‐centered communication

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Care team identification in the electronic health record: A critical first step for patient‐centered communication

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Address for correspondence and reprint requests: Anuj K. Dalal, MD, Assistant Professor, Harvard Medical School, Division of General Internal Medicine, Brigham and Women's Hospital, Brigham Circle, 1620 Tremont Street, Suite BC‐3‐002HH, Boston, MA 02120‐1613; Telephone: 617‐525‐8891; Fax: 617‐732‐7072; E‐mail: [email protected]

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Culture if spikes? Indications and yield of blood cultures in hospitalized medical patients

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Kalpana Gupta, MD, MPH

Judith M. Strymish, MD

Muhammad Dhanani, MD

Stephen M. Brecher, PHD

Anthony C. Breu, MD

Blood cultures are the gold standard test for the diagnosis of bloodstream infections (BSI). Given the high mortality associated with BSI,[1, 2, 3] physicians have a low threshold to obtain blood cultures.[4, 5] Unfortunately, physicians are poor at predicting which hospitalized patients have BSI,[6, 7] and published guidelines do not provide clear indications for the use of blood cultures.[8] As a result, current practice follows a culture if spikes paradigm, whereby inpatient providers often obtain blood cultures in the setting of any fever. This is the most common anticipatory guidance communicated between providers, involving up to 75% of written sign‐out instructions.[9] The result is a low rate of true positive blood cultures (5%10%)[10, 11, 12] with only a slightly lower rate of false positive blood cultures (contaminants).[12, 13, 14] False positive blood cultures often lead to repeat blood cultures, unnecessary antibiotic use, and increased hospital cost and length of stay.[13]

Over the last several years, there has been an increased emphasis on practicing high‐value care by avoiding unnecessary and duplicate testing. In 2012, the American Board of Internal Medicine introduced the Choosing Wisely campaign, with specific initiatives to reduce medical waste and overuse. Given the low yield of blood cultures, guidance on patients in whom blood cultures are most appropriate would be welcome. Studies assessing risk factors for bacteremia have led to the development of multiple stratification systems without overall consensus.[10, 15, 16, 17, 18, 19, 20] Furthermore, much of the current literature on blood culture utilization includes cultures drawn in the emergency department (ED) or intensive care unit setting (ICU).[10, 18, 19, 20] Less is known regarding the rates of positivity and utility for blood cultures drawn on patients hospitalized on an acute care medical ward.

Our study had 3 main objectives: (1) determine the rates of true positive and false positive blood cultures among hospitalized medical patients, (2) determine the ability of physician‐selected indications and patient characteristics to predict BSI, and (3) identify populations in which blood cultures may not be necessary.

PATIENTS AND METHODS

Study Design

We conducted a prospective cohort study of all hospitalized medical patients for whom blood cultures were ordered and received by the microbiology laboratory. This investigation was approved by the Veterans Affairs (VA) Boston Healthcare System internal review board.

Patients and Setting

During a 7‐month period (October 1, 2014April 15, 2015), all blood culture orders were reviewed for indication and result each day (and on Monday for weekend blood cultures) at a large VA teaching hospital (approximately 6200 admissions each year). As part of the electronic medical order, providers selected from among a list of common indications. Options included various clinical signs and diagnoses, and providers could select more than 1 indication. Each blood culture order triggered a phlebotomist to draw 2 separate blood culture sets (each set consisted of 1 aerobic and 1 anaerobic blood culture bottle).

Inclusion criteria included admission to 1 of 5 general medical service teams or 1 of 2 cardiology teams. Given that the study hospital does not have dedicated subspecialty service teams (with the exception of cardiology), all patients with medical diagnoses are cared for on the general medical service.

Predictor and Outcome Variables

Patient characteristics were obtained via chart review. Fever was defined as a single temperature greater than 100.4F within 24 hours prior to a blood culture order. Leukocytosis was defined as a white blood cell count greater than 10,000 within 24 hours of a blood culture order. Patients were considered to have received antibiotics if an order for an antibacterial or antifungal agent was active within 72 hours prior to the blood culture order. Each blood culture order was assigned a working diagnosis that prompted the order. These working diagnoses were identified by chart review as documented under the provider's assessment and plan and were not necessarily the primary diagnosis prompting hospitalization.

Classification of positive blood cultures into true and false positive was determined by consensus among the microbiology and the infectious disease departments after review of clinical and laboratory data, consistent with a previously established practice at the hospital. A true negative culture consisted of any culture that was not a true positive or a false positive. A blood culture order was defined as an electronic entry and included all sets of blood cultures drawn as a result of that order. Consistent with previous literature, a blood culture episode was defined as all blood cultures ordered within a 48‐hour period starting at the time of the first culture.[10] For patients with multiple admissions during the study period, each admission was considered a unique patient.

Statistical Analysis

Rates of true and false positivity of blood cultures were calculated. In addition, positive likelihood ratios (LR+) for true positive blood cultures were calculated using JMP statistical software (SAS Institute, Inc., Cary, NC).

RESULTS

Overall

A total of 576 blood culture orders (467 blood culture episodes) were completed on 363 hospitalized medical patients during the study period. Five hundred forty orders were placed on patients on general medical services and 36 orders on patients on the cardiology services. Four hundred eighty‐seven (85%) orders resulted in 2 sets of cultures being drawn, 87 (15%) resulted in 1 set of cultures, and 2 (0.3%) resulted in 3 sets of cultures. The median time between admission and culture draw was 2 days (range, 072 days), with 57% of cultures drawn during hospital day 0 to 2, 24.5% drawn between hospital day 3 to 7, and 19.4% drawn after hospital day 7. The average age of the patients was 70.4 years, and 94% were men. Additional patient characteristics are shown in Table 1.

Table 1.Clinical Characteristics of the Cohort
Clinical Characteristic	Total, n = 363 (%)	True Positive Blood Cultures, n = 14 (%)	P Value
NOTE: Abbreviations: MRSA, methicillin‐resistant staphylococcus aureus. *Documented in admission note. Includes urinary and central venous catheters. Within 90 days of current hospitalization.
Mean age, y	70.4	73.9	0.4
Male sex	350 (96%)	14 (100%)	1
White race	308 (85%)	11 (79%)	0.7
Location prior to admission
Community	276 (76%)	11 (79%)	1
Hospital	51 (14%)	1 (7%)	0.7
Long‐term care facility	36 (10%)	2 (14%)	0.6
Comorbidities
Diabetes	136 (37%)	5 (36%)	1
Malignancy	100 (28%)	4 (31%)	1
Alcohol abuse	89 (25%)	2 (14%)	0.5
Cirrhosis	31 (9%)	1 (7%)	1
End‐stage renal disease	21 (6%)	1 (7%)	1
Active drug use*	16 (4%)	1 (7%)	0.5
Catheter	93 (26%)	3 (21%)	0.8
Recent hospitalization	145 (40%)	6 (43%)	1
History of MRSA colonization	72 (20%)	5 (36%)	0.16
Cultures drawn in emergency department	69 (19%)	6 (43%)	0.03

The true positive and false positive rates per blood culture order were 3.6% (21/576) and 2.3% (13/576), respectively (Table 2). Similar values were seen per blood cultures episode (3.4% and 2.7%, respectively). The true positive blood culture rates per order and episode were significantly lower than those drawn on emergency room patients during the study period (41/570, 7.2%, P < 0.05).

Table 2.Rates of True Positive, False Positive, and True Negative Blood Cultures
	Total, n (%)	True Positive, n (%)	False Positive, n (%)	True Negative, n (%)
NOTE: *Includes pyelonephritis. Includes abdominal infections and meningitis. Includes non‐neutropenic and nonpostoperative fever. Includes seizure, syncope, delirium, and heart failure.
Per patient	363	14 (3.8)	13 (3.6)	336 (92.6)
Per blood culture episode	467	16 (3.4)	13 (2.7)	438 (93.8)
Per blood culture order	576	21 (3.6)	13 (2.3)	542 (94.1)
Rates per blood culture order
Physician‐selected indication, n = 530
Fever	136 (25.6)	3 (2.2)	3 (2.2)	130 (95.6)
Fever and additional indication(s)	118 (22.2)	5 (4.2)	3 (2.5)	110 (93.2)
Fever and leukocytosis	50 (9.4)	4 (8.0)	3 (6.0)	43 (86.0)
Leukocytosis	50 (9.4)	2 (4.0)	0 (0)	48 (96.0)
Follow‐up previous positive	60 (11.3)	7 (11.7)	0 (0)	53 (88.3)
Working diagnosis, n = 576
Pneumonia	101 (17.5)	0 (0)	4 (3.9)	97 (96.0)
Bacteremia/endocarditis	97 (16.8)	12 (12.3)	1 (1.0)	84 (86.6)
Urinary tract infection*	95 (16.4)	5 (5.3)	2 (2.1)	88 (92.6)
Other infection	46 (8.0)	0 (0)	0 (0)	46 (100)
Skin and soft‐tissue infection	39 (6.8)	1 (2.6)	0 (0)	38 (97.4)
Neutropenic fever	28 (4.9)	0 (0)	0 (0)	28 (100)
Sepsis	27 (4.7)	0 (0)	0 (0)	27 (100)
Fever	18 (3.1)	1 (5.5)	1 (5.5)	16 (88.9)
Bone and join infection	15 (2.6)	1 (6.7)	0 (0)	14 (93.3)
Postoperative fever	9 (1.6)	0 (0)	0 (0)	9 (100)
Noninfectious diagnosis	101 (17.5)	1 (1.0)	5 (5.0)	95 (94.1)
Antibiotic exposure
Yes	354 (61.5)	5 (1.4)	5 (1.4)	344 (97.1)
No	222 (38.6)	16 (7.2)	8 (3.6)	198 (89.1)
Previous documented positive culture via chart review
Yes	155 (26.9)	9 (5.8)	2 (1.3)	144 (92.9)
No	421 (73.1)	12 (2.9)	11 (2.6)	398 (94.5)

