Digital health policy decoded: Mapping national strategies using Donabedian's model

Abstract

National strategies are essential driving forces behind governments taking responsibility for setting the direction of digital health on a national level. This study employed a novel mixed-methods approach, integrating topic modeling, co-occurrence analysis, and qualitative content analysis, to comprehensively examine 22 national digital health strategies through the lens of Donabedian's structure-process-outcome model. The quantitative analysis identified 14 prevalent topics, while the qualitative analysis provided nuanced insights into the contexts underlying these topics. Leveraging Donabedian's framework, the topics were categorized into structure (training and digital health professionals, governance frameworks, computing infrastructure, public-private partnerships, regulatory frameworks), process (AI and big data, decision-support systems, shared digital health records, disease surveillance, information system interoperability), and outcome dimensions (improved health and social care, privacy and security, quality and efficiency of health services, universal coverage, sustainable development goals). This hybrid methodology offers a unique contribution by mapping the identified themes onto a widely accepted quality of care model, bridging the gap between policy analysis and healthcare quality assessment. The study unveils underaddressed themes, highlights the interrelationships between policy components, and provides a comprehensive understanding of the global digital health policy landscape. The findings inform future strategies, academic research directions, and potential policy considerations for governments formulating digital health regulations.