1
|
Price G, Peek N, Eleftheriou I, Spencer K, Paley L, Hogenboom J, van Soest J, Dekker A, van Herk M, Faivre-Finn C. An Overview of Real-World Data Infrastructure for Cancer Research. Clin Oncol (R Coll Radiol) 2024:S0936-6555(24)00108-0. [PMID: 38631976 DOI: 10.1016/j.clon.2024.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 02/27/2024] [Accepted: 03/13/2024] [Indexed: 04/19/2024]
Abstract
AIMS There is increasing interest in the opportunities offered by Real World Data (RWD) to provide evidence where clinical trial data does not exist, but access to appropriate data sources is frequently cited as a barrier to RWD research. This paper discusses current RWD resources and how they can be accessed for cancer research. MATERIALS AND METHODS There has been significant progress on facilitating RWD access in the last few years across a range of scales, from local hospital research databases, through regional care records and national repositories, to the impact of federated learning approaches on internationally collaborative studies. We use a series of case studies, principally from the UK, to illustrate how RWD can be accessed for research and healthcare improvement at each of these scales. RESULTS For each example we discuss infrastructure and governance requirements with the aim of encouraging further work in this space that will help to fill evidence gaps in oncology. CONCLUSION There are challenges, but real-world data research across a range of scales is already a reality. Taking advantage of the current generation of data sources requires researchers to carefully define their research question and the scale at which it would be best addressed.
Collapse
Affiliation(s)
- G Price
- Division of Cancer Sciences, University of Manchester, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK.
| | - N Peek
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK; The Healthcare Improvement Studies Institute (THIS Institute), University of Cambridge, Cambridge, UK
| | - I Eleftheriou
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK
| | - K Spencer
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK; Leeds Teaching Hospitals NHS Trust, Leeds, UK; National Disease Registration Service, NHS England, UK
| | - L Paley
- National Disease Registration Service, NHS England, UK
| | - J Hogenboom
- Department of Radiation Oncology (Maastro), GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - J van Soest
- Department of Radiation Oncology (Maastro), GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands; Brightlands Institute for Smart Society (BISS), Faculty of Science and Engineering, Maastricht University, Maastricht, The Netherlands
| | - A Dekker
- Department of Radiation Oncology (Maastro), GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - M van Herk
- Division of Cancer Sciences, University of Manchester, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK
| | - C Faivre-Finn
- Division of Cancer Sciences, University of Manchester, Manchester, UK; The Christie NHS Foundation Trust, Manchester, UK
| |
Collapse
|
2
|
Németh B, Kamusheva M, Mitkova Z, Petykó ZI, Zemplényi A, Dimitrova M, Tachkov K, Balkányi L, Czech M, Dawoud D, Goettsch W, Hren R, Knies S, Lorenzovici L, Maravic Z, Piniazhko O, Zerovnik S, Kaló Z. Guidance on using real-world evidence from Western Europe in Central and Eastern European health policy decision making. J Comp Eff Res 2023; 12:e220157. [PMID: 36861458 PMCID: PMC10402755 DOI: 10.57264/cer-2022-0157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 02/14/2023] [Indexed: 03/03/2023] Open
Abstract
Aim: Real-world data and real-world evidence (RWE) are becoming more important for healthcare decision making and health technology assessment. We aimed to propose solutions to overcome barriers preventing Central and Eastern European (CEE) countries from using RWE generated in Western Europe. Materials & methods: To achieve this, following a scoping review and a webinar, the most important barriers were selected through a survey. A workshop was held with CEE experts to discuss proposed solutions. Results: Based on survey results, we selected the nine most important barriers. Multiple solutions were proposed, for example, the need for a European consensus, and building trust in using RWE. Conclusion: Through collaboration with regional stakeholders, we proposed a list of solutions to overcome barriers on transferring RWE from Western Europe to CEE countries.