Table 3.Likelihood of True Positive and False Positive Blood Cultures Orders
	LR+ (95% CI), True Positive Blood Culture	LR+ (95% CI), False Positive Blood Culture
NOTE: Abbreviations: CI, confidence interval; LR+, likelihood ratio positive.
Physician‐selected indication
Fever	0.6 (0.21.7)	0.9 (0.32.5)
Fever and additional indication(s)	1.1 (0.52.4)	1.0 (0.42.8)
Fever and leukocytosis	2.2 (0.95.6)	2.5 (0.97.1)
Leukocytosis	1.1 (0.34.0)	0.4 (0.05.6)
Follow‐up previous positive	3.4 (1.86.5)	0.3 (0.04.7)
Diagnosis
Pneumonia	0.1 (0.01.9)	1.8 (0.84.1)
Bacteremia/endocarditis	3.7 (2.55.7)	0.5 (0.13.0)
Urinary tract infection	1.5 (0.73.2)	0.9 (0.33.4)
Noninfectious diagnosis	0.3 (0.01.8)	2.3 (1.14.6)
Recent antibiotic exposure
Yes	0.4 (0.20.8)	0.6 (0.31.2)
No	2.1 (1.62.7)	1.6 (1.02.5)
No with fever	2.4 (1.24.9)	0.8 (0.23.6)
No with fever and leukocytosis	5.6 (1.818.2)	0.4 (0.12.6)
Prior positive cultures
Yes	1.6 (1.02.7)	0.6 (0.22.0)

For the true positive cultures, gram‐positive organisms were isolated most frequently (14/21, 67%) with Staphylococcus aureus identified in 2/21 (10%) positive cultures and Enterococcus faecalis identified in 7/21 (33%) positive cultures. Gram‐negative organisms were isolated in 6/21 (29%) cultures, and 1/21 (5%) culture grew 2 organisms (Enterococcus faecalis and Nocardia). The majority of false positive cultures isolated 1 or more species of coagulase‐negative Staphylococcus (11/13, 85%).

Predictors of True Bacteremia

The 4 most common working diagnoses prompting a blood culture order were pneumonia, bacteremia/endocarditis, urinary tract infection, and a noninfectious diagnosis (eg, syncope), with each prompting approximately 17% of the total orders (Table 2). Of these, only a primary diagnosis of bacteremia/endocarditis was predictive of a true positive culture, yielding a rate of 12.3% (LR+ 3.7, 95% confidence interval [CI]: 2.5‐5.7). No other diagnosis was predictive of true positivity. A diagnosis of pneumonia yielded no true positive and 4 false positive blood cultures (3.9%), whereas a noninfectious diagnosis yielded only 1 true positive (1.0%) and 5 false positives (5.0%). The positive likelihood ratios for these 2 diagnoses were 0.1 (95% CI: 0.00‐1.9) and 0.3 (95% CI: 0.04‐1.8), respectively.

Indications were selected for 530 of 576 (92%) blood culture orders (Table 2). The most common indication was fever alone (25.6%), followed by fever with an additional indication (22.2%), follow‐up positive blood cultures (11.3%), fever and leukocytosis (9.4%), and leukocytosis alone (9.4%). Only follow‐up positive blood cultures was predictive of a true positive, with a LR+ of 3.4 (95% CI: 1.8‐6.5).

A total of 14 patients (3.9%) had true positive blood cultures. For these patients, 10/14 (71%) had 1 true positive blood culture, 3/14 (21%) had 2 true positive blood cultures, and 1/14 (7%) had 5 true positive blood cultures. The average number of cultures drawn was 4.9. The clinical characteristic most predictive of a true positive blood culture was the absence of recent antibiotic administration. If the blood culture was ordered on a patient not receiving antibiotics (true positivity rate 7.2%, 16/222), the LR+ was 2.1 (95% CI: 1.6‐2.7). In a patient not receiving antibiotics who was also noted to have fever and leukocytosis (true positivity rate 17.6%, 3/17), the LR+ was 5.6 (95% CI: 1.8‐18.2). Conversely, patients receiving antibiotics were rarely found to have true positive blood cultures (true positivity rate 1.4%, 5/354) with a LR+ of 0.4 (95% CI: 0.2‐0.8).

DISCUSSION

In this prospective study, we determined the diagnostic yield of blood cultures ordered on hospitalized medical patients to be low, with just 3.6% of orders identifying a true BSI. This was coupled with a similar false positive rate of 2.3%. Our study found rates of true positive blood cultures much lower in hospitalized medical patients than in rates previously described when ED and ICU patients were included.[11, 16]

Although ordering blood cultures is a routine clinical behavior when there is concern for an infection, a clinician's ability to subjectively predict who has a BSI only improves the likelihood 2‐fold.[6] Despite the availability of multiple scoring systems to aid the clinicians,[10, 21, 22] our study found that over 50% of cultures were ordered in the setting of fever or leukocytosis, potentially demonstrating a triggered response to an event, rather than a decision based on probabilities. This common clinician instinct to culture if spikes is an ineffective practice if not coupled with additional clinical information. In fact, in 1 retrospective study, there was no association between fever spike and blood culture positivity.[23]

Our study suggests that objective and easily obtainable clinical characteristics may be effective in helping determine the probability of blood cultures revealing a BSI. Although more robust prediction models have value, they often require multiple inputs, limiting their utility to the bedside clinician. Stratifying patients by either antibiotic exposure or working diagnosis may provide the most benefit for the hospitalized medical patient. For those on antibiotics, the yield of true positive blood cultures is so low that they are unlikely to provide clinically useful information. In fact, although nearly two‐thirds of cultures were obtained after antibiotic exposure, only 1 (0.2%) of these patients had a culture that provided additional information regarding a BSI. Bacteremia had already been established for the other 4 patients. These results are similar to a prior study, which concluded that physicians should wait 72 hours from time of preantibiotic cultures before considering additional blood cultures given the lack of additional information provided.[24]

The working diagnosis also drives the probability of a positive blood culture. As has been shown with other studies, blood cultures are unlikely to diagnose a BSI for patients being treated for either cellulitis or pneumonia.[25, 26, 27] In our study, the working diagnosis prompting the most blood cultures was pneumonia, with the false positive rate exceeding the true positive rate, a finding consistent with previous literature. This situation may lead to the addition of unnecessary antibiotics while waiting for a positive culture to be confirmed as a false positive (eg, vancomycin for a preliminary culture showing gram‐positive cocci in clusters).

There are a number of limitations to our study. Physician‐chosen indication may not correlate with the actual clinical picture and/or may not represent the full set of variables involved in the clinical decision to order a blood culture. However, the subjective clinical indication and the objective clinical criteria found in the chart provided similar LRs. Our study did not evaluate the potential harm of not ordering a blood culture. We also did not assess the value of a true negative culture particularly in patients with endovascular infections where additional cultures are often required to document clearance of bacteremia. Lastly, our study applies to patients on a hospitalized medical service and was performed at a VA hospital with a specific population of elderly male patients, which may limit the generalizability of our results.

Despite these limitations, this study benefits from its prospective design, along with the fact that >90% of blood culture orders placed included a corresponding indication. This provides insight into physician clinical reasoning at the time the blood culture was ordered. In addition, our ability to calculate likelihood ratios provides bedside physicians with an easy and powerful way of modifying the probability of BSI prior to ordering blood cultures, aiding them in providing high‐value clinical care while potentially reducing testing overuse.

The acceptability of not obtaining blood cultures may vary by clinical experience and by specialty. Physicians must weigh the low true positive rate against the consequences of missing a BSI. Although not a substitute for clinical judgement, the LRs in this study can provide a framework to aid in clinical decision making. For example, assuming a pretest probability of 3.6% (the rate of true positive for our entire cohort), blood cultures may not be equally as compelling in 2 similar patients with fever. The first is not on antibiotics and also has a leukocytosis. The second is being treated for pneumonia and is already on antibiotics. For the first patient, using a LR+ of 5.6 (for the fever and leukocytosis in the absence of antibiotics) modifies the patient's probability of a true positive blood culture to 17.3%. Blood cultures should be ordered. In contrast, for the second patient, using a LR+ of 0.4 (for the presence of antibiotics) decreases the patient's probability of a true positive blood culture to 1.5%. Armed with these data, the bedside clinician can now decide whether this rate of true positivity warrants blood cultures. For some, this rate will be comfortably low. For others, this rate will not assuage them; only the negative culture will. Our data are not meant to make this decision, but may aid in making it a probability‐based decision.

