Recommendations to overcome barriers to the use of artificial intelligence-driven evidence in health technology assessment
BackgroundArtificial 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 re...
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Frontiers Media S.A.
2023-04-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1088121/full |
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| author | Antal Zemplényi Antal Zemplényi Konstantin Tachkov Laszlo Balkanyi Bertalan Németh Zsuzsanna Ida Petykó Guenka Petrova Marcin Czech Dalia Dawoud Dalia Dawoud Wim Goettsch Wim Goettsch Inaki Gutierrez Ibarluzea Rok Hren Saskia Knies László Lorenzovici László Lorenzovici Zorana Maravic Oresta Piniazhko Alexandra Savova Alexandra Savova Manoela Manova Manoela Manova Tomas Tesar Spela Zerovnik Zoltán Kaló Zoltán Kaló |
| author_facet | Antal Zemplényi Antal Zemplényi Konstantin Tachkov Laszlo Balkanyi Bertalan Németh Zsuzsanna Ida Petykó Guenka Petrova Marcin Czech Dalia Dawoud Dalia Dawoud Wim Goettsch Wim Goettsch Inaki Gutierrez Ibarluzea Rok Hren Saskia Knies László Lorenzovici László Lorenzovici Zorana Maravic Oresta Piniazhko Alexandra Savova Alexandra Savova Manoela Manova Manoela Manova Tomas Tesar Spela Zerovnik Zoltán Kaló Zoltán Kaló |
| author_sort | Antal Zemplényi |
| collection | DOAJ |
| description | BackgroundArtificial 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.MethodsWe 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.ResultsRecommendations 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.ConclusionIn 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. |
| first_indexed | 2024-04-09T13:07:57Z |
| format | Article |
| id | doaj.art-5ea38b6b6fdb4470a15843849d12de56 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-09T13:07:57Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-5ea38b6b6fdb4470a15843849d12de562023-05-12T10:32:36ZengFrontiers Media S.A.Frontiers in Public Health2296-25652023-04-011110.3389/fpubh.2023.10881211088121Recommendations to overcome barriers to the use of artificial intelligence-driven evidence in health technology assessmentAntal Zemplényi0Antal Zemplényi1Konstantin Tachkov2Laszlo Balkanyi3Bertalan Németh4Zsuzsanna Ida Petykó5Guenka Petrova6Marcin Czech7Dalia Dawoud8Dalia Dawoud9Wim Goettsch10Wim Goettsch11Inaki Gutierrez Ibarluzea12Rok Hren13Saskia Knies14László Lorenzovici15László Lorenzovici16Zorana Maravic17Oresta Piniazhko18Alexandra Savova19Alexandra Savova20Manoela Manova21Manoela Manova22Tomas Tesar23Spela Zerovnik24Zoltán Kaló25Zoltán Kaló26Center for Health Technology Assessment and Pharmacoeconomics Research, Faculty of Pharmacy, University of Pécs, Pécs, HungarySyreon Research Institute, Budapest, HungaryDepartment of Organization and Economics of Pharmacy, Faculty of Pharmacy, Medical University of Sofia, Sofia, BulgariaMedical Informatics R&D Center, Pannon University, Veszprém, HungarySyreon Research Institute, Budapest, HungarySyreon Research Institute, Budapest, HungaryDepartment of Organization and Economics of Pharmacy, Faculty of Pharmacy, Medical University of Sofia, Sofia, BulgariaDepartment of Pharmacoeconomics, Institute of Mother and Child, Warsaw, PolandScience Policy and Research Programme, Science Evidence and Analytics Directorate, National Institute for Health and Care Excellence (NICE), London, United KingdomCairo University, Faculty of Pharmacy, Cairo, EgyptDivision of Pharmacoepidemiology and Clinical Pharmacology, Utrecht University, Utrecht, NetherlandsNational Health Care Institute, Diemen, Netherlands0Basque Foundation for Health Innovation and Research, Barakaldo, Spain1Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, SloveniaNational Health Care Institute, Diemen, Netherlands2Syreon Research Romania, Tirgu Mures, Romania3G. E. Palade University of Medicine, Pharmacy, Science and Technology, Tirgu Mures, Romania4Digestive Cancers Europe, Brussels, Belgium5HTA Department of State Expert Centre of the Ministry of Health of Ukraine, Kyiv, UkraineDepartment of Organization and Economics of Pharmacy, Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria6National Council of Prices and Reimbursement of Medicinal Products, Sofia, BulgariaDepartment of Organization and Economics of Pharmacy, Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria6National Council of Prices and Reimbursement of Medicinal Products, Sofia, Bulgaria7Department of Organisation and Management of Pharmacy, Faculty of Pharmacy, Comenius University in Bratislava, Bratislava, Slovakia8Ministry of Health, Ljubljana, SloveniaSyreon Research Institute, Budapest, Hungary9Centre for Health Technology Assessment, Semmelweis University, Budapest, HungaryBackgroundArtificial 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.MethodsWe 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.ResultsRecommendations 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.ConclusionIn 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.https://www.frontiersin.org/articles/10.3389/fpubh.2023.1088121/fullartificial intelligence—AImachine learninghealth technology assessmentevidence generationCentral and Eastern Europe |
| spellingShingle | Antal Zemplényi Antal Zemplényi Konstantin Tachkov Laszlo Balkanyi Bertalan Németh Zsuzsanna Ida Petykó Guenka Petrova Marcin Czech Dalia Dawoud Dalia Dawoud Wim Goettsch Wim Goettsch Inaki Gutierrez Ibarluzea Rok Hren Saskia Knies László Lorenzovici László Lorenzovici Zorana Maravic Oresta Piniazhko Alexandra Savova Alexandra Savova Manoela Manova Manoela Manova Tomas Tesar Spela Zerovnik Zoltán Kaló Zoltán Kaló Recommendations to overcome barriers to the use of artificial intelligence-driven evidence in health technology assessment Frontiers in Public Health artificial intelligence—AI machine learning health technology assessment evidence generation Central and Eastern Europe |
| title | Recommendations to overcome barriers to the use of artificial intelligence-driven evidence in health technology assessment |
| title_full | Recommendations to overcome barriers to the use of artificial intelligence-driven evidence in health technology assessment |
| title_fullStr | Recommendations to overcome barriers to the use of artificial intelligence-driven evidence in health technology assessment |
| title_full_unstemmed | Recommendations to overcome barriers to the use of artificial intelligence-driven evidence in health technology assessment |
| title_short | Recommendations to overcome barriers to the use of artificial intelligence-driven evidence in health technology assessment |
| title_sort | recommendations to overcome barriers to the use of artificial intelligence driven evidence in health technology assessment |
| topic | artificial intelligence—AI machine learning health technology assessment evidence generation Central and Eastern Europe |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1088121/full |
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