Health systems worldwide are increasingly realizing the need for well-timed, relevant, and patient-centred evidence to supplement the adoption and reimbursement decisions of new health technologies. Conventional clinical trial data, while crucial for validating safety and efficacy, often fail to represent real-world populations, long-term outcomes, or challenges in the routine clinical practice. Patient registries, which systematically collect patient data on a particular disease or treatment, can be useful in this context. Registry data are being increasingly emphasized as their application expands beyond the traditional role in post-marketing safety surveillance to the foundation for depicting real-world value and efficacy of health technologies.(1)
Registries offer an exclusive, longitudinal view into patient outcomes, treatments, and healthcare resource utilization; making them a remarkably valuable source of real-world data (RWD) in the shift toward value-based care, where decisions made by healthcare systems must focus on the performance of health interventions in not just the randomized controlled trial (RCT) settings but across diverse and changing real-world populations.(2-4) For health technology assessment (HTA) agencies, registry data offer the ability to assess the efficacy of interventions in routine clinical practice, enable long-term outcome monitoring, and develop more rigorous economic models that account for real-life variables and healthcare resource utilization.(2, 5, 6)
European and several other HTA bodies are increasingly adopting registry data to make decisions on disease burden, treatment patterns, cost-effectiveness, and comparative effectiveness, especially in areas where conventional RCTs are limited, such as rare diseases or personalized medicine. Registries also facilitate integration of vital insights into long-term safety profiles and the advancing patient experiences over time, elements that are usually beyond the scope of pre-market studies. For payers, this facilitates more certain and customized decisions about coverage, pricing, and resource allocation, particularly when connected with cost and budget impact models.(2, 5, 6)
However, even with the growing significance of registry data, several structural and technical limitations restrict their widespread adoption in HTA and payer guidance. Data quality, standardization, and interoperability continue to pose challenges, while many registries still depend on internally specified criteria with few universally accepted standards, making cross-registry comparison difficult, which can even hamper the reliability of findings. Also, registries often struggle to keep up with the rapidly progressing health technologies, leaving evidence gaps when new interventions are introduced. These challenges highlight the growing need for standardized quality guidelines, transparent governance, and active data infrastructures that can adapt to the emerging evidence needs.(2, 5-8)
Having said that, recent digital health advancements, decentralized study models, and integration with electronic health records (EHRs) and claims data are transforming the collection and use of registry data. These innovations lessen the patient and clinician burden, enhance data granularity and relevance, and improve interoperability across systems. They also facilitate connection with refined datasets to address a variety of regulatory, clinical, and economic queries, making registry-based real-world evidence (RWE) more robust, receptive, and relevant to decisions.(9, 10)
With changes in regulatory landscapes, such as the introduction of European HTA Regulation (11) or the U.S. Inflation Reduction Act,(12) manufacturers, HTA agencies, and payers must rethink evidence types required for health technology submissions. A growing perception suggests that early and sustained collaboration between manufacturers, registries, regulators, and payers is crucial for aligning evidence generation approaches with decision-making requirements. This collective approach ensures the relevance of registries for future, while also supporting smarter, more unbiased, and impactful healthcare decisions.(2, 5, 6)
Strategically expanding the use of registry data can help the healthcare ecosystem progress towards a patient-centred model, where access, affordability, and outcomes are driven by RWE. Recognizing this potential will require collected effort and careful investment, but it can provide significant value to patients, providers, and payers alike.
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References
- Gliklich RE, Dreyer NA, Leavy MB, editors. Registries for Evaluating Patient Outcomes: A User’s Guide [Internet]. 3rd edition. Rockville (MD): Agency for Healthcare Research and Quality (US); 2014 Apr. 1, Patient Registries. Available at: https://www.ncbi.nlm.nih.gov/books/NBK208643/
- Murphy LA, Akehurst R, Cunningham D, et al. Real-world evidence to support health technology assessment and payer decision making: is it now or never? Int J Technol Assess Health Care. 2025 Mar 31;41(1):e20.
- MDIC HEPV Initiative. Payer and HTA Perspectives on Real-World Evidence for Medical Devices – Final Report. November 2021.
- Jaksa A, Arena PJ, Hanisch M, Marsico M. Use of Real-World Evidence in Health Technology Reassessments Across 6 Health Technology Assessment Agencies. Value Health. 2025; 28(6):898-906.
- Windfuhr F, de Vries ST, Melinder M, et al. Stakeholders’ Perspectives Toward the Use of Patient Registry Data for Decision-Making on Medicines: A Cross-Sectional Survey. Drug Saf. 2025 Feb 23.
- Evidera. Advancing the Use of Registry Data to Improve Health Technology Assessment and Payer Evidence. ISPOR 2024. Available at: https://www.ispor.org/docs/default-source/intl2024/120eviderav2.pdf?sfvrsn=a1d01ffe_0
- Rubinger L, Ekhtiari S, Gazendam A, Bhandari M. Registries: Big data, bigger problems? Injury. 2023; 54(3):S39-S42.
