Healthcare Data Science and Clinical Analytics Training Course

Course overview

Healthcare organisations hold rich clinical, operational and financial data, yet many analytics teams struggle to turn fragmented EHR extracts, claims files, laboratory feeds and scheduling data into trustworthy evidence. Poorly defined cohorts, inconsistent coding, untested models and unclear visualisations can lead to flawed utilisation forecasts, misleading quality metrics and decisions clinicians do not trust. This course equips data professionals to build analyses that respect clinical context, patient privacy and the realities of care delivery.

Participants learn an end-to-end healthcare analytics workflow: profiling EHR and claims data; applying ICD-10, CPT, LOINC and medication concepts; designing patient cohorts; handling missingness and longitudinal records; and preparing analysis-ready datasets. They use SQL and Python to develop descriptive, predictive and time-to-event analyses, including readmission risk, length-of-stay forecasting and population segmentation. The programme also covers model validation, calibration, bias assessment, explainability, dashboard design and the governance controls needed when analytics informs clinical or operational decisions.

Teaching combines instructor-led technical sessions with hands-on work using realistic de-identified healthcare datasets. Teams work through a connected case involving hospital readmissions and capacity management, moving from a data dictionary and cohort definition to a validated model and decision-ready dashboard. Each participant leaves with a healthcare analytics project pack containing a data-quality assessment, reproducible analysis notebook, model evaluation report, dashboard specification and implementation plan for a workplace use case.

The course is designed for analysts, data scientists, informatics professionals and healthcare managers who already work with data and need to produce defensible analytics for providers, payers, life sciences organisations or public health teams.

Course objectives

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

• Construct reproducible patient cohorts using inclusion, exclusion, index-date and follow-up rules
• Profile EHR and claims datasets to identify coding gaps, duplicates, missingness and implausible clinical values
• Write SQL queries that join encounters, diagnoses, procedures, laboratory results and medication records at patient level
• Build Python pipelines for feature engineering from longitudinal clinical and operational data
• Develop and validate readmission or length-of-stay prediction models using discrimination, calibration and threshold metrics
• Assess model bias across clinically relevant demographic and risk subgroups using fairness diagnostics
• Create decision-focused Tableau dashboards for quality, utilisation and population-health measures
• Produce a governed analytics project pack with data lineage, validation evidence, model documentation and implementation actions

Benefits of attending

Target competencies

Who should attend

• Healthcare Data Analysts — who need to turn EHR, claims and operational extracts into reliable performance insight
• Clinical Data Scientists — who build predictive models that must be clinically credible and safely evaluated
• Clinical Informatics Specialists — who translate care workflows and coding structures into usable analytical datasets
• BI and Reporting Managers — who need dashboards that support quality, utilisation and capacity decisions
• Population Health Analysts — who define risk cohorts and target interventions across patient populations
• Healthcare Operations Managers — who commission analytics for flow, readmissions, length of stay and service demand

Requirements and prerequisites

Participants should be comfortable working with structured datasets and spreadsheets or tables, and should understand basic descriptive statistics, including averages, distributions, rates and correlation. Prior practical experience writing simple SQL queries and using Python for data analysis is expected; participants should recognise tables, joins, data frames and common file formats such as CSV. Familiarity with healthcare terminology, EHR systems or coding schemes is useful but not essential, as clinical data structures and coding context are taught. Advanced machine learning, prior Tableau experience, production deployment skills and clinical qualifications are not required.

Training methodology

Each day combines instructor-led demonstrations with guided work in SQL, Python and Tableau using de-identified healthcare datasets. Participants analyse connected EHR, claims and operational scenarios rather than isolated technical exercises: defining cohorts, tracing data provenance, engineering clinical features and evaluating models against a decision use case. Small-group case discussions test how findings would be interpreted by clinicians, quality teams and operations leaders. The final two days are structured as an applied project workshop, where participants assemble their own analytics project pack and receive instructor feedback on feasibility, evidence and governance.

Course outline

Day 1: Healthcare data landscape and analytical framing

• Healthcare provider, payer and population-health data ecosystems
• EHR encounter, patient, order, result and medication data structures
• Claims, billing and utilisation data relationships
• Clinical coding systems: ICD-10, CPT, LOINC and RxNorm
• Analytical questions for quality, access, flow and outcomes
• Unit-of-analysis selection: patient, encounter, episode and provider
• Data governance, privacy and minimum-necessary access principles

Workshop: Participants map a readmissions business question to required source tables, measures, stakeholders and governance constraints.