Collapse
Affiliation(s)
| | - Maria Kamusheva
- Department of Organization & Economics of Pharmacy, Faculty of Pharmacy, Medical University of Sofia, Sofia, BG 1000, Bulgaria
| | - Zornitsa Mitkova
- Department of Organization & Economics of Pharmacy, Faculty of Pharmacy, Medical University of Sofia, Sofia, BG 1000, Bulgaria
| | | | - Antal Zemplényi
- Syreon Research Institute, Budapest, HU 1142, Hungary
- Center for Health Technology Assessment & Pharmacoeconomics Research, Faculty of Pharmacy, University of Pécs, Pécs, Hungary
| | - Maria Dimitrova
- Department of Organization & Economics of Pharmacy, Faculty of Pharmacy, Medical University of Sofia, Sofia, BG 1000, Bulgaria
| | - Konstantin Tachkov
- Department of Organization & Economics of Pharmacy, Faculty of Pharmacy, Medical University of Sofia, Sofia, BG 1000, Bulgaria
| | - László Balkányi
- Medical Informatics R&D Center, Pannon University, Veszprém, HU 8200, Hungary
| | - Marcin Czech
- Department of Pharmacoeconomics, Institute of Mother & Child, Warsaw, PL 01-211, Poland
| | - Dalia Dawoud
- Science Policy & Research Programme, Science Evidence & Analytics Directorate, National Institute for Health & Care Excellence (NICE), London, United Kingdom
- Cairo University, Faculty of Pharmacy, Cairo, Egypt
| | - Wim Goettsch
- Division of Pharmacoepidemiology & Clinical Pharmacology, Utrecht University, Utrecht, The Netherlands
- National Health Care Institute, Diemen, NL 1120 AH, The Netherlands
| | - Rok Hren
- Faculty of Mathematics & Physics, University of Ljubljana, Ljubljana, Slovenia
| | - Saskia Knies
- National Health Care Institute, Diemen, NL 1120 AH, The Netherlands
| | - László Lorenzovici
- Syreon Research Romania, Tirgu Mures, RO 540004, Romania
- G. E. Palade University of Medicine, Pharmacy, Science & Technology, Tirgu Mures, RO 540142, Romania
| | | | - Oresta Piniazhko
- HTA Department of State Expert Centre of the Ministry of Health of Ukraine, Kyiv, Ukraine
| | | | - Zoltán Kaló
- Syreon Research Institute, Budapest, HU 1142, Hungary
- Centre for Health Technology Assessment, Semmelweis University, Budapest, HU 1091 Hungary
| |
Collapse
|
3
|
Zemplényi A, Tachkov K, Balkanyi L, Németh B, Petykó ZI, Petrova G, Czech M, Dawoud D, Goettsch W, Gutierrez Ibarluzea I, Hren R, Knies S, Lorenzovici L, Maravic Z, Piniazhko O, Savova A, Manova M, Tesar T, Zerovnik S, Kaló Z. Recommendations to overcome barriers to the use of artificial intelligence-driven evidence in health technology assessment. Front Public Health 2023; 11:1088121. [PMID: 37181704 PMCID: PMC10171457 DOI: 10.3389/fpubh.2023.1088121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 04/03/2023] [Indexed: 05/16/2023] Open
Abstract
Background Artificial intelligence (AI) has attracted much attention because of its enormous potential in healthcare, but uptake has been slow. There are substantial barriers that challenge health technology assessment (HTA) professionals to use AI-generated evidence for decision-making from large real-world databases (e.g., based on claims data). As part of the European Commission-funded HTx H2020 (Next Generation Health Technology Assessment) project, we aimed to put forward recommendations to support healthcare decision-makers in integrating AI into the HTA processes. The barriers, addressed by the paper, are particularly focusing on Central and Eastern European (CEE) countries, where the implementation of HTA and access to health databases lag behind Western European countries. Methods We constructed a survey to rank the barriers to using AI for HTA purposes, completed by respondents from CEE jurisdictions with expertise in HTA. Using the results, two members of the HTx consortium from CEE developed recommendations on the most critical barriers. Then these recommendations were discussed in a workshop by a wider group of experts, including HTA and reimbursement decision-makers from both CEE countries and Western European countries, and summarized in a consensus report. Results Recommendations have been developed to address the top 15 barriers in areas of (1) human factor-related barriers, focusing on educating HTA doers and users, establishing collaborations and best practice sharing; (2) regulatory and policy-related barriers, proposing increasing awareness and political commitment and improving the management of sensitive information for AI use; (3) data-related barriers, suggesting enhancing standardization and collaboration with data networks, managing missing and unstructured data, using analytical and statistical approaches to address bias, using quality assessment tools and quality standards, improving reporting, and developing better conditions for the use of data; and (4) technological barriers, suggesting sustainable development of AI infrastructure. Conclusion In the field of HTA, the great potential of AI to support evidence generation and evaluation has not yet been sufficiently explored and realized. Raising awareness of the intended and unintended consequences of AI-based methods and encouraging political commitment from policymakers is necessary to upgrade the regulatory and infrastructural environment and knowledge base required to integrate AI into HTA-based decision-making processes better.
