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Transformative new pharmaceutical treatments hold the promise of changing patients’ lives by slowing and curing disease, improving well-being, and advancing quality of life. Yet, as treatments come to the market, they highlight tensions within our system. On one hand, new treatments must be affordable for patients to access them. On the other hand, medical innovation requires continued investment and the promise of future financial returns for investors. That these two factors sometimes come into conflict is not a new idea. It has long been the subject of debate on Capitol Hill, among policy makers, and across private insurers. But if we could better understand the dynamic balance between these forces, perhaps we could develop more effective tools for advancing both goals—access and innovation.

The first step is knowing which innovations improve health outcomes for which patients. But too often, the efforts to track the outcomes of patients receiving any given treatment are inconclusive, incomplete, inefficient, or ill-timed to guide payer decision making and coverage.

In this article, we explore whether the growing availability of real-world data and advancements in technology will be sufficient to enable outcomes tracking and ultimately support new payment models. And if not now, what will be required?

Necessary But Not Sufficient

In evaluating new treatments, the term real-world data is used to distinguish data gathered from a variety of sources including electronic health records, medical claims, and registries about a wide array of patients receiving the drug or intervention in real-world settings. These data are typically far more expansive than those discrete samples gathered in highly controlled clinical trials that the Food and Drug Administration has historically insisted on when first evaluating a drug for approval. In addition, real-world data offer payer organizations more relevant and representative information about their member populations and provider networks.

Recent and notable investments in real-world data platforms are making data more available and connected. Technological advancements, such as artificial intelligence (AI), can address data complexity and connectivity while also allowing data privacy and security. Together, these efforts hold the promise to make tracking individual patient outcomes more robust, repeatable, and rapid.

In parallel, payers are exploring new innovative value-based payment models to mitigate financial risk and uncertainties regarding the clinical durability of these new treatments. For example, private insurers have announced outcomes tracking contracts for several new treatments, including sickle cell disease gene therapy. Similarly, the Centers for Medicare and Medicaid Services Innovation Center recently announced further details on the Cell and Gene Therapy Access Model. This program provides infrastructure for state Medicaid organizations and biopharmaceutical manufacturers to collect outcomes and negotiate payments based on those individual patient outcomes.

These existing and emerging capabilities are promising, but we believe that they will be insufficient to address affordability and patient access for new treatments. We identify three remaining challenges that researchers, regulators, and payers must overcome to accelerate outcomes tracking and payment innovation.

Existing And Emerging Capabilities

Increasingly Efficient And Scalable Data Collection, Connection, And Adjudication

The fact that care delivery occurs both within and across health care systems often generates silos of clinical and claims data. And, as patients move throughout the patchwork health insurance coverage system, the ability to track outcomes over time is often disrupted. Creating longitudinal patient or population outcomes requires significant time and resources to transform these fragmented data systems.

Recent real-world data platforms created by publicly funded entities, private-sector organizations, multistakeholder consortia, and clinical societies hold promise. These efforts improve infrastructures for research, enhance understanding of specific diseases (for example, hemophilia), or enable learning about specific procedures or products (for example, transplantation or cellular therapies). However, few of these platforms collect both clinical and payment information.

Even when researchers have access, they face time and resource intensive tasks to transform data and adjudicate outcomes. New common data models, such as the National Patient-Centered Clinical Research Network (PCORnet) model, streamline efforts by allowing data to be maintained locally while expediting the collation of results. Other efforts provide standard data use agreements, create contract-specific limited data networks to address privacy and security concerns, or develop data partnerships with centers of excellence to simplify data aggregation.

Together, the efforts to create data collection infrastructures and streamline processes can accelerate outcomes tracking. These efforts enable repeatable and scalable processes across clinical conditions and organizations but have not been widely used for payment innovation.

Emerging Privacy And Security-Preserving Technologies

Legislation meant to ensure patient consent, privacy, and secure data can often constrict data sharing and preclude outcomes tracking. To protect patient privacy, new technologies are increasingly replacing personal identifiers with placeholder values or pseudonymized data.

In addition, AI and machine learning technologies offer new capabilities to develop algorithms and adjudicate outcomes more quickly while protecting personal health information. Finally, other efforts create secure zero-trust data environments without exposing data or algorithms. These enclaves can preserve competitive advantages for stakeholders sharing data and enable longitudinal data tracking without creating complicated governance models.

Although technology can enable data sharing and safeguard privacy and security, there’s more work to do. To overcome data-sharing constraints and track outcomes, we must more broadly apply these rapidly emerging technologies while advancing stakeholders’ awareness and understanding of the opportunities they present.

Remaining Challenges

Despite the promise of emerging capabilities, challenges impede the scalable implementation of outcomes tracking and payment innovation.

Absent Meaningful And Measurable Metrics

Measures meaningful to patients and payers are rarely identified or collected. Patient fatigue, for example, is a key domain for many clinical conditions, but it is not captured by existing ICD-10 or administrative claims. To address these challenges, initiatives such as the Health Outcomes Observatory (H20) consortium bring stakeholders together to define, collect, and incorporate patient-reported outcomes into standard data collection. Agreed-upon outcomes metrics, endorsed by all stakeholders, which can be implemented and accessed in clinical practice, are critical for scalable payment innovation.

Efforts to enrich existing data with more clinically nuanced information are underway. For example, research networks have supplemented specific clinical measures for targeted conditions (for example, respiratory measures to assess COVID-19 outcomes). Disease or product registries such as the World Federation for Hemophilia Gene Therapy Registry often incorporate patient-generated data. Finally, clinical data collected during care delivery (for example, specialty pharmacy care or prior authorization attestation) can add clinical context.

These solutions offer important first steps, but they only provide point-in-time and one-off disease solutions rather than sustainable platforms to identify and integrate meaningful, measurable, and clinical- and patient-relevant outcomes over time across numerous conditions.

Misaligned Incentives For Sharing Data Across Stakeholders

Creating a holistic record of outcomes from diverse clinical interactions remains challenging. Too often, the benefits of data sharing are overshadowed by the costs and disincentives associated with transforming data into a connected and longitudinal record.

To make data more accessible, federal and state officials have created policies and regulations to make data sharing more seamless. The newly launched Trusted Exchange Framework and Common Agreement established by the 21st Century Cures Act simplifies electronic health information exchange by setting principles and minimum standards for data interoperability. In contrast, several US states require systematic submissions of medical, pharmacy, and dental payment information to state-specific all-payer claims databases in an effort to guide health care affordability, efficiency, and quality.

In the private sector, incentives for data sharing are emerging. For accountable care organizations, data sharing is critical to predict and manage risk. Some health plans are establishing network contracts with hospitals and providers that encourage more real-time data sharing in exchange for shorter revenue cycles for payment. Finally, new vertically integrated models create business incentives to enable the flow of data across the organization.

However, efforts to collect and share data that benefit multiple stakeholders are rare. One illustrative example is the Center for International Blood and Marrow Transplant Research (CIBMTR) registry. Required by laws permitting stem cell research, the CIBMTR collects data on outcomes associated with allogeneic hematopoietic transplants in the US. Gathering and maintaining this data is resource-intensive, but it is also used for credentialling Centers of Excellence—a critical designation for reimbursement from some payers. By leveraging the same outcomes data, the CIBMTR has found a powerful way to align incentives and ensure sustainable, high-quality data collection.

Data, once viewed as a means to preserve negotiating power, hold the potential to identify competitive advantages, reduce administrative costs, and improve care quality. To enable better and more complete data for outcomes tracking, we must pursue a mixture of public standards, bonus payments, penalties, and regulations.

Lack Of Leadership And Governance

Beyond a shift in incentives, leadership and governance are also essential. Government-led efforts in the US have sought to increase the adoption of electronic health records, improve data interoperability and exchange, and consider evidence based on real-world data to support regulatory decisions. However, few efforts address payment innovation beyond guidance for specific registries to enable coverage with evidence development policies.

Without a mandate or leadership infrastructure, tensions will persist. Ambiguities regarding the level and quality of evidence to track patient outcomes will remain. Without defined governance, stakeholders are unlikely to agree on who will contribute and access data. We must also align public, private, patient, and provider interests and that requires leadership. It’s necessary to: create and sustain momentum for tracking outcomes; sustain biomedical innovation; and balance patient access and affordability concerns.

An Urgent Call To Action

Care delivery and payment must catch up with the rapid advancements in biomedical innovation. Otherwise, patients struggling to afford these treatments may be unable to access them. As long as reimbursement continues to be based on cost, instead of value or outcomes achieved, innovation will suffer, and so too will patient health.

While real-world data platforms evolve in exciting ways, too often, these efforts are not fit for payment. To minimize waste and inefficiency, we must leverage these infrastructures rather than create a parallel set of data platforms to meet payer needs. As we outline, the most critical first step is to expand emerging real-world data and systems’ capabilities aimed at enhancing the efficiency and scale of data collection and outcomes adjudication while preserving patient privacy and data security. However, existing challenges due to the absence of meaningful and measurable metrics, misalignment of stakeholder incentives, and lack of leadership and governance will impede outcomes tracking at scale. To overcome these challenges, we have sought here to crystallize a core set of actionable principles and requirements for payer-ready outcomes tracking.

This is an ambitious and attainable goal. There is precedent for complex and collaborative action to standardize outcomes, adopt adaptive regulatory approaches, and design new financing and reimbursement models. These precompetitive multistakeholder initiatives offer important lessons for those of us committed to tracking outcomes for payment innovation and patient access. They make clear the potential impact of taking at least three key actions: First, focus on significant challenges that cannot be tackled by any single stakeholder. Second, align incentives among all stakeholders to improve patient outcomes. Third, build trust across stakeholders through sustainable, neutral third-party leadership and shared governance.

Today, affordability concerns threaten patient access. Health care systems can preserve biomedical innovation by rewarding value and improving patient outcomes. This requires building upon existing real-world data platforms and leveraging emerging technologies and capabilities. However, it will also necessitate defining and sustainably collecting meaningful metrics, incentivizing data sharing and partnerships aligning stakeholder benefits, and establishing multistakeholder leadership and governance.

Fostering these environmental changes to accelerate outcomes tracking and enable payment innovation is more than any stakeholder can solve on their own through operations, regulations, or legislation. To address affordability and access concerns associated with the new and transformative therapies, we must take collective and aligned action.

Authors’ Note

Authors Hirsch and Barlow are employees of Tufts NEWDIGS, which includes members and collaborators from the academic, biopharmaceutical, foundation, health care policy and regulatory organizations, legal advisers, patient advocacy commercial and private payers, public payers, pharmacy benefit managers, provider systems, self-insured employers, solution providers, and reinsurance and stop-loss organizations. Stamm and Styliadou are co-leads at the H20 project, which is supported by Takeda and coordinated by the Medical University of Vienna. Styliadou is employed by Takeda. Huang is a former employee of Takeda.

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