Preparing for the Future: A Mixed Methods Study Protocol on AI Awareness and Educational Integration in Qatars Primary Health Care Workforce.

This mixed-methods study protocol outlines a comprehensive plan to assess AI awareness, attitudes, and educational needs among Qatar's primary healthcare workforce, aiming to develop an evidence-based readiness framework and training roadmap to facilitate the effective and ethical integration of artificial intelligence in primary care.

The Gist

Imagine Qatar's Primary Health Care system (PHCC) as a massive, high-tech train station. The tracks, signals, and the trains themselves (the digital infrastructure and Electronic Health Records) are being upgraded to be the fastest and smartest in the world. They are ready to run on "Artificial Intelligence" (AI) fuel.

However, there's a problem: The drivers, conductors, and station staff (the doctors, nurses, and pharmacists) haven't been taught how to drive these new trains yet.

This research paper is essentially a pre-trip inspection and training needs assessment. The authors want to find out exactly how ready the staff is to handle this new technology before they try to switch it on.

Here is a breakdown of the study in simple terms:

1. The Big Question: "Are We Ready?"

The researchers are asking: Do the healthcare workers in Qatar know what AI is? Do they trust it? Are they scared of it? And most importantly, do they know how to use it without crashing the train?

They aren't just looking at the technology; they are looking at the people using it. They know that even the best car is useless if the driver doesn't have a license or doesn't know how to steer.

2. How They Are Checking (The Mixed-Methods Approach)

To get the full picture, they are using two different tools, like checking a car with both a computer scanner and a mechanic's eyes:

• The "Census" Survey (The Computer Scanner):
  They are sending a digital questionnaire to everyone working in the health centers (except two very small stations). It's like asking every single employee, "On a scale of 1 to 10, how comfortable are you with AI?" They want to count the numbers: How many people know what AI is? How many have used it? How many are worried about it?

  • Goal: To get a big, clear map of the current situation.

• The "Coffee Chat" Interviews (The Mechanic's Eyes):
  Numbers can tell you what is happening, but not why. So, the researchers will also sit down with small groups of doctors and nurses for deep conversations. They will ask things like, "Why are you hesitant to use AI?" or "What kind of training would actually help you?"

  • Goal: To understand the feelings, fears, and real-life stories behind the numbers.

3. The "Readiness Score" (The Dashboard)

The researchers are building a special "AI Readiness Score." Think of this like a dashboard in a car that shows two things:

1. Infrastructure Readiness: Is the engine (the computers and internet) strong enough?
2. Human Readiness: Is the driver (the staff) skilled enough?

If the engine is perfect but the driver is asleep, the car won't move. If the driver is great but the engine is broken, the car won't move. This study aims to balance both.

4. Why Do This? (The Destination)

Qatar has a big vision (Qatar National Vision 2030) to make healthcare super modern. They have already built the "roads" (digital systems). But if they don't train the "drivers," the new roads will sit empty.

This study wants to create a Training Roadmap.

• If the survey shows doctors are scared of AI making mistakes, the training will focus on safety and trust.
• If the survey shows nurses don't know what AI is, the training will start with the basics.
• If the staff says, "We need hands-on workshops," the plan will include that instead of just boring online videos.

5. The Safety Net (Ethics)

The researchers promise that this is a safe space.

• No Bosses Watching: The doctors and nurses can answer honestly without their managers knowing.
• No Punishment: Saying "I don't know what AI is" won't get anyone in trouble.
• Privacy: All answers are anonymous, like putting a note in a sealed box.

The Bottom Line

This paper is a blueprint for the future. Instead of just buying expensive AI software and hoping the staff figures it out, the researchers are saying, "Let's stop, check our map, see where our drivers need the most help, and then build a custom training program."

By the end of this study, Qatar's Primary Health Care will have a clear plan on how to turn their workforce into AI-savvy experts, ensuring that when the "AI train" starts running, everyone is ready to ride it safely and efficiently.

Technical Summary

Based on the provided preprint, here is a detailed technical summary of the study protocol:

1. Problem Statement

Artificial Intelligence (AI) is rapidly transforming global healthcare through clinical decision support, workflow optimization, and population health management. However, successful integration relies heavily on the readiness, awareness, and educational preparedness of the healthcare workforce.

