Health Informatics and Analytics Training Course

Duration 5 days 10 modules · 33 contact hours
Level Intermediate Certificate on completion
Who attends Health Service Managers, Clinical Informatics Professionals and others
Fee from USD 1,100 KSh 95,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 9 Nov 2026 Course code SD-HSM-022

Before you join: Participants should have experience working with health services, health programmes, clinical records, service reporting, digital health projects or operational improvement.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Health systems generate large volumes of clinical, operational, financial and population-health data, yet many managers struggle to turn these records into dependable decisions. Poor data definitions, inconsistent coding, fragmented systems and weak governance can distort performance reporting, delay care coordination and create privacy risks. Health informatics provides the disciplines needed to organise information, connect systems and use evidence responsibly.

This course develops practical capability in health information systems, clinical workflows, data quality, interoperability, governance, analytics and performance reporting. Participants learn to map patient and information flows, define minimum data sets, assess data-quality problems, interpret health indicators and specify dashboards that support action. The course addresses widely used standards and methods including HL7 FHIR, SNOMED CT, LOINC, ICD-10, RACI, BPMN, statistical process control and Plan-Do-Study-Act cycles.

Teaching combines focused instructor briefings with health-system cases, facilitated group analysis, workflow mapping, indicator design and dashboard critique. Cases reflect common challenges such as reducing avoidable readmissions, improving laboratory-result exchange, monitoring outpatient access and strengthening health-record privacy controls. Participants leave with a structured health informatics improvement pack containing workflow, governance, data-quality, analytics and implementation materials that can be adapted to their own service.

The course is suited to professionals who work between care delivery, service management, digital health, information management, quality improvement and reporting. It is appropriate for hospital, primary-care, public-health, insurance, social-care and NGO settings where decisions depend on reliable, timely and well-governed information.

What you will be able to do

  • Map patient and information workflows using swimlane diagrams and BPMN notation
  • Assess health-data quality using defined completeness, validity, timeliness and consistency rules
  • Apply HL7 FHIR, SNOMED CT, LOINC and ICD-10 concepts to information exchange requirements
  • Design governance arrangements using stewardship roles, RACI matrices and access-control principles
  • Define health-service indicators with clear numerators, denominators and case definitions
  • Interpret variation using run charts, statistical process control and risk-adjusted measures
  • Specify decision-focused dashboards using wireframes, thresholds, drill-downs and alert criteria
  • Develop an implementation and evaluation plan for a health informatics improvement initiative

Benefits of attending

For you

  • Produce clearer requirements for electronic health record and reporting projects
  • Identify data-quality defects before they affect management or clinical decisions
  • Interpret service-performance indicators without confusing variation with deterioration
  • Communicate effectively with clinicians, analysts, vendors and information governance teams
  • Build a credible improvement plan for a current health information problem

For your organisation

  • Improves consistency of service measures used in management reporting
  • Reduces avoidable rework caused by unclear data definitions and ownership
  • Supports safer information exchange between care settings and partner organisations
  • Strengthens privacy, access-control and audit-trail practices for health records
  • Creates better evidence for quality improvement, capacity planning and investment decisions

Course modules

10 modules over 5 days

Participants can explain how health information systems support care delivery, management and population-health decisions.

  • Health information system components: people, processes, applications, data and infrastructure
  • Map a patient journey from registration through discharge and follow-up
  • Distinguish operational, clinical, public-health and financial information requirements
  • Identify data producers, custodians, users and decision rights
  • Apply sociotechnical thinking to workflow, usability, incentives and adoption barriers
  • Trace information flows between primary care, hospitals, laboratories and payers

Participants can document clinical workflows and convert service needs into testable information requirements.

  • Document current-state workflows using swimlane diagrams and BPMN notation
  • Separate clinical workflow variation from data-entry defects and workarounds
  • Define minimum data sets for referral, admission and discharge
  • Use RACI matrices to assign data stewardship responsibilities
  • Assess usability issues through task analysis and cognitive walkthroughs
  • Specify future-state requirements with user stories and acceptance criteria

Participants can assess health-data quality and establish practical controls for improving information reliability.

  • Classify structured, semi-structured and unstructured health data sources
  • Apply data-quality dimensions: completeness, validity, timeliness, consistency, uniqueness and accuracy
  • Profile missingness, duplicates, outliers and invalid codes using data-quality rules
  • Construct data dictionaries with definitions, formats, permitted values and owners
  • Reconcile patient identities through deterministic matching and master patient index principles
  • Create data-quality issue logs with severity, ownership and remediation dates

Participants can select appropriate interoperability approaches and terminology standards for common health data exchanges.

  • Explain HL7 messaging, FHIR resources and API-based exchange patterns
  • Match SNOMED CT concepts to clinical documentation and reporting needs
  • Use LOINC codes for laboratory observations and interoperable result exchange
  • Differentiate ICD-10 diagnosis coding from clinical terminology and problem lists
  • Review CDA documents, FHIR bundles and interface specification elements
  • Test interoperability scenarios for referrals, medications, results and discharge summaries

Participants can establish governance, privacy and access-control arrangements for health information assets.

  • Establish data governance councils, stewardship roles and escalation routes
  • Apply purpose limitation, minimum necessary access and consent principles
  • Classify protected health information across clinical, administrative and research uses
  • Build role-based access control matrices for common health-system roles
  • Assess confidentiality risks using threat modelling and privacy impact assessments
  • Plan audit trails, breach response and retention schedules for records

Participants can frame analytic questions and calculate meaningful health-service performance measures.