Disclosures

Presented in part at the Infectious Diseases Society of America Annual Meeting in San Diego, California in 2015. This material is the result of work supported in part with resources and the use of facilities at the VA Boston HCS, West Roxbury, MA. Katherine Linsenmeyer, MD, 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.

Files

References

Laupland KB, Church DL. Population‐based epidemiology and microbiology of community‐onset bloodstream infections. Clin Microbiol Rev. 2014;27(4):647–664.
Weinstein MP, Towns ML, Quartey SM, et al. The clinical significance of positive blood cultures in the 1990s: a prospective comprehensive evaluation of the microbiology, epidemiology, and outcome of bacteremia and fungemia in adults. Clin Infect Dis. 1997;24(4):584–602.
Weinstein MP, Murphy JR, Reller LB, Lichtenstein KA. The clinical significance of positive blood cultures: a comprehensive analysis of 500 episodes of bacteremia and fungemia in adults. II. Clinical observations, with special reference to factors influencing prognosis. Rev Infect Dis. 1983;5(1):54–70.
Kumar A, Roberts D, Wood KE, et al. Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Crit Care Med. 2006;34(6):1589–1596.
Sands KE, Bates DW, Lanken PN, et al. Epidemiology of sepsis syndrome in 8 academic medical centers. JAMA. 1997;278(3):234–240.
Pfitzenmeyer P, Decrey H, Auckenthaler R, Michel JP. Predicting bacteremia in older patients. J Am Geriatr Soc. 1995;43(3):230–235.
Makadon HJ, Bor D, Friedland G, Dasse P, Komaroff AL, Aronson MD. Febrile inpatients: house officers' use of blood cultures. J Gen Intern Med. 1987;2(5):293–297.
Baron EJ, Miller JM, Weinstein MP, et al. Executive summary: a guide to utilization of the microbiology laboratory for diagnosis of infectious diseases: 2013 recommendations by the Infectious Diseases Society of America (IDSA) and the American Society for Microbiology (ASM)(a). Clin Infect Dis. 2013;57(4):485–488.
Horwitz LI, Moin T, Krumholz HM, Wang L, Bradley EH. What are covering doctors told about their patients? Analysis of sign‐out among internal medicine house staff. Qual Saf Health Care. 2009;18(4):248–255.
Bates DW, Cook EF, Goldman L, Lee TH. Predicting bacteremia in hospitalized patients. A prospectively validated model. Ann Intern Med. 1990;113(7):495–500.
Aronson MD, Bor DH. Blood cultures. Ann Intern Med. 1987;106(2):246–253.
Roth A, Wiklund AE, Palsson AS, et al. Reducing blood culture contamination by a simple informational intervention. J Clin Microbiol. 2010;48(12):4552–4558.
Bates DW, Goldman L, Lee TH. Contaminant blood cultures and resource utilization. The true consequences of false‐positive results. JAMA. 1991;265(3):365–369.
Dawson S. Blood culture contaminants. J Hosp Infect. 2014;87(1):1–10.
Rangel‐Frausto MS, Pittet D, Costigan M, Hwang T, Davis CS, Wenzel RP. The natural history of the systemic inflammatory response syndrome (SIRS). A prospective study. JAMA. 1995;273(2):117–123.
Bates DW, Sands K, Miller E, et al. Predicting bacteremia in patients with sepsis syndrome. Academic Medical Center Consortium Sepsis Project Working Group. J Infect Dis. 1997;176(6):1538–1551.
Jones GR, Lowes JA. The systemic inflammatory response syndrome as a predictor of bacteraemia and outcome from sepsis. QJM. 1996;89(7):515–522.
Shapiro NI, Wolfe RE, Wright SB, Moore R, Bates DW. Who needs a blood culture? A prospectively derived and validated prediction rule. J Emerg Med. 2008;35(3):255–264.
Wildi K, Tschudin‐Sutter S, Dell‐Kuster S, Frei R, Bucher HC, Nuesch R. Factors associated with positive blood cultures in outpatients with suspected bacteremia. Eur J Clin Microbiol Infect Dis. 2011;30(12):1615–1619.
Yehezkelli Y, Subah S, Elhanan G, et al. Two rules for early prediction of bacteremia: testing in a university and a community hospital. J Gen Intern Med. 1996;11(2):98–103.
Coburn B, Morris AM, Tomlinson G, Detsky AS. Does this adult patient with suspected bacteremia require blood cultures? JAMA. 2012;308(5):502–511.
Nakamura T, Takahashi O, Matsui K, et al. Clinical prediction rules for bacteremia and in‐hospital death based on clinical data at the time of blood withdrawal for culture: an evaluation of their development and use. J Eval Clin Pract. 2006;12(6):692–703.
Riedel S, Bourbeau P, Swartz B, et al. Timing of specimen collection for blood cultures from febrile patients with bacteremia. J Clin Microbiol. 2008;46(4):1381–1385.
Grace CJ, Lieberman J, Pierce K, Littenberg B. Usefulness of blood culture for hospitalized patients who are receiving antibiotic therapy. Clin Infect Dis. 2001;32(11):1651–1655.
Chalasani NP, Valdecanas MA, Gopal AK, McGowan JE, Jurado RL. Clinical utility of blood cultures in adult patients with community‐acquired pneumonia without defined underlying risks. Chest. 1995;108(4):932–936.
Luna CM. Blood cultures in community‐acquired pneumonia: are we ready to quit? Chest. 2003;123(4):977–978.
Craven DE. Blood cultures for community‐acquired pneumonia: piecing together a mosaic for doing less. Am J Respir Crit Care Med. 2004;169(3):327–328.

Article PDF

Issue

Journal of Hospital Medicine - 11(5)

Page Number

336-340

Read more about Yield of Blood Cultures

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Original Research

Author(s)

Kalpana Gupta, MD, MPH

Judith M. Strymish, MD

Muhammad Dhanani, MD

Stephen M. Brecher, PHD

Anthony C. Breu, MD

Author(s)

Kalpana Gupta, MD, MPH

Judith M. Strymish, MD

Muhammad Dhanani, MD

Stephen M. Brecher, PHD

Anthony C. Breu, MD

Files

Files

Article PDF

Article PDF

PATIENTS AND METHODS

Study Design

Patients and Setting

Predictor and Outcome Variables

Statistical Analysis

RESULTS

Overall

Table 1.Clinical Characteristics of the Cohort
Clinical Characteristic	Total, n = 363 (%)	True Positive Blood Cultures, n = 14 (%)	P Value
NOTE: Abbreviations: MRSA, methicillin‐resistant staphylococcus aureus. *Documented in admission note. Includes urinary and central venous catheters. Within 90 days of current hospitalization.
Mean age, y	70.4	73.9	0.4
Male sex	350 (96%)	14 (100%)	1
White race	308 (85%)	11 (79%)	0.7
Location prior to admission
Community	276 (76%)	11 (79%)	1
Hospital	51 (14%)	1 (7%)	0.7
Long‐term care facility	36 (10%)	2 (14%)	0.6
Comorbidities
Diabetes	136 (37%)	5 (36%)	1
Malignancy	100 (28%)	4 (31%)	1
Alcohol abuse	89 (25%)	2 (14%)	0.5
Cirrhosis	31 (9%)	1 (7%)	1
End‐stage renal disease	21 (6%)	1 (7%)	1
Active drug use*	16 (4%)	1 (7%)	0.5
Catheter	93 (26%)	3 (21%)	0.8
Recent hospitalization	145 (40%)	6 (43%)	1
History of MRSA colonization	72 (20%)	5 (36%)	0.16
Cultures drawn in emergency department	69 (19%)	6 (43%)	0.03

Table 2.Rates of True Positive, False Positive, and True Negative Blood Cultures
	Total, n (%)	True Positive, n (%)	False Positive, n (%)	True Negative, n (%)
NOTE: *Includes pyelonephritis. Includes abdominal infections and meningitis. Includes non‐neutropenic and nonpostoperative fever. Includes seizure, syncope, delirium, and heart failure.
Per patient	363	14 (3.8)	13 (3.6)	336 (92.6)
Per blood culture episode	467	16 (3.4)	13 (2.7)	438 (93.8)
Per blood culture order	576	21 (3.6)	13 (2.3)	542 (94.1)
Rates per blood culture order
Physician‐selected indication, n = 530
Fever	136 (25.6)	3 (2.2)	3 (2.2)	130 (95.6)
Fever and additional indication(s)	118 (22.2)	5 (4.2)	3 (2.5)	110 (93.2)
Fever and leukocytosis	50 (9.4)	4 (8.0)	3 (6.0)	43 (86.0)
Leukocytosis	50 (9.4)	2 (4.0)	0 (0)	48 (96.0)
Follow‐up previous positive	60 (11.3)	7 (11.7)	0 (0)	53 (88.3)
Working diagnosis, n = 576
Pneumonia	101 (17.5)	0 (0)	4 (3.9)	97 (96.0)
Bacteremia/endocarditis	97 (16.8)	12 (12.3)	1 (1.0)	84 (86.6)
Urinary tract infection*	95 (16.4)	5 (5.3)	2 (2.1)	88 (92.6)
Other infection	46 (8.0)	0 (0)	0 (0)	46 (100)
Skin and soft‐tissue infection	39 (6.8)	1 (2.6)	0 (0)	38 (97.4)
Neutropenic fever	28 (4.9)	0 (0)	0 (0)	28 (100)
Sepsis	27 (4.7)	0 (0)	0 (0)	27 (100)
Fever	18 (3.1)	1 (5.5)	1 (5.5)	16 (88.9)
Bone and join infection	15 (2.6)	1 (6.7)	0 (0)	14 (93.3)
Postoperative fever	9 (1.6)	0 (0)	0 (0)	9 (100)
Noninfectious diagnosis	101 (17.5)	1 (1.0)	5 (5.0)	95 (94.1)
Antibiotic exposure
Yes	354 (61.5)	5 (1.4)	5 (1.4)	344 (97.1)
No	222 (38.6)	16 (7.2)	8 (3.6)	198 (89.1)
Previous documented positive culture via chart review
Yes	155 (26.9)	9 (5.8)	2 (1.3)	144 (92.9)
No	421 (73.1)	12 (2.9)	11 (2.6)	398 (94.5)

