The American Journal of Managed Care
October 2022
Volume 28
Issue 10

Implementation and Cost Validation of a Real-time Benefit Tool

This study evaluates impact of a real-time benefit tool on medication access and physician and pharmacy workflows at a large academic medical center.


Objectives: To assess the accuracy of a real-time benefit tool (RTBT) that is compliant with the standards of the National Council for Prescription Drug Programs (NCPDP) in a large academic medical center.

Study Design: Observational study of electronic health records and pharmacy records from July 14, 2019, through January 14, 2020, across all ambulatory clinics and outpatient pharmacies in the health system.

Methods: Main assessments included (1) demographic characteristics of patients in whom the RTBT was used and those in whom it was not used, (2) types of changes most frequently made to medication orders upon reviewing the RTBT, and (3) comparison of the out-of-pocket costs for prescriptions vs the RTBT-generated estimates.

Results: The most common modifications made to prescriptions due to RTBT use were changes in days’ supply (44%) and the quantity of medication (69%). In more than 98% of prescription orders, patients’ out-of-pocket costs were either equivalent to or lower than the estimates generated by the RTBT.

Conclusions: Current standards established by NCPDP yield accurate patient out-of-pocket estimates and could serve as a national standard for all Part D sponsors.

Am J Manag Care. 2022;28(10):e363-e369.


Takeaway Points

  • Medicare Part D plans are required to provide an out-of-pocket cost estimate for drugs (via real-time benefit tools [RTBTs]) at the point of care (POC) for at least 1 electronic health record system and provider within network.
  • Impact of RTBTs on medication access, physician workflow, and pharmacy workflow has not been published.
  • Use of an RTBT improved medication access and reduced burdens on physician and pharmacy workflows.
  • Providing accurate cost estimates to patients at the POC may lead to them having greater medication access by supporting successful fulfillment and use of new prescriptions.


More than a decade ago, the Institute for Healthcare Improvement launched the Triple Aim initiative, which strives to help health care organizations improve patients’ individual experiences of care and improve the overall health of the population they serve, while reducing the per capita cost of care.1 Despite this, the high cost of drugs continues to play a major role in medication nonadherence and abandonment,2-8 which may inadvertently lead to adverse health outcomes and greater health resource utilization.9-11 Medication nonadherence and suboptimal medication management have recently been estimated to cost the US health care system approximately $528 billion annually.12

Data suggest that patients desire lower-cost prescriptions13 and that patient-physician discussion on cost and affordability may lead to overall reduction in out-of-pocket (OOP) costs.14 Providing physicians with information on prices of tests and procedures has been generally associated with selection of cost-effective tests,15,16 although the results for cost-effective medication selection have been mixed.17,18 In an effort to make a meaningful impact on patient medication use and adherence, a prescriber would need patient-specific information for OOP costs.

In 2018, the HHS Blueprint to Lower Drug Prices and Reduce Out-of-Pocket Costs identified several potential mechanisms to reduce pharmaceutical spending, including price transparency.19 The attempt to increase price transparency included the CMS rule in May 2019 requiring pharmaceutical manufacturers to display in direct-to-consumer advertisements an estimated 30-day supply wholesale acquisition cost for the drug being promoted.20 It is unclear, however, whether this rule has had any direct impact on patients’ preferences due to significant heterogeneity in benefit design, coverage, and formularies in the United States.21

Soon after the CMS rule on direct-to-consumer advertisement, another rule was finalized on May 23, 2019; its numerous provisions include the requirement for implementation of a real-time benefit tool (RTBT).22 According to the rule, effective January 1, 2021, all Medicare Part D sponsors would be required to implement at least 1 intermediary vendor’s RTBT and ensure its compatibility with 1 or more providers’ electronic health record (EHR) system. The language within the final rule does not cite any existing or proposed standard and instead calls for “accurate,” “timely,” and “clinically appropriate” information to be returned by the tool.22,23

The National Council for Prescription Drug Programs (NCPDP) is a CMS Designated Standards Maintenance Organization and is instrumental in developing and maintaining standards for a wide variety of Medicare Part D rules. The NCPDP Real-Time Prescription Benefit task group has created a set of proposed standards for real-time information exchange among pharmacy benefit managers (PBMs), intermediaries, and the EHRs for several use case scenarios.

Johns Hopkins Medicine (JHM) received funding from the NCPDP Foundation to evaluate the effectiveness of these standards in providing accurate and timely information to clinicians and to assess the overall impact on OOP costs for patients. Another study at JHM has found a positive association between clinician RTBT use and greater prescription obtainment by patients.24


The Real-Time Benefit Transaction Process

A real-time benefit transaction process starts with the provider selecting a drug within their EHR, which then triggers the request for information regarding coverage status, estimated OOP costs, utilization management (UM) edits, and less costly alternatives within the patient’s formulary, if available.

