ArticleBMC primary care2024
Healthcare leaders' experiences of implementing artificial intelligence for medical history-taking and triage in Swedish primary care: an interview study.
Article in BMC primary care, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Patient safety practices in an innovative digital landscape: an interview study on triage in Swedish digital primary care.Scandinavian journal of primary health care · 2026Article
- Latent profiles of nurses' attitudes toward artificial intelligence in nursing and associated factors: a cross-sectional study.BMC nursing · 2026Article
- The Interface of Artificial Intelligence and the Electronic Medical Record in Orthopaedic Surgery: Current Applications and Future Directions.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026Review
- Employee perceptions of AI adoption across service domains in a Finnish public health and social care organization: a cross-sectional mixed-methods study.BMC health services research · 2026Article
- Artificial intelligence and person-centred practice: a critical reflection.NPJ digital medicine · 2026Review
- Investigating the Implementation and Impact of AI-Assisted Fall Prevention in Hospitals: Protocol for a Multicenter, Multimethod Observational Study in Sweden (SAFE).JMIR research protocols · 2026Observational
- AI Triage in Primary Care: Building Safer and More Equitable Real-World Evidence.Journal of medical Internet research · 2026Article
- Advancing medical AI through benchmarking and competition for specialty triage.NPJ digital medicine · 2026Article
- Symptoms and causes of obstacles in AI-based telemonitoring: strategies for system development through a CRISP-DM lens.Frontiers in artificial intelligence · 2026Article
- Mapping Clinical Intent in Internet Hospitals: Identifying and Profiling Patient Demand via Natural Language Processing.Computational and structural biotechnology journal · 2026Article
- Review
- Integrating AI-based triage in primary care: a qualitative study of Swedish healthcare professionals' experiences applying normalization process theory.BMC primary care · 2025Article
- Establishing organizational AI governance in healthcare: a case study in Canada.NPJ digital medicine · 2025Article
- Enhancing the Innovation Ecosystem: Overcoming Challenges to Introducing Information-Driven Technologies in Health Care.Journal of medical Internet research · 2025Article
- Influences on trust in the use of AI-based triage-an interview study with primary healthcare professionals and patients in Sweden.Frontiers in digital health · 2025Article
- Artificial intelligence, medications, pharmacogenomics, and ethics.Pharmacogenomics · 2024Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) holds significant promise for enhancing the efficiency and safety of medical history-taking and triage within primary care. However, there remains a dearth of knowledge concerning the practical implementation of AI systems for these purposes, particularly in the context of healthcare leadership. This study explores the experiences of healthcare leaders regarding the barriers to implementing an AI application for automating medical history-taking and triage in Swedish primary care, as well as the actions they took to overcome these barriers. Furthermore, the study seeks to provide insights that can inform the development of AI implementation strategies for healthcare.
methodsWe adopted an inductive qualitative approach, conducting semi-structured interviews with 13 healthcare leaders representing seven primary care units across three regions in Sweden. The collected data were subsequently analysed utilizing thematic analysis. Our study adhered to the Consolidated Criteria for Reporting Qualitative Research to ensure transparent and comprehensive reporting.
resultsThe study identified implementation barriers encountered by healthcare leaders across three domains: (1) healthcare professionals, (2) organization, and (3) technology. The first domain involved professional scepticism and resistance, the second involved adapting traditional units for digital care, and the third inadequacies in AI application functionality and system integration. To navigate around these barriers, the leaders took steps to (1) address inexperience and fear and reduce professional scepticism, (2) align implementation with digital maturity and guide patients towards digital care, and (3) refine and improve the AI application and adapt to the current state of AI application development.
conclusionThe study provides valuable empirical insights into the implementation of AI for automating medical history-taking and triage in primary care as experienced by healthcare leaders. It identifies the barriers to this implementation and how healthcare leaders aligned their actions to overcome them. While progress was evident in overcoming professional-related and organizational-related barriers, unresolved technical complexities highlight the importance of AI implementation strategies that consider how leaders handle AI implementation in situ based on practical wisdom and tacit understanding. This underscores the necessity of a holistic approach for the successful implementation of AI in healthcare.
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