Artificial Intelligence for M&E Data Analysis and Reporting in the Health Sector Course

The Artificial Intelligence for M&E Data Analysis and Reporting in the Health Sector Course is designed to address the growing need for health-sector professionals to manage increasing volumes of programme, routine health, HMIS, facility, community, and survey data. The course demonstrates how artificial intelligence can strengthen the efficiency, accuracy, and timeliness of monitoring, evaluation, analysis, and performance reporting processes.

Health programmes depend on reliable data to track service delivery, assess programme performance, identify implementation gaps, and guide resource allocation. This practical course equips participants with AI-supported approaches for cleaning, validating, organizing, analysing, and interpreting health data while identifying missing information, inconsistencies, anomalies, unusual trends, and other data-quality challenges that may affect reporting and decision-making.

Participants will gain practical skills in applying AI to quantitative and qualitative health information, analysing indicators, comparing programme performance, identifying trends, and transforming complex datasets into meaningful evidence. The course also covers AI-assisted charts, dashboards, visualisations, analytical summaries, and reports that enable M&E and HMIS teams to communicate programme achievements, challenges, risks, and emerging priorities more effectively.

The course recognizes that health information frequently contains confidential and sensitive beneficiary, patient, facility, and programme data. Participants will therefore examine responsible AI practices covering data privacy, confidentiality, cybersecurity, ethical use, algorithmic bias, transparency, human oversight, and verification of AI-generated outputs to ensure that technological innovation does not compromise established health data governance standards.

Through practical exercises and health-sector scenarios, participants will learn how to translate AI-supported analysis into actionable recommendations for programme planning and improvement. By reducing repetitive manual work and accelerating analytical processes, AI can enable M&E and HMIS professionals to dedicate greater attention to interpretation, learning, programme adaptation, performance improvement, and evidence-based decision-making.

Who Should Attend

• Monitoring and Evaluation Officers and Specialists working in health programmes
• Health Management Information System (HMIS) Officers and Coordinators
• Monitoring, Evaluation, Research and Learning (MERL) Professionals
• Health Programme Managers and Project Coordinators
• Public Health Officers, Specialists and Programme Professionals
• Health Data Analysts and Data Management Officers
• Epidemiologists and Health Surveillance Professionals
• Health Information Management and Digital Health Professionals
• Biostatisticians, Statisticians and Health Research Professionals
• Results-Based Management and Programme Performance Specialists
• Health Planning, Policy and Decision-Support Professionals
• Quality Improvement and Health Systems Strengthening Officers
• Donor-Funded Health Programme and Project Teams
• NGO, INGO and Development Partner Health Programme Professionals
• Government Ministry and Department of Health M&E Personnel
• Health Facility Managers and Performance Monitoring Officers
• Survey, Research and Evaluation Professionals working in the health sector
• Data Visualization, Reporting and Knowledge Management Professionals

Course Objectives

• Apply artificial intelligence within health-sector M&E and HMIS workflows to improve the efficiency, accuracy, timeliness, and analytical depth of routine monitoring, evaluation, learning, and programme reporting.
• Use AI-assisted techniques to clean and validate health datasets by identifying missing values, duplicate records, inconsistencies, outliers, unusual observations, and other data-quality problems requiring corrective action.
• Analyse routine health, programme, facility, community, and survey data using AI to identify meaningful patterns, relationships, variations, performance gaps, and emerging trends across different populations and locations.
• Strengthen health data-quality assessment and assurance processes by applying AI-supported checks for completeness, accuracy, consistency, timeliness, integrity, and reliability before analysis and reporting.
• Apply AI to monitor health programme indicators and performance targets by identifying achievement gaps, unexpected changes, implementation bottlenecks, underperforming areas, and issues requiring management attention.
• Detect anomalies and reporting inconsistencies in health information systems using AI-assisted analytical approaches that highlight unusual indicator movements, questionable records, and potential reporting errors.
• Develop AI-supported charts, visualisations, and interactive dashboards that communicate health indicators, programme achievements, trends, performance gaps, risks, and priority areas clearly to decision-makers.
• Use generative AI to strengthen health-sector M&E reporting by developing analytical narratives, executive summaries, performance reports, management briefs, and evidence-based recommendations from verified programme data.
• Apply predictive analytics to health programme monitoring to forecast indicator performance, identify emerging implementation risks, anticipate potential deviations from targets, and support proactive management responses.
• Translate health data and AI-generated analytical findings into actionable recommendations that support programme planning, resource prioritisation, corrective actions, service improvement, and evidence-based decision-making.
• Apply responsible and ethical AI principles when handling sensitive health information by maintaining confidentiality, data privacy, cybersecurity, transparency, human oversight, and appropriate safeguards against algorithmic bias.
• Develop practical approaches for integrating AI into existing M&E and HMIS systems while maintaining data governance, institutional accountability, human verification, organizational capacity, and sustainable adoption.