The information flow is then triaged through an intermediary entity, which relays data elements to the patient’s PBM claim processor. The estimated OOP costs account for the patient’s benefit design, including deductible thresholds. RTBT implementation in our institution was programmed to display information on alternative medications to the provider when the cost of the alternatives’ 30-day supply was less than that for the medication selected by the provider. No minimum threshold for difference in costs was implemented. Figure 1 displays this process for the intermediary used by our EHR system. A more detailed clinical workflow for the RTBT can be found in the eAppendix Figure (eAppendix available at

At our institution, the RTBT was implemented as part of an upgrade to the EHR without formal training or guidance to the prescribers regarding the tool. An RTBT estimate was shown to the provider only if it met the following predefined criteria: the alternative price difference threshold must be greater than zero (ie, any cheaper medication), the maximum number of alternatives to be shown must not exceed 5, the sorting method is based on cost (showing drugs with lowest cost first), and the provider must not have opted out of RTBT prompt generation. The PBMs and claims processors create and implement UM strategies. In the Surescripts system, there is no mechanism to ascertain accuracy of these UM edits based on the intention of the PBM; therefore, we presume that these edits are implemented accurately.

Data Sources

The data for our analysis were collected from the ambulatory divisions of JHM. Within JHM, the RTBT was implemented in July 2019. Data were collected post implementation of the tool, through January 2020. All providers had the capability of opting out of participation in RTBT, thereby suppressing all prompts from the tool.

The RTBT implemented at JHM during the analysis time frame was closely aligned with the beta version of the NCPDP standard and was administered by Surescripts, which serves as the intermediary and communicates data elements to claims processors.25

We gathered all patient and physician demographic data, as well as details of RTBT use, from our EHR, Epic; specifically, these data were collected from outpatient and ambulatory care encounters in which a medication order was placed during the study period. Data were further divided by those encounters in which the RTBT generated alternatives within the study period. For all medication orders sent to a Johns Hopkins outpatient pharmacy, the system database EnterpriseRx McKesson Pharmacy Systems was utilized. The OOP cost validation assessed the co-payment paid by the patients at pharmacy point of sale (POS) compared with what was estimated by the RTBT.

Data Analyses

In our descriptive results, we outline patient and physician demographics and impact of RTBT utilization. Among the key components we assessed were the potential cost savings, the types of changes most frequently made to the prescriptions, and differences in estimated OOP cost at the point of care (POC) and the co-payment paid at the pharmacy POS.


From July 14, 2019, through January 14, 2020, there were 368,655 medication orders placed for 125,498 patients. There were 1572 orders (0.43%) generated due to modifications suggested within the RTBT at the POC for 1446 patients (1.2%). Table 1 [part A and part B] consists of demographic information for providers who made changes to an initial prescription per RTBT suggestion and patients for whom these changes were made. The overall population (hereafter referred to as OP) consists of patients and providers with any encounter in which a medication order was placed in the study period, regardless of RTBT use. Distribution of provider types was similar between the RTBT and the OP groups, with physicians being the most common prescriber type at 82% and 84%, respectively. Internal medicine and family medicine departments accounted for 51% of the orders for which RTBT-related changes occurred, whereas these departments were only 21% of all encounters during study. Approximately 28% of the encounters in the OP group were for infusion therapy and radiology; this finding is expected because pharmacotherapy initiated and modified within these departments is typically not covered under a prescription benefit, thereby leading to underutilization of the RTBT by these specialists.

Compared with the OP group, the RTBT group tended to consist of more Black patients (39% vs 31%), fewer White patients (48% vs 55%), patients belonging to higher Adjusted Clinical Group resource utilization bands (low morbidity, 6% vs 10%; very high morbidity, 23% vs 16%), and patients with higher chronic condition counts (median, 4 vs 3). The health insurance market segments were similar in both groups.

Changes Made to Prescriptions

Table 2 provides a summary of the types of changes in the 1572 orders. An order may be counted more than once in the changes outlined within the table, as multiple changes could have been made within a single order (eg, change in both days’ supply and pharmacy within a single order). The most common type of change made was to the quantity (69%). Selection of recommendations within the RTBT resulted in more favorable coverage status 78% of the time. Such changes also resulted in more favorable prior authorization status for a drug 67% of the time.

Changes in quantities included both those that resulted in greater days’ supply and those that were consistent with available packaging (eg, changing quantity for a mupirocin ointment tube from “1” to “30 grams”). The vast majority of the days’ supply changes consisted of changing a prescription from a 30-day supply to a 90-day supply (76%).

Drug identifier (ID) changes occurred in 33% of the changed prescriptions. These reflect both changes to the actual drug molecule (eg, captopril to lisinopril) and changes to formulation (eg, changing a more expensive formulation of extended-release metformin to a less expensive one). The most common drug ID changes may be found in eAppendix Table 1.

