Course overview
The Electronic Medical Records Analytics & Healthcare Reporting Training Workshop is a practical professional training programme designed to equip participants with the skills required to extract, manage, analyse and report data generated by Electronic Medical Records (EMR) and Electronic Health Records (EHR) systems.
The workshop focuses on transforming routine clinical records into reliable information for healthcare management, clinical service monitoring, quality improvement, patient safety, resource planning and evidence-based decision-making. Participants learn how to assess medical record completeness, validate clinical data, analyse patient encounters, monitor service utilisation, evaluate clinical outcomes and develop healthcare performance reports and dashboards.
The Electronic Medical Records Analytics & Healthcare Reporting Training Workshop equips healthcare professionals, medical records officers, hospital administrators and health informatics specialists with practical skills in electronic medical records analysis, clinical data management and healthcare reporting. Participants learn to validate clinical datasets, calculate healthcare indicators, analyse patient cohorts, monitor service utilisation, develop dashboards and prepare reliable reports for clinical quality improvement and hospital management. The training supports better use of digital health information across hospitals, clinics and healthcare programmes.
Course objectives
- Understand EMR data structures and workflows and identify the clinical, administrative and operational information available for healthcare analytics.
- Apply ethical data governance and evidence-based reporting practices to support healthcare planning, clinical quality improvement and responsible use of patient information.
- Develop healthcare reports and interactive dashboards that communicate clinical performance, patient safety indicators and operational results to relevant decision-makers.
- Conduct EMR-based statistical analysis and cohort reporting to identify clinical trends, patient characteristics, service delivery gaps and opportunities for quality improvement.
- Calculate and interpret clinical and operational healthcare indicators relating to patient encounters, diagnoses, treatments, follow-up, outcomes and service utilisation.
- Extract, clean, validate and organise electronic medical records data while maintaining data integrity, confidentiality and traceability.
Who should attend
Health information management professionals, medical records officers, hospital administrators, clinical data analysts, health informatics specialists, medical officers, nurses, clinical officers, quality improvement officers, monitoring and evaluation professionals, hospital operations managers, health programme coordinators, digital health implementation teams, healthcare researchers, medical audit personnel and health information technology professionals.
Course content
- Module 1: Foundations of Electronic Medical Records and Healthcare Analytics Electronic Medical Records and Electronic Health Records concepts, differences between EMR and EHR systems, role of electronic records in clinical service delivery, components of
- Module 2: EMR Data Architecture, Clinical Workflows and Data Sources Patient master indexes and unique patient identifiers, patient registration and demographic records, encounter and visit records, clinical notes and consultation documentation, diagnosis
- Module 3: EMR Data Extraction, Cleaning and Preparation for Analysis Data extraction from authorised EMR reports and database exports, selection of relevant patient and encounter fields, data dictionaries and metadata documentation, standardisation of var
- Module 4: EMR Data Quality Assessment and Clinical Documentation Auditing Dimensions of healthcare data quality, completeness of patient demographics and clinical records, accuracy of diagnosis and treatment documentation, consistency between clinical not
- Module 5: Healthcare Indicators and Clinical Performance Measurement Patient registration and encounter indicators, outpatient attendance and inpatient admission statistics, new versus returning patient visits, average length of stay, bed occupancy and be
- Module 6: Patient Cohort Analysis and Longitudinal Healthcare Data Definition of patient cohorts and eligibility criteria, identification of patients using diagnosis and encounter histories, incident versus prevalent patient groups, new and returning pati
- Module 7: Clinical Data Analysis and Statistical Reporting Descriptive statistics for patient demographics, age and sex distributions, diagnosis frequencies, proportions and percentages, measures of central tendency and variability, cross-tabulation of cl
- Module 8: Disease-Specific EMR Analytics and Chronic Care Monitoring Identification of disease-specific patient cohorts, analysis of diabetes and hypertension records, monitoring of blood pressure and glucose measurements, assessment of chronic disease fo
- Module 9: Clinical Quality, Patient Safety and Healthcare Audit Analytics Clinical quality indicators and audit objectives, completeness of clinical assessments, documentation of allergies and adverse reactions, medication reconciliation and prescription
- Module 10: Healthcare Reporting, Dashboards and Performance Visualisation Principles of healthcare report design, clinical and operational reporting requirements, monthly and quarterly hospital reports, outpatient and inpatient performance summaries, pati
