Association of Display of Patient Photographs in the Electronic Health Record With Wrong-Patient Order Entry Errors | Electronic Health Records | JAMA Network Open | JAMA Network
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1.
Adelman  JS, Kalkut  GE, Schechter  CB,  et al.  Understanding and preventing wrong-patient electronic orders: a randomized controlled trial.   J Am Med Inform Assoc. 2013;20(2):305-310. doi:10.1136/amiajnl-2012-001055 PubMedGoogle ScholarCrossref
2.
Green  RA, Hripcsak  G, Salmasian  H,  et al.  Intercepting wrong-patient orders in a computerized provider order entry system.   Ann Emerg Med. 2015;65(6):679-686.e1. doi:10.1016/j.annemergmed.2014.11.017 PubMedGoogle ScholarCrossref
3.
Kannampallil  TG, Manning  JD, Chestek  DW,  et al.  Effect of number of open charts on intercepted wrong-patient medication orders in an emergency department.   J Am Med Inform Assoc. 2018;25(6):739-743. doi:10.1093/jamia/ocx099 PubMedGoogle ScholarCrossref
4.
Adelman  JS, Applebaum  JR, Schechter  CB,  et al.  Effect of restriction of the number of concurrently open records in an electronic health record on wrong-patient order errors: a randomized clinical trial.   JAMA. 2019;321(18):1780-1787. doi:10.1001/jama.2019.3698 PubMedGoogle ScholarCrossref
5.
Pham  JC, Story  JL, Hicks  RW,  et al.  National study on the frequency, types, causes, and consequences of voluntarily reported emergency department medication errors.   J Emerg Med. 2011;40(5):485-492. doi:10.1016/j.jemermed.2008.02.059 PubMedGoogle ScholarCrossref
6.
 Oops, sorry, wrong patient! A patient verification process is needed everywhere, not just at the bedside.  Alta RN. 2012;67(6):18-22.
7.
The Joint Commission. The Joint Commission’s 2011 National Patient Safety Goals. Accessed June 30, 2020. https://www.jointcommission.org/standards_information/npsgs.aspx
8.
Salmasian H, Green R, Friedman C, Hripcsak G, Vawdrey DK. Are patients with similar names at greater risk for wrong-patient orders? AMIA Summits Clinical Research Informatics. Accessed June 20, 2020. https://knowledge.amia.org/amia-59309-cri2015-1.2002246/t-005-1.2003490/a-078-1.2003539/a-078-1.2003540/ap-078-1.2003541?qr=1
9.
Wears  RL.  “Just a few seconds of your time…” at least 130 million times a year.   Ann Emerg Med. 2015;65(6):687-689. doi:10.1016/j.annemergmed.2015.02.006 PubMedGoogle ScholarCrossref
10.
Hancock  PJB, Bruce  V, Burton  AM.  Recognition of unfamiliar faces.   Trends Cogn Sci. 2000;4(9):330-337. doi:10.1016/S1364-6613(00)01519-9 PubMedGoogle ScholarCrossref
11.
Hyman  D, Laire  M, Redmond  D, Kaplan  DW.  The use of patient pictures and verification screens to reduce computerized provider order entry errors.   Pediatrics. 2012;130(1):e211-e219. doi:10.1542/peds.2011-2984 PubMedGoogle ScholarCrossref
12.
National Quality Forum. Patient safety 2015. Accessed June 30, 2020. http://www.qualityforum.org/Projects/n-r/Patient_Safety_Measures_2015/Final_Report.aspx
13.
Adelman  J, Aschner  J, Schechter  C,  et al.  Use of temporary names for newborns and associated risks.   Pediatrics. 2015;136(2):327-333. doi:10.1542/peds.2015-0007 PubMedGoogle ScholarCrossref
14.
Adelman  JS, Aschner  JL, Schechter  CB,  et al.  Evaluating serial strategies for preventing wrong-patient orders in the NICU.   Pediatrics. 2017;139(5):e20162863. doi:10.1542/peds.2016-2863 PubMedGoogle Scholar
15.
Lombardi  D, Gaston-Kim  J, Perlstein  D,  et al.  Preventing wrong-patient electronic orders in the emergency department.   Journal of Clinical Outcomes Management. 2016;23(12):550-554. https://www.mdedge.com/jcomjournal/article/146067/emergency-medicine/preventing-wrong-patient-electronic-orders-emergencyGoogle Scholar
16.
R Core Team. R: a language and environment for statistical computing. Accessed June 30, 2020. https://www.r-project.org
17.
von Elm  E, Altman  DG, Egger  M, Pocock  SJ, Gøtzsche  PC, Vandenbroucke  JP; STROBE Initiative.  The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.   Prev Med. 2007;45(4):247-251. doi:10.1016/j.ypmed.2007.08.012 PubMedGoogle ScholarCrossref
18.
Hulley  SB, Cummings  SR, Browner  WS, Grady  DG, Newman  TB.  Designing Clinical Research. 3rd ed. Lippincott Williams & Wilkins; 2006.
19.
Wu  AW, Marks  CM.  Close calls in patient safety: should we be paying closer attention?   CMAJ. 2013;185(13):1119-1120. doi:10.1503/cmaj.130014 PubMedGoogle ScholarCrossref
20.
World Health Organization. WHO draft guidelines for adverse event reporting and learning systems: from information to action. Accessed August 9, 2019. https://apps.who.int/iris/bitstream/handle/10665/69797/WHO-EIP-SPO-QPS-05.3-eng.pdf
21.
Institute for Healthcare Improvement. Create a reporting system. Accessed August 9, 2019. http://www.ihi.org/resources/Pages/Changes/CreateaReportingSystem.aspx
22.
Thomas  JJ, Yaster  M, Guffey  P.  The use of patient digital facial images to confirm patient identity in a children’s hospital’s anesthesia information management system.   Jt Comm J Qual Patient Saf. 2020;46(2):118-121. doi:10.1016/j.jcjq.2019.10.007PubMedGoogle Scholar
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    1 Comment for this article
    EXPAND ALL
    Overstated number of orders?
    David Polaner, MD, FAAP | Seattle Children's Hospital and University of Washington School of Medicine
    One of the usual ways of increasing efficiency and decreasing the likelihood of missing orders in all EHR systems is that orders can be grouped into order sets, in which multiple orders are accumulated into a single purpose specific order set (e.g., admission orders, sepsis work-up orders, etc.). These order sets commonly comprise 10 or more individual orders across multiple categories. While the individual items in the order set might require selection or clarification, the order set itself is called up with a single click.