The pharmacy type change most commonly reflects a change from retail pharmacy to mail order pharmacy.

Changes in quantity unit were highly heterogeneous and included changes from “grams” to “mL,” “units” to “grams,” and the like. The most common changes, however, were tablets to capsules (32%) and vice versa (20%).

Cost Validation

The validation of RTBT-generated OOP cost estimates could be completed only for patients who picked up their medications at a Johns Hopkins outpatient pharmacy. These prescription costs could be reliably compared when the days’ supply ordered matched the days’ supply dispensed and received by the patient. Such criteria narrowed down the number of evaluable prescriptions to 64. In Figure 2, the differences between the estimated OOP cost in the RTBT and the price paid at the pharmacy POS are displayed. For 70% of the prescriptions, there were no discrepancies. In 94% of the prescriptions, the discrepancy was less than $10. In all but 2 instances, the discrepancies were due to patients having a lower actual co-payment than what was displayed in the RTBT. This is likely due to use of pharmaceutical manufacturer co-payment assistance or use of a supplemental benefit. Altogether, for greater than 98% of the orders, the OOP costs were either equivalent to or lower than the RTBT-generated estimates.


Our findings suggest that implementation of an RTBT system utilizing NCPDP standards leads to accurate information at the POC regarding patients’ OOP costs, potentially leading to fewer issues with medication therapy initiation and adherence.

The RTBT population had a higher proportion of Black patients, was generally older, and had higher morbidity and more chronic conditions compared with the OP. These characteristics are often associated with more vulnerable populations with advanced conditions and polypharmacy. These also tend to be the very patients who have difficulty affording their medications.26 Increases in medication affordability and adherence play a role in improving overall health outcomes.9,27,28

PBMs often employ UM strategies, such as prior authorization, to promote cost containment and appropriate medication use. Significant heterogeneity in coverage policies and UM edits for different insurers make it difficult for providers to know the details of each patient’s insurance coverage and the financial burden a patient may face with various pharmacological therapies. Moreover, many clinicians may not realize differences in coverage policy for various formulations (eg, ointments vs creams) or different brands of the same molecule (eg, amoxicillin formulations). Such differences may not have clinical relevance for most patients but could have substantial impact on OOP costs. Knowledge of coverage and price at the POC may prevent or alleviate arduous coverage determination and appeals processes and reduce callbacks from patients’ pharmacies (in states where prescription quantity and refills cannot be modified at the pharmacy level), all of which often lead to significant provider dissatisfaction and high administrative burden.29-32 Use of an RTBT may also result in lower administrative costs for PBMs, health plans, and pharmacies.

The OOP cost estimates generated by the RTBT using NCPDP’s proposed standard were largely accurate in our sample, with only 3% of prescriptions having an RTBT estimate that was lower than the actual OOP cost. All other discrepancies tended to result in lower actual OOP cost for the patients and were due to use of drug manufacturer coupon, dual prescription benefit coverage (eg, Medicaid or Program of All-Inclusive Care for the Elderly for Medicare beneficiaries), or other charitable secondary coverages at the pharmacy POS. Our decision to set $10 as the cutoff for an acceptable difference between projected and actual prescription cost was made in the setting of a lack of rigorous discrete choice experiments or other studies evaluating the acceptable thresholds in the literature for RTBT for patients and clinicians. It is possible that in aggregate, a $10 difference may be unacceptably high or that even a higher threshold could be reasonably set. Future studies should evaluate this question. We acknowledge inherent bias in our cost validation analysis, as we could review only prescriptions filled by the Johns Hopkins pharmacy and picked up by the patient, which resulted in a small sample size. Our analysis would not have identified any inconsistencies in the RTBT estimate and POS cost, which may have led to medication abandonment. Moreover, our analysis of RTBT accuracy was limited to cost comparisons, whereas in the real world, several factors may contribute to the effectiveness and accuracy of RTBTs, including accurate implementation of UM edits. Currently, there exists no mechanism to evaluate the accuracy of UM implementation due to potentially wide variations in these edits across different PBMs and claims processors. Additional studies are ongoing to evaluate the impact of RTBTs on pharmacy workflows and prescription abandonment.