Collapse
Affiliation(s)
- Antal Zemplényi
- Center for Health Technology Assessment and Pharmacoeconomics Research, Faculty of Pharmacy, University of Pécs, Pécs, Hungary
- Syreon Research Institute, Budapest, Hungary
- *Correspondence: Antal Zemplényi,
| | - Konstantin Tachkov
- Department of Organization and Economics of Pharmacy, Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria
| | - Laszlo Balkanyi
- Medical Informatics R&D Center, Pannon University, Veszprém, Hungary
| | | | | | - Guenka Petrova
- Department of Organization and Economics of Pharmacy, Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria
| | - Marcin Czech
- Department of Pharmacoeconomics, Institute of Mother and Child, Warsaw, Poland
| | - Dalia Dawoud
- Science Policy and Research Programme, Science Evidence and Analytics Directorate, National Institute for Health and Care Excellence (NICE), London, United Kingdom
- Cairo University, Faculty of Pharmacy, Cairo, Egypt
| | - Wim Goettsch
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht University, Utrecht, Netherlands
- National Health Care Institute, Diemen, Netherlands
| | | | - Rok Hren
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
| | - Saskia Knies
- National Health Care Institute, Diemen, Netherlands
| | - László Lorenzovici
- Syreon Research Romania, Tirgu Mures, Romania
- G. E. Palade University of Medicine, Pharmacy, Science and Technology, Tirgu Mures, Romania
| | | | - Oresta Piniazhko
- HTA Department of State Expert Centre of the Ministry of Health of Ukraine, Kyiv, Ukraine
| | - Alexandra Savova
- Department of Organization and Economics of Pharmacy, Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria
- National Council of Prices and Reimbursement of Medicinal Products, Sofia, Bulgaria
| | - Manoela Manova
- Department of Organization and Economics of Pharmacy, Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria
- National Council of Prices and Reimbursement of Medicinal Products, Sofia, Bulgaria
| | - Tomas Tesar
- Department of Organisation and Management of Pharmacy, Faculty of Pharmacy, Comenius University in Bratislava, Bratislava, Slovakia
| | | | - Zoltán Kaló
- Syreon Research Institute, Budapest, Hungary
- Centre for Health Technology Assessment, Semmelweis University, Budapest, Hungary
| |
Collapse
|
4
|
Jaksa A, Arena PJ, Chan KKW, Ben-Joseph RH, Jónsson P, Campbell UB. Transferability of real-world data across borders for regulatory and health technology assessment decision-making. Front Med (Lausanne) 2022; 9:1073678. [PMID: 36465931 PMCID: PMC9709526 DOI: 10.3389/fmed.2022.1073678] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 10/31/2022] [Indexed: 08/11/2023] Open
Abstract
Recently, there has been increased consideration of real-world data (RWD) and real-world evidence (RWE) in regulatory and health technology assessment (HTA) decision-making. Due to challenges in identifying high-quality and relevant RWD sources, researchers and regulatory/HTA bodies may turn to RWD generated in locales outside of the locale of interest (referred to as "transferring RWD"). We therefore performed a review of stakeholder guidance as well as selected case studies to identify themes for researchers to consider when transferring RWD from one jurisdiction to another. Our review highlighted that there is limited consensus on defining decision-grade, transferred RWD; certain stakeholders have issued relevant guidance, but the recommendations are high-level and additional effort is needed to generate comprehensive guidance. Additionally, the case studies revealed that RWD transferability has not been a consistent concern for regulatory/HTA bodies and that more focus has been put on the evaluation of internal validity. To help develop transferability best practices (alongside internal validity best practices), we suggest that researchers address the following considerations in their justification for transferring RWD: treatment pathways, nature of the healthcare system, incidence/prevalence of indication, and patient demographics. We also recommend that RWD transferability should garner more attention as the use of imported RWD could open doors to high-quality data sources and potentially reduce methodological issues that often arise in the use of local RWD; we thus hope this review provides a foundation for further dialogue around the suitability and utility of transferred RWD in the regulatory/HTA decision-making space.
