By skyforbes 
While there is government assistance through Medicaid, Medicare, and Children’s Health Insurance Program for low-income adults, families and children, pregnant women, the elderly, and differently abled people, Americans still exist in the context of a majority private sector medical system with no universal coverage and decreasing public control.
Patients face challenges in receiving the best care for their circumstances and needs when being prescribed medication due to private companies playing a big factor into what medications are available and their costs, slow insurance processes, and whether their needs are taken fully into account. Additionally, insurance calculation processes can be a black box because it is a multi-step system involving manufacturers, distributors, pharmacies, and insurance companies with private negotiations between various steps. This can take the power away from patients when it comes to being prescribed what’s best for their overall health.
Real Time Prescription Benefit (RTPB) Tools
In order to provide patients with more knowledge on drug pricing and how factors like insurance, pharmacy, type of medication, prior authorization (PA), et cetera affect medication costs and turnaround time, Real Time Prescription Benefit (RTPB) tools were introduced starting in 2019. These electronic clinical decision support tools are integrated into existing Electronic Health Record (EHR) tools such as Epic to provide price transparency at the point of care. They provide patient and medication specific information like out-of-pocket cost of medication, drug benefit coverage and restrictions, and alternative medications from Pharmacy Benefit Managers (PBM) to providers. Prior to this, pricing information was available only to pharmacists when submitting claims. Overall, RTPB tools aim to lower out-of-pocket costs for patients through cost transparency.
RTPB tools also alert providers if a medication requires PA and suggest alternatives that do not. This feature is vital as acquiring PA approval from insurance companies can significantly delay how long it takes for a patient’s medication to reach their hands. There are a lot of contributing factors such as long processing times, lack of standardized systems for submitting PA requests, excessive paperwork, and unpredictability in general. This can compromise patients who need to receive their medication quickly. With RTPB tools, providers and patients can collaboratively discuss other faster alternatives.
Workflow of RTPB Tools
As demonstrated in demos by vendors like SureScripts, DoseSpot, and NextGen, the general workflow for RTPB tools is as follows:
- The provider selects which insurance a patient wishes to use and initiates a new prescription order
- The provider selects a medication from a database and enters dosage, refills, and pharmacy information
- The RTPB presents that medication’s estimated cost, alternative medications and their costs, their preferred level, and alternative pharmacies. It also displays whether prior auth is needed
- The provider reviews these with the patient and discusses what medication best adheres to their needs
- The provider sends the order to the pharmacy and the patient picks it up when it’s ready
Research and Limitations
Research papers on RTPB tools and their efficacy gather that “RTPB activation was associated with a higher prescription fill rate (79.8% vs 71.7%) and lower cancellation rate (9.34% vs 14.89%)” and “an adjusted 11.2% OOP cost reduction for medications” (Wong et al.). However, most also concur that there is more work to be done when it comes to data accuracy and medication inclusivity — “only a small percentage of medications (4.2%) had recommendations for medication substitutions” (Wong et al.).
Additionally, we must account for potential side effects like increased disparity for uninsured patients, advocate for the widespread implementation of RTPB tools, and advance RTPB tools themselves.
History and Development
RTPB tools are a newer tool with considerable potential but room to grow. Looking back, RTPB tools have come a long way since 2019 when the National Council for Prescription Drug Programs (NCPDP) approved a beta version of the NCPDP Real-Time Prescription Benefit Standard. In 2021, the Centers for Medicare & Medicaid Services (CMS) mandated that Medicare Part D sponsors are required to implement at least one RTPB tool by January 1, 2023, with two studies conducted on their efficacy since then. In 2024, Congress passed the Real-Time Benefit Tool Implementation Act requiring that Medicare prescription drug plan (PDP) sponsors implement at least one electronic, real-time benefit tool by January 1, 2027. These legislative milestones reflect growing recognition of the tool’s potential in supporting prescribing decisions.
As we look forward to the future of what RTPB tools can be, we must explore existing gaps to best improve provider experience and patient care.
Broader Considerations
While cost is an important factor, it’s not the only factor that patients consider. Aside from the necessary considerations — whether a new medication is incompatible with their current medication, allergies, existing health conditions, etc — quality of life, side effects, and much more play into what’s right for a patient.
According to the paper “Factors Contributing to Medication Adherence in Patients with a Chronic Condition: A Scoping Review of Qualitative Research”, Kvarnström et al. highlights that concerns like how well a medication fits into a patient’s work schedule influences how well it integrates into a patient’s life and medication adherence. “Difficulties in integrating medication into daily life can prevent patients from taking medication as prescribed. Working life may require shift work, and night shifts may make it difficult to have regular routines” (Kvarnström et al.).
Even whether a medication contains side effects like vivid dreams and nightmares can be an important factor to a patient. Through a conversation about different patient needs, Fabio Castellanos, a peer of mine at ArtCenter’s Masters of Design in Interaction Design program, described his experience where his doctor inquired about his sleep quality when prescribing him Malaria prevention medication for his trip to the Amazon. One of the side effects was nightmares, and his doctor wanted to verify if he had preexisting sleep problems so that they could look into alternatives if so.
