Indication alerts to improve problem list documentation. (27th December 2021)

• Record Type: Journal Article
• Authors: Grauer, Anne; Kneifati-Hayek, Jerard; Reuland, Brian; Applebaum, Jo R; Adelman, Jason S; Green, Robert A; Lisak-Phillips, Jeanette; Liebovitz, David; Byrd, Thomas F; Kansal, Preeti; Wilkes, Cheryl; Falck, Suzanne; Larson, Connie; Shilka, John; VanDril, Elizabeth; Schiff, Gordon D; Galanter, William L; Lambert, Bruce L
• Is Part Of: Journal of the American Medical Informatics Association. Volume 29:Number 5(2022)
• Page Start: 909
• Page End: 917
• Publication Date: 2021-12-27
• Subjects: decision support systems -- clinical -- medical records -- problem-oriented -- indication-based prescribing -- problem list; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocab285
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Background: Problem lists represent an integral component of high-quality care. However, they are often inaccurate and incomplete. We studied the effects of alerts integrated into the inpatient and outpatient computerized provider order entry systems to assist in adding problems to the problem list when ordering medications that lacked a corresponding indication. Methods: We analyzed medication orders from 2 healthcare systems that used an innovative indication alert. We collected data at site 1 between December 2018 and January 2020, and at site 2 between May and June 2021. We reviewed random samples of 100 charts from each site that had problems added in response to the alert. Outcomes were: (1) alert yield, the proportion of triggered alerts that led to a problem added and (2) problem accuracy, the proportion of problems placed that were accurate by chart review. Results: Alerts were triggered 131 134, and 6178 times at sites 1 and 2, respectively, resulting in a yield of 109 055 (83.2%) and 2874 (46.5%), P < .001. Orders were abandoned, for example, not completed, in 11.1% and 9.6% of orders, respectively, P <.001. Of the 100 sample problems, reviewers deemed 88% ± 3% and 91% ± 3% to be accurate, respectively, P = .65, with a mean of 90% ± 2%. Conclusions: Indication alerts triggered by medication orders initiated in the absence of a justifying diagnosis were useful for populating problem lists, with yields of 83.2% and 46.5% at 2 healthcare systems. Problems were placed with a reasonable level of accuracy, with 90% ± 2% of problems deemed accurate based on chart review.