    In this study the unit of analysis was one order. If the primary analysis
    de-aggregates orders in order sets, it would overestimate the number of WPOE errors- the physician in reality made a single error in pulling up the order set for the wrong patient even though the number of orders in that order set is greater than one. This methodology thereby inflates both the "n" of orders and the number of WPOE orders (assuming all of the orders in the set are tagged as WPOE) in the analysis. Because the number of individual orders in a set varies, the variability of how this affects the data is unknown. It appears that the authors have tried to mitigate this limitation in their secondary analysis by looking at order sessions, but it is still not clear if these sessions comprised individual orders, order sets, or a mixture of the two.
    CONFLICT OF INTEREST: None Reported
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    Original Investigation
    Emergency Medicine
    November 11, 2020

    Association of Display of Patient Photographs in the Electronic Health Record With Wrong-Patient Order Entry Errors

    Author Affiliations
    • 1Department of Quality and Safety, Brigham and Women’s Hospital, Boston, Massachusetts
    • 2Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
    • 3Harvard T.H. Chan School of Public Health, Boston, Massachusetts
    • 4Vanderbilt University Medical Center, Nashville, Tennessee
    • 5Department of Emergency Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
    • 6Department of Emergency Medicine, Rhode Island Hospital, Providence
    • 7Alpert Medical School of Brown University, Providence, Rhode Island
    • 8Department of Quality and Safety, NewYork-Presbyterian Hospital, New York, New York
    JAMA Netw Open. 2020;3(11):e2019652. doi:10.1001/jamanetworkopen.2020.19652
    Key Points

    Question  Can wrong-patient order entry errors be reduced with noninterruptive display of patient photographs?

    Findings  In this cohort study involving 2 558 746 orders that were placed for 71 851 patients, displaying a patient’s photograph in the banner of the electronic health record was associated with a significant reduction in the rate of wrong-patient order entry errors. Unlike prior interventions, this solution required no added practitioner time burden or risk of alert fatigue.

    Meaning  The results of this study suggest that capturing patient photographs and displaying them in the electronic health record may be a simple and cost-effective strategy for reducing wrong-patient errors.

    Abstract

    Importance  Wrong-patient order entry (WPOE) errors have a high potential for harm; these errors are particularly frequent wherever workflows are complex and multitasking and interruptions are common, such as in the emergency department (ED). Previous research shows that interruptive solutions, such as electronic patient verification forms or alerts, can reduce these types of errors but may be time-consuming and cause alert fatigue.

    Objective  To evaluate whether the use of noninterruptive display of patient photographs in the banner of the electronic health record (EHR) is associated with a decreased rate of WPOE errors.

    Design, Setting, and Participants  In this cohort study, data collected as part of care for patients visiting the ED of a large tertiary academic urban hospital in Boston, Massachusetts, between July 1, 2017, and June 31, 2019, were analyzed.

    Exposures  In a quality improvement initiative, the ED staff encouraged patients to have their photographs taken by informing them of the intended safety impact.

    Main Outcomes and Measures  The rate of WPOE errors (measured using the retract-and-reorder method) for orders placed when the patient’s photograph was displayed in the banner of the EHR vs the rate for patients without a photograph displayed. The primary analysis focused on orders placed in the ED; a secondary analysis included orders placed in any care setting.

    Results  A total of 2 558 746 orders were placed for 71 851 unique patients (mean [SD] age, 49.2 [19.1] years; 42 677 (59.4%) female; 55 109 (76.7%) non-Hispanic). The risk of WPOE errors was significantly lower when the patient’s photograph was displayed in the EHR (odds ratio, 0.72; 95% CI, 0.57-0.89). After this risk was adjusted for potential confounders using multivariable logistic regression, the effect size remained essentially the same (odds ratio, 0.57; 95% CI, 0.52-0.61). Risk of error was significantly lower in patients with higher acuity levels and among patients whose race was documented as White.

    Conclusions and Relevance  This cohort study suggests that displaying patient photographs in the EHR provides decision support functionality for enhancing patient identification and reducing WPOE errors while being noninterruptive with minimal risk of alert fatigue. Successful implementation of such a program in an ED setting involves a modest financial investment and requires appropriate engagement of patients and staff.

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