Define population segments and build a disease or risk registry from clinical, claims and social data sources.
Healthcare & Pharmaceutical Management
Population Health Management and Risk Stratification Techniques
Build population health registries and risk stratification models that route the right patients to the right level of care management, then measure outcomes against value-based contract targets.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
A health system that treats every patient the same way spends its limited care management resources on people who barely need them while high-risk patients cycle through emergency departments unmanaged. This course teaches population health leaders to fix that mismatch through disciplined risk stratification and registry design. Participants learn to define population segments and build disease and risk registries from clinical, claims and social data sources, then construct predictive risk models that score patients using established scoring methods and validate those scores against actual utilisation outcomes. A dedicated module addresses incorporating social determinants of health data, such as housing instability and transport access, into a risk score that would otherwise miss why a clinically stable patient keeps being readmitted. The programme design modules cover building tiered care management interventions matched to risk level, and hotspotting techniques used to identify and support the small number of high utilisers who account for a disproportionate share of cost and harm. The course closes with outcome measurement and dashboard design that tracks whether stratification is actually changing utilisation and cost, and governance methods for aligning population health analytics with value-based contract targets and shared savings arrangements.
Expected Learning Outcomes
Construct a predictive risk model using an established scoring method and validate it against utilisation data.
Incorporate social determinants of health data into a risk score to explain otherwise unaccounted-for risk.
Design a tiered care management programme that matches intervention intensity to patient risk level.
Apply hotspotting techniques to identify high utilisers and design targeted support interventions for them.
Design an outcome measurement dashboard that tracks stratification impact on utilisation and cost.
Align population health analytics governance with value-based contract targets and shared savings arrangements.
Who Should Attend
Population health managers building or refining risk stratification programmes
Care management leaders designing tiered intervention models for at-risk patients
Health system analytics teams building predictive risk scoring and registries
Value-based care contract managers tracking utilisation and cost outcomes
Community health workers and case managers supporting high-utiliser patients
Health plan medical directors overseeing population risk segmentation strategy
Course Modules
Select any module to see its sessions and points.
01Population Health Management Foundations
2 sessions · 8 points
Session 1Defining Population Segments and Registries
- Define population segments based on condition burden, risk level and care management need.
- Build a disease registry that reliably captures the eligible patient population for a given programme.
- Establish inclusion and exclusion criteria that keep a registry accurate as patient status changes.
- Audit registry completeness against known clinical prevalence to detect under-capture.
Session 2Data Sources and Integration for Population Analytics
- Identify clinical, claims, pharmacy and social data sources relevant to a population analytics programme.
- Design a data integration process that reconciles patient identity across disparate source systems.
- Assess data quality and completeness limitations before relying on a source for risk scoring.
- Establish data governance rules covering access, refresh frequency and update responsibility.
02Risk Stratification Model Design
2 sessions · 8 points
Session 1Predictive Risk Models and Scoring Methods
- Select a risk scoring method appropriate to the population and available data completeness.
- Validate a risk model's predictive accuracy against observed utilisation and cost outcomes.
- Recalibrate a risk model periodically as population characteristics and care patterns change.
- Communicate risk score limitations clearly to clinical teams who will act on the results.
Session 2Social Determinants of Health Integration into Risk Scores
- Identify social determinants of health data, such as housing and transport access, relevant to risk.
- Integrate social determinants data into a risk score without introducing unjustified bias.
- Design a screening process that captures social determinants data directly from patients or partners.
- Evaluate whether adding social determinants data meaningfully improves risk prediction accuracy.
03Care Management Tiering and Intervention Design
2 sessions · 8 points
Session 1Tiered Care Management Programme Design
- Design care management tiers with intervention intensity matched to defined risk score bands.
- Assign care team roles, such as care coordinator or community health worker, to each tier.
- Set caseload sizes appropriate to the intensity of intervention required at each tier.
- Design a step-up and step-down process as a patient's risk level changes over time.
Session 2Hotspotting and High-Utiliser Intervention Strategy
- Apply hotspotting analysis to identify the small patient subset driving disproportionate cost and utilisation.
- Design an intensive, individualised intervention plan for identified high-utiliser patients.
- Coordinate multidisciplinary outreach for high utilisers with complex medical and social needs.
- Track whether targeted high-utiliser interventions reduce avoidable emergency department visits.
04Measurement, Governance and Value-Based Alignment
2 sessions · 8 points
Session 1Outcome Measurement and Dashboard Design
- Design a dashboard that tracks utilisation, cost and clinical outcome trends by risk tier.
- Select leading and lagging indicators that show whether stratification is changing care patterns.
- Present dashboard findings to clinical and executive stakeholders in an actionable format.
- Distinguish programme impact from secular trends when interpreting outcome dashboard data.
Session 2Governance and Alignment with Value-Based Contracts
- Align population health analytics governance with the metrics defined in value-based contracts.
- Establish a governance committee that reviews stratification methodology and programme performance.
- Report population health outcomes to payers in a format that supports shared savings reconciliation.
- Plan periodic review of stratification methodology as contract terms and population needs evolve.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
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