Day 2: Data quality and healthcare data preparation

• Clinical data profiling with completeness, validity and timeliness measures
• Detecting duplicate patients and fragmented patient identities
• Missing-data mechanisms in clinical and administrative datasets
• Outlier checks for laboratory, vital-sign and utilisation values
• Date logic for admissions, discharges, transfers and observation stays
• Code mapping and terminology normalisation strategies
• Data-quality reporting with issue severity and remediation actions

Workshop: Participants produce a data-quality scorecard and remediation log for a de-identified hospital encounter extract.

Day 3: SQL for patient-level analytics

• Relational modelling of patient, encounter and event tables
• Inner, left and full joins for clinical record assembly
• Common table expressions for staged cohort construction
• Window functions for prior events and longitudinal sequencing
• Index-date logic and observation-window queries
• Diagnosis and procedure code filtering with value sets
• SQL validation checks for row inflation and denominator errors

Workshop: Participants write and validate a SQL cohort query for adult inpatient discharges with a 30-day follow-up window.

Day 4: Cohort design and longitudinal clinical features

• Inclusion and exclusion criteria for observational healthcare analysis
• Index dates, washout periods and censoring rules
• Episode-of-care construction from encounters and transfers
• Comorbidity features using diagnosis history
• Medication, laboratory and utilisation feature engineering
• Avoiding target leakage in temporal healthcare datasets
• Documenting cohort definitions for reproducibility

Workshop: Participants create a cohort specification and feature dictionary for a length-of-stay analysis use case.

Day 5: Python analytics for healthcare datasets

• Pandas data frames for healthcare data transformation
• Reusable data-cleaning functions and validation assertions
• Categorical encoding for diagnosis, payer and discharge fields
• Numerical scaling and imputation approaches
• Exploratory analysis of rates, distributions and service variation
• Stratified analysis by service line and patient subgroup
• Jupyter notebook structure for reproducible analytical work

Workshop: Participants build a documented Python notebook that cleans, joins and explores a multi-table patient dataset.

Day 6: Predictive modelling for clinical and operational decisions

• Problem formulation for readmission and length-of-stay prediction
• Training, validation and test splits using temporal logic
• Logistic regression and tree-based model selection
• Feature selection with clinical plausibility review
• Class imbalance and precision-recall trade-offs
• Risk-score thresholds linked to intervention capacity
• Model documentation using intended-use statements

Workshop: Participants train candidate readmission-risk models and select an approach based on operational use criteria.

Day 7: Model validation, safety and fairness

• Discrimination metrics: ROC-AUC, PR-AUC, sensitivity and specificity
• Calibration curves and Brier score interpretation
• Confusion matrices at operational decision thresholds
• Subgroup performance analysis by age, sex, ethnicity and payer
• Fairness risks from proxy variables and historical inequity
• Explainability using feature importance and SHAP values
• Clinical validation, monitoring and model-change controls

Workshop: Participants produce a validation report comparing model calibration and subgroup performance before recommending deployment conditions.

Day 8: Clinical analytics visualisation and communication

• Measure design for rates, risk adjustment and denominator integrity
• Tableau data connections and calculated fields
• Run charts, control charts and variation interpretation
• Patient-flow and bed-capacity visualisation patterns
• Risk stratification and cohort drill-down dashboards
• Designing dashboards for clinicians versus executive users
• Annotation, caveats and action-oriented narrative reporting

Workshop: Participants create a Tableau dashboard showing readmission patterns, risk segments and service-line variation.

Day 9: Interoperability, governance and implementation

• HL7 FHIR resources for patient, encounter, observation and medication data
• OMOP Common Data Model concepts and standard vocabularies
• Data lineage from source extract to analytical output
• Privacy-preserving de-identification and disclosure controls
• Model governance roles, approval gates and audit evidence
• Monitoring drift, performance decay and intervention effects
• Implementation planning with clinical, operational and technical stakeholders

Workshop: Participants develop a governance and deployment checklist for their readmission analytics solution.

Day 10: Applied healthcare analytics project

• Problem statement and decision-owner refinement
• Cohort definition and data-lineage review
• Data-quality evidence and limitation statements
• Model result interpretation and threshold recommendation
• Dashboard walkthrough and user-action design
• Implementation risks, dependencies and monitoring measures
• Executive presentation and peer challenge session

Workshop: Participants present a complete healthcare analytics project pack and receive structured feedback on analytical validity, usability and governance.

Tools & standards covered

Python, PostgreSQL, Tableau, OMOP Common Data Model

A typical training day

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

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