Table 3.Likelihood of True Positive and False Positive Blood Cultures Orders
	LR+ (95% CI), True Positive Blood Culture	LR+ (95% CI), False Positive Blood Culture
NOTE: Abbreviations: CI, confidence interval; LR+, likelihood ratio positive.
Physician‐selected indication
Fever	0.6 (0.21.7)	0.9 (0.32.5)
Fever and additional indication(s)	1.1 (0.52.4)	1.0 (0.42.8)
Fever and leukocytosis	2.2 (0.95.6)	2.5 (0.97.1)
Leukocytosis	1.1 (0.34.0)	0.4 (0.05.6)
Follow‐up previous positive	3.4 (1.86.5)	0.3 (0.04.7)
Diagnosis
Pneumonia	0.1 (0.01.9)	1.8 (0.84.1)
Bacteremia/endocarditis	3.7 (2.55.7)	0.5 (0.13.0)
Urinary tract infection	1.5 (0.73.2)	0.9 (0.33.4)
Noninfectious diagnosis	0.3 (0.01.8)	2.3 (1.14.6)
Recent antibiotic exposure
Yes	0.4 (0.20.8)	0.6 (0.31.2)
No	2.1 (1.62.7)	1.6 (1.02.5)
No with fever	2.4 (1.24.9)	0.8 (0.23.6)
No with fever and leukocytosis	5.6 (1.818.2)	0.4 (0.12.6)
Prior positive cultures
Yes	1.6 (1.02.7)	0.6 (0.22.0)

Predictors of True Bacteremia

DISCUSSION

Disclosures

PATIENTS AND METHODS

Study Design

Patients and Setting

Predictor and Outcome Variables

Statistical Analysis

RESULTS

Overall

Table 1.Clinical Characteristics of the Cohort
Clinical Characteristic	Total, n = 363 (%)	True Positive Blood Cultures, n = 14 (%)	P Value
NOTE: Abbreviations: MRSA, methicillin‐resistant staphylococcus aureus. *Documented in admission note. Includes urinary and central venous catheters. Within 90 days of current hospitalization.
Mean age, y	70.4	73.9	0.4
Male sex	350 (96%)	14 (100%)	1
White race	308 (85%)	11 (79%)	0.7
Location prior to admission
Community	276 (76%)	11 (79%)	1
Hospital	51 (14%)	1 (7%)	0.7
Long‐term care facility	36 (10%)	2 (14%)	0.6
Comorbidities
Diabetes	136 (37%)	5 (36%)	1
Malignancy	100 (28%)	4 (31%)	1
Alcohol abuse	89 (25%)	2 (14%)	0.5
Cirrhosis	31 (9%)	1 (7%)	1
End‐stage renal disease	21 (6%)	1 (7%)	1
Active drug use*	16 (4%)	1 (7%)	0.5
Catheter	93 (26%)	3 (21%)	0.8
Recent hospitalization	145 (40%)	6 (43%)	1
History of MRSA colonization	72 (20%)	5 (36%)	0.16
Cultures drawn in emergency department	69 (19%)	6 (43%)	0.03

Table 2.Rates of True Positive, False Positive, and True Negative Blood Cultures
	Total, n (%)	True Positive, n (%)	False Positive, n (%)	True Negative, n (%)
NOTE: *Includes pyelonephritis. Includes abdominal infections and meningitis. Includes non‐neutropenic and nonpostoperative fever. Includes seizure, syncope, delirium, and heart failure.
Per patient	363	14 (3.8)	13 (3.6)	336 (92.6)
Per blood culture episode	467	16 (3.4)	13 (2.7)	438 (93.8)
Per blood culture order	576	21 (3.6)	13 (2.3)	542 (94.1)
Rates per blood culture order
Physician‐selected indication, n = 530
Fever	136 (25.6)	3 (2.2)	3 (2.2)	130 (95.6)
Fever and additional indication(s)	118 (22.2)	5 (4.2)	3 (2.5)	110 (93.2)
Fever and leukocytosis	50 (9.4)	4 (8.0)	3 (6.0)	43 (86.0)
Leukocytosis	50 (9.4)	2 (4.0)	0 (0)	48 (96.0)
Follow‐up previous positive	60 (11.3)	7 (11.7)	0 (0)	53 (88.3)
Working diagnosis, n = 576
Pneumonia	101 (17.5)	0 (0)	4 (3.9)	97 (96.0)
Bacteremia/endocarditis	97 (16.8)	12 (12.3)	1 (1.0)	84 (86.6)
Urinary tract infection*	95 (16.4)	5 (5.3)	2 (2.1)	88 (92.6)
Other infection	46 (8.0)	0 (0)	0 (0)	46 (100)
Skin and soft‐tissue infection	39 (6.8)	1 (2.6)	0 (0)	38 (97.4)
Neutropenic fever	28 (4.9)	0 (0)	0 (0)	28 (100)
Sepsis	27 (4.7)	0 (0)	0 (0)	27 (100)
Fever	18 (3.1)	1 (5.5)	1 (5.5)	16 (88.9)
Bone and join infection	15 (2.6)	1 (6.7)	0 (0)	14 (93.3)
Postoperative fever	9 (1.6)	0 (0)	0 (0)	9 (100)
Noninfectious diagnosis	101 (17.5)	1 (1.0)	5 (5.0)	95 (94.1)
Antibiotic exposure
Yes	354 (61.5)	5 (1.4)	5 (1.4)	344 (97.1)
No	222 (38.6)	16 (7.2)	8 (3.6)	198 (89.1)
Previous documented positive culture via chart review
Yes	155 (26.9)	9 (5.8)	2 (1.3)	144 (92.9)
No	421 (73.1)	12 (2.9)	11 (2.6)	398 (94.5)

Table 3.Likelihood of True Positive and False Positive Blood Cultures Orders
	LR+ (95% CI), True Positive Blood Culture	LR+ (95% CI), False Positive Blood Culture
NOTE: Abbreviations: CI, confidence interval; LR+, likelihood ratio positive.
Physician‐selected indication
Fever	0.6 (0.21.7)	0.9 (0.32.5)
Fever and additional indication(s)	1.1 (0.52.4)	1.0 (0.42.8)
Fever and leukocytosis	2.2 (0.95.6)	2.5 (0.97.1)
Leukocytosis	1.1 (0.34.0)	0.4 (0.05.6)
Follow‐up previous positive	3.4 (1.86.5)	0.3 (0.04.7)
Diagnosis
Pneumonia	0.1 (0.01.9)	1.8 (0.84.1)
Bacteremia/endocarditis	3.7 (2.55.7)	0.5 (0.13.0)
Urinary tract infection	1.5 (0.73.2)	0.9 (0.33.4)
Noninfectious diagnosis	0.3 (0.01.8)	2.3 (1.14.6)
Recent antibiotic exposure
Yes	0.4 (0.20.8)	0.6 (0.31.2)
No	2.1 (1.62.7)	1.6 (1.02.5)
No with fever	2.4 (1.24.9)	0.8 (0.23.6)
No with fever and leukocytosis	5.6 (1.818.2)	0.4 (0.12.6)
Prior positive cultures
Yes	1.6 (1.02.7)	0.6 (0.22.0)