There are several limitations to the data that we used. First, several patient groups were excluded from our analyses because their PBMs and/or claims processors did not participate in Surescripts’ RTBT. The Surescripts RTBT also excludes uninsured patients. Second, the data from our EHR allow us to ascertain whether the RTBT suggested an alternative with expected OOP costs, but not whether the provider viewed this information. Third, the cost estimates generated were based on the PBM information within the EHR and could not account for change in coverage (eg, health plan, benefit design, health insurance type, calendar year) or change in PBM, and we were unable to verify whether change in coverage led to discrepancy in OOP cost estimates. Fourth, the RTBT system is reliant on costs generated by claims processors for PBMs, thereby excluding estimates for laboratory tests, procedures, durable medical equipment, or medications that are generally not processed by PBMs (eg, intravenous infusions). Finally, we could only validate the expected OOP costs provided by the RTBT using prescriptions fulfilled by a Johns Hopkins outpatient pharmacy where providers utilized the RTBT, as it is only for the Johns Hopkins outpatient pharmacies that we have the POS payment information for all prescriptions sold. Excluding orders where providers did not utilize the RTBT precludes evaluation of accuracy of UM edits for such orders. A larger study with higher RTBT adoption and data from several pharmacies is needed to validate our findings in heterogeneous populations. As the estimates are solely generated by the PBM, any further savings with use of drug manufacturer assistance are not accounted for within the estimate. Furthermore, the RTBT at our institution provides OOP cost estimates based on available information on file, which may be limited to primary prescription insurance coverage and may not account for any secondary prescription coverage. A high OOP cost estimate at the POC could lead to lack of prescription fulfillment by a patient who is unaware of the impact of secondary prescription coverage.

With its use affecting just 1.2% of patients, the overall rate of adoption of the RTBT at our institution was low, which is consistent with adoption of other EHR-based clinical decision support tools.33 The reasons for this may be multifactorial, including lack of training of providers in use of the tool, inadequate integration into clinical workflows, or lack of provider incentive to use a tool that patients and families are unaware of or are not requesting. Clinicians may find the OOP cost information helpful but may feel frustrated with the lack of information for all patients, which may result from missing information regarding patients’ PBMs in the EHR, or because of a lack of participation of the claims processor with the institution’s intermediary entity that is responsible for implementing the RTBT. We are currently exploring mechanisms to derive PBM information in the EHR with patients’ medical insurance details. We also believe that adoption of the NCPDP standard for RTBTs will lead to rapid adoption of the RTBT and coordination among various claims processors and intermediaries.

Policy Implications

Our findings are relevant for the currently implemented CMS rule on RTBT use integration in EHRs for use by providers. Consistent standards applied across a myriad of intermediaries, PBMs, and EHRs would ensure accurate and consistent information flow for all beneficiaries.

Our findings will inform all stakeholders for another recently finalized rule, which seeks to implement RTBTs for Medicare beneficiaries effective January 1, 2023.34 This rule would require the existing RTBT capabilities to be expanded directly to beneficiaries. It would result in provision of real-time OOP cost information via patient portals or telephone calls and would allow plans to incentivize their members to use the RTBT. Adoption and utilization of this standard would ensure that members view consistent and accurate information at the POC and via a patient portal. Direct-to-consumer RTBTs informed by high-quality standards could provide the push to broad adoption by providers and health systems, which does not currently exist for provider-facing processes.


The results from our 6-month evaluation of implementing an RTBT closely aligned with the NCPDP beta standard show a high degree of accuracy in estimates of OOP costs for drugs among patients who presented to ambulatory care clinics and obtained their prescriptions from our institution’s outpatient pharmacies. Advancement of the NCPDP standard is important to the implementation and uptake of RTBTs in health care institutions and will provide value to patients, as well as health care providers. Integration of such a standard in a formal CMS rulemaking process will further ensure consistent delivery of accurate information across all payers, intermediaries, and EHR platforms.


The authors would like to acknowledge Dr Danny Lee, chief medical informatics officer at the Johns Hopkins Community Physicians program.

Author Affiliations: The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington (SB), Seattle, WA; Johns Hopkins Bloomberg School of Public Health (AD), Baltimore, MD; Division of Pediatric Gastroenterology, Department of Pediatrics, The Johns Hopkins University School of Medicine (SDM), Baltimore, MD; The Johns Hopkins University School of Medicine (AB, KS, AD), Baltimore, MD; The Johns Hopkins Hospital (JM), Baltimore, MD.

Source of Funding: The funding for this project was provided by the National Council for Prescription Drug Programs (NCPDP) Foundation.

Author Disclosures: Drs Merrey and Davison report receiving grant funding for this project from the NCPDP Foundation. Dr Merrey also reports additional grant funding from the Pharmacy Quality Alliance and National Pharmaceutical Council to evaluate the impact of real-time benefit tools on patient access to prescriptions. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (AB, JM, AD); acquisition of data (SB, KS, JM); analysis and interpretation of data (SB, SDM, AB, KS, JM, AD); drafting of the manuscript (SB, SDM); critical revision of the manuscript for important intellectual content (SB, SDM, JM, AD); provision of patients or study materials (JM, AD); obtaining funding (JM, AD); administrative, technical, or logistic support (AB, KS, JM); and supervision (AD).

Address Correspondence to: Shiven Bhardwaj, PharmD, The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, 1959 NE Pacific St, Box 357630, Seattle, WA 98195-7630. Email:


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