A digital tool designed to show prescription costs at the moment a clinician orders a medication did not change overall prescription fill rates in a post hoc analysis of a cluster randomized clinical trial. Yet the same intervention appeared to make a meaningful difference for a narrower and economically important group: patients prescribed high-cost drugs. The increase was especially pronounced among patients living in low-income communities, suggesting that access to real-time pricing information may influence medication decisions when financial barriers are most likely to affect whether a prescription reaches the pharmacy counter.
The findings, published in JAMA Health Forum, examine the impact of a real-time prescription benefit, or RTPB, tool. These systems connect electronic health records with insurance and pharmacy-benefit data to estimate what a patient may pay for a medication before the prescription is finalized. Depending on the patient’s insurance coverage, the technology can display expected out-of-pocket costs, identify lower-cost alternatives, or suggest medications that are covered more favorably by a health plan. The goal is to replace the uncertainty that often surrounds prescription prices with actionable information during a clinical encounter.
Prescription affordability is a major source of medication nonadherence. A clinician may prescribe an evidence-based treatment, but the patient’s ability to begin or continue that treatment can depend on a copayment, deductible, coinsurance requirement, or lack of coverage. In conventional prescribing workflows, clinicians may not know the final cost until after the patient reaches the pharmacy. At that point, a prescription may be abandoned, delayed, or replaced through a time-consuming process involving the patient, pharmacist, insurer, and medical office. RTPB tools attempt to intervene earlier, when the treatment plan can still be changed.
The trial used cluster randomization, a design in which groups rather than individual patients are assigned to an intervention or comparison condition. In this case, clinical practices or other care-delivery units were randomized, reducing the risk that clinicians exposed to the tool would inadvertently apply its recommendations to patients in the control group. The current report was a post hoc analysis, meaning that investigators examined specific patterns within data collected from the original trial rather than testing a newly randomized intervention. Such analyses can reveal which patients or prescriptions may benefit most, although they generally provide more limited evidence than outcomes specified in advance.
Across the randomized population, the tool did not produce a measurable change in the overall rate at which prescriptions were filled. That result is important because it indicates that simply displaying price information does not automatically overcome every obstacle between prescribing and medication use. Prescription fulfillment is influenced by many factors beyond price, including transportation, pharmacy access, health literacy, competing household expenses, clinical uncertainty, side effects, changing symptoms, and whether a patient believes the medication is necessary. A cost transparency tool can address one component of that chain, but it cannot by itself resolve all of the others.
The pattern changed when researchers focused on high-cost medications. Within that subset, RTPB recommendations were associated with increased prescription fill rates. The finding suggests that the tool may be most consequential when the financial difference between available therapies is large enough to affect a patient’s decision. A recommendation for a lower-cost or better-covered alternative can reduce the likelihood that a patient will encounter an unaffordable bill after leaving the clinic. It may also help clinicians select treatments that preserve therapeutic intent while reducing the patient’s immediate financial exposure.
The association was particularly strong among patients from low-income communities, a result that underscores the unequal impact of prescription prices. For patients with limited financial resources, even modest copayments can compete with basic household needs, while a high deductible or coinsurance obligation may make a medication effectively inaccessible. Real-time benefit information could therefore function as a targeted form of financial risk reduction, allowing clinicians to account for affordability as part of treatment selection. The analysis does not establish that the tool eliminated disparities, but it indicates that its effects may be concentrated among populations most vulnerable to cost-related nonadherence.
At the same time, the investigators emphasize a critical limitation: RTPB recommendations were generated for only a small proportion of prescription orders. This restricted exposure means that the results apply primarily to the narrow segment of prescriptions for which the system produced a recommendation, rather than to every medication ordered in the randomized practices. A tool may be installed throughout a health system, yet its practical influence will remain limited if insurance data are incomplete, drug pricing information is unavailable, the medication is not included in the platform, or clinicians do not receive a usable alternative. The distinction between having access to a technology and receiving a relevant recommendation is central to interpreting the study.
The findings point toward a more precise role for digital price transparency in clinical medicine. Rather than serving as a universal solution for medication adherence, RTPB technology may be most useful as a decision-support system for prescriptions with substantial expected costs and for patients likely to face financial constraints. Future research will need to determine whether broader integration with pharmacy, insurer, and electronic-record systems can increase the proportion of orders receiving actionable recommendations. Studies should also examine whether lower-cost substitutions maintain comparable clinical outcomes, whether patients continue taking the selected medications over time, and how tools affect clinician workload and prescribing quality. For now, the analysis suggests that when price threatens access, making the cost visible at the moment of prescribing can help—but only when the system has enough information to offer a practical alternative.
Subject of Research: The effect of real-time prescription benefit tools on medication prescription fill rates, with a focus on high-cost drugs and patients from low-income communities.
Web References: https://doi.org/10.1001/jamahealthforum.2026.2739
Keywords: Real-time prescription benefit tools, prescription fill rates, medication affordability, high-cost drugs, drug costs, prescription medications, clinical trials, cluster randomized trial, post hoc analysis, health disparities, low-income communities, electronic health records, prescribing, medication adherence.
