Kennedy, Brian D

Abstract 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.

Abstract Background Bamlanivimab and casirivimab-imdevimab are authorized for treatment of mild to moderate coronavirus disease 2019 (COVID-19) in high-risk patients. We compared the outcomes of patients who received these therapies to identify factors associated with hospitalization and other clinical outcomes. Methods Adult patients who received monoclonal antibody from 19 November 2020 to 11 February 2021 were selected and divided into those who received bamlanivimab (n = 2747) and casirivimab-imdevimab (n = 849). The 28-day all-cause and COVID-19–related hospitalizations were compared between the groups. Results The population included 3596 patients; the median age was 62 years, and 50% were female. All had ≥1 medical comorbidity; 55% had multiple comorbidities. All-cause and COVID-19–related hospitalization rates at 28 days were 3.98% and 2.56%, respectively. After adjusting for medical comorbidities, there was no significant difference in all-cause and COVID-19–related hospitalization rates between bamlanivimab and casirivimab-imdevimab (adjusted hazard ratios [95% confidence interval], 1.4 [.9–2.2] and 1.6 [.8–2.7], respectively). Chronic kidney, respiratory and cardiovascular diseases, and immunocompromised status were associated with higher likelihood of hospitalization. Conclusions This observational study on the use of bamlanivimab and casirivimab-imdevimab in high-risk patients showed similarly low rates of hospitalization. The number and type of medical comorbidities are associated with hospitalizations after monoclonal antibody treatment.