Preparing for the future: A mixed methods study protocol on AI awareness and educational integration in Qatar’s Primary Health Care Workforce
4 affiliations
- 1Consultant (Research), Clinical Affairs, Department of Clinical Research, Primary Health Care Corporation, Qatar
- 2Public Health Research Consultant, Clinical Affairs, Department of Clinical Research -Primary Health Care Corporation, Qatar
- 3Clinical Research Coordinator, Clinical Affairs, Department of Clinical Research, Primary Health Care Corporation, Qatar
- 4Acting Director of Clinical Research, Clinical Affairs, Department of Clinical Research, Primary Health Care Corporation, Qatar
Abstract
Background: Artificial intelligence (AI) is increasingly being integrated into healthcare systems, with growing applications in clinical decision support, workflow optimization, and population health management. While substantial investments have been made in digital infrastructure, the successful adoption of AI in primary care depends critically on the readiness, awareness, and educational preparedness of healthcare professionals. Global health authorities emphasize the need for ethically grounded and workforce‑focused approaches to AI integration; however, evidence on clinicians’ readiness for AI, particularly in primary care settings and in the Middle East region, remains limited. Objectives: This study aims to assess the level of awareness, perceptions, attitudes, and educational needs related to AI among healthcare professionals working within Qatar’s Primary Health Care Corporation (PHCC). In addition, it seeks to examine organizational factors influencing the integration of AI‑focused education in primary care and to develop an AI readiness framework that can inform targeted training strategies and policy planning. Methods: This study will adopt a mixed‑methods design guided by the Organizational Readiness for Change (ORC) framework, adapted for AI integration in primary care. The quantitative component will consist of an anonymous, census‑style online survey distributed to all healthcare professionals across PHCC health centers and headquarters, assessing AI awareness, attitudes, training needs, and perceived infrastructure readiness. Composite AI awareness and attitude scores will be calculated, and regression analyses will be used to explore factors associated with AI readiness. The qualitative component will include semi‑structured interviews and focus group discussions using maximum variation sampling to capture diverse professional perspectives. Qualitative data will be analyzed thematically, following COREQ and SRQR reporting standards. Quantitative and qualitative findings will be integrated to generate an AI readiness profile and an actionable education roadmap aligned with national digital health priorities. Discussion: This study will provide the first comprehensive assessment of AI readiness among primary care healthcare professionals in Qatar. By identifying knowledge gaps, training priorities, and organizational enablers and barriers, the findings are expected to inform the development of evidence‑based AI education strategies within continuing professional development frameworks. The proposed AI readiness framework may also offer a transferable model for other health systems seeking to align workforce development with responsible AI implementation in primary care.
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BibTeX
@article{syed2026preparing,
title = {{Preparing for the future: A mixed methods study protocol on AI awareness and educational integration in Qatar’s Primary Health Care Workforce}},
author = {Muslim Abbas Syed and Ahmed Sameer Alnuaimi and Dana Bilal El Kaissi and Mohamed Ahmed Syed},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0345347},
url = {https://doi.org/10.1371/journal.pone.0345347},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Preparing for the future: A mixed methods study protocol on AI awareness and educational integration in Qatar’s Primary Health Care Workforce
AU - Muslim Abbas Syed
AU - Ahmed Sameer Alnuaimi
AU - Dana Bilal El Kaissi
AU - Mohamed Ahmed Syed
PY - 2026
DA - 2026/09/15
JO - PLOS One
DO - 10.1371/journal.pone.0345347
UR - https://doi.org/10.1371/journal.pone.0345347
AB - Background: Artificial intelligence (AI) is increasingly being integrated into healthcare systems, with growing applications in clinical decision support, workflow optimization, and population health management. While substantial investments have been made in digital infrastructure, the successful adoption of AI in primary care depends critically on the readiness, awareness, and educational preparedness of healthcare professionals. Global health authorities emphasize the need for ethically grounded and workforce‑focused approaches to AI integration; however, evidence on clinicians’ readiness for AI, particularly in primary care settings and in the Middle East region, remains limited. Objectives: This study aims to assess the level of awareness, perceptions, attitudes, and educational needs related to AI among healthcare professionals working within Qatar’s Primary Health Care Corporation (PHCC). In addition, it seeks to examine organizational factors influencing the integration of AI‑focused education in primary care and to develop an AI readiness framework that can inform targeted training strategies and policy planning. Methods: This study will adopt a mixed‑methods design guided by the Organizational Readiness for Change (ORC) framework, adapted for AI integration in primary care. The quantitative component will consist of an anonymous, census‑style online survey distributed to all healthcare professionals across PHCC health centers and headquarters, assessing AI awareness, attitudes, training needs, and perceived infrastructure readiness. Composite AI awareness and attitude scores will be calculated, and regression analyses will be used to explore factors associated with AI readiness. The qualitative component will include semi‑structured interviews and focus group discussions using maximum variation sampling to capture diverse professional perspectives. Qualitative data will be analyzed thematically, following COREQ and SRQR reporting standards. Quantitative and qualitative findings will be integrated to generate an AI readiness profile and an actionable education roadmap aligned with national digital health priorities. Discussion: This study will provide the first comprehensive assessment of AI readiness among primary care healthcare professionals in Qatar. By identifying knowledge gaps, training priorities, and organizational enablers and barriers, the findings are expected to inform the development of evidence‑based AI education strategies within continuing professional development frameworks. The proposed AI readiness framework may also offer a transferable model for other health systems seeking to align workforce development with responsible AI implementation in primary care.
SN - 1932-6203
ER -