• The Gap: While Qatar has invested in digital infrastructure (e.g., centralized Electronic Health Records), there is limited evidence regarding the readiness of Primary Health Care (PHC) professionals to adopt AI.
• Specific Context: Previous studies in Qatar indicate high awareness (78.4% of family physicians) but significant gaps in formal training and competence. There is a lack of comprehensive data on the organizational and human factors influencing AI integration in the Middle East, specifically within Qatar's Primary Health Care Corporation (PHCC).
• The Need: A structured assessment is required to move beyond simple awareness to understanding attitudes, training needs, and organizational barriers to inform policy and curriculum development.

2. Methodology

This study employs a Mixed-Methods Design guided by the Organizational Readiness for Change (ORC) framework, adapted for AI integration. The framework evaluates two dimensions: Infrastructure Readiness (technical capacity) and Human Readiness (awareness, attitudes, competency).

A. Study Setting and Population

• Setting: All PHCC Health Centers and Headquarters in Qatar (excluding two small centers, AlJumailiya and Leghwairiya, due to small staff counts <50).
• Population: Approximately 7,975 healthcare professionals, including physicians, dentists, nurses, pharmacists, allied health staff, and administrative support.
• Sampling Strategy:
  • Quantitative: Census-style invitation (targeting the whole population).
  • Qualitative: Maximum variation sampling to ensure diverse representation across roles, experience levels, and locations.

B. Data Collection

1. Quantitative Component (Online Survey):
   • Tool: An anonymous questionnaire developed based on validated literature and expert review.
   • Validation: Face validity assessed via a pilot study (n=10); Content validity assessed via a panel of 10 experts using the Content Validity Ratio (CVR).
   • Domains: Demographics, AI Awareness/Knowledge (7 items), Training Needs (4 items), Attitudes/Perceptions (Trust, Benefits, Concerns), and Infrastructure Readiness.
   • Target Sample: Aiming for 1,000–2,000 responses (minimum calculated sample size for 95% CI is 367).
2. Qualitative Component:
   • Methods: Semi-structured interviews (n=12–20) and Focus Group Discussions (2 sessions with 10–15 participants each).
   • Guides: Structured around themes of awareness, training needs, perceptions, infrastructure, and recommendations.
   • Standards: Conducted in compliance with COREQ and SRQR guidelines.

C. Data Analysis Plan

• Quantitative:
  • Composite Scoring: Calculation of an AI Awareness Score and an AI Attitude Score (normalized to 100).
    • Awareness: Based on exposure to 13 AI application categories.
    • Attitude: Aggregated from trust, perceived benefits, concerns (inversely graded), and willingness to use.
  • Statistics: Descriptive statistics, Independent t-tests/ANOVA (or non-parametric equivalents) for group comparisons, and Logistic Regression to identify predictors of AI readiness.
• Qualitative:
  • Method: Thematic Analysis following Braun and Clarke's (2006) six-step approach.
  • Process: Independent coding by two researchers, development of a codebook, and thematic synthesis.
• Integration: Quantitative and qualitative findings will be synthesized using joint displays to create an actionable education roadmap.

3. Key Contributions

• First Comprehensive Assessment: This is the first study to comprehensively assess AI readiness among PHC professionals in Qatar and the broader Middle East region.
• Development of an AI Readiness Framework: The study will generate a validated AI Readiness Score and a transferable model for assessing workforce readiness in other health systems.
• Evidence-Based Roadmap: It will produce a targeted AI education roadmap aligned with Qatar's National Digital Health Strategy and National Health Strategy 3 (NHS3).
• Methodological Rigor: The study utilizes a robust mixed-methods approach with strict validation protocols (CVR, pilot testing) and adherence to international reporting standards (STROBE, COREQ, SRQR).

4. Results

• Status: As this is a study protocol, no empirical results are reported yet.
• Expected Outcomes: The study anticipates identifying specific knowledge gaps, training priorities, and organizational barriers. It aims to quantify the "Human Readiness" vs. "Infrastructure Readiness" gap to inform policy.

5. Significance

• Policy Impact: The findings will directly inform the Primary Health Care Corporation (PHCC) leadership and policymakers on how to allocate resources for training and infrastructure.
• Workforce Development: By identifying specific competency gaps, the study will guide the integration of AI education into Continuing Professional Development (CPD) and medical curricula.
• Patient Safety & Quality: Enhancing AI literacy ensures that clinicians can critically evaluate and safely use AI tools, preventing uncritical reliance on automated systems and protecting patient autonomy.
• Global Relevance: As one of the first empirical investigations in the region, it offers a model for other health systems navigating the transition to AI-integrated primary care.

Timeline: The study is tentatively scheduled to run from April 2026 to November 2026, with data collection occurring between May and July 2026.