General Medicine · Medical Literature
Real-Time Prescription Benefit Tool Availability and Prescription Medication Fill Rates: A Post Hoc Analysis of a Cluster Randomized Clinical Trial.
Abstract
Patients frequently forgo filling prescriptions due to high out-of-pocket costs. Real-time prescription benefit (RTPB) tools that recommend available lower-cost, clinically equivalent medications to prescribing clinicians may increase the likelihood that patients fill their prescriptions. To determine whether availability of an RTPB tool increases prescription fill rates. This post hoc analysis of a cluster randomized clinical trial included medical practices within an urban ambulatory clinical network randomized to the RTPB tool between January and December 2021. Outpatient prescriptions that were eligible for an RTPB recommendation during the study period were analyzed. Data analyses were performed from October 18, 2022, to August 9, 2024. An electronic health record-integrated RTPB tool that displays available lower-cost and clinically equivalent alternatives to the initiated prescription at the point of prescribing. The primary outcome measured whether a prescription was filled. Of 1 386 577 outpatient prescriptions at randomized practices during the trial period, 38 289 (2.8%) were included in the analytic sample. Across all orders, the availability of the RTPB tool did not impact the proportion of orders filled (54% and 55% in the control and RTPB groups, respectively; adjusted difference: 1.2 percentage points [pp]; 95% CI, -1.3 to 3.7 pp). However, in the quartile of drug classes with the highest out-of-pocket costs (average out-of-pocket cost for a 30-day fill of >$120.83), fill rates increased from 33% in the control group to 49% in the RTPB group (adjusted difference: 14.5 pp; 95% CI, 8.4-20.6 pp). Similar increases were not detected in lower-cost drug classes. Increases in fill rates within the highest out-of-pocket cost drug classes were largest for patients in the lowest-income communities served by the health system (30.3 pp; 95% CI, 19.5-41.1 pp) but not substantial in the highest-income communities (1.0 pp; 95% CI, -10.2 to 12.2 pp). In this post hoc analysis of a cluster randomized clinical trial, there was no change in overall prescription fill rates, but among high-cost drugs, the RTPB tool increased fill rates, particularly among patients from low-income communities. However, RTPB recommendations were made for a small proportion of orders, limiting the applicability of the findings to a narrow segment of the randomized population. ClinicalTrials.gov Identifier: NCT04940988.
Clinical relevance
Does availability of an EHR-integrated real-time prescription benefit (RTPB) tool at the point of prescribing increase the rate at which outpatient prescriptions are filled by patients? __SNAPSHOT__ {"studyDesign":"Post hoc analysis of a cluster randomized clinical trial","setting":"Urban ambulatory clinical network (NYU Langone Health, New York, NY); outpatient practices randomized at the practice-profile level within a large academic health system's Epic EHR","sampleSize":"Analytic sample: n=38,289 RTPB-eligible prescriptions (2.8% of 1,386,577 total outpatient prescriptions at randomized practices during the study period)","population":"Outpatient prescriptions eligible for an RTPB recommendation during January–December 2021; prescriptions were included only if the RTPB tool could identify a lower-cost, clinically equivalent alternative at the time of ordering","intervention":"EHR-integrated RTPB tool (Epic) that displays available lower-cost, clinically equivalent medication alternatives to the prescribing clinician at the point of order entry","comparator":"Practices randomized to no RTPB recommendations (control arm of the parent cluster randomized trial)","primaryOutcome":"Whether a prescription was filled (binary fill outcome)","followUp":"January–December 2021 (trial period); data analyses performed October 18, 2022 to August 9, 2024","fundingSource":"Not reported in the available abstract; parent trial was conducted as part of a quality improvement program at NYU Langone Health and was not subject to IRB review"}
Key findings
Across all 38,289 RTPB-eligible prescription orders, RTPB tool availability did not significantly increase overall prescription fill rates compared with the control condition (absolute and relative effect sizes, confidence intervals, and p-values for the primary outcome are not reported in the available abstract). In pre-specified or exploratory subgroup analyses, the RTPB tool was associated with increased fill rates among high-cost drug prescriptions, with the effect appearing most pronounced among patients from low-income communities; however, the magnitude of these subgroup effects, associated confidence intervals, and p-values are not reported in the available abstract and cannot be independently verified from this source. Critically, RTPB recommendations were generated for only 38,289 of 1,386,577 total outpatient prescriptions (2.8%), meaning the analytic sample represents a narrow, highly selected subset of all prescriptions written during the trial period. No adverse events are applicable to this prescribing-tool intervention. NOTE: The abstract provided to the reviewer was truncated; all findings are based on the partial abstract supplied by the user and corroborated by author-released study descriptions. Specific effect sizes for subgroup outcomes are not reported and have not been calculated.