- Allen A, Patrick H, Ruof J, et al. Development and Pilot Test of the Registry Evaluation and Quality Standards Tool: An Information Technology–Based Tool to Support and Review Registries. Value Health. 2022; 25(8):1390-1398.
- Christian J, Dasgupta N, Jordan M, et al. Digital Health and Patient Registries: Today, Tomorrow, and the Future. In: Gliklich RE, Dreyer NA, Leavy MB, et al., editors. 21st Century Patient Registries: Registries for Evaluating Patient Outcomes: A User’s Guide: 3rd Edition, Addendum [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2018 Mar. 3. Available at: https://www.ncbi.nlm.nih.gov/books/NBK493822/
- Miller RS, Mitchell K, Myslinski R, et al. Health Information Technology (IT) and Patient Registries. In: Gliklich RE, Leavy MB, Dreyer NA, editors. Tools and Technologies for Registry Interoperability, Registries for Evaluating Patient Outcomes: A User’s Guide, 3rd Edition, Addendum 2 [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2019 Oct. Chapter 1. Available at: https://www.ncbi.nlm.nih.gov/books/NBK551883/
- European Commission. Implementation of the Regulation on health technology assessment: Regulation (EU) 2021/2282 of The European Parliament and of The Council of 15 December 2021 on Health Technology Assessment and Amending Directive 2011/24/EU. Available at: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32021R2282
- IRS. Inflation Reduction Act of 2022. Available at: https://www.irs.gov/inflation-reduction-act-of-2022
The transformation of healthcare from volume-based to value-based mode has encouraged public and private payers to reform reimbursement models emphasizing on accountability for care quality and healthcare costs. Thanks to the ever-increasing healthcare costs, payers and providers are increasingly preferring bundled payments over fee-for-service (FFS) payment structures. (1,2)
Value-based healthcare is driven by ‘meaningful’ treatment that matters to the patient. The purpose of value-based healthcare is not minimizing the costs, but maximizing the ‘value’, which is defined by “patient outcomes divided by cost”; (1) thus emphasizing on a patient’s importance. (2)
In the US, ‘pay-for-performance’ (P4P) programs have gained popularity among healthcare policymakers as well as private and public players, including Medicaid and Medicare. The term ‘pay-for-performance’ covers all the initiatives that contribute to the quality, efficacy, and overall value of healthcare. (1) These programs provide financial incentives or disincentives to providers or institutions as per their performance on quality measures. Ideally, the well-targeted and designed P4P programs would improve the behaviour of providers and healthcare systems by enhancing the quality of care delivery, reducing redundant use of expensive healthcare services, and improving patient health outcomes. (2,3)
Drug prices in the US are the highest in the world, which necessitate new or alternate payment approaches. For instance, the new treatments for hepatitis C virus (HCV) infection are highly effective and very expensive at the same time, at least from the view of many payers, physicians, and patients. Even 5 years after these drugs were introduced, only 15% of the estimated population of more than 3 million HCV patients in the US have been treated. This can also be due to budgetary constraints of the state Medicaid programs. (1)
An excellent population health management approach considers multiple aspects of health, such as sufficiency of medical care, public health interventions, social and physical environments and related services, genetics, and individual behavior. (1) Data from multiple sources can be used to conduct population health management programs and to assess their value. In addition to these diverse data, data from big population health programs are driven by huge volume, high speed, and incoherent data flows. (2)
There are humungous quantities of data existing in healthcare; data from all kinds of sources, such as clinical, patient, payer, R&D, pharmacy as well as revolutionary technologies that are being quickly embraced, for e.g. data from wearable devices. According to a report by International Data Corporation (IDC), (1) the volume of healthcare data which was observed to be around 153 exabytes in 2013 is estimated to reach around 2,314 exabytes in the year 2020. Therefore, integrating data from all types of diverse sources and clinical systems is a fundamental challenge for any healthcare entity in order to enhance patient care and performance indicators. (2)
The healthcare industry is challenged with administrative and regulatory intricacies that make achieving the healthcare objectives, such as better patient outcomes and reduced costs, difficult. Difficulties faced while improving patient outcomes are predominantly taxing, since health systems measure and report thousands of outcomes annually. (1) In addition, healthcare industry is saturated with the need for improved quality and safety programs. (2) Quality healthcare refers to “the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge”. (3)
Health care is undergoing remarkable change with the global delivery system transitioning to a value-based system from the fee-for-service approach, which has been in place for the past half century. It is predicted that, in this ever evolving business model, hospitals, health care systems, physician groups, and other health care providers will take on more risk, along with the responsibility for delivering defined services to a specific population at a preset measures of price and quality. As a result, novel care delivery networks and value-based arrangements are emerging in the health care communities. While the rate of revolution varies in different communities, health care organizations must be proactive in order to not lag behind. (1,2)