Reinhard Jeindl1, Claudia Wild2. 1. Austrian Institute for Health Technology Assessment GmbH (AIHTA), Garnisongasse 7/20, 1090, Wien, Österreich. reinhard.jeindl@aihta.at. 2. Austrian Institute for Health Technology Assessment GmbH (AIHTA), Garnisongasse 7/20, 1090, Wien, Österreich.
Abstract
BACKGROUND: For most digital health applications (DiGA) only limited evidence of benefit is available. Currently available assessment frameworks do not cover all domains of a full health technology assessment (HTA). Additionally, technology-specific aspects are required for the evaluation of DiGA. This work aimed to analyze the available assessment frameworks and design an evaluation process for DiGA. METHODS: By a systematic literature search six assessment frameworks for DiGA were selected and analyzed. A hand search for strategies on DiGA of selected countries was conducted. RESULTS: Of the analyzed assessment frameworks four described study designs. One assessment framework proposed a risk classification of DiGA. Aspects of artificial intelligence were assessed by one assessment framework. The analyzed countries have differing strategies for reimbursement of DiGA. CONCLUSION: Assessment frameworks for DiGA are very heterogeneous. There are efforts to find regulations for DiGA on a national level. When evaluating DiGA, a staged approach considering risk classes with subsequent evaluation of relevant HTA aspects is recommended.
BACKGROUND: For most digital health applications (DiGA) only limited evidence of benefit is available. Currently available assessment frameworks do not cover all domains of a full health technology assessment (HTA). Additionally, technology-specific aspects are required for the evaluation of DiGA. This work aimed to analyze the available assessment frameworks and design an evaluation process for DiGA. METHODS: By a systematic literature search six assessment frameworks for DiGA were selected and analyzed. A hand search for strategies on DiGA of selected countries was conducted. RESULTS: Of the analyzed assessment frameworks four described study designs. One assessment framework proposed a risk classification of DiGA. Aspects of artificial intelligence were assessed by one assessment framework. The analyzed countries have differing strategies for reimbursement of DiGA. CONCLUSION: Assessment frameworks for DiGA are very heterogeneous. There are efforts to find regulations for DiGA on a national level. When evaluating DiGA, a staged approach considering risk classes with subsequent evaluation of relevant HTA aspects is recommended.