Indication alerts intercept drug name confusion errors during computerized entry of medication orders

Confusion between similar drug names is a common cause of potentially harmful medication errors. Interventions to prevent these errors at the point of prescribing have had limited success. The purpose of this study is to measure whether indication alerts at the time of computerized physician order e...

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Veröffentlicht in:PloS one 2014-07, Vol.9 (7), p.e101977-e101977
Hauptverfasser: Galanter, William L, Bryson, Michelle L, Falck, Suzanne, Rosenfield, Rachel, Laragh, Marci, Shrestha, Neeha, Schiff, Gordon D, Lambert, Bruce L
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container_end_page e101977
container_issue 7
container_start_page e101977
container_title PloS one
container_volume 9
creator Galanter, William L
Bryson, Michelle L
Falck, Suzanne
Rosenfield, Rachel
Laragh, Marci
Shrestha, Neeha
Schiff, Gordon D
Lambert, Bruce L
description Confusion between similar drug names is a common cause of potentially harmful medication errors. Interventions to prevent these errors at the point of prescribing have had limited success. The purpose of this study is to measure whether indication alerts at the time of computerized physician order entry (CPOE) can intercept drug name confusion errors. A retrospective observational study of alerts provided to prescribers in a public, tertiary hospital and ambulatory practice with medication orders placed using CPOE. Consecutive patients seen from April 2006 through February 2012 were eligible if a clinician received an indication alert during ordering. A total of 54,499 unique patients were included. The computerized decision support system prompted prescribers to enter indications when certain medications were ordered without a coded indication in the electronic problem list. Alerts required prescribers either to ignore them by clicking OK, to place a problem in the problem list, or to cancel the order. Main outcome was the proportion of indication alerts resulting in the interception of drug name confusion errors. Error interception was determined using an algorithm to identify instances in which an alert triggered, the initial medication order was not completed, and the same prescriber ordered a similar-sounding medication on the same patient within 5 minutes. Similarity was defined using standard text similarity measures. Two clinicians performed chart review of all cases to determine whether the first, non-completed medication order had a documented or non-documented, plausible indication for use. If either reviewer found a plausible indication, the case was not considered an error. We analyzed 127,458 alerts and identified 176 intercepted drug name confusion errors, an interception rate of 0.14±.01%. Indication alerts intercepted 1.4 drug name confusion errors per 1000 alerts. Institutions with CPOE should consider using indication prompts to intercept drug name confusion errors.
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Main outcome was the proportion of indication alerts resulting in the interception of drug name confusion errors. Error interception was determined using an algorithm to identify instances in which an alert triggered, the initial medication order was not completed, and the same prescriber ordered a similar-sounding medication on the same patient within 5 minutes. Similarity was defined using standard text similarity measures. Two clinicians performed chart review of all cases to determine whether the first, non-completed medication order had a documented or non-documented, plausible indication for use. If either reviewer found a plausible indication, the case was not considered an error. We analyzed 127,458 alerts and identified 176 intercepted drug name confusion errors, an interception rate of 0.14±.01%. Indication alerts intercepted 1.4 drug name confusion errors per 1000 alerts. Institutions with CPOE should consider using indication prompts to intercept drug name confusion errors.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>25025346</pmid><doi>10.1371/journal.pone.0101977</doi><oa>free_for_read</oa></addata></record>
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subjects Ambulatory care
Computer and Information Sciences
Computerized physician order entry
Confusion
Decision Support Systems, Clinical
Diabetes
Documentation
Drugs
Error analysis
Hospitals
Humans
Indication
Interception
Medical errors
Medical records
Medication errors
Medication Errors - prevention & control
Medicine
Medicine and Health Sciences
Names
Patient safety
Patients
Pharmacy
Physical Sciences
Physicians
Research and Analysis Methods
Retrospective Studies
Similarity
Visual perception
title Indication alerts intercept drug name confusion errors during computerized entry of medication orders
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