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Peer-reviewedCC BY-NC 4.0BMJ Open

Implementation of artificial intelligence in healthcare: a protocol for a phased mixed-methods multiple case study in the North Denmark Region

  • Kathrine Hald1,2,3
  • Maria Fagerström4,5
  • Janus Laust Thomsen1
  • Søren Paaske Johnsen2,3,6
  • Per Nilsen4
6 affiliations
  1. 1Center for General Practice, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
  2. 2Danish Center for Health Services Research, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
  3. 3Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
  4. 4Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
  5. 5Clinical Department of Activity and Health, Linköping University, Linköping, Sweden
  6. 6Danish Center for Health Services Research, Aalborg University Hospital, Aalborg, Denmark
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Abstract

Background: Artificial intelligence (AI) is increasingly being implemented across healthcare systems with the potential to improve diagnostic accuracy, support clinical decision-making and enhance healthcare delivery. Despite growing investments in AI, evidence on how AI innovations are successfully implemented, adopted and sustained in routine clinical practice remains limited. Existing research has predominantly focused on technical performance and clinical effectiveness, while implementation-related factors have received less attention. This study aims to evaluate the implementation of AI innovations across multiple healthcare settings over time.

Methods and analysis: This study applies a phased mixed-methods multiple case study design to evaluate the implementation of 11 AI innovations introduced across hospital-based and cross-sectoral healthcare settings in the North Denmark Region. Each AI innovation is conceptualised as an individual case. The study consists of three sequential phases: (1) exploratory cross-case mapping of implementation processes and contextual determinants. (2) In-depth longitudinal case studies of selected cases. (3) Cross-case synthesis and programme-level evaluation. Data collection includes interviews, observations, document analysis, questionnaires and regional register data. The study is informed by the Consolidated Framework for Implementation Research, Proctor et al’s Implementation Outcomes Taxonomy, the Quality Implementation Framework and the WHO’s Health System Performance Assessment framework. Qualitative and quantitative data will be analysed separately and integrated through triangulation at case and cross-case levels.

Ethics and dissemination: According to Danish legislation, this study does not require approval from a regional research ethics committee. The study has been assessed by the Aalborg University Research Ethics Committee and registered as a research project involving the processing of personal data at Aalborg University. All participants will provide informed consent prior to participation. Findings will be disseminated through peer-reviewed publications, conference presentations and reports targeting relevant stakeholders.

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Cite

BibTeXRIS
BibTeX
@article{hald2026implementation,
  title = {{Implementation of artificial intelligence in healthcare: a protocol for a phased mixed-methods multiple case study in the North Denmark Region}},
  author = {Kathrine Hald and Maria Fagerström and Janus Laust Thomsen and Søren Paaske Johnsen and Per Nilsen},
  journal = {BMJ Open},
  year = {2026},
  month = oct,
  doi = {10.1136/bmjopen-2026-124097},
  url = {https://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2026-124097},
  note = {CC BY-NC 4.0},
}
RIS
TY  - JOUR
TI  - Implementation of artificial intelligence in healthcare: a protocol for a phased mixed-methods multiple case study in the North Denmark Region
AU  - Kathrine Hald
AU  - Maria Fagerström
AU  - Janus Laust Thomsen
AU  - Søren Paaske Johnsen
AU  - Per Nilsen
PY  - 2026
DA  - 2026/10/30
JO  - BMJ Open
DO  - 10.1136/bmjopen-2026-124097
UR  - https://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2026-124097
AB  - Background: Artificial intelligence (AI) is increasingly being implemented across healthcare systems with the potential to improve diagnostic accuracy, support clinical decision-making and enhance healthcare delivery. Despite growing investments in AI, evidence on how AI innovations are successfully implemented, adopted and sustained in routine clinical practice remains limited. Existing research has predominantly focused on technical performance and clinical effectiveness, while implementation-related factors have received less attention. This study aims to evaluate the implementation of AI innovations across multiple healthcare settings over time.  Methods and analysis: This study applies a phased mixed-methods multiple case study design to evaluate the implementation of 11 AI innovations introduced across hospital-based and cross-sectoral healthcare settings in the North Denmark Region. Each AI innovation is conceptualised as an individual case. The study consists of three sequential phases: (1) exploratory cross-case mapping of implementation processes and contextual determinants. (2) In-depth longitudinal case studies of selected cases. (3) Cross-case synthesis and programme-level evaluation. Data collection includes interviews, observations, document analysis, questionnaires and regional register data. The study is informed by the Consolidated Framework for Implementation Research, Proctor et al’s Implementation Outcomes Taxonomy, the Quality Implementation Framework and the WHO’s Health System Performance Assessment framework. Qualitative and quantitative data will be analysed separately and integrated through triangulation at case and cross-case levels.  Ethics and dissemination: According to Danish legislation, this study does not require approval from a regional research ethics committee. The study has been assessed by the Aalborg University Research Ethics Committee and registered as a research project involving the processing of personal data at Aalborg University. All participants will provide informed consent prior to participation. Findings will be disseminated through peer-reviewed publications, conference presentations and reports targeting relevant stakeholders.
SN  - 2044-6055
SN  - 2044-6055
ER  -