Collapse
Affiliation(s)
- Ashley Jaksa
- Scientific Research and Strategy, Aetion, Inc., New York, NY, United States
| | - Patrick J. Arena
- Scientific Research and Strategy, Aetion, Inc., New York, NY, United States
- Department of Epidemiology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Kelvin K. W. Chan
- Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
- Canadian Centre for Applied Research in Cancer Control, Toronto, ON, Canada
| | - Rami H. Ben-Joseph
- Big Data Real World Evidence, Jazz Pharmaceuticals, Palo Alto, CA, United States
| | - Páll Jónsson
- National Institute for Health and Care Excellence, Manchester, United Kingdom
| | - Ulka B. Campbell
- Scientific Research and Strategy, Aetion, Inc., New York, NY, United States
| |
Collapse
|
5
|
Ádám I, Callenbach M, Németh B, Vreman RA, Tollin C, Pontén J, Dawoud D, Elvidge J, Crabb N, van Waalwijk van Doorn-Khosrovani SB, Pisters-van Roy A, Vincziczki Á, Almomani E, Vajagic M, Oner ZG, Matni M, Fürst J, Kahveci R, Goettsch WG, Kaló Z. Outcome-based reimbursement in Central-Eastern Europe and Middle-East. Front Med (Lausanne) 2022; 9:940886. [PMID: 36213666 PMCID: PMC9539523 DOI: 10.3389/fmed.2022.940886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/01/2022] [Indexed: 11/13/2022] Open
Abstract
Outcome-based reimbursement models can effectively reduce the financial risk to health care payers in cases when there is important uncertainty or heterogeneity regarding the clinical value of health technologies. Still, health care payers in lower income countries rely mainly on financial based agreements to manage uncertainties associated with new therapies. We performed a survey, an exploratory literature review and an iterative brainstorming in parallel about potential barriers and solutions to outcome-based agreements in Central and Eastern Europe (CEE) and in the Middle East (ME). A draft list of recommendations deriving from these steps was validated in a follow-up workshop with payer experts from these regions. 20 different barriers were identified in five groups, including transaction costs and administrative burden, measurement issues, information technology and data infrastructure, governance, and perverse policy outcomes. Though implementing outcome-based reimbursement models is challenging, especially in lower income countries, those challenges can be mitigated by conducting pilot agreements and preparing for predictable barriers. Our guidance paper provides an initial step in this process. The generalizability of our recommendations can be improved by monitoring experiences from pilot reimbursement models in CEE and ME countries and continuing the multistakeholder dialogue at national levels.
Collapse
Affiliation(s)
- Ildikó Ádám
- Center for Health Technology Assessment, Semmelweis University, Budapest, Hungary
| | - Marcelien Callenbach
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, Netherlands
| | | | - Rick A. Vreman
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, Netherlands
- National Health Care Institute, Zorginstituut Nederland, Diemen, Netherlands
| | - Cecilia Tollin
- The Dental and Pharmaceutical Benefits Agency, Tandvårds- och Låkemedelsförmånsverket, Stockholm, Sweden
| | - Johan Pontén
- The Dental and Pharmaceutical Benefits Agency, Tandvårds- och Låkemedelsförmånsverket, Stockholm, Sweden
| | - Dalia Dawoud
- National Institute for Health and Care Excellence, London, United Kingdom
- Faculty of Pharmacy, Cairo University, Cairo, Egypt
| | - Jamie Elvidge
- National Institute for Health and Care Excellence, London, United Kingdom
| | - Nick Crabb
- National Institute for Health and Care Excellence, London, United Kingdom
| | | | - Anke Pisters-van Roy
- Department of Medical Advisory and Innovation, Centraal Ziekenfonds (CZ) Health Insurance, Tilburg, Netherlands
| | - Áron Vincziczki
- National Health Insurance Fund of Hungary, Nemzeti Egészségbiztosítási Alapkezelõ, Budapest, Hungary
| | - Emad Almomani
- Department for Health Technology Assessment, Jordanian Royal Medical Services, Amman, Jordan
| | | | | | - Mirna Matni
- Social Security Main Office, Caisse Nationale de la Sécurité Sociale, Beirut, Lebanon
| | - Jurij Fürst
- Department of Drugs, Health Insurance Institute of Slovenia, Ljubljana, Slovenia
| | - Rabia Kahveci
- Pharmaceutical Policies and Governance, Management Sciences for Health, Kyiv, Ukraine
| | - Wim G. Goettsch
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, Netherlands
- National Health Care Institute, Zorginstituut Nederland, Diemen, Netherlands
| | - Zoltán Kaló
- Center for Health Technology Assessment, Semmelweis University, Budapest, Hungary
- Syreon Research Institute, Budapest, Hungary
| |
Collapse
|