BACKGROUND: Electronic prescribing is increasingly used, in part because of government incentives for its use. Many of its benefits come from clinical decision support (CDS), but often too many alerts are displayed, resulting in alert fatigue. OBJECTIVE: To characterize the override rates for medication-related CDS alerts in the outpatient setting, the reasons cited for overrides at the time of prescribing, and the appropriateness of overrides. METHODS: We measured CDS alert override rates and the coded reasons for overrides cited by providers at the time of prescribing. Our primary outcome was the rate of CDS alert overrides; our secondary outcomes were the rate of overrides by alert type, reasons cited for overrides at the time of prescribing, and override appropriateness for a subset of 600 alert overrides. Through detailed chart reviews of alert override cases, and selective literature review, we developed appropriateness criteria for each alert type, which were modified iteratively as necessary until consensus was reached on all criteria. RESULTS: We reviewed 157,483 CDS alerts (7.9% alert rate) on 2,004,069 medication orders during the study period. 82,889 (52.6%) of alerts were overridden. The most common alerts were duplicate drug (33.1%), patient allergy (16.8%), and drug-drug interactions (15.8%). The most likely alerts to be overridden were formulary substitutions (85.0%), age-based recommendations (79.0%), renal recommendations (78.0%), and patient allergies (77.4%). An average of 53% of overrides were classified as appropriate, and rates of appropriateness varied by alert type (p<0.0001) from 12% for renal recommendations to 92% for patient allergies. DISCUSSION: About half of CDS alerts were overridden by providers and about half of the overrides were classified as appropriate, but the likelihood of overriding an alert varied widely by alert type. Refinement of these alerts has the potential to improve the relevance of alerts and reduce alert fatigue.
BACKGROUND: Electronic prescribing is increasingly used, in part because of government incentives for its use. Many of its benefits come from clinical decision support (CDS), but often too many alerts are displayed, resulting in alert fatigue. OBJECTIVE: To characterize the override rates for medication-related CDS alerts in the outpatient setting, the reasons cited for overrides at the time of prescribing, and the appropriateness of overrides. METHODS: We measured CDS alert override rates and the coded reasons for overrides cited by providers at the time of prescribing. Our primary outcome was the rate of CDS alert overrides; our secondary outcomes were the rate of overrides by alert type, reasons cited for overrides at the time of prescribing, and override appropriateness for a subset of 600 alert overrides. Through detailed chart reviews of alert override cases, and selective literature review, we developed appropriateness criteria for each alert type, which were modified iteratively as necessary until consensus was reached on all criteria. RESULTS: We reviewed 157,483 CDS alerts (7.9% alert rate) on 2,004,069 medication orders during the study period. 82,889 (52.6%) of alerts were overridden. The most common alerts were duplicate drug (33.1%), patientallergy (16.8%), and drug-drug interactions (15.8%). The most likely alerts to be overridden were formulary substitutions (85.0%), age-based recommendations (79.0%), renal recommendations (78.0%), and patientallergies (77.4%). An average of 53% of overrides were classified as appropriate, and rates of appropriateness varied by alert type (p<0.0001) from 12% for renal recommendations to 92% for patientallergies. DISCUSSION: About half of CDS alerts were overridden by providers and about half of the overrides were classified as appropriate, but the likelihood of overriding an alert varied widely by alert type. Refinement of these alerts has the potential to improve the relevance of alerts and reduce alert fatigue.
Authors: Shobha Phansalkar; Amrita A Desai; Douglas Bell; Eileen Yoshida; John Doole; Melissa Czochanski; Blackford Middleton; David W Bates Journal: J Am Med Inform Assoc Date: 2012-04-26 Impact factor: 4.497
Authors: Shobha Phansalkar; Heleen van der Sijs; Alisha D Tucker; Amrita A Desai; Douglas S Bell; Jonathan M Teich; Blackford Middleton; David W Bates Journal: J Am Med Inform Assoc Date: 2012-09-25 Impact factor: 4.497
Authors: Ross Koppel; Joshua P Metlay; Abigail Cohen; Brian Abaluck; A Russell Localio; Stephen E Kimmel; Brian L Strom Journal: JAMA Date: 2005-03-09 Impact factor: 56.272
Authors: Patrick E Beeler; E John Orav; Diane L Seger; Patricia C Dykes; David W Bates Journal: J Am Med Inform Assoc Date: 2015-10-24 Impact factor: 4.497
Authors: Sara Ibáñez-Garcia; Carmen Rodriguez-Gonzalez; Vicente Escudero-Vilaplana; Maria Luisa Martin-Barbero; Belén Marzal-Alfaro; Jose Luis De la Rosa-Triviño; Irene Iglesias-Peinado; Ana Herranz-Alonso; Maria Sanjurjo Saez Journal: Appl Clin Inform Date: 2019-07-17 Impact factor: 2.342
Authors: Adrian Wong; Christine Rehr; Diane L Seger; Mary G Amato; Patrick E Beeler; Sarah P Slight; Adam Wright; David W Bates Journal: Drug Saf Date: 2019-04 Impact factor: 5.606
Authors: Emily M Powers; Richard N Shiffman; Edward R Melnick; Andrew Hickner; Mona Sharifi Journal: J Am Med Inform Assoc Date: 2018-11-01 Impact factor: 4.497
Authors: Anita N Bindraban; José Rolvink; Florine A Berger; Patricia M L A van den Bemt; Aaf F M Kuijper; Ruud T M van der Hoeven; Aukje K Mantel-Teeuwisse; Matthijs L Becker Journal: Int J Clin Pharm Date: 2018-07-26
Authors: Timothy M Herr; Josh F Peterson; Luke V Rasmussen; Pedro J Caraballo; Peggy L Peissig; Justin B Starren Journal: J Am Med Inform Assoc Date: 2019-02-01 Impact factor: 4.497
Authors: Alissa L Russ; Alan J Zillich; Brittany L Melton; Scott A Russell; Siying Chen; Jeffrey R Spina; Michael Weiner; Elizabette G Johnson; Joanne K Daggy; M Sue McManus; Jason M Hawsey; Anthony G Puleo; Bradley N Doebbeling; Jason J Saleem Journal: J Am Med Inform Assoc Date: 2014-03-25 Impact factor: 4.497