Understanding and preventing wrong-patient electronic orders

A randomized controlled trial

Jason S. Adelman, Gary E. Kalkut, Clyde B. Schechter, Jeffrey M. Weiss, Matthew Alan Berger, Stan H. Reissman, Hillel W. Cohen, Stephen J. Lorenzen, Daniel A. Burack, William N. Southern

Research output: Contribution to journalArticle

48 Citations (Scopus)

Abstract

Objective: To evaluate systems for estimating and preventing wrong-patient electronic orders in computerized physician order entry systems with a two-phase study. Materials and methods: In phase 1, from May to August 2010, the effectiveness of a 'retract-and-reorder' measurement tool was assessed that identified orders placed on a patient, promptly retracted, and then reordered by the same provider on a different patient as a marker for wrong-patient electronic orders. This tool was then used to estimate the frequency of wrong-patient electronic orders in four hospitals in 2009. In phase 2, from December 2010 to June 2011, a three-armed randomized controlled trial was conducted to evaluate the efficacy of two distinct interventions aimed at preventing these errors by reverifying patient identification: an 'ID-verify alert', and an 'ID-reentry function'. Results: The retract-and-reorder measurement tool effectively identified 170 of 223 events as wrong-patient electronic orders, resulting in a positive predictive value of 76.2% (95% CI 70.6% to 81.9%). Using this tool it was estimated that 5246 electronic orders were placed on wrong patients in 2009. In phase 2, 901 776 ordering sessions among 4028 providers were examined. Compared with control, the ID-verify alert reduced the odds of a retract-and-reorder event (OR 0.84, 95% CI 0.72 to 0.98), but the ID-reentry function reduced the odds by a larger magnitude (OR 0.60, 95% CI 0.50 to 0.71). Discussion and conclusion: Wrong-patient electronic orders occur frequently with computerized provider order entry systems, and electronic interventions can reduce the risk of these errors occurring.

Original languageEnglish (US)
Pages (from-to)305-310
Number of pages6
JournalJournal of the American Medical Informatics Association
Volume20
Issue number2
DOIs
StatePublished - 2013

Fingerprint

Randomized Controlled Trials
Medical Order Entry Systems

ASJC Scopus subject areas

  • Health Informatics

Cite this

Understanding and preventing wrong-patient electronic orders : A randomized controlled trial. / Adelman, Jason S.; Kalkut, Gary E.; Schechter, Clyde B.; Weiss, Jeffrey M.; Berger, Matthew Alan; Reissman, Stan H.; Cohen, Hillel W.; Lorenzen, Stephen J.; Burack, Daniel A.; Southern, William N.

In: Journal of the American Medical Informatics Association, Vol. 20, No. 2, 2013, p. 305-310.

Research output: Contribution to journalArticle

Adelman, Jason S. ; Kalkut, Gary E. ; Schechter, Clyde B. ; Weiss, Jeffrey M. ; Berger, Matthew Alan ; Reissman, Stan H. ; Cohen, Hillel W. ; Lorenzen, Stephen J. ; Burack, Daniel A. ; Southern, William N. / Understanding and preventing wrong-patient electronic orders : A randomized controlled trial. In: Journal of the American Medical Informatics Association. 2013 ; Vol. 20, No. 2. pp. 305-310.
@article{7b5ea232e2604c1d9128f245761b594d,
title = "Understanding and preventing wrong-patient electronic orders: A randomized controlled trial",
abstract = "Objective: To evaluate systems for estimating and preventing wrong-patient electronic orders in computerized physician order entry systems with a two-phase study. Materials and methods: In phase 1, from May to August 2010, the effectiveness of a 'retract-and-reorder' measurement tool was assessed that identified orders placed on a patient, promptly retracted, and then reordered by the same provider on a different patient as a marker for wrong-patient electronic orders. This tool was then used to estimate the frequency of wrong-patient electronic orders in four hospitals in 2009. In phase 2, from December 2010 to June 2011, a three-armed randomized controlled trial was conducted to evaluate the efficacy of two distinct interventions aimed at preventing these errors by reverifying patient identification: an 'ID-verify alert', and an 'ID-reentry function'. Results: The retract-and-reorder measurement tool effectively identified 170 of 223 events as wrong-patient electronic orders, resulting in a positive predictive value of 76.2{\%} (95{\%} CI 70.6{\%} to 81.9{\%}). Using this tool it was estimated that 5246 electronic orders were placed on wrong patients in 2009. In phase 2, 901 776 ordering sessions among 4028 providers were examined. Compared with control, the ID-verify alert reduced the odds of a retract-and-reorder event (OR 0.84, 95{\%} CI 0.72 to 0.98), but the ID-reentry function reduced the odds by a larger magnitude (OR 0.60, 95{\%} CI 0.50 to 0.71). Discussion and conclusion: Wrong-patient electronic orders occur frequently with computerized provider order entry systems, and electronic interventions can reduce the risk of these errors occurring.",
author = "Adelman, {Jason S.} and Kalkut, {Gary E.} and Schechter, {Clyde B.} and Weiss, {Jeffrey M.} and Berger, {Matthew Alan} and Reissman, {Stan H.} and Cohen, {Hillel W.} and Lorenzen, {Stephen J.} and Burack, {Daniel A.} and Southern, {William N.}",
year = "2013",
doi = "10.1136/amiajnl-2012-001055",
language = "English (US)",
volume = "20",
pages = "305--310",
journal = "Journal of the American Medical Informatics Association : JAMIA",
issn = "1067-5027",
publisher = "Oxford University Press",
number = "2",