  • Frame management questions as measurable analytic questions and hypotheses
  • Select descriptive, diagnostic, predictive or prescriptive analytics approaches
  • Build numerator, denominator, inclusion and exclusion definitions for indicators
  • Calculate rates, ratios, proportions, percent change and risk-adjusted measures
  • Interpret distributions using mean, median, quartiles, variance and confidence intervals
  • Recognise confounding, selection bias, seasonality and small-number instability

Participants can analyse performance variation and identify priority service-improvement opportunities.

  • Define balanced scorecard measures for quality, access, cost and workforce
  • Construct run charts and statistical process control charts
  • Stratify outcomes by age, sex, geography, payer and deprivation
  • Measure readmissions, length of stay, mortality and appointment no-shows
  • Compare observed and expected outcomes using case-mix adjustment methods
  • Prioritise performance gaps with Pareto analysis and root-cause categories

Participants can design decision-focused dashboards and communicate analytic findings clearly to stakeholders.

  • Design dashboard wireframes around decisions, users, thresholds and refresh cycles
  • Choose charts that reveal trends, variation, distributions and comparisons accurately
  • Apply annotation, colour, scale and labelling rules to clinical dashboards
  • Develop drill-down paths from executive indicators to patient-level records
  • Test dashboard interpretations for misleading scales, clutter and alert fatigue
  • Present findings using the situation-background-assessment-recommendation narrative structure for decisions

Participants can plan an information-system change from requirements through adoption and benefits review.

  • Develop a business case linking information investments to measurable outcomes
  • Sequence implementation work through discovery, design, build, test and deployment
  • Maintain requirements traceability from user need through acceptance testing
  • Plan data migration, interface testing, cutover and contingency arrangements
  • Use stakeholder maps, readiness assessments and communication plans for adoption
  • Monitor benefits realisation, incidents, adoption metrics and corrective actions

Participants can create a measured action plan for improving a defined health informatics issue.

  • Define evaluation questions, baselines, targets, measures and review cadence
  • Apply Plan-Do-Study-Act cycles to health information improvement initiatives
  • Create action plans with owners, milestones, dependencies and decision gates
  • Prioritise analytic requests using impact, effort, risk and feasibility criteria
  • Prepare governance updates using concise evidence, exceptions and recommendations
  • Set personal application commitments for the first ninety workplace days

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. Clinical workflow mapping template
  2. Health data-quality assessment checklist
  3. Data governance RACI matrix
  4. Health indicator definition sheet
  5. Decision-dashboard wireframe model
  6. Health informatics implementation action plan

Who should attend

  • Health Service Managers — need dependable information for service, quality and resource decisions
  • Clinical Informatics Professionals — translate clinical needs into information and system requirements
  • Health Information Managers — strengthen coding, records governance and data-quality controls
  • Quality Improvement Leads — use indicators and variation analysis to target care improvements
  • Digital Health Project Managers — plan interoperable systems, migration and user adoption
  • Public Health and NGO Programme Managers — monitor population outcomes, coverage and service performance

Skills and tools covered

Health data governanceClinical workflow mappingInteroperability standardsData quality managementHealth analyticsDashboard design

Tools and standards

HL7 FHIR, SNOMED CT, LOINC, Plan-Do-Study-Act

How the course runs

Training methodology

The course uses short instructor briefings to establish concepts, standards and methods, followed by health-service case work and facilitated group tasks. Participants map care and information flows, diagnose data-quality issues, define indicators, interpret charts and critique dashboard designs. Simulations address governance choices, interoperability requirements and implementation trade-offs. Each participant applies the methods to a realistic workplace issue and develops an action plan supported by peer and instructor feedback.

Requirements and prerequisites

Participants should have experience working with health services, health programmes, clinical records, service reporting, digital health projects or operational improvement. Familiarity with common health measures such as admissions, waiting times, service utilisation or outcomes is helpful. Participants should be comfortable reading tables, percentages and basic charts. Prior programming, database administration, advanced statistics, clinical licensure or prior knowledge of HL7 FHIR and coding terminologies is not required. The course explains technical concepts from a management and applied analytics perspective.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

Live online deliveries follow the same structure in East Africa Time, with shorter screen blocks and longer breaks.

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

You should understand how your health service, programme or reporting process operates and be comfortable reading basic tables and charts. You do not need programming, advanced statistics, clinical licensure or previous technical standards training.

Bring a current information, reporting, workflow, quality or digital health issue from your workplace if possible. A laptop is useful for working with templates and course materials, whether attending in class or live online.

The course suits managers, informatics staff, health information managers, quality leads, analysts, project managers and programme staff. It is designed for people who need to make or support decisions using health data.

This course focuses on the management, governance, workflow and analytic use of health information. It does not teach programming, database engineering or vendor-specific electronic health record configuration.

You can use the methods to clarify reporting measures, investigate data-quality concerns, improve information flows or prepare requirements for a digital health initiative. The action-planning materials are structured for direct adaptation to a current work priority.

The five days provide about 32.5 contact hours, delivered from 08:30 to 16:30 each day. Time is distributed across ten three-hour modules, with additional time for breaks, case discussions, group work and application planning.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

Related courses in Health Systems Management