Predictors of True Bacteremia

DISCUSSION

Disclosures

References

Laupland KB, Church DL. Population‐based epidemiology and microbiology of community‐onset bloodstream infections. Clin Microbiol Rev. 2014;27(4):647–664.
Weinstein MP, Towns ML, Quartey SM, et al. The clinical significance of positive blood cultures in the 1990s: a prospective comprehensive evaluation of the microbiology, epidemiology, and outcome of bacteremia and fungemia in adults. Clin Infect Dis. 1997;24(4):584–602.
Weinstein MP, Murphy JR, Reller LB, Lichtenstein KA. The clinical significance of positive blood cultures: a comprehensive analysis of 500 episodes of bacteremia and fungemia in adults. II. Clinical observations, with special reference to factors influencing prognosis. Rev Infect Dis. 1983;5(1):54–70.
Kumar A, Roberts D, Wood KE, et al. Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Crit Care Med. 2006;34(6):1589–1596.
Sands KE, Bates DW, Lanken PN, et al. Epidemiology of sepsis syndrome in 8 academic medical centers. JAMA. 1997;278(3):234–240.
Pfitzenmeyer P, Decrey H, Auckenthaler R, Michel JP. Predicting bacteremia in older patients. J Am Geriatr Soc. 1995;43(3):230–235.
Makadon HJ, Bor D, Friedland G, Dasse P, Komaroff AL, Aronson MD. Febrile inpatients: house officers' use of blood cultures. J Gen Intern Med. 1987;2(5):293–297.
Baron EJ, Miller JM, Weinstein MP, et al. Executive summary: a guide to utilization of the microbiology laboratory for diagnosis of infectious diseases: 2013 recommendations by the Infectious Diseases Society of America (IDSA) and the American Society for Microbiology (ASM)(a). Clin Infect Dis. 2013;57(4):485–488.
Horwitz LI, Moin T, Krumholz HM, Wang L, Bradley EH. What are covering doctors told about their patients? Analysis of sign‐out among internal medicine house staff. Qual Saf Health Care. 2009;18(4):248–255.
Bates DW, Cook EF, Goldman L, Lee TH. Predicting bacteremia in hospitalized patients. A prospectively validated model. Ann Intern Med. 1990;113(7):495–500.
Aronson MD, Bor DH. Blood cultures. Ann Intern Med. 1987;106(2):246–253.
Roth A, Wiklund AE, Palsson AS, et al. Reducing blood culture contamination by a simple informational intervention. J Clin Microbiol. 2010;48(12):4552–4558.
Bates DW, Goldman L, Lee TH. Contaminant blood cultures and resource utilization. The true consequences of false‐positive results. JAMA. 1991;265(3):365–369.
Dawson S. Blood culture contaminants. J Hosp Infect. 2014;87(1):1–10.
Rangel‐Frausto MS, Pittet D, Costigan M, Hwang T, Davis CS, Wenzel RP. The natural history of the systemic inflammatory response syndrome (SIRS). A prospective study. JAMA. 1995;273(2):117–123.
Bates DW, Sands K, Miller E, et al. Predicting bacteremia in patients with sepsis syndrome. Academic Medical Center Consortium Sepsis Project Working Group. J Infect Dis. 1997;176(6):1538–1551.
Jones GR, Lowes JA. The systemic inflammatory response syndrome as a predictor of bacteraemia and outcome from sepsis. QJM. 1996;89(7):515–522.
Shapiro NI, Wolfe RE, Wright SB, Moore R, Bates DW. Who needs a blood culture? A prospectively derived and validated prediction rule. J Emerg Med. 2008;35(3):255–264.
Wildi K, Tschudin‐Sutter S, Dell‐Kuster S, Frei R, Bucher HC, Nuesch R. Factors associated with positive blood cultures in outpatients with suspected bacteremia. Eur J Clin Microbiol Infect Dis. 2011;30(12):1615–1619.
Yehezkelli Y, Subah S, Elhanan G, et al. Two rules for early prediction of bacteremia: testing in a university and a community hospital. J Gen Intern Med. 1996;11(2):98–103.
Coburn B, Morris AM, Tomlinson G, Detsky AS. Does this adult patient with suspected bacteremia require blood cultures? JAMA. 2012;308(5):502–511.
Nakamura T, Takahashi O, Matsui K, et al. Clinical prediction rules for bacteremia and in‐hospital death based on clinical data at the time of blood withdrawal for culture: an evaluation of their development and use. J Eval Clin Pract. 2006;12(6):692–703.
Riedel S, Bourbeau P, Swartz B, et al. Timing of specimen collection for blood cultures from febrile patients with bacteremia. J Clin Microbiol. 2008;46(4):1381–1385.
Grace CJ, Lieberman J, Pierce K, Littenberg B. Usefulness of blood culture for hospitalized patients who are receiving antibiotic therapy. Clin Infect Dis. 2001;32(11):1651–1655.
Chalasani NP, Valdecanas MA, Gopal AK, McGowan JE, Jurado RL. Clinical utility of blood cultures in adult patients with community‐acquired pneumonia without defined underlying risks. Chest. 1995;108(4):932–936.
Luna CM. Blood cultures in community‐acquired pneumonia: are we ready to quit? Chest. 2003;123(4):977–978.
Craven DE. Blood cultures for community‐acquired pneumonia: piecing together a mosaic for doing less. Am J Respir Crit Care Med. 2004;169(3):327–328.

References

Laupland KB, Church DL. Population‐based epidemiology and microbiology of community‐onset bloodstream infections. Clin Microbiol Rev. 2014;27(4):647–664.
Weinstein MP, Towns ML, Quartey SM, et al. The clinical significance of positive blood cultures in the 1990s: a prospective comprehensive evaluation of the microbiology, epidemiology, and outcome of bacteremia and fungemia in adults. Clin Infect Dis. 1997;24(4):584–602.
Weinstein MP, Murphy JR, Reller LB, Lichtenstein KA. The clinical significance of positive blood cultures: a comprehensive analysis of 500 episodes of bacteremia and fungemia in adults. II. Clinical observations, with special reference to factors influencing prognosis. Rev Infect Dis. 1983;5(1):54–70.
Kumar A, Roberts D, Wood KE, et al. Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Crit Care Med. 2006;34(6):1589–1596.
Sands KE, Bates DW, Lanken PN, et al. Epidemiology of sepsis syndrome in 8 academic medical centers. JAMA. 1997;278(3):234–240.
Pfitzenmeyer P, Decrey H, Auckenthaler R, Michel JP. Predicting bacteremia in older patients. J Am Geriatr Soc. 1995;43(3):230–235.
Makadon HJ, Bor D, Friedland G, Dasse P, Komaroff AL, Aronson MD. Febrile inpatients: house officers' use of blood cultures. J Gen Intern Med. 1987;2(5):293–297.
Baron EJ, Miller JM, Weinstein MP, et al. Executive summary: a guide to utilization of the microbiology laboratory for diagnosis of infectious diseases: 2013 recommendations by the Infectious Diseases Society of America (IDSA) and the American Society for Microbiology (ASM)(a). Clin Infect Dis. 2013;57(4):485–488.
Horwitz LI, Moin T, Krumholz HM, Wang L, Bradley EH. What are covering doctors told about their patients? Analysis of sign‐out among internal medicine house staff. Qual Saf Health Care. 2009;18(4):248–255.
Bates DW, Cook EF, Goldman L, Lee TH. Predicting bacteremia in hospitalized patients. A prospectively validated model. Ann Intern Med. 1990;113(7):495–500.
Aronson MD, Bor DH. Blood cultures. Ann Intern Med. 1987;106(2):246–253.
Roth A, Wiklund AE, Palsson AS, et al. Reducing blood culture contamination by a simple informational intervention. J Clin Microbiol. 2010;48(12):4552–4558.
Bates DW, Goldman L, Lee TH. Contaminant blood cultures and resource utilization. The true consequences of false‐positive results. JAMA. 1991;265(3):365–369.
Dawson S. Blood culture contaminants. J Hosp Infect. 2014;87(1):1–10.
Rangel‐Frausto MS, Pittet D, Costigan M, Hwang T, Davis CS, Wenzel RP. The natural history of the systemic inflammatory response syndrome (SIRS). A prospective study. JAMA. 1995;273(2):117–123.
Bates DW, Sands K, Miller E, et al. Predicting bacteremia in patients with sepsis syndrome. Academic Medical Center Consortium Sepsis Project Working Group. J Infect Dis. 1997;176(6):1538–1551.
Jones GR, Lowes JA. The systemic inflammatory response syndrome as a predictor of bacteraemia and outcome from sepsis. QJM. 1996;89(7):515–522.
Shapiro NI, Wolfe RE, Wright SB, Moore R, Bates DW. Who needs a blood culture? A prospectively derived and validated prediction rule. J Emerg Med. 2008;35(3):255–264.
Wildi K, Tschudin‐Sutter S, Dell‐Kuster S, Frei R, Bucher HC, Nuesch R. Factors associated with positive blood cultures in outpatients with suspected bacteremia. Eur J Clin Microbiol Infect Dis. 2011;30(12):1615–1619.
Yehezkelli Y, Subah S, Elhanan G, et al. Two rules for early prediction of bacteremia: testing in a university and a community hospital. J Gen Intern Med. 1996;11(2):98–103.
Coburn B, Morris AM, Tomlinson G, Detsky AS. Does this adult patient with suspected bacteremia require blood cultures? JAMA. 2012;308(5):502–511.
Nakamura T, Takahashi O, Matsui K, et al. Clinical prediction rules for bacteremia and in‐hospital death based on clinical data at the time of blood withdrawal for culture: an evaluation of their development and use. J Eval Clin Pract. 2006;12(6):692–703.
Riedel S, Bourbeau P, Swartz B, et al. Timing of specimen collection for blood cultures from febrile patients with bacteremia. J Clin Microbiol. 2008;46(4):1381–1385.
Grace CJ, Lieberman J, Pierce K, Littenberg B. Usefulness of blood culture for hospitalized patients who are receiving antibiotic therapy. Clin Infect Dis. 2001;32(11):1651–1655.
Chalasani NP, Valdecanas MA, Gopal AK, McGowan JE, Jurado RL. Clinical utility of blood cultures in adult patients with community‐acquired pneumonia without defined underlying risks. Chest. 1995;108(4):932–936.
Luna CM. Blood cultures in community‐acquired pneumonia: are we ready to quit? Chest. 2003;123(4):977–978.
Craven DE. Blood cultures for community‐acquired pneumonia: piecing together a mosaic for doing less. Am J Respir Crit Care Med. 2004;169(3):327–328.