Quality of life is an important yet often overlooked need. Fabio’s experience is a good case study on the positive effects of attending to needs like these. On the other hand, his experience highlights potential cascading negative effects if quality of life needs are disregarded. A medication with side effects that include nightmares will affect his sleep, and consequently his well-being in all factors of life.
Currently, RTPB tools are effective simulators where providers can see how different factors affect a patient’s out-of-pocket expenses in live time. Still, they only provide pharmacies, medications, insurances, dosage, and day’s supply as adjustable factors. Other critical patient needs remain unaddressed within these tools.
Proposition
To be inclusive for a wider array of patient needs, I propose building RTPB tools on top of a recommender system. This expanded tool uses machine learning to recommend the best fit medication for a patient’s unique needs as well as alternatives — similar to how services like Netflix and Youtube analyze user data and media information to build predictive models that suggest media tailored for the user. While cost is an important consideration, I envision the future of RTPB tools to account for needs like medication compatibility, allergies, existing health conditions, quality of life, side effects, and anything patients prioritize as the tool evolves.
Features
This proposed tool will include a real time, dynamic feature where providers can interact with the recommendation results and suggest another direction they’d like to nudge it towards. For example, if a patient conveys that they want to prioritize having less side effects, or they want to account for a new need not previously mentioned after seeing the recommended medication and alternatives, the provider can interact with their RTPB tool to receive new recommendations. This empowers providers and patients to have a space for open communication on their needs, backed by data.
By directing the future of RTPB tools towards recommender systems, we can also improve the provider’s user experience by making it easier to process the data existing RTPB tools supply them with. With a complex system that accounts for so many factors, it’s more important now than ever to focus on lessening providers’ cognitive load and manual labor. Existing RTPB tools provide raw data with less consideration for information architecture. They also require ample manual labor from providers as they have to scroll through all alternative options in each factor’s section and select different options.
For example, DoseSpot’s RTPB tool lists alternative pharmacies and medications in a vertical scrolling list, making it so that providers have to scroll to access all information about these alternatives. This makes it difficult for providers to parse through them. Shifting from manual browsing to data-backed suggestions would streamline decision-making and optimize its workflow.
Another area of growth this tool can advocate for is counteracting the black box precedent regarding information transparency that insurance companies created. While it would require governmental interference to improve cost and other source transparency, we can take advantage of how recommender systems work by including how it arrived at its conclusion in the RTPB tool itself. This empowers providers and patients to have as much information as possible about why a medication is a good fit before prescribing it.
Technical Feasibility
Moving forward, we must consider what kinds of data and technology are needed to make this vision a reality.
RPTB tools currently source their data from Pharmacy Benefit Managers (PBM). The paper “Where Do Real-Time Prescription Benefit Tools Fit in the Landscape of High US Prescription Medication Costs? A Narrative Review” discusses this process where “the PBM determines eligibility by cross-referencing enrollment data from the EHR with the PBM’s insurance and prescription coverage information. If the patient has medication coverage through the PBM, it returns prescription benefits information (specific to that patient) to the RTPB vendor” (Wong et al.). PBMs contain data about drug utilization, formulary structure, and benefit design. It’s private because it’s regulated by the Health Insurance Portability and Accountability Act (HIPAA).
In order to have the data needed for this proposed tool, we would need data about how effective a drug is for different needs. Providers will most likely need to input their patient’s needs into their EHR profile, which could then be used by PBMs to create their recommendation models through supervised algorithms like neural networks, graphical data structures, and other machine learning methods. PBMs do not have this data, but they could build off their existing data such as patient adherence and medical utilization patterns. Furthermore, they could draw from the FDA’s post-market database for drug safety and effectiveness. By linking existing data to new patient needs data, they could evaluate whether a medication fits a patient’s needs and use that to make future decisions.
Interoperability and Adoption
Two key factors for successful implementation are interoperability — how well different healthcare systems integrate with each other — and how best to assimilate providers to this new system.
According to Arotin Hartounian, an Experience Development Manager at Ascension who I interviewed, “technology from healthcare vendors are fragmented because there is less technological support in healthcare”. He also discussed how “services and systems already exist based on the current load of providers, and we don’t want to add to their tasks. This is especially important in healthcare since stakes are high as it directly affects patients, and we need to make sure it doesn’t affect the quality and safety of care”.
Fortunately, RTPB tools are already integrated into EHRs’ prescription management screens which makes it easier to integrate a new RTPB tool as it would just replace the existing tool. This way, we avoid adding new screens to make this transition smoother for providers in an already complex system. Within the tool itself, employing modular design could help support interoperability as it allows the tool’s design to be more adaptable, scalable, and standardized, especially for more complex interfaces like this one.
Conclusion
RTPB tools are an up and coming tool with great potential towards supporting providers and patients in choosing the best medication for their needs. However, to fully realize their potential, they must evolve beyond cost considerations. By incorporating machine learning-powered recommender systems, these tools can become more inclusive of all patient needs, from necessary considerations like medication compatibility to quality of life needs like medication side effects. This evolution would not only empower patients and providers but also challenge systemic limitations in the U.S. healthcare system.
Citations
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