Healthcare Data Analytics & Performance Dashboards Training Course

Course Overview

Learning Objectives

• Identify the main healthcare data sources and assess their fitness for analysis.
• Define healthcare performance indicators with precise numerators, denominators, exclusions and attribution rules.
• Analyze length of stay, readmission, mortality, access and utilization measures.
• Explain case-mix classification and its role in comparing hospital activity and cost.
• Apply risk-adjustment and standardization methods to produce fair comparisons between units and providers.
• Link records across health records, claims and exchange data while protecting confidentiality.
• Distinguish real performance change from random variation using statistical process control and funnel plots.
• Benchmark performance against suitable peer groups and interpret differences cautiously.
• Evaluate predictive models and AI analytics for risk stratification and demand planning.
• Design tiered dashboards that present healthcare indicators clearly for boards, executives and departments.
• Establish governance for indicator catalogs, validation, ownership, access and change control.
• Develop a review process that converts dashboard findings into improvement decisions.

Who Should Attend

Learning Outcomes

• Assess the coverage and limitations of healthcare data sources used for analysis.
• Write indicator specifications that produce consistent results across departments and periods.
• Calculate and interpret length of stay, readmission, mortality, access and utilization indicators.
• Use case-mix information to explain differences in activity and cost per case.
• Produce risk-adjusted and standardized indicators and explain their assumptions to managers.
• Plan record linkage across clinical, administrative and claims data with privacy safeguards.
• Read control charts and funnel plots to separate meaningful signals from normal variation.
• Select appropriate peer groups and benchmarks for comparing hospitals and services.
• Judge whether a predictive model or AI output is fit for management use.
• Design board, executive and operational dashboards that support focused decisions.
• Apply governance controls for indicator ownership, validation, access and versioning.
• Prepare a performance analytics and dashboard plan for their organization.

Course Outline