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Address for correspondence and reprint requests: Katherine Linsenmeyer, MD, Boston University School of Medicine, 850 Harrison Ave., Dowling Building 3rd Floor, Boston MA 02118; Telephone: 857‐203‐5121; Fax: 857‐203‐5622; E‐mail: [email protected]

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Joseph S. Eastern, MD

No matter how complete your insurance portfolio, there is one policy – one you probably never heard of – that you should definitely consider adding to it.

A few years ago, I spoke with a dermatologist in California who experienced every employer’s nightmare: He fired an incompetent employee who promptly sued him for wrongful termination and accused him of sexual harassment to boot. The charges were completely false, and the employee’s transgressions were well documented, but defending the lawsuit, successfully or not, would have been expensive. So the dermatologist’s lawyer persuaded him to settle it for a significant sum of money.

Disasters like that are becoming more common. Plaintiffs’ attorneys know all too well that most small businesses, including medical practices, are not insured against employees’ legal actions, and usually cannot afford to defend them in court.

Fortunately, there is a relatively inexpensive way to protect yourself: Employment Practices Liability Insurance (EPLI) provides protection against many kinds of employee lawsuits not covered by conventional liability insurance. These include wrongful termination, sexual harassment, discrimination, breach of employment contract, negligent hiring or evaluation, failure to promote, wrongful discipline, mismanagement of benefits, and the ever-popular “emotional distress.”

EPLI would have covered the California dermatologist, had he carried it, against his employee’s charges. In fact, there is a better-than-even chance that the plaintiff’s attorney would have dropped the lawsuit entirely once informed that it would be aggressively defended.

Some liability carriers are beginning to cover some employee-related issues in “umbrella” policies, so check your current insurance coverage first. Then, as with all insurance, you should shop around for the best price, and carefully read the policies on your short list. All EPLI policies cover claims against your practice and its owners and employees, but some cover claims against full-time employees only. Try to obtain the broadest coverage possible so that part-time, temporary, and seasonal employees, and if possible, even applicants for employment and former employees, also are covered.

Look for the most comprehensive policy in terms of coverage. Almost every EPLI policy covers the allegations mentioned above, but some offer a more comprehensive list of covered acts, such as invasion of privacy and defamation of character.

Also be aware of precisely what each policy does not cover. Most contain exclusions for punitive damages and court-imposed fines, as well as for criminal acts, fraud, and other clearly illegal conduct. For example, if it can be proved that you fired an employee because he or she refused to falsify insurance claims, any resulting civil suit against you will not be covered by any type of insurance.

Depending on where you practice, it may be necessary to ask an employment lawyer to evaluate your individual EPLI needs. An underwriter cannot anticipate every eventuality for you, particularly if he or she does not live in your area and is not familiar with employment conditions in your community.

Try to get a clause added that permits you to choose your own defense attorney. Better still, pick a specific attorney or firm that you trust, and have that counsel named in an endorsement to the policy. Otherwise, the insurance carrier will select an attorney from its own panel who may not consider your interests a higher priority than those of the insurer itself.

If you must accept the insurer’s choice of counsel, you should find out whether that attorney is experienced in employment law, which is a very specialized area. And, just as with your malpractice policy, you will want to maintain as much control as possible over the settlement of claims. Ideally, no claim should be settled without your expressed permission.

As with any insurance policy you buy, be sure to choose an established carrier with ample experience in the field and solid financial strength. A low premium is no bargain if the carrier is new to EPLI, or goes broke, or decides to cease covering employment practices liability.

Above all, make sure that you can live with the claims definition and exclusions in the policy you choose, and seek advice if you are unsure what your specific needs are before you sign on the dotted line.

Dr. Eastern practices dermatology and dermatologic surgery in Belleville, N.J. He is the author of numerous articles and textbook chapters, and is a longtime monthly columnist for Dermatology News. Write to him at [email protected].

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Nocturnists Daniele Olveczky, MD, MS, of Beth Israel Deaconess Medical Center in Boston, and Eric Martin, MD, of the University of Colorado, provide insight and tips for tackling night shifts and avoiding burnout.

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What Is Your Diagnosis? Mycosis Fungoides

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Carol B. Thompson, MD

Lesley L. Starnes, MD

Rebecca Todd Bell, MD

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Cristina Shimek, MD

Robert B. Skinner Jr, MD

The Diagnosis: Mycosis Fungoides

Physical examination revealed erythematous polycyclic and arcuate plaques with fine overlying scale on the right arm and shoulder (Figure 1). Mild wrinkling and telangiectasias were noted on the skin surrounding the lesions. Laboratory tests showed normal values for antinuclear antibodies, anti–Sjögren syndrome–related antigen A, and anti–Sjögren syndrome–related antigen B.

Figure 1. Erythematous polycyclic and targetoid plaques with fine overlying scale on the right arm.

A skin biopsy of a plaque on the right upper arm showed enlarged pleomorphic lymphocytes arranged along the basal layer and in focal collections within the epidermis (Figure 2). Within the dermis were wiry bundles of collagen, a sparse superficial and patchy infiltrate of lymphocytes, and scattered large mononuclear cells (Figure 3). Immunoperoxidase staining revealed large intraepidermal lymphocytes positive for CD4 (Figure 4A) and CD5. Notably, these lymphocytes also stained positive for CD30 (Figure 4B). Staining for CD8, CD1a, CD56, and anaplastic lymphoma kinase was negative, with aberrant loss of CD3. The morphology and pattern of immunoreactivity supported the diagnosis of mycosis fungoides (MF).

Mycosis fungoides is the most common form of cutaneous T-cell lymphoma.¹ Its progression is classified in 3 stages: (1) early (patch) stage, (2) plaque stage, and (3) tumor stage. Conclusive diagnosis of early stage MF often is difficult due to its clinical features that are similar to more common benign dermatoses (eg, atopic dermatitis, psoriasis, lichen planus), leading to shortcomings in determining prognosis and selecting an appropriate treatment regimen. With this diagnositic difficulty in mind, guidelines have been created to aid in the diagnosis of early stage MF.²

Clinical features consistent with early stage MF include multiple erythematous, well-demarcated lesions with varying shapes that typically are greater than 5 cm in diameter.² Lesions usually are flat or thinly elevated and may exhibit slight scaling. As was noted in our patient, poikiloderma of the surrounding skin is fairly specific for early stage MF, as it is not a feature associated with common clinical mimics of MF (eg, atopic dermatitis, psoriasis, lichen planus). The distribution of skin lesions in non–sun-exposed areas is common. The eruption is persistent, though it may wax and wane in severity.²

Figure 2. Enlarged pleomorphic lymphocytes arranged along the basal layer and in focal collections were noted within the epidermis (H&E, original magnification ×200).		Figure 3. Lymphocytes arranged along the basal layer and in focal collections within the epidermis. Wiry bundles of collagen, a sparse superficial and patchy infiltrate of lymphocytes, and scattered large mononuclear cells were noted within the dermis (H&E, original magnification ×20).

Histopathologic examination is necessary to confirm a diagnosis of MF. Typically, early stage MF is marked by enlarged T lymphocytes within the epidermis as well as the papillary and superficial reticular dermis. Cerebriform nuclei are a key finding in the diagnosis of MF. Lymphocytes frequently are arranged linearly along the basal layer of the epidermis. Within the epidermis, clusters of atypical lymphocytes (Pautrier microabscesses) without spongiosis are uncommon but are a characteristic finding of MF if present.¹ Papillary dermal fibrosis also may be evident.²


Figure 4. Large intraepidermal lymphocytes were highlighted on CD4 (A) and CD30 immunostaining (B)(original magnification ×200 and ×200).

