Design and evaluation of an electronic prospective medication order review system for medication orders in the inpatient setting.

Autor:	Ojha P; Department of Pharmacy Services, Mayo Clinic, Rochester, MN 55905, United States., Anderson BJ; Department of Pharmacy Services, Mayo Clinic, Rochester, MN 55905, United States., Draper EW; Department of Pharmacy Services, Mayo Clinic, Rochester, MN 55905, United States., Flaker SM; Department of Pharmacy Services, Mayo Clinic, Rochester, MN 55905, United States., Siska MH; Department of Pharmacy Services, Mayo Clinic, Rochester, MN 55905, United States., Mara KC; Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, United States., Kennedy BD; Department of Pharmacy Services, Mayo Clinic, Rochester, MN 55905, United States., Schreier DJ; Department of Pharmacy Services, Mayo Clinic, Rochester, MN 55905, United States.
Jazyk:	angličtina
Zdroj:	JAMIA open [JAMIA Open] 2024 Jan 27; Vol. 7 (1), pp. ooae003. Date of Electronic Publication: 2024 Jan 27 (Print Publication: 2024).
DOI:	10.1093/jamiaopen/ooae003
Abstrakt:	Objectives: Since the 1970s, a plethora of tools have been introduced to support the medication use process. However, automation initiatives to assist pharmacists in prospectively reviewing medication orders are lacking. The review of many medications may be protocolized and implemented in an algorithmic fashion utilizing discrete data from the electronic health record (EHR). This research serves as a proof of concept to evaluate the capability and effectiveness of an electronic prospective medication order review (EPMOR) system compared to pharmacists' review. Materials and Methods: A subset of the most frequently verified medication orders were identified for inclusion. A team of clinical pharmacist experts developed best-practice EPMOR criteria. The established criteria were incorporated into conditional logic built within the EHR. Verification outcomes from the pharmacist (human) and EPMOR (automation) were compared. Results: Overall, 13 404 medication orders were included. Of those orders, 13 133 passed pharmacist review, 7388 of which passed EPMOR. A total of 271 medication orders failed pharmacist review due to order modification or discontinuation, 105 of which passed EPMOR. Of the 105 orders, 19 were duplicate orders correctly caught by both EPMOR and pharmacists, but the opposite duplicate order was rejected, 51 orders failed due to scheduling changes. Discussion: This simulation was capable of effectively discriminating and triaging orders. Protocolization and automation of the prospective medication order review process in the EHR appear possible using clinically driven algorithms. Conclusion: Further research is necessary to refine such algorithms to maximize value, improve efficiency, and minimize safety risks in preparation for the implementation of fully automated systems. Competing Interests: None declared. (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu