PMED: APPM Workshop: Evidence-Based Practice v Practice-Based Evidence - Jason Burke, March 15, 2019
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This document discusses using machine learning and analytics to improve clinical decision making. It outlines some of the shortcomings of current evidence-based practices, including biases and limited data. The document proposes that machines could help overcome these issues by analyzing large amounts of data from real-world practice to uncover patterns that may improve care delivery and outcomes. It presents a model for developing analytics as reusable products that learn over time from new data, to help create a sustainable "learning health system." Examples of potential applications discussed include analyzing clinical variation and developing condition-specific insights.
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PMED: APPM Workshop: Evidence-Based Practice v Practice-Based Evidence - Jason Burke, March 15, 2019
- 1. UNCHEALTHCARESYSTEMUNCHEALTHCARESYSTEM DRAFT – FOR DISCUSSION PURPOSES ONLY – INFORMATION SUBJECT TO CHANGE Evidence-based Practice vs. Practice-based Evidence March 2019 Innovations in Precision Medicine within Health Care Delivery Jason Burke Chief Analytics Officer & System VP @jaburke jasonburke
- 2. 2 UNCHEALTHCARESYSTEM DRAFT – FOR DISCUSSION PURPOSES ONLY – INFORMATION SUBJECT TO CHANGE The Future is Here
- 3. 3 UNCHEALTHCARESYSTEM DRAFT – FOR DISCUSSION PURPOSES ONLY – INFORMATION SUBJECT TO CHANGE How do you make clinical decisions? Source Short-comings (not exhaustive) Published Research Other patients in practice Gut instinct Sales reps • Sample bias • Publication bias • Information overload / innovation pace • Increasing specialization / complexity exceeds cognition • Data sparsity / completeness • Psychological / cognitive biases • Commercial bias • Limited scopes of inquiry • Bench to bedside timeframes What if machines could help overcome these shortcomings? • Crunch the sources and factors • Model more representative populations • Uncover patterns too complex for the human brain to readily identify • Serve as an aide
- 4. 4 UNCHEALTHCARESYSTEM DRAFT – FOR DISCUSSION PURPOSES ONLY – INFORMATION SUBJECT TO CHANGE Image via Flickr user horiavarlan PRACTICE-BASED EVIDENCE Growing: Expanding the Opportunity Funnel global institutional individual multi-institutional SCOPE controlled semi-controlled real world cohorts CONSTRAINTS QUALITY IMPROVEMENT CLINICAL RESEARCH
- 5. 5 UNCHEALTHCARESYSTEM DRAFT – FOR DISCUSSION PURPOSES ONLY – INFORMATION SUBJECT TO CHANGE Growing: Increasing Analytical Complexity for Population Health Utilization Explorer Care Variation Analytics
- 6. 6 UNCHEALTHCARESYSTEM DRAFT – FOR DISCUSSION PURPOSES ONLY – INFORMATION SUBJECT TO CHANGE Example: Bundled Payments The “obvious answers” are sometimes neither obvious nor answers. So what is needed to create sustainable change? Increasing complexity %target
- 7. 7 UNCHEALTHCARESYSTEM DRAFT – FOR DISCUSSION PURPOSES ONLY – INFORMATION SUBJECT TO CHANGE Rethinking Analytics in Health Care • Stakeholders are project area experts • Effort is focused on predefined questions • Work is relevant to project team • Timeline is project driven • Data definitions are project specific • Data structured for single use • Little-to-no analytical code reuse • Release available to project stakeholders • Stakeholders are functional experts • Questions are not predefined • Work must be relevant to multiple customers • Timeline is engineering driven • Data definitions are enterprise-wide • Data is structured for broad re-use • Analytical models are built for multiple projects • Release available to entire enterprise Most health industry analyses are conducted as a PROJECT Our work is managed more like a PRODUCT
- 8. 8 UNCHEALTHCARESYSTEM DRAFT – FOR DISCUSSION PURPOSES ONLY – INFORMATION SUBJECT TO CHANGE COGNITIVEPHASEASSOCIATIVE PHASE AUTONOMOUS PHASE Learning Health System “Learning Model” COLLECT Gather Aggregate INFLUENCE Iteration Implementation Automation Validation OPTIMIZE Refactoring Tuning Maintenance PLAY Explorations Novel data What-ifs Discovery New Research SYNTHESIZE Factors Models Relationships Patterns DerivedfromFitts&Posner’s3StagesofCognitiveLearing(1967) OBSERVE Describe Profile Report EXTEND New data Modeling Integration Simplification Jason Burke
- 9. 9 UNCHEALTHCARESYSTEM DRAFT – FOR DISCUSSION PURPOSES ONLY – INFORMATION SUBJECT TO CHANGE Clinical Variation: Supporting Value-Based Care Opportunity Identification Cohort Insights Outcomes Comparison Cost Comparison Condition Specific Insights
- 10. 10 UNCHEALTHCARESYSTEM DRAFT – FOR DISCUSSION PURPOSES ONLY – INFORMATION SUBJECT TO CHANGE Discussion Jason Burke @jaburke jasonburke
Editor's Notes
- Health care’s ongoing evolution (e.g., accountable care, risk delegation, population health management, consumer technologies, Internet of Things) is creating new imperatives for the way both physicians and patients approach heath care delivery. Clinicians are faced with competing demands: standardize care across patients, but ensure optimal individual health outcomes. Care pathways targeting broad-based patient populations derived from clinical research (i.e., evidence-based practice) must be reconciled with real-world observations (i.e., practice-based evidence). The intersection of these two forces create innovative opportunities for transforming patient care through richer understandings of patients, clinicians, health outcomes, quality measures, processes, and costs. Capturing these opportunities requires new competencies in harnessing data sciences and analytics to make more tailored care decisions, rationalize sparse delivery resources, manage risks, and drive efficiencies that reduce the overall cost of care.