Duration: 2 hours Prerequisites: Basic data analysis knowledge Session Type: Technical Skills

• Analyze healthcare data patterns and identify meaningful insights
• Create impactful reports for clinical and administrative stakeholders
• Understand clinical quality indicators and their business impact
• Apply analytics methodologies to healthcare-specific datasets

• Patient Demographics: Age, gender, insurance, contact information
• Clinical Observations: Vital signs, lab results, diagnostic findings
• Treatment Data: Medications, procedures, interventions
• Outcomes: Length of stay, readmissions, mortality, complications

• Financial: Charges, payments, costs, revenue by service line
• Operational: Wait times, throughput, resource utilization
• Quality: Patient satisfaction, safety events, compliance metrics
• Workforce: Staffing levels, productivity, turnover

• Diagnosis Codes: ICD-10 codes and clinical classifications
• Procedure Codes: CPT codes and revenue codes
• Payment Data: Insurance payments, patient responsibility, adjustments
• Denial Management: Claim rejections and appeal outcomes

• Dashboard Creation: Real-time operational metrics
• Trend Analysis: Patient volume patterns, seasonal variations
• Benchmarking: Comparing performance to national standards
• Reporting: Regulatory reports and quality measure submissions

• Risk Stratification: Identifying high-risk patients
• Readmission Prediction: 30-day readmission probability models
• Length of Stay: Predicting resource needs and discharge planning
• No-Show Modeling: Appointment optimization and overbooking strategies

• Clinical Decision Support: Treatment protocol recommendations
• Resource Optimization: Staffing and bed management
• Supply Chain: Inventory optimization and demand forecasting
• Revenue Optimization: Charge capture and coding improvements

• Patient Safety: Hospital-acquired infections, medication errors
• Clinical Effectiveness: Evidence-based care protocols adherence
• Patient Experience: HCAHPS scores, patient satisfaction ratings
• Care Coordination: Transitions of care, follow-up rates

• Efficiency: Turnaround times, throughput, capacity utilization
• Financial Performance: Operating margins, cost per case, revenue per patient
• Access: Wait times, appointment availability, emergency department metrics
• Workforce: FTE per occupied bed, overtime rates, turnover

• CMS Core Measures: Hospital quality reporting requirements
• The Joint Commission: Accreditation performance measures
• Public Health Reporting: Notifiable disease surveillance
• Meaningful Use: EHR adoption and usage metrics

• Completeness: Missing data elements affecting analysis
• Accuracy: Documentation errors and data entry mistakes
• Consistency: Variations in coding and terminology usage
• Timeliness: Delays in data entry affecting real-time analytics

• System Silos: Data trapped in departmental systems
• Format Variations: Different data structures and schemas
• Master Data: Patient matching and provider directory management
• Historical Data: Legacy system data migration and normalization

• De-identification: Removing protected health information for analytics
• Access Controls: Role-based data access and audit logging
• Business Associate Agreements: Third-party analytics vendor compliance
• Data Governance: Policies for data usage and sharing

• Key Metrics: High-level performance indicators
• Exception Reporting: Alerts for metrics outside normal ranges
• Trend Visualization: Time-series charts showing performance over time
• Benchmarking: Comparison to peer organizations and national standards

• Quality Measures: Clinical outcome metrics and improvement opportunities
• Patient Population: Demographics and risk factor analysis
• Provider Performance: Individual and group performance metrics
• Care Management: High-risk patient identification and intervention tracking

• Capacity Management: Bed occupancy, OR utilization, staff productivity
• Financial Performance: Revenue, costs, profit margins by service line
• Patient Flow: Admission patterns, length of stay, discharge planning
• Supply Chain: Inventory turnover, cost per case, vendor performance

1. Data Quality Assessment: Evaluate sample healthcare dataset for completeness and accuracy
2. KPI Dashboard Design: Create mockup dashboard for hospital executives
3. Trend Analysis: Identify patterns in patient admission data
4. Report Creation: Design clinical quality report for medical staff

• Epic Reporting: SlicerDicer, Reporting Workbench
• Cerner Analytics: PowerChart, HealtheLife Analytics
• Business Intelligence: Tableau, Power BI, QlikView
• Statistical Analysis: R, Python, SAS, SPSS
• Database Tools: SQL Server, Oracle, MySQL

1. Data Stewardship: Assign ownership for each data domain
2. Quality Standards: Define acceptable data quality thresholds
3. Access Policies: Establish role-based data access controls
4. Change Management: Process for data definition changes
5. Audit and Monitoring: Regular data quality assessments

• Healthcare analytics requires understanding both clinical and business contexts
• Data quality is foundational to meaningful analysis
• Different stakeholders need different types of reports and visualizations
• Privacy and security considerations must be embedded in all analytics work

• Start with business questions, not available data
• Validate findings with clinical experts
• Focus on actionable insights, not just interesting patterns
• Consider the impact of data quality on analysis conclusions
• Always maintain patient privacy and data security

Session 6 will explore current digital health trends for 2026, including AI integration, remote patient monitoring, and telemedicine, and their implications for business analysts.