Duration: 2 hours Prerequisites: Data analysis experience Session Type: Advanced Technical Skills

• Understand predictive models used by 71% of hospitals with EHR-integrated AI
• Analyze population health data for actionable insights
• Design clinical decision support features powered by analytics
• Evaluate the business impact of predictive modeling initiatives

• 71% of hospitals now run at least one EHR-integrated predictive AI model
• Common applications: Risk stratification, readmission prediction, sepsis detection
• ROI indicators: Reduced readmissions, improved clinical outcomes, cost savings
• Growth trajectory: Expanding from pilot projects to enterprise-wide deployments

• Clinical Risk Models: Patient deterioration, mortality prediction
• Operational Models: Length of stay, resource utilization, staffing needs
• Financial Models: Cost prediction, revenue optimization, denial probability
• Population Health: Disease outbreaks, chronic disease progression
• Quality Models: Patient satisfaction, safety events, compliance metrics

• Data Inputs: Vital signs, lab values, medication administration
• Model Output: Sepsis risk score with confidence intervals
• Clinical Integration: Real-time alerts in EHR workflow
• Validation Requirements: Clinical effectiveness studies and FDA approval

• Risk Factors: Demographics, comorbidities, social determinants
• Prediction Window: 30-day, 90-day, and annual readmission probability
• Intervention Triggers: High-risk patient identification for care management
• Outcome Measurement: Actual vs. predicted readmission rates

• Assessment Factors: Age, medications, mobility, cognitive status
• Dynamic Scoring: Continuous risk assessment based on current status
• Prevention Protocols: Automated care plan adjustments
• Staff Notifications: Real-time alerts to nursing staff

• Diabetes Population: HbA1c trends, medication adherence, complications
• Hypertension Management: Blood pressure control, medication optimization
• Heart Disease: Risk stratification and intervention effectiveness
• Mental Health: Depression screening, treatment response, suicide risk

• Data Sources: Census data, insurance claims, patient surveys
• Risk Factors: Housing instability, food insecurity, transportation barriers
• Intervention Targeting: Resource allocation for high-risk populations
• Community Partnerships: Collaboration with social service organizations

• Disease Outbreak Detection: Unusual pattern identification
• Vaccination Coverage: Population immunity assessment
• Health Disparities: Identifying and addressing care gaps
• Environmental Health: Air quality, water safety impact analysis

• Census Prediction: Daily patient volume forecasting
• Bed Management: Discharge timing and bed availability
• OR Scheduling: Surgery duration prediction and optimization
• Staffing Models: Workload prediction and staff allocation

• Demand Forecasting: Medication and supply usage prediction
• Inventory Management: Automated reordering and stock optimization
• Vendor Performance: Delivery reliability and quality metrics
• Cost Optimization: Price negotiation and contract management

• Claim Denial Prediction: Identifying high-risk claims before submission
• Payment Timeline: Cash flow forecasting and collection optimization
• Coding Accuracy: Automated coding suggestions and error prevention
• Payer Contract Analysis: Performance against contract terms

• Drug-Drug Interactions: Real-time interaction checking
• Dosing Recommendations: Weight and kidney function-based adjustments
• Allergy Alerts: Cross-sensitivity and severity assessment
• Formulary Management: Cost-effective medication alternatives

• Image Analysis: AI-powered radiology and pathology assistance
• Laboratory Values: Critical value identification and trending
• Clinical Guidelines: Evidence-based treatment recommendations
• Risk Calculators: Standardized assessment tools integration

• Protocol Adherence: Evidence-based care pathway compliance
• Outcome Prediction: Treatment response probability assessment
• Resource Utilization: Efficient care delivery optimization
• Quality Metrics: Real-time quality indicator monitoring

• Feature Engineering: Creating meaningful variables from raw data
• Data Quality: Handling missing values and outliers
• Bias Detection: Ensuring model fairness across populations
• Privacy Protection: De-identification and secure analysis

• Algorithm Selection: Choosing appropriate machine learning techniques
• Cross-Validation: Robust model performance assessment
• Hyperparameter Tuning: Optimizing model parameters
• Performance Metrics: Sensitivity, specificity, positive predictive value

• Retrospective Analysis: Historical data validation
• Prospective Studies: Real-world performance evaluation
• Clinical Workflow Integration: Usability and adoption testing
• Outcome Measurement: Impact on clinical care and patient outcomes

Scenario: 30-day readmission prediction for heart failure patients

• Identify relevant risk factors and data sources
• Design model validation approach
• Create clinical workflow integration plan
• Define success metrics and monitoring strategy

Scenario: Chronic disease management for diabetes population

• Design key performance indicators
• Create visualization strategy for different stakeholders
• Plan data refresh and update procedures
• Develop intervention trigger mechanisms

Scenario: Sepsis early warning system implementation

• Map current clinical workflow
• Design alert fatigue prevention strategies
• Plan clinical validation studies
• Create training and adoption plan

• Epic Cognitive Computing: Integrated EHR analytics platform
• Cerner Intelligence: Clinical and operational analytics suite
• IBM Watson Health: AI-powered healthcare analytics (legacy)
• Microsoft Healthcare Bot: Conversational AI for healthcare

• Python/R: Statistical analysis and machine learning
• TensorFlow/PyTorch: Deep learning model development
• Tableau/Power BI: Data visualization and dashboards
• Snowflake/Databricks: Cloud-based analytics platforms

• Change Healthcare: Revenue cycle and clinical analytics
• Optum: Population health and risk adjustment
• Premier/Vizient: Benchmarking and performance improvement
• Apervita: Real-world evidence and outcomes research

• Model Drift: Monitoring performance degradation over time
• Bias Mitigation: Ensuring fair treatment across patient populations
• Transparency: Explainable AI for clinical decision making
• Fallback Procedures: Manual processes when models fail

• FDA Approval: Software as Medical Device (SaMD) requirements
• Clinical Validation: Demonstrating safety and effectiveness
• Quality Management: Design controls and risk management
• Post-Market Surveillance: Ongoing safety monitoring

• Patient Consent: Transparent use of patient data for analytics
• Algorithmic Bias: Preventing discrimination in predictive models
• Clinical Autonomy: Supporting rather than replacing clinical judgment
• Data Privacy: Protecting patient confidentiality in analytics

• Predictive analytics is becoming mainstream in healthcare with 71% hospital adoption
• Clinical validation and regulatory compliance are essential for patient safety
• Population health analytics enables proactive intervention and resource allocation
• Operational analytics drives efficiency and cost optimization
• Model governance and ethics are critical for sustainable AI implementation
• Business analysts play a key role in translating analytics insights into actionable improvements

• Strong clinical champion engagement
• Robust data quality and governance
• Comprehensive training and change management
• Continuous monitoring and optimization
• Integration with existing clinical workflows
• Clear ROI measurement and communication

Session 13 will explore mobile health (mHealth) and remote monitoring, covering the 8.82% annual growth in RPM and integration strategies with HMIS platforms.