Transferability of new methods for health technology assessment in the field of diabetes between early and late adopters’ countries
This study aimed to investigate the transferability of novel artificial intelligence (AI) methods for prediction modelling of diabetes based on real-world data (RWD) between early and late adopters of emerging health technologies from the perspective of developers and health technology assessment (H...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Biotechnology & Biotechnological Equipment |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/13102818.2024.2371354 |
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| author | Konstantin Tachkov Francisco Somolinos-Simón Jose Tapia-Galisteo Maria Elena Hernando Gema García-Sáez Maria Dimitrova Maria Kamusheva Zornitsa Mitkova Zsuzsanna Petyko Bertalan Nemeth Zoltan Kalo Tomas Tesar Marian-Sorin Paveliu Oresta Piniazhko Iga Lipska Adina Turcu-Stiolica Alexandra Savova Manoela Manova Rok Hren Petra Došenović Bonča Saskia Knies Michal Stanak Tomáš Doležal Dinko Vitezic Guenka Petrova |
| author_facet | Konstantin Tachkov Francisco Somolinos-Simón Jose Tapia-Galisteo Maria Elena Hernando Gema García-Sáez Maria Dimitrova Maria Kamusheva Zornitsa Mitkova Zsuzsanna Petyko Bertalan Nemeth Zoltan Kalo Tomas Tesar Marian-Sorin Paveliu Oresta Piniazhko Iga Lipska Adina Turcu-Stiolica Alexandra Savova Manoela Manova Rok Hren Petra Došenović Bonča Saskia Knies Michal Stanak Tomáš Doležal Dinko Vitezic Guenka Petrova |
| author_sort | Konstantin Tachkov |
| collection | DOAJ |
| description | This study aimed to investigate the transferability of novel artificial intelligence (AI) methods for prediction modelling of diabetes based on real-world data (RWD) between early and late adopters of emerging health technologies from the perspective of developers and health technology assessment (HTA) experts. A two-step approach was used. Developers of the new AI methods within HTx consortium completed a survey about the benefits, usability, barriers associated with implementing the new prediction models in routine HTA practices. Then, HTA experts from Central and Eastern European (CEE) countries participated in a focus group discussion. Developers generally expressed optimism regarding the transferability of the methods, while acknowledging potential disparities across CEE countries. Key benefits that were identified included enhanced understanding of diabetes, improved cost-effectiveness modelling, and refined patient stratification, all of which could contribute to clinical and reimbursement decisions across various jurisdictions. The focus group underscored the value of real-world data for diabetes prediction modelling, serving as a beneficial resource for both clinicians and HTA agencies. However, there was a recognized need to clarify the processes of integrating randomized clinical trial data with real-world data. For the other stakeholders, the advancement of the methodology will improve the diagnosis and therapy during the process of decision making. Experts from CEE countries recognized the potential of artificial intelligence-based methods employing real-world data for diabetes modelling. These methods are seen as instrumental in elucidating the heterogeneous nature of the disease, supporting clinician decision-making and holding promises for HTA purposes. |
| format | Article |
| id | doaj-art-6868ed8c409341fb99bd9cda75a78575 |
| institution | Kabale University |
| issn | 1310-2818 1314-3530 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Biotechnology & Biotechnological Equipment |
| spelling | doaj-art-6868ed8c409341fb99bd9cda75a785752024-12-10T05:42:51ZengTaylor & Francis GroupBiotechnology & Biotechnological Equipment1310-28181314-35302024-12-0138110.1080/13102818.2024.2371354Transferability of new methods for health technology assessment in the field of diabetes between early and late adopters’ countriesKonstantin Tachkov0Francisco Somolinos-Simón1Jose Tapia-Galisteo2Maria Elena Hernando3Gema García-Sáez4Maria Dimitrova5Maria Kamusheva6Zornitsa Mitkova7Zsuzsanna Petyko8Bertalan Nemeth9Zoltan Kalo10Tomas Tesar11Marian-Sorin Paveliu12Oresta Piniazhko13Iga Lipska14Adina Turcu-Stiolica15Alexandra Savova16Manoela Manova17Rok Hren18Petra Došenović Bonča19Saskia Knies20Michal Stanak21Tomáš Doležal22Dinko Vitezic23Guenka Petrova24Department of Organization and Economics of Pharmacy, Faculty of Pharmacy, Medical University Sofia, Sofia, BulgariaCentre for Biomedical Technology (CTB), ETSI de Telecomunicación, Universidad Politécnica de Madrid, Madrid, SpainCentre for Biomedical Technology (CTB), ETSI de Telecomunicación, Universidad Politécnica de Madrid, Madrid, SpainCentre for Biomedical Technology (CTB), ETSI de Telecomunicación, Universidad Politécnica de Madrid, Madrid, SpainCentre for Biomedical Technology (CTB), ETSI de Telecomunicación, Universidad Politécnica de Madrid, Madrid, SpainDepartment of Organization and Economics of Pharmacy, Faculty of Pharmacy, Medical University Sofia, Sofia, BulgariaDepartment of Organization and Economics of Pharmacy, Faculty of Pharmacy, Medical University Sofia, Sofia, BulgariaDepartment of Organization and Economics of