From Health Data to Action – AI for District Health Service Monitoring and Planning

Mission and objectives

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The workshop aims to enable participants to:

1. Interpret routine health indicators and transform health data into actionable insights for district-level service monitoring and planning.
2. Apply accessible AI-enabled tools to support data interpretation, report summarisation, trend identification and prioritisation of health service gaps.
3. Develop a preliminary district health performance dashboard or evidence-informed action plan through a practical, collaborative exercise.
4. Critically evaluate AI-generated outputs for accuracy, bias, confidentiality, contextual appropriateness and operational feasibility.
5. Identify practical opportunities and safeguards for integrating AI-assisted analysis into existing health information and district review systems.

Themes and relevance to Digital Public Health

Digital public health increasingly depends on the ability of health systems to turn routine data into decisions that improve service delivery. However, technological innovation must be accompanied by the skills, governance arrangements and organisational processes needed to make it useful in practice.

This workshop addresses the implementation gap between collecting health data and using it to guide action. It focuses on practical applications of AI and accessible analytical tools rather than advanced programming or the development of new algorithms. Participants will explore how these tools can support the interpretation of service indicators, identify potential performance gaps, generate questions for further investigation and inform resource prioritisation.

The workshop will use district health management in Sindh as an applied case context, while developing an approach transferable to other LMIC settings. It will also address the limitations of AI-assisted decision-making, emphasising that generated insights must be checked against source data, local knowledge, professional judgement and established accountability mechanisms.

The central proposition is that AI should complement, rather than replace, the expertise and responsibility of public health professionals.

Intended audience

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The workshop is intended for:

• District health managers, District Health Officers and health service administrators.
• Public health practitioners and policymakers.
• Health information system professionals and monitoring and evaluation specialists.
• Digital health researchers, data analysts and AI practitioners.
• Academics, implementation researchers and professionals working in health systems
  strengthening.

No advanced programming or specialist AI expertise will be required. Participants should have a basic familiarity with public health indicators or health service delivery; a technical background will not be a prerequisite.

The session will be particularly useful for participants interested in practical, affordable approaches to evidence-informed management in resource-constrained settings.

Expected Outcomes and Practical Outputs

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By the end of the workshop, participants are expected to have:

1. Practised interpreting health service indicators and identifying potential performance gaps.
2. Used an AI-assisted workflow to support data analysis and planning.
3. Developed a draft district health performance dashboard or data-informed action plan.
4. Applied a basic framework for checking AI-generated findings before using them in management decisions.
5. Identified practical steps for embedding data-informed decision-making into existing district health review processes.

The collective outputs will include draft action plans or dashboards and a transferable checklist for responsible AI-assisted health data use. These outputs will help identify common implementation challenges and inform future capacity-strengthening activities in digital public health.

Format and schedule

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The 90-minute session will combine a short introduction, a live demonstration, collaborative problem-solving and critical reflection.

Duration	Session	Description
10 min	Opening and problem framing	The chair will introduce the challenge of translating routine health information into managerial action. Participants will briefly share examples of data-use challenges encountered in their professional settings.
10 min	Demonstration – From data to insight	A facilitator will introduce a hypothetical district health dataset and demonstrate how spreadsheets, simple visualisations and an accessible AI application can help summarise indicators, identify potential outliers and formulate questions for further investigation. The demonstration will distinguish between descriptive findings, AI-generated hypotheses and conclusions that require additional verification.
25 min	Hands-on group challenge	Participants will work in small groups using a shared, hypothetical or anonymised dataset covering selected indicators, such as: – Maternal and child health service utilisation. – Routine immunisation coverage. – Health facility readiness and essential medicine availability. – Service utilisation trends and selected resource constraints. Each group will identify priority performance gaps, examine possible explanations, formulate follow-up questions and select one or two areas for intervention. Groups will use appropriate analytical or AI-assisted tools while documenting any assumptions and limitations.
20 min	From insight to action	Each group will develop a draft district health action plan or dashboard. The output will identify the priority problem, supporting evidence, proposed intervention, responsible stakeholder, an indicative timeline and a measurable monitoring indicator.
15 min	Peer review and responsible AI challenge	Groups will exchange their findings and critically review the proposed recommendations. They will assess whether the conclusions are supported by the data, whether AI-generated outputs contain errors or unsupported assumptions, and whether the recommendations are equitable, feasible and appropriate to the local context.
10 min	Reporting, reflection and take-home framework	Groups will share their main findings and proposed actions. The chair will synthesise lessons, highlight responsible AI practices and facilitate a final discussion on integrating data-informed analysis into routine district health review meetings.

Speakers

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