TY - JOUR
T1 - Recommendations to overcome barriers to the use of artificial intelligence-driven evidence in health technology assessment
AU - Zemplényi, Antal
AU - Tachkov, Konstantin
AU - Balkanyi, Laszlo
AU - Németh, Bertalan
AU - Petykó, Zsuzsanna Ida
AU - Petrova, Guenka
AU - Czech, Marcin
AU - Dawoud, Dalia
AU - Goettsch, Wim
AU - Gutierrez Ibarluzea, Inaki
AU - Hren, Rok
AU - Knies, Saskia
AU - Lorenzovici, László
AU - Maravic, Zorana
AU - Piniazhko, Oresta
AU - Savova, Alexandra
AU - Manova, Manoela
AU - Tesar, Tomas
AU - Zerovnik, Spela
AU - Kaló, Zoltán
N1 - Publisher Copyright:
Copyright © 2023 Zemplényi, Tachkov, Balkanyi, Németh, Petykó, Petrova, Czech, Dawoud, Goettsch, Gutierrez Ibarluzea, Hren, Knies, Lorenzovici, Maravic, Piniazhko, Savova, Manova, Tesar, Zerovnik and Kaló.
PY - 2023/4/26
Y1 - 2023/4/26
N2 - 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.
AB - 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.
KW - Central and Eastern Europe
KW - artificial intelligence—AI
KW - evidence generation
KW - health technology assessment
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85158971431&partnerID=8YFLogxK
U2 - 10.3389/fpubh.2023.1088121
DO - 10.3389/fpubh.2023.1088121
M3 - Article
C2 - 37181704
SN - 2296-2565
VL - 11
JO - Frontiers in Public Health
JF - Frontiers in Public Health
M1 - 1088121
ER -