ArticleHealth affairs (Project Hope)2026
Prescription Real-Time Benefit Tools: Clinicians Receive Frequent Alerts Yet Rarely Accept The Suggested Changes.
Article in Health affairs (Project Hope), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- User-Directed vs Interruptive Real-Time Benefit Tools for Medication Changes in Primary Care.JAMA network open · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Health systems are increasingly integrating real-time benefit tools into their electronic health records (EHRs). These tools are designed to improve the affordability of and adherence to medications by alerting clinicians to out-of-pocket prescription price estimates and lower-price alternatives. Clinicians' engagement with real-time benefit tools is critical to the tools' success, yet it has not been comprehensively examined. Using 2019-22 EHR data from 803 ambulatory clinics and 4,894 clinicians in a large health system, we found that clinicians received a median of 19 alerts per 100 orders; of the 87 percent of clinicians who received at least one alert, 91 percent changed an order in response to an alert at least once. Clinicians made changes in response to 11 percent of all alerts they received, but they rarely accepted the alternatives originally suggested by the tool. Changes most often involved the pharmacy (60 percent), followed by quantity (39 percent), formulation (6 percent), and medication (4 percent). Primary care clinicians received the most alerts per month, while medical and surgical subspecialists made changes for a larger share of alerts. For these tools to be successfully implemented, leaders must consider the quality and usability of alerts, time burdens, and alert fatigue.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.