ArticleHealthcare (Amsterdam, Netherlands)2023
Implementing real-time prescription benefit tools: Early experiences from 5 academic medical centers.
Article in Healthcare (Amsterdam, Netherlands), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- The Promises and Pitfalls of Real-time Benefit Tools: A Qualitative Study of Primary Care Providers.Journal of general internal medicine · 2026Article
- User-Directed vs Interruptive Real-Time Benefit Tools for Medication Changes in Primary Care.JAMA network open · 2026Article
- Examining usability of RxFill: integrating health IT to support medication adherence.BMC medical informatics and decision making · 2026Article
- Prescription Real-Time Benefit Tools: Clinicians Receive Frequent Alerts Yet Rarely Accept The Suggested Changes.Health affairs (Project Hope) · 2026Article
- Price transparency at the point of prescribing with real-time prescription benefits.American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists · 2024Article
- Real-world use of a medication out-of-pocket cost estimator in primary care one year after Medicare regulation.Journal of the American Geriatrics Society · 2024Article
- Medicare Coverage for Diabetes Management: A Primary Care Professional's Guide to Advocacy and Navigation.Diabetes, obesity, and cardiometabolic CAREArticle
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Authors and funding
11 authors.
Funding
Abstract
backgroundMedication price transparency tools are increasingly available, but data on their use, and their potential effects on prescribing behavior, patient out of pocket (OOP) costs, and clinician workflow integration, is limited.
objectiveTo describe the implementation experiences with real-time prescription benefit (RTPB) tools at 5 large academic medical centers and their early impact on prescription ordering.
designand Participants: In this cross-sectional study, we systematically collected information on the characteristics of RTPB tools through discussions with key stakeholders at each of the five organizations. Quantitative encounter data, prescriptions written, and RTPB alerts/estimates and prescription adjustment rates were obtained at each organization in the first three months after "go-live" of the RTPB system(s) between 2019 and 2020. MAIN MEASURES: Implementation characteristics, prescription orders, cost estimate retrieval rates, and prescription adjustment rates. KEY
resultsDifferences were noted with respect to implementation characteristics related to RTPB tools. All of the organizations with the exception of one chose to display OOP cost estimates and suggested alternative prescriptions automatically. Differences were also noted with respect to a patient cost threshold for automatic display. In the first three months after "go-live," RTPB estimate retrieval rates varied greatly across the five organizations, ranging from 8% to 60% of outpatient prescriptions. The prescription adjustment rate was lower, ranging from 0.1% to 4.9% of all prescriptions ordered.
conclusionsIn this study reporting on the early experiences with RTPB tools across five academic medical centers, we found variability in implementation characteristics and population coverage. In addition RTPB estimate retrieval rates were highly variable across the five organizations, while rates of prescription adjustment ranged from low to modest.
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