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Report
Aims and strategy for the implementation of machine learning in evidence synthesis in the Cluster for Reviews and Health Technology Assessments for 2021-2022
Published
This report describes suggestions for a strategic approach to meeting the continued need for innovation, evaluation, and implementation of ML for health technology assessments, systematic reviews, and other evidence syntheses.
This report describes suggestions for a strategic approach to meeting the continued need for innovation, evaluation, and implementation of ML for health technology assessments, systematic reviews, and other evidence syntheses.
About this publication
- Year: 2021
- By: Norwegian Institute of Public Health
- Authors Muller AE, Ames H, Himmels J, Jardim PJ, Nguyen L, Rose C, Van De Velde S.
- ISBN (digital): 978-82-8406-234-1
Key message
In 2020-2021, a team in the Cluster for Reviews and Health Technology Assessments, Division for Health Services at the Norwegian Institute of Public Health (NIPH) ran a project on machine learning (ML) related to the conduct of evidence syntheses. Part of the work involved creating a vision and proposals for expanding ML activities in 2021-2022.
This report describes the team’s suggestion for a strategic approach to meeting the continued need for innovation, evaluation, and implementation of ML for health technology assessments, systematic reviews, and other evidence syntheses. We propose a vision and goals, and a novel and flexible team structure. We divide activities into innovation, evaluation, and implementation, and present a risk assessment to inform the roll-out of a future team working on ML activities.