Pharmacy, Faculty of Pharmacy, Medical University Sofia, Sofia, BulgariaSYREON Research Institute, Budapest, HungarySYREON Research Institute, Budapest, HungarySYREON Research Institute, Budapest, HungaryDepartment of Organisation and Management in Pharmacy, Faculty of Pharmacy, Comenius University in Bratislava, Bratislava, SlovakiaFarmacologie Clinica/Farmacoeconomie, Titu Maiorescu University, Bucharest, RomaniaHTA Department of State Expert Centre of Ministry of Health, UkraineHealth Policy Institute, Warsaw, PolandFaculty of Pharmacy, University of Medicine and Pharmacy of Craiova, Craiova, RomaniaDepartment of Organization and Economics of Pharmacy, Faculty of Pharmacy, Medical University Sofia, Sofia, BulgariaDepartment of Organization and Economics of Pharmacy, Faculty of Pharmacy, Medical University Sofia, Sofia, BulgariaSYREON Research Institute, Budapest, HungarySchool of Economics and Business, University of Ljubljana, Ljubljana, SloveniaNational Institute for Value and Technologies in Healthcare, SlovakiaNational Institute for Value and Technologies in Healthcare, Bratislava, SlovakiaInstitute of Health Economics and Technology Assessment, Praga, CzechiaUniversity of Rijeka Medical School and University Hospital Centre, Rijeka, Rijeka, CroatiaDepartment of Organization and Economics of Pharmacy, Faculty of Pharmacy, Medical University Sofia, Sofia, BulgariaThis study aimed to investigate the transferability of novel artificial intelligence (AI) methods for prediction modelling of diabetes based on real-world data (RWD) between early and late adopters of emerging health technologies from the perspective of developers and health technology assessment (HTA) experts. A two-step approach was used. Developers of the new AI methods within HTx consortium completed a survey about the benefits, usability, barriers associated with implementing the new prediction models in routine HTA practices. Then, HTA experts from Central and Eastern European (CEE) countries participated in a focus group discussion. Developers generally expressed optimism regarding the transferability of the methods, while acknowledging potential disparities across CEE countries. Key benefits that were identified included enhanced understanding of diabetes, improved cost-effectiveness modelling, and refined patient stratification, all of which could contribute to clinical and reimbursement decisions across various jurisdictions. The focus group underscored the value of real-world data for diabetes prediction modelling, serving as a beneficial resource for both clinicians and HTA agencies. However, there was a recognized need to clarify the processes of integrating randomized clinical trial data with real-world data. For the other stakeholders, the advancement of the methodology will improve the diagnosis and therapy during the process of decision making. Experts from CEE countries recognized the potential of artificial intelligence-based methods employing real-world data for diabetes modelling. These methods are seen as instrumental in elucidating the heterogeneous nature of the disease, supporting clinician decision-making and holding promises for HTA purposes.https://www.tandfonline.com/doi/10.1080/13102818.2024.2371354Transferabilityhealth technology assessmentCentral and Easter European Countriesdiabetesartificial intelligence |
| spellingShingle | Konstantin Tachkov Francisco Somolinos-Simón Jose Tapia-Galisteo Maria Elena Hernando Gema García-Sáez Maria Dimitrova Maria Kamusheva Zornitsa Mitkova Zsuzsanna Petyko Bertalan Nemeth Zoltan Kalo Tomas Tesar Marian-Sorin Paveliu Oresta Piniazhko Iga Lipska Adina Turcu-Stiolica Alexandra Savova Manoela Manova Rok Hren Petra Došenović Bonča Saskia Knies Michal Stanak Tomáš Doležal Dinko Vitezic Guenka Petrova Transferability of new methods for health technology assessment in the field of diabetes between early and late adopters’ countries Biotechnology & Biotechnological Equipment Transferability health technology assessment Central and Easter European Countries diabetes artificial intelligence |
| title | Transferability of new methods for health technology assessment in the field of diabetes between early and late adopters’ countries |
| title_full | Transferability of new methods for health technology assessment in the field of diabetes between early and late adopters’ countries |
| title_fullStr | Transferability of new methods for health technology assessment in the field of diabetes between early and late adopters’ countries |
| title_full_unstemmed | Transferability of new methods for health technology assessment in the field of diabetes between early and late adopters’ countries |
| title_short | Transferability of new methods for health technology assessment in the field of diabetes between early and late adopters’ countries |
| title_sort | transferability of new methods for health technology assessment in the field of diabetes between early and late adopters countries |
| topic | Transferability health technology assessment Central and Easter European Countries diabetes artificial intelligence |
| url | https://www.tandfonline.com/doi/10.1080/13102818.2024.2371354 |
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