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Original article

Vol. 156 No. 2 (2026)

A qualitative assessment of the use of artificial intelligence in public sector health organisations in Ontario, Canada

Cite this as:
Swiss Med Wkly. 2026;156:4942
Published
20.02.2026

Summary

BACKGROUND: Advancements in artificial intelligence (AI) and machine learning (ML) capabilities and increased access to methods have enabled population-level analysis for research and health system monitoring. However, governments and public authorities may not be aware of the full range of applications, opportunities, and barriers and challenges. The purpose of the present study was to understand the barriers and facilitators of AI/ML and whether (and how) AI/ML was being used (or could be used) in public sector health organisations in a publicly funded health system.

METHODS: Thirteen key informant interviews at three public sector health organisations were conducted. Common themes were identified. An in-person workshop was held in Toronto on 15 May 2024. We identified barriers and enablers and make recommendations for advancing AI/ML in the broader public sector.

RESULTS: A total of six barriers were identified by participants: 1) knowledge, education and expertise; 2) privacy, ethics and security; 3) technology and infrastructure; 4) financial barriers; 5) competing priorities; and 6) fear of being replaced. Key enablers include 1) partnerships; 2) buy-in and support from senior leadership; 3) management and operational supports; and 4) actionable use cases. One prominent use case objective was to enhance healthcare system efficiency, including automating routine or unskilled operations to allow healthcare professionals to focus on higher-value tasks.

CONCLUSION: Demonstrating success from focused small-scale AI applications, partnering with academia, and engaging senior leadership may build confidence and capability, but it is important to share knowledge, experiences, and measure success for the perspectives of different partners.

