Repurposing Clinical Decision Support System Data to Measure Dosing Errors and Clinician-Level Quality of Care
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SYSTEMS-LEVEL QUALITY IMPROVEMENT
Repurposing Clinical Decision Support System Data to Measure Dosing Errors and Clinician-Level Quality of Care David L. Chin 1 & Michelle H. Wilson 2 & Ashley S. Trask 3 & Victoria T. Johnson 4 & Brittanie I. Neaves 5 & Andrea Gojova 3 & Michael A. Hogarth 6 & Heejung Bang 7 & Patrick S. Romano 7 Received: 18 February 2020 / Accepted: 15 July 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract We aimed to develop and validate an instrument to detect hospital medication prescribing errors using repurposed clinical decision support system data. Despite significant efforts to eliminate medication prescribing errors, these events remain common in hospitals. Data from clinical decision support systems have not been used to identify prescribing errors as an instrument for physician-level performance. We evaluated medication order alerts generated by a knowledge-based electronic prescribing system occurring in one large academic medical center’s acute care facilities for patient encounters between 2009 and 2012. We developed and validated an instrument to detect medication prescribing errors through a clinical expert panel consensus process to assess physician quality of care. Six medication prescribing alert categories were evaluated for inclusion, one of which – dose – was included in the algorithm to detect prescribing errors. The instrument was 93% sensitive (recall), 51% specific, 40% precise, 62% accurate, with an F1 score of 55%, positive predictive value of 96%, and a negative predictive value of 32%. Using repurposed electronic prescribing system data, dose alert overrides can be used to systematically detect medication prescribing errors occurring in an inpatient setting with high sensitivity. Keywords Decision support systems, clinical . Quality of health care . Outcome and process assessment (health care) . Medication errors . Medical informatics applications . Electronic health records
Introduction * David L. Chin [email protected] 1
Department of Health Promotion and Policy, School of Public Health and Health Sciences, University of Massachusetts Amherst, Arnold House 331 | 715 N. Pleasant Street, MA 01003 Amherst, USA
2
Department of Internal Medicine, Santa Clara Valley Medical Center, San Jose, CA, USA
3
Department of Pharmacy, University of California Davis Health System, Sacramento, CA, USA
4
Consultative Internal Medicine, for Group Health Physicians Washington Permanente Medical Group, Seattle, WA, USA
5
Department of Allergy and Immunology, Wilford Hall Ambulatory Surgical Center, San Antonio, TX, USA
6
Department of Internal Medicine, Division of Biomedical Informatics, University of California San Diego, School of Medicine, San Diego, CA, USA
7
Center for Healthcare Policy and Research, University of California, Davis, Sacramento, CA, USA
Clinical decision support (CDS) systems embedded in Electronic Health Records (EHR) are common in modern healthcare delivery, particularly in electronic prescribing functions. Although
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