- Module 11: Healthcare Database Queries, Interoperability and System Integration Fundamentals of relational healthcare databases, tables and relationships in clinical information systems, patient and encounter identifiers, basic SQL concepts for authorised
- Module 12: Healthcare Data Governance, Privacy and Security Principles of healthcare data governance, patient confidentiality and professional responsibilities, lawful handling of personal and sensitive health data, role-based access control, minimum nece
- Module 13: Advanced Healthcare Analytics and Outcome Monitoring Development of analytical questions from clinical and operational priorities, risk stratification concepts, analysis of repeated clinical measurements, patient outcome comparisons, regression
- Module 14: Healthcare Reporting Automation and Reproducible Analytical Workflows Standardisation of recurring healthcare reports, reusable report templates, consistent indicator definitions, automated data import and transformation workflows, refreshable
- Module 15: Integrated EMR Analytics and Healthcare Reporting Capstone Project Selection of a healthcare reporting problem, definition of analytical questions and target users, identification of authorised EMR data sources, selection of relevant patient an
General notes
- Practical Exercises and Expected Outputs: EMR data structure assessment — EMR data source and field mapping document, clinical data cleaning — validated analytical dataset and data cleaning log, record quality audit — EMR data quality assessment report, healthcare indicator calculation — clinical and operational indicator workbook, patient cohort analysis — cohort definition and patient follow-up summary, disease-specific analytics — chronic disease or other clinical service analysis report, clinical quality review — clinical documentation audit and improvement recommendations, healthcare dashboard development — interactive hospital performance dashboard, database query exercise — authorised clinical data extraction and summary outputs, reporting automation — reusable reporting template and validation checklist, governance exercise — healthcare data privacy and access-control checklist, final capstone project — integrated EMR analytics report, dashboard and presentation.
- Recommended Software and Tools : Microsoft Excel, Power BI, SQL, DHIS2, REDCap, OpenMRS, OpenEMR, Bahmni, electronic medical record systems, electronic health record systems, R, SPSS, Stata, Python, FHIR, HL7. Software selection will depend on institutional access, training objectives and the systems used by participating healthcare organisations. Clinical information should only be accessed through authorised accounts and approved datasets.
- Training Methodology : The workshop combines instructor-led presentations, practical software demonstrations, guided exercises, case studies, group assignments and an integrated capstone project. Participants work with synthetic or appropriately de-identified clinical datasets to practise data validation, indicator calculation, cohort analysis, clinical reporting and dashboard development. The methodology emphasises the interpretation of clinical and operational results, data quality, privacy protection and practical application in hospitals, clinics and digital health programmes.
- Electronic medical records training, EMR analytics course, EHR data analysis, healthcare reporting training, clinical data analytics, hospital data management, medical records analysis, healthcare dashboards, health informatics training, clinical quality reporting, digital health analytics.
Frequently asked questions
1. Do I need experience with an EMR system?
Basic familiarity with healthcare records is helpful, but the workshop introduces key EMR concepts before progressing to analytical workflows. Advanced database exercises may require additional technical experience.
2. Will the workshop cover patient-level data analysis?
Yes. Participants learn about patient and encounter records, longitudinal data, cohort definitions, follow-up patterns and clinical indicator analysis using authorised sample datasets.
3. Which software will be used?
Depending on the training configuration, sessions may use Excel, Power BI, SQL, R, SPSS, Stata or Python, alongside EMR platforms such as OpenMRS or OpenEMR where suitable.
4. Does the course cover healthcare dashboards?
Yes. Participants learn to develop dashboards for patient activity, service utilisation, clinical documentation, selected outcomes and hospital performance monitoring.
5. Will the training address patient privacy?
Yes. Data confidentiality, access control, de-identification, secure data handling, ethical use and relevant data protection requirements are incorporated throughout the programme.
6. 7. What practical outputs will participants produce?
Expected outputs include a cleaned clinical dataset, data quality assessment, indicator workbook, cohort analysis, healthcare dashboard, clinical or operational report and a final project presentation.
Schedule & fees
| Dates | Location | Duration | Fee (USD) |
|---|
| 01/02/2027 - 12/02/2027 |
Nairobi,kenya or Virtual |
10 Days |
$3,000 |
| 05/04/2027 - 16/04/2027 |
Lusaka,Zambia |
10 Days |
$3,500 |
| 20/09/2027 - 01/10/2027 |
Mombasa,Kenya |
10 Days |
$3,000 |
| 04/10/2027 - 15/10/2027 |
Accra,Ghana |
10 Days |
$4,000 |