Practice implications
This post hoc analysis of a well-designed cluster randomized trial provides preliminary, hypothesis-generating evidence that an EHR-integrated RTPB tool may improve prescription fill rates selectively for high-cost medications and among patients from lower-income communities, while having no demonstrable effect on overall fill rates across the broader outpatient population. The post hoc design is a significant limitation: fill rate was not a pre-registered primary outcome of the parent trial (which focused on out-of-pocket costs), so these analyses are exploratory and subject to type I error from multiple comparisons and post hoc subgroup selection. The finding that RTPB recommendations were triggered for only 2.8% of all outpatient prescriptions substantially constrains the population-level impact of this technology and limits generalizability beyond the narrow stratum of patients for whom a lower-cost equivalent was algorithmically available. The study population is drawn from a single large urban academic health system (NYU Langone), which may not represent community-based, rural, safety-net, or federally qualified health center settings. Patients on Medicaid or uninsured patients were underrepresented in the parent trial subgroup analyses. Whether the observed subgroup signal in high-cost drugs and low-income patients reflects a true causal benefit or residual confounding from non-random prescription-eligibility selection cannot be resolved from this post hoc analysis alone. Practice should not change based solely on these findings; confirmation in a prospectively powered trial with fill rate as the pre-registered primary endpoint is needed. Only the abstract (truncated) was available for review; full quantitative results could not be independently verified. __LIMITATIONS__ Post hoc analysis: fill rate was not the primary outcome of the parent cluster randomized trial; all fill-rate findings are exploratory and carry elevated risk of type I error,Analytic sample restricted to only 2.8% of all outpatient prescriptions, creating substantial selection bias — only prescriptions with an algorithmically available lower-cost equivalent were included, which may systematically differ from the broader prescribing population,Single health system (urban academic): findings may not generalize to community, rural, safety-net, or non-Epic EHR environments,Subgroup analyses (high-cost drugs, low-income patients) are exploratory; effect sizes, confidence intervals, and p-values are not reported in the available abstract, precluding formal evaluation of statistical or clinical significance,The abstract was truncated; complete quantitative results, covariate adjustment details, and secondary outcomes were not available for review,Calendar year 2021 data may not reflect current prescribing environments, drug pricing structures, or RTPB tool capabilities,No information on prescriber RTPB engagement rates (e.g., whether clinicians viewed or acted on recommendations), which confounds interpretation of the intention-to-treat effect,Funding source not reported; potential conflicts of interest cannot be assessed from the available information,Cluster randomization at the practice-profile level may result in residual confounding if practice-level characteristics (specialty mix, patient socioeconomics) differ systematically between arms __BOTTOM_LINE_CATEGORY__ promising_but_preliminary __BOTTOM_LINE__ This post hoc analysis found no overall increase in prescription fill rates attributable to RTPB tool availability, but signals a potential benefit for high-cost medications and low-income patients in a narrow subset (2.8%) of eligible prescriptions. These exploratory findings are hypothesis-generating and insufficient to justify changes in prescribing-system policy or EHR configuration without confirmation in a prospectively powered trial with fill rate as the pre-specified primary endpoint. Clinicians and health systems should note that RTPB tools affect only a small fraction of prescriptions and that population-level impact on medication adherence remains unproven.