Immunostaining typically reveals positivity for CD3 and CD4, as well as for lymphocyte antigens CD2 and CD5.¹ CD30 positivity in early stage MF rarely has been reported in the literature.^3,4 Such cases appear histologically similarly to CD30‒negative cases in other respects. One study showed that the presence of CD30-positive lymphocytes does not alter the clinical course of MF.³ Another study found that, while epidermal CD30-postive lymphocytes had no prognostic relevance, an increased percentage of dermal CD30-positive cells was linked to a higher stage at diagnosis and worse overall prognosis.⁵ Pathogenesis underlying CD30 positivity in early MF is unknown. It is important to note that CD30-positive cells commonly are seen in lymphomatoid papulosis and anaplastic large cell lymphoma, as well as a variety of nonneoplastic conditions.^3,6,7

References

Smoller BR. Mycosis fungoides: what do/do not we know? J Cutan Pathol. 2008;35(suppl 2):35-39.
Pimpinelli N, Olsen EA, Santucci M, et al. Defining early mycosis fungoides. J Am Acad Dermatol. 2005;53:1053-1063.
Wu H, Telang GH, Lessin SR, et al. Mycosis fungoides with CD30-positive cells in the epidermis. Am J Dermatopathol. 2000;22:212-216.
Ohtani T, Kikuchi K, Koizumi H, et al. A case of CD30⁺ large-cell transformation in a patient with unilesional patch-stage mycosis fungoides. Int J Dermatol. 2009;48:623-626.
Edinger JT, Clark BZ, Pucevich BE, et al. CD30 expression and proliferative fraction in nontransformed mycosis fungoides. Am J Surg Pathol. 2009;33:1860-1868.
Resnik KS, Kutzner H. Of lymphocytes and cutaneous epithelium: keratoacanthomatous hyperplasia in CD30⁺ lymphoproliferative disorders and CD30⁺ cells associated with keratoacanthoma. Am J Dermatopathol. 2010;32:314-315.
Kempf W. CD30⁺ lymphoproliferative disorders: histopathology, differential diagnosis, new variants, and simulators. J Cutan Pathol. 2006;33(suppl 1):58-70.

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Dr. Thompson is from the Division of Dermatology, University of Louisville, Kentucky. Dr. Starnes is from The Jackson Clinic, Tennessee. Dr. Bell is from Central Dermatology, Chapel Hill, North Carolina. Drs. Randall and Skinner are from the Department of Dermatology, University of Tennessee Health Science Center, Memphis. Dr. Shimek is from Duckworth Pathology Group, Memphis.

The authors report no conflict of interest.

Correspondence: Lesley L. Starnes, MD, 930 Madison Ave, Ste 840, Memphis, TN 38103 ([email protected]).

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Lesley L. Starnes, MD

Rebecca Todd Bell, MD

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Robert B. Skinner Jr, MD

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The Diagnosis: Mycosis Fungoides

Figure 2. Enlarged pleomorphic lymphocytes arranged along the basal layer and in focal collections were noted within the epidermis (H&E, original magnification ×200).		Figure 3. Lymphocytes arranged along the basal layer and in focal collections within the epidermis. Wiry bundles of collagen, a sparse superficial and patchy infiltrate of lymphocytes, and scattered large mononuclear cells were noted within the dermis (H&E, original magnification ×20).


Figure 4. Large intraepidermal lymphocytes were highlighted on CD4 (A) and CD30 immunostaining (B)(original magnification ×200 and ×200).

The Diagnosis: Mycosis Fungoides

Figure 2. Enlarged pleomorphic lymphocytes arranged along the basal layer and in focal collections were noted within the epidermis (H&E, original magnification ×200).		Figure 3. Lymphocytes arranged along the basal layer and in focal collections within the epidermis. Wiry bundles of collagen, a sparse superficial and patchy infiltrate of lymphocytes, and scattered large mononuclear cells were noted within the dermis (H&E, original magnification ×20).


Figure 4. Large intraepidermal lymphocytes were highlighted on CD4 (A) and CD30 immunostaining (B)(original magnification ×200 and ×200).

References

Smoller BR. Mycosis fungoides: what do/do not we know? J Cutan Pathol. 2008;35(suppl 2):35-39.
Pimpinelli N, Olsen EA, Santucci M, et al. Defining early mycosis fungoides. J Am Acad Dermatol. 2005;53:1053-1063.
Wu H, Telang GH, Lessin SR, et al. Mycosis fungoides with CD30-positive cells in the epidermis. Am J Dermatopathol. 2000;22:212-216.
Ohtani T, Kikuchi K, Koizumi H, et al. A case of CD30⁺ large-cell transformation in a patient with unilesional patch-stage mycosis fungoides. Int J Dermatol. 2009;48:623-626.
Edinger JT, Clark BZ, Pucevich BE, et al. CD30 expression and proliferative fraction in nontransformed mycosis fungoides. Am J Surg Pathol. 2009;33:1860-1868.
Resnik KS, Kutzner H. Of lymphocytes and cutaneous epithelium: keratoacanthomatous hyperplasia in CD30⁺ lymphoproliferative disorders and CD30⁺ cells associated with keratoacanthoma. Am J Dermatopathol. 2010;32:314-315.
Kempf W. CD30⁺ lymphoproliferative disorders: histopathology, differential diagnosis, new variants, and simulators. J Cutan Pathol. 2006;33(suppl 1):58-70.

References

Smoller BR. Mycosis fungoides: what do/do not we know? J Cutan Pathol. 2008;35(suppl 2):35-39.
Pimpinelli N, Olsen EA, Santucci M, et al. Defining early mycosis fungoides. J Am Acad Dermatol. 2005;53:1053-1063.
Wu H, Telang GH, Lessin SR, et al. Mycosis fungoides with CD30-positive cells in the epidermis. Am J Dermatopathol. 2000;22:212-216.
Ohtani T, Kikuchi K, Koizumi H, et al. A case of CD30⁺ large-cell transformation in a patient with unilesional patch-stage mycosis fungoides. Int J Dermatol. 2009;48:623-626.
Edinger JT, Clark BZ, Pucevich BE, et al. CD30 expression and proliferative fraction in nontransformed mycosis fungoides. Am J Surg Pathol. 2009;33:1860-1868.
Resnik KS, Kutzner H. Of lymphocytes and cutaneous epithelium: keratoacanthomatous hyperplasia in CD30⁺ lymphoproliferative disorders and CD30⁺ cells associated with keratoacanthoma. Am J Dermatopathol. 2010;32:314-315.
Kempf W. CD30⁺ lymphoproliferative disorders: histopathology, differential diagnosis, new variants, and simulators. J Cutan Pathol. 2006;33(suppl 1):58-70.

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An otherwise healthy 62-year-old man presented for evaluation of multiple scaly erythematous plaques on the right upper arm and shoulder of 10 years’ duration. The patient reported a burning sensation but no exacerbation of the lesions upon sun exposure. He previously had been treated for a presumed clinical diagnosis of erythema annulare centrifugum but experienced only modest improvement with topical corticosteroids and tacrolimus ointment 0.1%. Previous trials of systemic antifungals also yielded minimal benefit.

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William G. Wilkoff, MD

A few years ago I wrote a column about what promised to be an exciting development in blood testing technology. Using the money her parents had set aside for her education, a young woman dropped out of Stanford University at age 19 and started a company that she claimed would be able to offer hundreds of lab tests on just a few drops of blood. Results would be available in just minutes instead of hours or days. At the time I wrote the column, the company had just landed a contract with a large drug store chain with an arrangement that would eventually allow nearly every resident of the United States to be within a few miles of a site that would offer rapid response blood tests with nothing more than a finger prick.

It seemed a little hard to believe, but the prospect of pediatricians being able to make a diagnosis without running the risk of exsanguinating our smallest patients sounded appealing. On the other hand, I worried that a quick and easy technology might encourage some physicians to use a shotgun approach to diagnosing illness rather than a more rational and cost-effective process based on the traditional skills of history taking and physical examination. Some patients who foolishly wanted to know “everything” about themselves might be tempted to ask their physicians to order the whole smorgasbord of tests. “Hey, it’s only a few drops of blood.”

Turns out there were enough people with more money than reservations and the company quickly attracted hundreds of millions of dollars in venture capital. The company, now calling itself Theranos, has been valued at nine billion dollars. But, recently this startup star has encountered some serious bumps in the road to a full-scale launch (“Hot Startup Theranos Has Struggled With Its Blood-Test Technology” by John Carreyrou, The Wall Street Journal, updated Oct. 16, 2015). The Wall Street Journal reported that despite promises, only a few of the 240 tests offered by the company are currently performed using their proprietary microtechnique. In the days following the Journal article, the Food and Drug Administration warned Theranos that their “nanotainer” is considered a new medical device that must first clear the agency’s time consuming and costly vetting process (“Hot Startup Theranos Dials Back Lab Tests at FDA’s Behest” by John Carreyrou, The Wall Street Journal, updated Oct. 16, 2015).

The venture capitalists who had climbed on the Theranos bandwagon tempted by the just-a-few-drops promise may end up seeing their bank accounts hemorrhage. But I don’t think we should be too critical of their investment decision. It was and may still be good idea that has simply run afoul of the details. However, I recently learned about another new business that I don’t consider to have even started with a good idea, but still has managed to attract enough capital to get itself off the ground (“Should Breast Milk Be Nutritionally Analyzed?” by Laura Johannes, The Wall Street Journal, Dec. 28, 2015).

I’m sure you have seen some new mothers who were concerned that their breast milk was not enough for their babies. But how many of them would pay $150 for a start-up kit and then more than $300 to find out the nutritional content of their breast milk? What if it meant pumping and freezing three samples 2 or 3 days apart and then shipping them in a cooler to a lab? What if you told them that neither you nor anyone else could reliably interpret the results because there aren’t any published guidelines for the optimal composition of human breast milk? Even if your practice is packed to the rafters with anxiety-driven, irrational parents, I don’t think you would find many takers. But that doesn’t seem to have bothered the folks who have invested in Happy Vitals, a company in Washington that is offering a service similar to the one I have just described.

