Towards a Continuous Learning Ecosystem: Data Innovations and Collaborations to Improve Clinical...
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Transcript of Towards a Continuous Learning Ecosystem: Data Innovations and Collaborations to Improve Clinical...
Towards a Continuous Learning Ecosystem: Data Innovations and Collaborations to Improve Clinical Outcomes and
Reduce Cost of Care
Vipul Kashyap*, Ph.D. Cigna Healthcare
[email protected] April 9, 2013
Medical Informatics World, 2013, Boston, MA
* Acknowledgments – Jeff Catteau, Knowledgent ([email protected]) Discussions and feedback on the Economic Decision Model
Outline
• The Healthcare Ecosystem:
– 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
The Healthcare Ecosystem: Current State
Hospitals
Providers
Individuals
Provider Perspective
Pharmaceutical
Companies
Clinical Research
Organizations (CROs)
Individuals
(Populations?)
Pharma Perspective
Siloized Ecosystem – Lack of collaboration/coordination – Inefficient!
Individuals
(Populations?)
Employers Health
Advocacy
Employer/Payor Perspective
Health Plans/
Payors
The Continuous Learning Ecosystem
Patient/ Individual
Lifelong Continuous Engagement
Ability to share and exchange clinical information and knowledge is a critical enabler
Continuous Learning
Interactions at the Point of Care
Medical History? Medications? Allergies? Contraindications? Referrals? Follow Up? Reimbursement
Follow Instructions: Lab Tests, Medications, others
Continuous Learning Ecosystem: Roadblock Does the physician have time and incentive to identify insights, research new ideas and share them with other stakeholders?
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
Data, Analytics & Knowledge
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
Interactions at the Point of Care: Populations
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!
Medication/Therapy compliance Discuss Alternatives, Activation and Engagement
Interactions at the Point of Care: Research
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?
Comparative Effectiveness Research Clinical Trials Clinical Studies
Aligning Research and Practice
Ideation
Opportunity Analysis
Clinical Trials/ Studies
Outcomes Measurement
Populations/ Segments
Hypothesis Generation
History & Physical
Care Archetypes
Possible Diagnoses
Differential Diagnosis/ Likelihood Estimation
Therapeutic Intervention
Lab Tests/ Longitudinal Follow Up
Do physicians have the orientation to do research in the context of clinical practice?
Physicians continue as usual – Automated Infrastructure pulls data and performs “Research Analytics”
Data, Analytics and Knowledge Population Clinical Care
Dimensions* Genome Toxicity/
Efficacy Cost Risk
Quality
Behavioral/Activation
Therapeutic Intervention
Engagement/ Outreach
Payment Incentives
Patient Incentives
Popltn 1
Patient 1
Patient 2
Patient 3
Patient 4
Patient 5
Popltn 2
Patient 6
Patient 7
Patient 8
Patient 9
Patient 10
Popltn 3
Patient 11
Patient 12
Patient 13
Patient 14
Interventions that need to be studied/evaluated
Pop
ulatio
ns
Activatio
n Segm
ents
Alignment Patient Care Archetypes Cost/Quality Populations Behavioral/Activation Segments Patient Stratification
Patient Stratificatio
n
Care A
rchetyp
es
Coordinated Healthcare Intervention
Therapeutic Intervention - Drugs, Procedures - Devices
Informational - Outreach - Wellness Apps
Motivational - Engagement - Personal Incentive
Cost and Risk Sharing - Provider Incentive
Coordinated Health Intervention
• Optimal Health Interventions will require collaborations between stakeholders across the ecosystem
• Optimizes a set of criteria: outcomes, cost, toxicity, efficacy, utilization, economic benefit
Scenario: Emergence of the Third Party RO/A
• 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”
Behavioral Data/Insights
Research/ Validation
Medication Adherence Insights
Incentives, Infrastructure Investment
Medication Compliance Insights
Licensing Model
Licensing Model
Engagement Protocols
Prescribing/ Administration Protocols
Towards an Economic Decision Model U = R – (Ic + Ir) Ir consists of one time and incremental investments: ui = ri + d – Ii
• 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
U = R – (Ip + Ir) Ir consists of investments in utilization and incentives
Ui = U + (ri) – (Ic + Ir) Ir consists of investments in compliance and incentives
Um/2 = (Uprovider + Upayor + Upharma)
U = utility, R = Revenue I: Investment in Care (Ic), Research (Clinical, Utilization, Incentives) (Ir), Payment (Ip)
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!
Blog
http://continuouslearningecosystem.blogspot.com
Look forward to comments, feedback, critique, suggestions!