}

TY - JOUR

T1 - Understanding and preventing wrong-patient electronic orders

T2 - A randomized controlled trial

AU - Adelman, Jason S.

AU - Kalkut, Gary E.

AU - Schechter, Clyde B.

AU - Weiss, Jeffrey M.

AU - Berger, Matthew Alan

AU - Reissman, Stan H.

AU - Cohen, Hillel W.

AU - Lorenzen, Stephen J.

AU - Burack, Daniel A.

AU - Southern, William N.

PY - 2013

Y1 - 2013

N2 - Objective: To evaluate systems for estimating and preventing wrong-patient electronic orders in computerized physician order entry systems with a two-phase study. Materials and methods: In phase 1, from May to August 2010, the effectiveness of a 'retract-and-reorder' measurement tool was assessed that identified orders placed on a patient, promptly retracted, and then reordered by the same provider on a different patient as a marker for wrong-patient electronic orders. This tool was then used to estimate the frequency of wrong-patient electronic orders in four hospitals in 2009. In phase 2, from December 2010 to June 2011, a three-armed randomized controlled trial was conducted to evaluate the efficacy of two distinct interventions aimed at preventing these errors by reverifying patient identification: an 'ID-verify alert', and an 'ID-reentry function'. Results: The retract-and-reorder measurement tool effectively identified 170 of 223 events as wrong-patient electronic orders, resulting in a positive predictive value of 76.2% (95% CI 70.6% to 81.9%). Using this tool it was estimated that 5246 electronic orders were placed on wrong patients in 2009. In phase 2, 901 776 ordering sessions among 4028 providers were examined. Compared with control, the ID-verify alert reduced the odds of a retract-and-reorder event (OR 0.84, 95% CI 0.72 to 0.98), but the ID-reentry function reduced the odds by a larger magnitude (OR 0.60, 95% CI 0.50 to 0.71). Discussion and conclusion: Wrong-patient electronic orders occur frequently with computerized provider order entry systems, and electronic interventions can reduce the risk of these errors occurring.

AB - Objective: To evaluate systems for estimating and preventing wrong-patient electronic orders in computerized physician order entry systems with a two-phase study. Materials and methods: In phase 1, from May to August 2010, the effectiveness of a 'retract-and-reorder' measurement tool was assessed that identified orders placed on a patient, promptly retracted, and then reordered by the same provider on a different patient as a marker for wrong-patient electronic orders. This tool was then used to estimate the frequency of wrong-patient electronic orders in four hospitals in 2009. In phase 2, from December 2010 to June 2011, a three-armed randomized controlled trial was conducted to evaluate the efficacy of two distinct interventions aimed at preventing these errors by reverifying patient identification: an 'ID-verify alert', and an 'ID-reentry function'. Results: The retract-and-reorder measurement tool effectively identified 170 of 223 events as wrong-patient electronic orders, resulting in a positive predictive value of 76.2% (95% CI 70.6% to 81.9%). Using this tool it was estimated that 5246 electronic orders were placed on wrong patients in 2009. In phase 2, 901 776 ordering sessions among 4028 providers were examined. Compared with control, the ID-verify alert reduced the odds of a retract-and-reorder event (OR 0.84, 95% CI 0.72 to 0.98), but the ID-reentry function reduced the odds by a larger magnitude (OR 0.60, 95% CI 0.50 to 0.71). Discussion and conclusion: Wrong-patient electronic orders occur frequently with computerized provider order entry systems, and electronic interventions can reduce the risk of these errors occurring.

UR - http://www.scopus.com/inward/record.url?scp=84874761572&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84874761572&partnerID=8YFLogxK

U2 - 10.1136/amiajnl-2012-001055

DO - 10.1136/amiajnl-2012-001055

M3 - Article

VL - 20

SP - 305

EP - 310

JO - Journal of the American Medical Informatics Association : JAMIA

JF - Journal of the American Medical Informatics Association : JAMIA

SN - 1067-5027

IS - 2

ER -