References

  1. 1. Huang KA, Choudhary HK, Kuo PC. Artificial Intelligent Agent Architecture and Clinical Decision-Making in the Healthcare Sector. Cureus. 2024 Jul;16(7):e64115. doi: https://doi.org/10.7759/cureus.64115
  2. 2. Gupta A, Singh A. Healthcare 4.0: recent advancements and futuristic research directions. Wirel Pers Commun. 2023;129(2):933–52. doi: https://doi.org/10.1007/s11277-022-10164-8
  3. 3. Olawade DB, Wada OJ, David-Olawade AC, Kunonga E, Abaire O, Ling J. Using artificial intelligence to improve public health: a narrative review. Front Public Health. 2023 Oct;11:1196397. doi: https://doi.org/10.3389/fpubh.2023.1196397
  4. 4. Russell S, Norvig P. Artificial Intelligence: A Modern Approach, Global Edition 4th.Ed. (4th ed.). Pearson Education. 2021
  5. 5. O’Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. Standards for reporting qualitative research: a synthesis of recommendations. Acad Med. 2014 Sep;89(9):1245–51. doi: https://doi.org/10.1097/ACM.0000000000000388
  6. 6. Rahimi S, Khatooni M. Saturation in qualitative research: an evolutionary concept analysis. Int J Nurs Stud Adv. 2024 Jan;6:100174. doi: https://doi.org/10.1016/j.ijnsa.2024.100174
  7. 7. Devers KJ. How will we know “good” qualitative research when we see it? Beginning the dialogue in health services research. Health Serv Res. 1999 Dec;34(5 Pt 2):1153–88.
  8. 8. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101. doi: https://doi.org/10.1191/1478088706qp063oa
  9. 9. Byrne D. A worked example of Braun and Clarke’s approach to reflexive thematic analysis. Qual Quant. 2022 Jun;56(3):1391–412. doi: https://doi.org/10.1007/s11135-021-01182-y
  10. 10. O’Connor C, Joffe H. Intercoder Reliability in Qualitative Research: Debates and Practical Guidelines. Int J Qual Methods. 2020;19:19. doi: https://doi.org/10.1177/1609406919899220
  11. 11. Lombard M, Snyder-Duch J, Bracken CC. Content Analysis in Mass Communication: Assessment and Reporting of Intercoder Reliability. Hum Commun Res. 2002 Oct;28(4):587–604. doi: https://doi.org/10.1111/j.1468-2958.2002.tb00826.x
  12. 12. Ramakrishnan K, Abuhamad G, Chantry C, Diamond SP, Donelson P, Ebert L, et al. The AI Maturity Framework: A strategic guide to operationalize and scale enterprise AI solutions [Internet]Element AI; 2020.
  13. 13. Artificial Intelligence in the Public Sector. A Maturity Model | IBM Center for The Business of Government [Internet]. [cited 2025 Oct 22]. Available from: https://www.businessofgovernment.org/report/artificial-intelligence-public-sector-maturity-model
  14. 14. Carter N, Bryant-Lukosius D, DiCenso A, Blythe J, Neville AJ. The use of triangulation in qualitative research. Oncol Nurs Forum. 2014 Sep;41(5):545–7. doi: https://doi.org/10.1188/14.ONF.545-547
  15. 15. Noble H, Heale R. Triangulation in research, with examples. Evid Based Nurs. 2019 Jul;22(3):67–8. doi: https://doi.org/10.1136/ebnurs-2019-103145
  16. 16. Artificial Intelligence and Data Act [Internet]. [cited 2024 Dec 18]. Available from: https://ised-isde.canada.ca/site/innovation-better-canada/en/artificial-intelligence-and-data-act
  17. 17. Bill 194, Strengthening Cyber Security and Building Trust in the Public Sector Act, 2024 - Legislative Assembly of Ontario [Internet]. [cited 2025 Feb 4]. Available from: https://www.ola.org/en/legislative-business/bills/parliament-43/session-1/bill-194
  18. 18. Neumann O, Guirguis K, Steiner R. Exploring artificial intelligence adoption in public organizations: a comparative case study. Public Manage Rev. 2024 Jan;26(1):114–41. doi: https://doi.org/10.1080/14719037.2022.2048685
  19. 19. Fisher S, Rosella LC. Priorities for successful use of artificial intelligence by public health organizations: a literature review. BMC Public Health. 2022 Nov;22(1):2146. doi: https://doi.org/10.1186/s12889-022-14422-z
  20. 20. Esmaeilzadeh P. Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations. Artif Intell Med. 2024 May;151:102861. doi: https://doi.org/10.1016/j.artmed.2024.102861
  21. 21. Riedemann L, Labonne M, Gilbert S. The path forward for large language models in medicine is open. NPJ Digit Med. 2024 Nov;7(1):339. doi: https://doi.org/10.1038/s41746-024-01344-w
  22. 22. Sachdeva S, Bhatia S, Al Harrasi A, Shah YA, Anwer K, Philip AK, et al. Unraveling the role of cloud computing in health care system and biomedical sciences. Heliyon. 2024 Apr;10(7):e29044. doi: https://doi.org/10.1016/j.heliyon.2024.e29044
  23. 23. Kusunose M, Muto K. Public attitudes toward cloud computing and willingness to share personal health records (PHRs) and genome data for health care research in Japan. Hum Genome Var. 2023 Mar;10(1):11. doi: https://doi.org/10.1038/s41439-023-00240-1
