Patrick E Beeler1, E John Orav2, Diane L Seger3, Patricia C Dykes4, David W Bates5. 1. Division of General Internal Medicine & Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA Harvard Medical School, Boston, Massachusetts, USA Research Center for Medical Informatics, University Hospital Zurich, Switzerland. 2. Division of General Internal Medicine & Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA. 3. Partners Healthcare Systems, Inc, Wellesley, Massachusetts, USA. 4. Division of General Internal Medicine & Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA Harvard Medical School, Boston, Massachusetts, USA. 5. Division of General Internal Medicine & Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA Harvard Medical School, Boston, Massachusetts, USA Partners Healthcare Systems, Inc, Wellesley, Massachusetts, USA dbates@partners.org.
Abstract
OBJECTIVE: Variation in the use of tests and treatments has been demonstrated to be substantial between providers and geographic regions. This study assessed variation between outpatient providers in overriding electronic prescribing warnings. METHODS: Responses to warnings were prospectively logged. Random effects models were used to calculate provider-to-provider variation in the rates for the decisions to override warnings in 6 different clinical domains: medication allergies, drug-drug interactions, duplicate drugs, renal recommendations, age-based recommendations, and formulary substitutions. RESULTS: A total of 157 482 responses were logged. Differences between 1717 providers accounted for 11% of the overall variability in override rates, so that while the average override rate was 45.2%, individual provider rates had a wide range with a 95% confidence interval (CI) (13.7%-76.7% ). The highest variations between providers were observed in the categories age-based (25.4% of total variability; average override rate 70.2% [95% CI, 29.1%-100% ]) and renal recommendations (24.2%; average 70% [95% CI, 29.5%-100% ]), and provider responses within these 2 categories were most often clinically inappropriate according to prior work. Among providers who received at least 10 age-based recommendations, 64 of 238 (27%) overrode ≥ 90% of the warnings and 13 of 238 (5%) overrode all of them. Of those who received at least 10 renal recommendations, 36 of 92 (39%) overrode ≥ 90% of the alerts and 9 of 92 (10%) overrode all of them. CONCLUSIONS: The decision to override prescribing warnings shows variation between providers, and the magnitude of variation differs among the clinical domains of the warnings; more variation was observed in areas with more inappropriate overrides.
OBJECTIVE: Variation in the use of tests and treatments has been demonstrated to be substantial between providers and geographic regions. This study assessed variation between outpatient providers in overriding electronic prescribing warnings. METHODS: Responses to warnings were prospectively logged. Random effects models were used to calculate provider-to-provider variation in the rates for the decisions to override warnings in 6 different clinical domains: medication allergies, drug-drug interactions, duplicate drugs, renal recommendations, age-based recommendations, and formulary substitutions. RESULTS: A total of 157 482 responses were logged. Differences between 1717 providers accounted for 11% of the overall variability in override rates, so that while the average override rate was 45.2%, individual provider rates had a wide range with a 95% confidence interval (CI) (13.7%-76.7% ). The highest variations between providers were observed in the categories age-based (25.4% of total variability; average override rate 70.2% [95% CI, 29.1%-100% ]) and renal recommendations (24.2%; average 70% [95% CI, 29.5%-100% ]), and provider responses within these 2 categories were most often clinically inappropriate according to prior work. Among providers who received at least 10 age-based recommendations, 64 of 238 (27%) overrode ≥ 90% of the warnings and 13 of 238 (5%) overrode all of them. Of those who received at least 10 renal recommendations, 36 of 92 (39%) overrode ≥ 90% of the alerts and 9 of 92 (10%) overrode all of them. CONCLUSIONS: The decision to override prescribing warnings shows variation between providers, and the magnitude of variation differs among the clinical domains of the warnings; more variation was observed in areas with more inappropriate overrides.
Keywords:
attitude of health personnel; clinical decision support systems; clinical practice variation; computer-assisted drug therapy; medical order entry systems
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