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Predictive Analytics: Overview 3. Predictive Analytics: Various Approaches CLUSTERING FORECASTING MONITORING & ADVISING SIMULATION &SCENARIO PLANNING DECISION TREE The use of current and past data, in conjunction with statistical, structural or other analytical models and methods, to determine the likelihood of certain future events Predictive methods cover the spectrum from relatively simple classification and forecasting to more advanced techniques such as simulation and advising As you move up the spectrum, the complexity of the approaches and their implementation increase 4. Clustering as Classification
Yeatman, et al: JNCI, April 2002 Osteopontin 5. Class Discovery and Class Prediction Golub, et al: SCIENCE, October 1999
6. Class Distinction, Combined with Pathway Model Wei, et al: Cancer Cell, October 2006 GC apoptosis (cell death) rapamycin MCL1 7. Decision Tree: Progressive Class Distinction
8. Forecasting:Process Model Structural Model: Bill of Resources Patient Seen in Emergency Dept Admit Patient: Presumptive Diagnosis: Pneumonia Discharge Monitor Care Delivery Standard Order Sets Equipment Labor Materials Facilities Nursing Orders: Respiratory Therapy: Medication Orders: Resource Demand Day 5 Day 4 Day 3 Day 2 Day 1 9. Forecasting: Resource Demand vs. Capacity Standard Order Sets Equipment Labor Materials Facilities Nursing Orders: Respiratory Therapy: Medication Orders: Day 5 Day 4 Day 3 Day 2 Day 1 10. Simulation: Resource Demand vs. Capacity What if incidence of disease X increases 2x? process X increases throughput 1.5x? market share in geography X (with Y / 1000 cases) increases by Z? we focus our service lines into centers of excellence, shifting our patient mix across our facilities within the system? Facility A Facility B Facility C 11. Monitoring & Advising: Risk Management Patient & Case Profiled Against Risk Model Incrementally Accumulate Evidence of Emerging Risk Retain Case Instance &Feedback to Risk Model Notify Risk Mgmt Team of Need for Corrective Action Monitor Care Delivery Isolate Root Causes Track Negative Outcomes Track Key Events Within a Process Track Incidents
Forecast Non- Reimbursement Loss Tie to Claims Data Tie to Clinical Data Track Litigation Improve Quality, Avoid Future Incidents 12. Implementation: System & Data Architecture Clinical Data Operations Data Financial Data External Data Data Warehouse Data Files Data Sets Parameter Management DATA PRESENTATION/ CONSUMPITON LAYER APPLICATION/ MODEL MANAGEMENT LAYER DATA STORAGE LAYER DATA INTEGRATION LAYER DATASOURCELAYER ODS Multi-Dimensional Data Store Integration Cleansing Data Quality Formatting Aggregation Predictive Models Test Management Model/Version Management 13. Process Framework KPI Definition& Decomposition
Prototyping and Refinement
Establish Architecture Iterative Prototyping & Validation Release toProduction Maintain, Improve, Expand
14. Extending Visibility Into The Enterprise Executive User Functional User Power User Highly Aggregated More Detail Complete Raw Data
15. www.edgewater.com October 1, 2008 Predictive Analytics in Healthcare