  24. 24. Greenhalgh T, Robert G, Macfarlane F, Bate P, Kyriakidou O. Diffusion of innovations in service organizations: systematic review and recommendations. Milbank Q. 2004;82(4):581–629. doi: https://doi.org/10.1111/j.0887-378X.2004.00325.x
  25. 25. Siu EC, Levinton C, Brown AD. The Value of Performance Measurement in Promoting Improvements in Women’s Health. Healthc Policy. 2009 Nov;5(2):52–67. doi: https://doi.org/10.12927/hcpol.2013.21233
  26. 26. Kamyabi A, Iyamu I, Saini M, May C, McKee G, Choi A. Advocating for population health: the role of public health practitioners in the age of artificial intelligence. Can J Public Health. 2024 Jun;115(3):473–6. doi: https://doi.org/10.17269/s41997-024-00881-x
  27. 27. Chan WW, Fitzsimmons-Craft EE, Smith AC, Firebaugh ML, Fowler LA, DePietro B, et al. The Challenges in Designing a Prevention Chatbot for Eating Disorders: observational Study. JMIR Form Res. 2022 Jan;6(1):e28003. doi: https://doi.org/10.2196/28003
  28. 28. Chen RJ, Wang JJ, Williamson DF, Chen TY, Lipkova J, Lu MY, et al. Algorithmic fairness in artificial intelligence for medicine and healthcare. Nat Biomed Eng. 2023 Jun;7(6):719–42. doi: https://doi.org/10.1038/s41551-023-01056-8
  29. 29. Gentzel M. Biased Face Recognition Technology Used by Government: A Problem for Liberal Democracy. Philos Technol. 2021;34(4):1639–63. doi: https://doi.org/10.1007/s13347-021-00478-z
  30. 30. Vyas DA, Eisenstein LG, Jones DS. Hidden in Plain Sight - Reconsidering the Use of Race Correction in Clinical Algorithms. N Engl J Med. 2020 Aug;383(9):874–82. doi: https://doi.org/10.1056/NEJMms2004740
  31. 31. Koo C, Yang A, Welch C, Jadav V, Posch L, Thoreson N, et al. Validating racial and ethnic non-bias of artificial intelligence decision support for diagnostic breast ultrasound evaluation. J Med Imaging (Bellingham). 2023 Nov;10(6):061108. doi: https://doi.org/10.1117/1.JMI.10.6.061108
  32. 32. Perets O, Stagno E, Ben Yehuda E, McNichol M, Celi LA, Rappoport N, et al. Inherent Bias in Electronic Health Records: A Scoping Review of Sources of Bias. medRxiv [preprint]. 2024 Apr 12;2024.04.09.24305594. doi: https://doi.org/10.1101/2024.04.09.24305594
  33. 33. Abujaber AA, Nashwan AJ. Ethical framework for artificial intelligence in healthcare research: A path to integrity. World J Methodol. 2024 Sep;14(3):94071. doi: https://doi.org/10.5662/wjm.v14.i3.94071
  34. 34. Park HJ. Patient perspectives on informed consent for medical AI: A web-based experiment. Digit Health. 2024 Apr;10:20552076241247938. doi: https://doi.org/10.1177/20552076241247938
  35. 35. Panteli D, Adib K, Buttigieg S, Goiana-da-Silva F, Ladewig K, Azzopardi-Muscat N, et al. Artificial intelligence in public health: promises, challenges, and an agenda for policy makers and public health institutions. Lancet Public Health. 2025 May;10(5):e428–32. doi: https://doi.org/10.1016/S2468-2667(25)00036-2
  36. 36. Responsible Use of Artificial Intelligence Directive | ontario.ca [Internet]. [cited 2025 Oct 23]. Available from: https://www.ontario.ca/page/responsible-use-artificial-intelligence-directive
  37. 37. Braun V, Clarke V. Supporting best practice in reflexive thematic analysis reporting in Palliative Medicine: A review of published research and introduction to the Reflexive Thematic Analysis Reporting Guidelines (RTARG). Palliat Med. 2024 Jun;38(6):608–16. doi: https://doi.org/10.1177/02692163241234800
  38. 38. Hou W, Ji Z. Comparing Large Language Models and Human Programmers for Generating Programming Code. Adv Sci (Weinh). 2025 Feb;12(8):e2412279. doi: https://doi.org/10.1002/advs.202412279
  39. 39. Ahmed SK. The pillars of trustworthiness in qualitative research. J Med Surg Public Health. 2024 Apr;2:100051. doi: https://doi.org/10.1016/j.glmedi.2024.100051
  40. 40. S4-Episode 9: Technology in the classroom: Digital education, privacy, and student well-being | Information and Privacy Commissioner of Ontario [Internet]. [cited 2025 Oct 20]. Available from: https://www.ipc.on.ca/en/media-centre/podcast/s4-episode-9-technology-classroom-digital-education-privacy-and-student-well-being
  41. 41. Ye J, Woods D, Jordan N, Starren J. The role of artificial intelligence for the application of integrating electronic health records and patient-generated data in clinical decision support. AMIA Jt Summits Transl Sci Proc. 2024 May;2024:459–67.
  42. 42. AI Strategy for the Federal Public Service 2025-2027: Priority - Canada.ca [Internet]. []. Available from: https://www.canada.ca/en/government/system/digital-government/digital-government-innovations/responsible-use-ai/gc-ai-strategy-priority-areas.html