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Towards a Continuous Learning Ecosystem: Data Innovations and Collaborations to Improve Clinical Outcomes and Reduce Cost of Care
The document discusses the transition from a siloed healthcare ecosystem to a continuous learning ecosystem aimed at improving clinical outcomes and reducing costs through data innovation and collaboration. It emphasizes the need for coordination among stakeholders, the importance of sharing clinical insights, and the necessity of incentivizing research to address barriers in the current system. Conclusively, it proposes the development of an economic decision model and a roadmap to enhance collaboration and information sharing within the healthcare ecosystem.
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Towards a Continuous Learning Ecosystem: Data Innovations and Collaborations to Improve Clinical Outcomes and Reduce Cost of Care
- 1. Towards a ContinuousLearning Ecosystem: Data Innovations and Collaborations to Improve Clinical Outcomes and Reduce Cost of Care Vipul Kashyap*, Ph.D. Cigna Healthcare vipul.kashyap@cigna.com April 9, 2013 Medical Informatics World, 2013, Boston, MA * Acknowledgments – Jeff Catteau, Knowledgent (jeff.catteau@knowledgent.com) Discussions and feedback on the Economic Decision Model
- 2. Outline • The HealthcareEcosystem: – Current State Continuous Learning Ecosystem • Interactions at the Point of Care • “Coordinated Healthcare Intervention” • Making it Happen: Incentivizing Research & Knowledge Sharing – Economic Decision Model • Conclusions and Next Steps
- 3. The Healthcare Ecosystem:Current State Individuals Individuals Individuals (Populations?) (Populations?) Hospitals Employers Health Pharmaceutical Advocacy Companies Providers Health Plans/ Clinical Research Payors Organizations (CROs) Employer/Payor Perspective Pharma Perspective Provider Perspective Siloized Ecosystem – Lack of collaboration/coordination – Inefficient!
- 4. The Continuous LearningEcosystem Patient/ Individual Lifelong Continuous Engagement Continuous Learning Ability to share and exchange clinical information and knowledge is a critical enabler
- 5. Interactions at thePoint of Care Follow Instructions: Lab Tests, Medications, others Medical History? Medications? Allergies? Contraindications? Referrals? Follow Up? Reimbursement Continuous Learning Ecosystem: Roadblock Does the physician have time and incentive to identify insights, research new ideas and share them with other stakeholders?
- 6. Data, Analytics &Knowledge Demographics Diagnosis Medication Procedure Allergy Contraindication Order Referral Result Reimb Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Patient 6 Patient 7 Patient 8 Patient 9 Patient 10 Patient 11 Patient 12 Patient 13 Patient 14 Observation/Hypothesis: Physicians implicitly leverage experience to create patterns or “Care Archetypes ” Opportunity: Leverage “Care Archetypes” as an organizational framework for improving cost/outcomes Sharing and communication of “Care Archetypes” across the Ecosystem
- 7. Interactions at thePoint of Care: Populations Medication/Therapy compliance Discuss Alternatives, Activation and Engagement Cost Effective? Affordable? Good Outcomes? Alternate Treatments? Risk? Engagement/Outreach? Behavioral Archetype? Compliance? Incentivized? Continuous Learning Ecosystem: Roadblock partially addressed Transition to Pay for Performance has begun! However: Improving Performance requires research – and sharing of insights and results Pay for Insight/Pay for Research is not yet on the agenda!
- 8. Interactions at thePoint of Care: Research Comparative Effectiveness Research Clinical Trials Clinical Studies New Interventions (Therapeutic, Incentives, Engagement)? Impact of new ongoing research? Patient Participation? Contribution of Insights: New Cost Effective approach, Drug Side Effects, New Behavioral Incentives Granular Care Delivery contexts where Interventions are effective? Genomic correlates of behavioral/activation characteristics?
- 9. Aligning Research andPractice Do physicians have the orientation to do research in the context of clinical practice? Opportunity Analysis Hypothesis Ideation Generation Clinical Trials/ Populations/ Studies Segments Outcomes Measurement Differential Diagnosis/ Possible Likelihood Estimation Diagnoses Therapeutic Intervention Care Archetypes History Lab Tests/ & Physical Longitudinal Follow Up Physicians continue as usual – Automated Infrastructure pulls data and performs “Research Analytics”
- 10. Data, Analytics andKnowledge Population Clinical Care Genome Toxicity/ Cost Risk Quality Behavioral/ Therapeutic Engagement/ Payment Patient Dimensions* Efficacy Activation Intervention Outreach Incentives Incentives Popltn 1 Patient 1 Interventions that need Patient 2 to be studied/evaluated Patient 3 Patient 4 Activation Segments Care Archetypes Patient Stratification Patient 5 Populations Popltn 2 Patient 6 Patient 7 Patient 8 Patient 9 Patient 10 Popltn 3 Patient 11 Patient 12 Patient 13 Patient 14 Alignment Patient Care Archetypes Cost/Quality Populations Behavioral/Activation Segments Patient Stratification
- 11. Coordinated Healthcare Intervention Therapeutic Intervention Informational - Drugs, Procedures - Outreach - Devices - Wellness Apps Coordinated Health Intervention Motivational Cost and Risk Sharing - Engagement - Provider Incentive - Personal Incentive • Optimal Health Interventions will require collaborations between stakeholders across the ecosystem • Optimizes a set of criteria: outcomes, cost, toxicity, efficacy, utilization, economic benefit
- 12. Scenario: Emergence ofthe Third Party RO/A Engagement Protocols Medication Incentives, Compliance Infrastructure Investment Insights Research/ Behavioral Validation Data/Insights Licensing Model Prescribing/ Administration Medication Adherence Licensing Model Protocols Insights • The Healthcare Ecosystem as a “Continuous Learning System” – Driven by Research • Reduce barriers to participation: Provide common infrastructure and a set of incentives • Need to incentivize sharing data generated from on going observations and measurement • Need for a trusted “Third Party Research Organization/Aggregator”
- 13. Towards an EconomicDecision Model U = R – (Ic + Ir) U = R – (Ip + Ir) Ir consists of one time and incremental Ir consists of investments in utilization investments: ui = ri + d – Ii and incentives Um/2 = (Uprovider + Upayor + Upharma) U = utility, R = Revenue Ui = U + (ri) – (Ic + Ir) I: Investment in Care (Ic), Ir consists of investments in compliance Research (Clinical, Utilization, Incentives) (Ir), and incentives Payment (Ip) • Utilization Model for Research drives investment in research to address the information gap • Providers will rationally consume results to drive cost/outcomes improvement in clinical care • Payors and Pharma will drive investments in infrastructure and incentives to drive research • Optimization curve for market based on marginal gains for various components
- 14. Conclusions • The Health Ecosystem needs to evolve into a Continuous Learning Ecosystem to achieve cost/outcome objectives • Need for Deep Collaborations & Information Sharing to close the Information Gap Care Archetypes Populations Activation/Behavioral Segmentations Toxicity/Efficacy Stratifications • Need to invest in infrastructure and incentivize research and knowledge sharing – Reducing Information Gap creates value (in terms of cost/outcomes) which is consumed by different stakeholders in the ecosystem – Information Gap always exists (due to new knowledge and information) new economic incentives market evolution/continuous learning • Trust – a key roadblock – may lead to the emergence of third party research organizations/aggregators • Next steps: Develop Economic Decision Model and Work on a Roadmap of Incentives to achieve a Continuous Learning Ecosystem!
- 15. Blog http://continuouslearningecosystem.blogspot.com Look forward tocomments, feedback, critique, suggestions!