Comprehensive Course OutlineTop of Form

Module 1: Artificial Intelligence for Health-Sector M&E and HMIS

• Application of artificial intelligence, machine learning, and generative AI within health-sector M&E and HMIS environments
• Mapping AI opportunities across routine health information, programme monitoring, surveys, evaluations, analysis, and reporting workflows
• Using AI to reduce repetitive manual M&E tasks while improving analytical efficiency, accuracy, and evidence-based decision-making
• Emerging AI applications for health programme monitoring, surveillance, performance management, and intelligent decision-support systems

Module 2: AI-Assisted Health Data Cleaning, Validation and Quality Assurance

• Using AI tools to identify missing values, duplicate records, inconsistencies, outliers, and potential errors in health datasets
• Automated validation of routine health, programme, facility, community, and survey data against established data-quality requirements
• Applying AI-supported data-quality assessments to examine completeness, accuracy, consistency, timeliness, integrity, and reliability
• Developing automated quality checks and human verification procedures before health data is approved for analysis and reporting

Module 3: AI for Routine Health, Programme and Survey Data Analysis

• Applying AI-assisted techniques to analyse routine HMIS, programme monitoring, household survey, facility, and beneficiary-level datasets
• Conducting descriptive and comparative analysis of health indicators across facilities, districts, populations, programmes, and reporting periods
• Using AI to identify relationships, variations, patterns, gaps, and significant changes within health programme performance data
• Integrating multiple health data sources to generate comprehensive analytical insights for programme management and decision-making

Module 4: AI for Health Data Trends, Anomalies and Performance Analysis

• Using AI to detect unusual changes, anomalies, reporting inconsistencies, and unexpected patterns within routine health information
• Analysing indicator trends against programme targets, baselines, benchmarks, previous reporting periods, and expected performance levels
• Identifying underperforming facilities, geographical areas, interventions, indicators, and population groups requiring management attention
• Applying AI-supported diagnostic analysis to investigate potential causes of performance gaps and emerging health programme challenges

Module 5: AI-Enhanced Health Indicators and Results-Based Monitoring

• Intelligent monitoring of health programme inputs, activities, outputs, outcomes, targets, milestones, and key performance indicators
• Using AI to analyse results frameworks, logical frameworks, theories of change, indicator matrices, and programme performance plans
• Automated tracking of indicator achievement, target variances, implementation progress, delayed activities, and emerging programme risks
• Comparative analysis of programme performance across health facilities, implementing partners, districts, interventions, and reporting periods

Module 6: Predictive Analytics and Early-Warning Systems for Health Programmes

• Applying predictive analytics to forecast health indicators, programme performance, service utilization, and implementation trends
• Using historical health and programme data to anticipate potential deviations from targets and identify emerging performance challenges
• Developing AI-supported early-warning approaches for service disruptions, programme delays, unusual indicator movements, and implementation risks
• Assessing predictive model accuracy, uncertainty, limitations, and appropriate human oversight when interpreting health-sector forecasts

Module 7: AI-Powered Health Data Visualization and Dashboards

• Transforming routine health, survey, and programme monitoring data into clear charts, graphs, maps, dashboards, and analytical displays
• Designing interactive health dashboards for tracking indicators, targets, trends, geographical variations, and programme performance
• Using AI to recommend appropriate visualisations for different health indicators, analytical questions, audiences, and reporting requirements
• Developing management and stakeholder dashboards that communicate achievements, gaps, risks, trends, and priority areas for intervention

Module 8: Generative AI for Health M&E Reporting and Analytical Summaries

• Using generative AI to develop accurate analytical narratives from verified health programme, HMIS, survey, and monitoring data
• AI-assisted preparation of monthly, quarterly, annual, donor, management, programme performance, and other health-sector reports
• Generating executive summaries, indicator narratives, key findings, performance explanations, and evidence-based recommendations using AI
• Applying prompt engineering and structured instructions to improve the accuracy, consistency, relevance, and usefulness of M&E reports

Module 9: Translating Health Data into Evidence-Based Decisions

• Converting AI-generated analytical findings into actionable recommendations for health planning, implementation, and programme improvement
• Using health data to identify priority interventions, resource requirements, implementation gaps, and opportunities for corrective action
• Communicating analytical findings effectively to programme managers, health authorities, donors, implementing partners, and decision-makers
• Integrating AI-generated insights with professional judgement and contextual knowledge to strengthen evidence-based health decision-making

Module 10: Responsible AI, Health Data Confidentiality, Security and Ethics

• Protecting confidential patient, beneficiary, facility, programme, and organizational information when using artificial intelligence tools
• Applying responsible AI principles covering privacy, informed use, cybersecurity, fairness, transparency, accountability, and human oversight
• Identifying algorithmic bias, hallucinations, unsupported conclusions, data leakage, and other risks affecting AI-generated health evidence
• Establishing verification, access control, data governance, and ethical safeguards for responsible use of AI within M&E and HMIS functions

Module 11: Practical Health M&E AI Application and Implementation Roadmap

• Practical application of AI to clean, validate, analyse, visualise, interpret, and report realistic health-sector M&E datasets
• Developing an integrated workflow connecting routine health information, programme monitoring data, analysis, dashboards, and reporting
• Producing an AI-assisted health performance report containing analytical findings, visualisations, identified gaps, and actionable recommendations
• Developing an organizational roadmap for integrating AI into existing M&E and HMIS processes while maintaining security and human oversight

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808 

Terms of Payment

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better