You and I might not have invested in a company whose business plan was to offer such a service. But I fear there may be enough health care “providers” practicing without the benefit of an evidence-based education that what I consider a capital misadventure may actually be able to pay back its investors.

Dr. Wilkoff practiced primary care pediatrics in Brunswick, Maine, for nearly 40 years. He has authored several books on behavioral pediatrics including “How to Say No to Your Toddler.”

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A 19-year-old man was admitted to our hospital to begin treatment for acute myeloid leukemia that had been diagnosed 2 days prior. Three days after completing a 10-day regimen of induction chemotherapy, he developed bilateral, well-demarcated erythematous patches on the palmar surfaces of the proximal phalanges of the third, fourth, and fifth fingers (Figure 1) and 2 patches on the right palm. The patient was referred to dermatology for evaluation. He recalled no trauma to these sites although he reported pushing his intravenous pole with the right hand when walking. Of note, he had become neutropenic and thrombocytopenic following chemotherapy

Figure 1. Bilateral, well-demarcated erythematous patches on the palmar surfaces of the proximal phalanges of the third, fourth, and fifth fingers and on the right palm.

On physical examination, the patches measured 1- to 1.5-cm in diameter and were mildly tender to palpation. The 2 patches on the right palm were much smaller than those on the fingers but were otherwise similar in appearance.

A punch biopsy of the erythematous lesion on the left third digit was performed. Histologic examination revealed extensive epidermal denudation associated with vascular proliferation and congestion as well as hemorrhage and a sparse lymphocytic infiltrate (Figures 2–4). There was no evidence of a leukemic infiltrate, and stains for fungal elements and bacteria were negative. Eccrine ducts appeared normal with no evidence of necrosis or metaplasia. These findings were suggestive of a frictional etiology.

Figure 2. Loss of epidermis with prominent dermal papillae and minimal inflammation (H&E, original magnification ×4).

Figure 3. Loss of epidermis with preservation of dermal papillae. Vascular ectasia and congestion along with a paucicellular inflammatory infiltrate are noted (H&E, original magnification ×20).

Figure 4. Eccrine sweat gland with normal secretory coils and excretory ducts. No inflammation or syringosquamous metaplasia were noted (H&E, original magnification ×20).

Due to the distribution of the skin lesions on the hands, it was suspected that the source of friction was a video game controller. Although the patient denied playing video games since his admission to the hospital, he reported heavy video game use during the weeks prior to admission. We postulated that the thrombocytopenia the patient developed following chemotherapy along with prior friction injury sustained from heavy video game play led to traumatic subcorneal hemorrhage on the hands at the points of contact with the video game controller (Figure 5). The subcorneal hematomas resolved completely over the next 2 months during which the patient abstained from video game play.

Figure 5. Points of contact on the hands are demonstrated when holding a video game controller.

This case demonstrates the importance of obtaining a detailed patient history, as our patient’s history of video game play prior to hospitalization proved to be of major diagnostic importance. Although the location, distribution, and well-demarcated nature of the patient’s lesions suggested an external source of trauma and biopsy definitively ruled out leukemia cutis, Sweet syndrome, and eccrine hidradenitis,¹ the final diagnosis of traumatic subcorneal hematomas was only possible with specific knowledge of the patient’s video game controller use.

Comment

History of video game play has been key to the diagnosis of a variety of cutaneous lesions documented in the medical literature. Robertson et al² attributed a similar case of traumatic subcorneal hematomas of the hands in an otherwise healthy 16-year-old boy to excessive use of a video game controller. Similarly, Kasraee et al³ attributed a case of idiopathic eccrine hidradenitis in an otherwise healthy 12-year-old girl to excessive video game use. In both of these reported cases, bilateral skin lesions on the palms of the hands appeared acutely in a pattern consistent with the points of contact of a video game controller. Excessive video game play has also been associated with unilateral dermatologic lesions on the hands, such as knuckle pads,⁴ onycholysis,⁵ friction blisters,⁶ pressure ulcers,⁷ and hemorrhagic lesions.^5,6,8

Video game–related pathologies are not limited to the skin and have been implicated in a variety of clinical presentations. In 1987, Osterman et al⁹ published an early account of repetitive strain injury (RSI) related to video game use in which the investigators reported 2 cases of video game–related volar flexor tenosynovitis (or trigger finger), which they termed “joystick digit.” Since that time, video game play has greatly evolved along with the types and nature of RSI cases reported in the medical literature. In 1990, Brasington¹⁰ described acute tendinopathy of the extensor pollicis longus tendon caused by excessive video game play, which was termed “Nintendinitis.” This term has since been used in reference to any video game–related RSI and reports have increased over time, likely due to the proliferation of an increasing array of video game systems.^5,11-16 In recent years, a number of traumatic injuries including fractures, joint dislocations, head injuries, hemothorax, and lacerations have been attributed to interactive gaming systems.^6,11,17-20 In rare cases, video game play also has been associated with enuresis,²¹ encopresis,²² and epilepsy.²³

According to a 2011 report from the Entertainment Software Association, women over the age of 18 years now represent a greater proportion of the video game–playing population than boys aged 17 years and younger.²⁴ This same report also noted that the average video game player is 35 years old; 44% of all players are female; and 27% of Americans over the age of 50 years play video games. This shifting demographic data, including the fact that 80% of American households reportedly play video games, reveals the expanding depth and breadth of the market.²⁴ However, the pediatric population is still a high-volume player demographic. Average time per session peaks between 10 to 12 years of age and then falls through the teenage and adults years.²⁴ Hence, the pediatric population is at high risk for clinical pathology because of the increased repetitive movements associated with video game play. Overall, cognizance of the popularity of video games and related pathologies can be an asset for dermatologists who evaluate pediatric patients.

References

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2. Robertson SJ, Leonard J, Chamberlain AJ. PlayStation purpura. Australas J Dermatol. 2010;51:220-222.

3. Kasraee B, Masouyé I, Piguet V. PlayStation palmar hidradenitis. Br J Dermatol. 2009;160:892-894.

4. Rushing ME, Sheehan DJ, Davis LS. Video game induced knuckle pad. Pediatr Dermatol. 2006;23:455-457.

5. Bakos RM, Bakos L. Use of dermoscopy to visualize punctate hemorrhages and onycholysis in “playstation thumb.” Arch Dermatol. 2006;142:1664-1665.

6. Wood DJ. The “How!” sign—a central palmar blister induced by overplaying on a Nintendo console. Arch Dis Child. 2001;84:288.

7. Koh TH. Ulcerative “nintendinitis”: a new kind of repetitive strain injury. Med J Aust. 2000;173:671.

8. Bernabeu-Wittel J, Domínguez-Cruz J, Zulueta T, et al. Hemorrhagic parallel-ridge pattern on dermoscopy in “Playstation fingertip.” J Am Acad Dermatol. 2011;65:238-239.

9. Osterman AL, Weinberg P, Miller G. Joystick digit. JAMA. 1987;257:782.

10. Brasington R. Nintendinitis. N Engl J Med. 1990;322:1473-1474.

11. Sparks DA, Coughlin LM, Chase DM. Did too much Wii cause your patient’s injury? J Fam Pract. 2011;60:404-409.

12. Bright DA, Bringhurst DC. Nintendo elbow. West J Med. 1992;156:667-668.

13. Vaidya HJ. Playstation thumb. Lancet. 2004;363:1080.

14. Bonis J. Acute Wiiitis. N Engl J Med. 2007;356:2431-2432.

15. Boehm KM, Pugh A. A new variant of Wiiitis [published online ahead of print June 13, 2008]. J Emerg Med. 2009;36:80.

16. Beddy P, Dunne R, de Blacam C. Achilles wiiitis. AJR Am J Roentgenol. 2009;192:W79.

17. Eley KA. A Wii fracture. N Engl J Med. 2010;362:473-474.

18. Wells JJ. An 8-year-old girl presented to the ER after accidentally being hit by a Wii remote control swung by her brother. J Trauma. 2008;65:1203.

19. Fysh T, Thompson JF. A Wii problem. J R Soc Med. 2009;102:502.

20. George AJ. Musculo-ske Wii tal medicine. Injury. 2012;43:390-391.

21. Schink JC. Nintendo enuresis. Am J Dis Child. 1991;145:1094.

22. Corkery JC. Nintendo power. Am J Dis Child. 1990;144:959.

23. Hart EJ. Nintendo epilepsy. N Engl J Med. 1990;322:1473.

24. Entertainment Software Association. 2015 sales, demographic, and usage data. essential facts about the computer and video game industry. Entertainment Software Association Web site. http://www.theesa.com/wp-content/uploads/2015/04/ESA-Essential-Facts-2015.pdf. Accessed October 16, 2015.

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Author and Disclosure Information

Drs. M. Lennox, Rizzo, and L. Lennox are from the School of Medicine and Biomedical Sciences, State University of New York at Buffalo. Dr. Rothman is from Women & Children’s Hospital of Buffalo.

The authors report no conflict of interest.

Correspondence: Luke Lennox, MD, 782 Potomac Ave, Buffalo, NY 14209 ([email protected]).

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