Improving Acceptance of Computerized Prescribing Alerts in Ambulatory Care |
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| Affiliation: | 1. Sleep Disorders Center, Department of Neurology, University of Michigan, Ann Arbor, MI;2. Emerging Technologies, Taubman Health Sciences Library, University of Michigan, Ann Arbor, MI;3. Learning Design & Publishing/Medical School Information Systems, University of Michigan, Ann Arbor, MI;2. Children’s Hospital of Philadelphia, Penn Medicine, Hospital of University of Pennsylvania, Philadelphia, PA;1. Michigan Pediatric Device Consortium, University of Michigan, CS Mott Children’s Hospital, Ann Arbor, 1540 E Hospital Dr SPC 4211, Michigan 48109;2. Section of Pediatric Surgery, University of Michigan, CS Mott Children’s Hospital, Ann Arbor, Michigan;1. Menzies Health Institute QLD, Griffith University, Nathan, Brisbane, Queensland, Australia;2. School of Nursing and Midwifery, Nathan Campus, Griffith University, Nathan, Brisbane, Queensland, Australia;3. Department of Public Health, College of Health Sciences, Qatar University, Qatar;4. School of Nursing, Queensland University of Technology, Kelvin Grove, Brisbane, Queensland, Australia;5. School of Applied Psychology, Mt Gravatt Campus, Griffith University, Brisbane, Queensland, Australia;6. Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China;7. Medical School, University of Exeter, Exeter, United Kingdom;8. Centre for Applied Health Economics, School of Medicine, Nathan Campus, Griffith University, Nathan, Brisbane, Queensland, Australia;9. School of Psychiatry, University of New South Wales, Sydney, Australia;7. Fondation France-Japon, l’École des hautes études en sciences sociales, Paris, France |
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| Abstract: | ![]()
Computerized drug prescribing alerts can improve patient safety, but are often overridden because of poor specificity and alert overload. Our objective was to improve clinician acceptance of drug alerts by designing a selective set of drug alerts for the ambulatory care setting and minimizing workflow disruptions by designating only critical to high-severity alerts to be interruptive to clinician workflow. The alerts were presented to clinicians using computerized prescribing within an electronic medical record in 31 Boston-area practices. There were 18,115 drug alerts generated during our six-month study period. Of these, 12,933 (71%) were noninterruptive and 5,182 (29%) interruptive. Of the 5,182 interruptive alerts, 67% were accepted. Reasons for overrides varied for each drug alert category and provided potentially useful information for future alert improvement. These data suggest that it is possible to design computerized prescribing decision support with high rates of alert recommendation acceptance by clinicians. |
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