{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,24]],"date-time":"2025-08-24T22:53:16Z","timestamp":1756075996012},"reference-count":16,"publisher":"Oxford University Press (OUP)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>All default electronic health record and drug reference database vendor drug-dose alerting recommendations (single dose, daily dose, dose frequency, and dose duration) were silently turned on in inpatient, outpatient, and emergency department areas for pediatric-only and nonpediatric-only populations. Drug-dose alerts were evaluated during a 3-month period. Drug-dose alerts fired on 12% of orders (104 098\/834 911). System-level and drug-specific strategies to decrease drug-dose alerts were analyzed. System-level strategies included: (1) turning off all minimum drug-dosing alerts, (2) turning off all incomplete information drug-dosing alerts, (3) increasing the maximum single-dose drug-dose alert threshold to 125%, (4) increasing the daily dose maximum drug-dose alert threshold to 125%, and (5) increasing the dose frequency drug-dose alert threshold to more than 2 doses per day above initial threshold. Drug-specific strategies included changing drug-specific maximum single and maximum daily drug-dose alerting parameters for the top 22 drug categories by alert frequency. System-level approaches decreased alerting to 5% (46 988\/834 911) and drug-specific approaches decreased alerts to 3% (25 455\/834 911). Drug-dose alerts varied between care settings and patient populations.<\/jats:p>","DOI":"10.1093\/jamia\/ocx031","type":"journal-article","created":{"date-parts":[[2017,3,11]],"date-time":"2017-03-11T12:13:22Z","timestamp":1489234402000},"page":"1149-1154","source":"Crossref","is-referenced-by-count":12,"title":["Optimizing drug-dose alerts using commercial software throughout an integrated health care system"],"prefix":"10.1093","volume":"24","author":[{"given":"Salim M","family":"Saiyed","sequence":"first","affiliation":[{"name":"Division of Health Sciences Informatics, Johns Hopkins School of Medicine, Baltimore, MD, USA"},{"name":"Department of Family Medicine, St. Joseph\u2019s Hospital and Medical Center, Phoenix, AZ, USA"},{"name":"CaroMont Health, Gastonia, NC, USA"}]},{"given":"Peter J","family":"Greco","sequence":"additional","affiliation":[{"name":"Department of Information Services, the MetroHealth System, Case Western Reserve University, Cleveland, OH, USA"},{"name":"Department of Internal Medicine, the MetroHealth System, Case Western Reserve University"},{"name":"Department of Pediatrics, the MetroHealth System, Case Western Reserve University"}]},{"given":"Glenn","family":"Fernandes","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, the MetroHealth System, Case Western Reserve University"}]},{"given":"David C","family":"Kaelber","sequence":"additional","affiliation":[{"name":"Department of Information Services, the MetroHealth System, Case Western Reserve University, Cleveland, OH, USA"},{"name":"Department of Internal Medicine, the MetroHealth System, Case Western Reserve University"},{"name":"Department of Pediatrics, the MetroHealth System, Case Western Reserve University"},{"name":"Department of Epidemiology and Biostatistics, the MetroHealth System, Case Western Reserve University,"},{"name":"Center for Clinical Informatics Research and Education, the MetroHealth System, Case Western Reserve 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