Healthcare Data Analytics for Quality and Patient Outcomes Training Course

Course overview

Healthcare quality teams often hold fragmented data across electronic health records, claims, incident systems, patient surveys and operational databases, yet struggle to turn it into defensible action. Measures such as 30-day readmissions, hospital-acquired infections, length of stay, medication safety events and emergency department wait times can be distorted by incomplete coding, inconsistent denominators, small samples or unadjusted case mix. This course equips professionals to build analyses that clinical leaders, quality committees and regulators can trust when prioritising improvement work.

Participants learn to frame outcome questions, define cohorts, assess data quality and calculate healthcare measures using reproducible analytical methods. The programme covers SQL-based extraction, data preparation, descriptive and statistical analysis, risk adjustment concepts, statistical process control, dashboard design and interpretation of variation. Participants use Power BI and Python/pandas to produce stratified quality measures, run charts, control charts and outcome dashboards, while applying governance principles for protected health information and HL7 FHIR-enabled data exchange.

Teaching combines instructor-led demonstrations with healthcare datasets modelled on admissions, discharge, infection surveillance and patient-experience records. Each day includes guided analysis and peer review of findings, with emphasis on communicating uncertainty, avoiding misleading comparisons and converting results into improvement priorities. By the end of the week, participants leave with a completed quality analytics pack: a measure specification, cleaned analytical dataset, documented calculations, Power BI dashboard, control-chart interpretation and a 90-day action plan for applying the method in their service.

The course is designed for analysts and improvement professionals who already work with health data and need stronger analytical discipline, as well as managers who must commission, assess or act on quality intelligence. It is particularly valuable for teams supporting clinical governance, population health, hospital operations, safety and performance reporting.

Course objectives

By the end of this course, participants will be able to:

• Define numerator, denominator, inclusion, exclusion and attribution rules in a healthcare quality measure specification
• Extract and join admissions, encounters, diagnoses and outcome records using SQL queries
• Profile healthcare datasets for missingness, duplicates, coding anomalies and denominator bias
• Calculate stratified rates, risk ratios and confidence intervals for priority patient-outcome measures
• Apply case-mix adjustment concepts to compare outcomes across services or patient cohorts
• Construct run charts and statistical process control charts to distinguish common-cause from special-cause variation
• Build an interactive Power BI dashboard with drill-downs, measure definitions and clinically meaningful filters
• Produce a documented quality analytics pack that links findings to an improvement action plan

Benefits of attending

Target competencies

Who should attend

• Healthcare Data Analysts — who need to convert EHR, claims and operational data into reliable quality measures
• Quality Improvement Managers — who must identify variation and target improvement initiatives with evidence
• Clinical Governance Leads — who need defensible performance intelligence for committees, audits and escalation
• Population Health Analysts — who compare outcomes across patient cohorts and care pathways
• Hospital Operations Analysts — who monitor flow, length of stay, readmissions and service performance
• Clinical Informatics Specialists — who prepare interoperable clinical data for analytics and quality reporting

Requirements and prerequisites

Participants should be comfortable working with tabular data and should understand basic healthcare concepts such as encounters, diagnoses, procedures, admissions, discharges and clinical outcomes. Prior experience creating spreadsheets, filtering data and interpreting percentages and rates is expected. Familiarity with basic SQL SELECT statements or Power BI is helpful, but detailed expertise in either is not required; guided templates are provided. Participants should bring a laptop able to run a modern web browser and, where permitted by their organisation, Power BI Desktop. No prior Python programming, advanced statistics, machine-learning experience or access to live patient data is required.

Training methodology

The programme uses short instructor-led briefings followed by hands-on work with realistic, de-identified healthcare datasets. Participants write SQL queries, profile records in Python/pandas, calculate quality measures and build Power BI visuals under instructor guidance. Case discussions examine readmissions, infection surveillance, emergency flow and equity-related variation, including how poor definitions can mislead decision-makers. Small groups review each other’s measure logic and dashboard choices as if preparing for a clinical governance meeting. The final session converts each participant’s analysis into a practical application plan for their own service.

Course outline

Day 1: Quality measurement foundations and healthcare data structures

• Healthcare quality domains: safety, effectiveness, timeliness, equity and patient-centred care
• Outcome, process, balancing and structural measures
• Measure specifications: numerators, denominators, cohorts and exclusions
• EHR, claims, registry, incident and patient-reported data sources
• Encounter, admission, discharge and episode-of-care data models
• ICD-10, procedure codes and diagnosis coding considerations
• HL7 FHIR resources for interoperable clinical data

Workshop: Participants draft a complete measure specification for 30-day readmissions, including cohort logic, exclusions, data fields and reporting cadence.

Day 2: Data extraction, preparation and quality assurance

• SQL SELECT, WHERE, GROUP BY and HAVING for healthcare datasets
• Joining patient, encounter, diagnosis and outcome tables
• Date logic for index admissions, follow-up windows and readmissions
• Duplicate-record detection and patient-identity reconciliation
• Missing-data profiling and clinically implausible value checks
• Python pandas data frames, merges and transformation workflows
• Data lineage, protected health information and minimum-necessary access

Workshop: Participants build a reproducible extraction and cleaning workflow that produces an analysis-ready admissions and outcomes dataset.

Day 3: Outcome analysis, stratification and risk adjustment

• Crude rates, standardised rates and rate denominators
• Stratification by age, sex, diagnosis, payer and service line
• Confidence intervals and small-number suppression decisions
• Risk ratios, odds ratios and interpretation limits
• Case-mix adjustment concepts and confounding variables
• Observed-versus-expected outcome comparisons
• Equity analysis and identification of outcome disparities

Workshop: Participants analyse variation in readmission outcomes across service lines and produce a stratified findings table with interpretation notes.

Day 4: Statistical process control and quality dashboards

• Run charts and median-based shift rules
• Shewhart control charts and control-limit interpretation
• Common-cause and special-cause variation
• Power BI data model relationships and calculated measures
• DAX measures for rates, rolling periods and benchmark comparisons
• Dashboard drill-through, slicers and clinical user journeys
• Visual design choices that prevent misleading quality reporting

Workshop: Participants create a Power BI quality dashboard with a control chart, stratified outcome views and documented measure definitions.

Day 5: From analysis to improvement decisions

• Translating analytical findings into improvement hypotheses
• Root-cause analysis links: Pareto analysis, process mapping and fishbone diagrams
• Prioritising interventions by impact, feasibility and measurement confidence
• Pre-post evaluation design and balancing measures
• Clinical governance reporting and evidence narratives
• Dashboard assurance, peer review and version control
• Ninety-day implementation planning for quality analytics

Workshop: Participants present their quality analytics pack to a simulated governance panel and produce a 90-day measurement and improvement action plan.

Tools & standards covered

Microsoft Power BI, Microsoft SQL Server, Python pandas, HL7 FHIR

A typical training day

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

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions