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Health IT Summit San Diego 2015 - Case Study "Analytics Strategy: Enablement, Innovation, Transformation" with Eric Hixson, PhD., Senior Program Administrator, Business Intelligence, Cleveland Clinic
The document outlines an analytics strategy for Cleveland Clinic, focusing on enablement, innovation, and transformation to manage complex data and improve decision-making in healthcare. Key principles include minimizing data latency, promoting a culture of information sharing, and ensuring alignment of initiatives with strategic priorities. The document emphasizes the importance of communication plans and measurable outcomes in the successful implementation of analytics solutions.
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Health IT Summit San Diego 2015 - Case Study "Analytics Strategy: Enablement, Innovation, Transformation" with Eric Hixson, PhD., Senior Program Administrator, Business Intelligence, Cleveland Clinic
- 1. Analytics Strategy: Enablement, Innovation, Transformation AnalyticsStrategy: Enablement, Innovation, Transformation Eric D. Hixson PhD, MBA Senior Program Administrator Cleveland Clinic Business Intelligence
- 2. Cleveland ClinicCleveland Clinic •5.5 million patient visits • 157,000 admissions • 202,000 surgical cases • 4,450 inpatient beds • 75 outpatient locations • 42,000 employees • 3,000+ physicians and scientists
- 3. Multiple Implicit Directives withUncertain Directions Multiple Implicit Directives with Uncertain Directions • Prevention • Accountability • Consumerism • Competition • Complexity
- 4. Rapidly Increasing DataDomainsRapidly Increasing Data Domains • Geolocation • Physical environment • Economic conditions • Public health • EHR • Finance and claims • Marketing • Mobile / wearables • Patient reported outcomes • Device / Sensors • Genomics • Family history • Social media • Preferences • Process / Operations • Health services continuum • Trials • Patient - Activation - Engagement - Diagnosis - Options - Treatment / Intervention - Prevention - Prognosis and Progress - Resources • Care Delivery - People - Process - Outcomes - Efficiency - Alternatives - Recommendations - Partnerships - Channel Selection
- 5. Increasing Decision ComplexityIncreasingDecision Complexity
- 7.
- 8. Guiding PrinciplesGuiding Principles •‘Global’, enterprise perspective • Consolidated and distributed services • Prioritization aligned to strategic priorities • Align initiatives with the customer • Develop culture and community around information sharing and delivery • Account for staff, architecture, life-cycle processes, and governance
- 9. Key CharacteristicsKey Characteristics •Minimize data latency • Reporting tailored for target audiences • Harmonized vertical and horizontal messaging • Transparency • Actionable feedback
- 10. OutlineOutline • First Principles •Structure and strategy are intertwined
- 11. Business Analysts The InformationManagement FactoryThe Information Management Factory Data Sources Data Aggregation & Transformation Internal Data Store Data Store Data Store Data Mart Financial External Visualization / Reporting Data Mart Data Mart Data Mart External Reporting Executive Dashboards Internal Mgt Reporting Clinical Operational Operational Dashboards Population Analysis Infrastructure Analytics Other Governance Clinical Operations Other Payer Quality Alliance Other Data Warehouse Technical Resources Data Analysts Other Data Stewards
- 12. OutlineOutline • First Principles •Structure and strategy are intertwined • Data cultures require time and leadership
- 13. Performance Management Evolution 19932004 2005 20061995 20021998 Monthly Wheels Monthly Operational Indicators Continuous Improvement Summits Web Based Wheels Daily Dashboard Dashboard Tools and Data Warehouse Executive Dashboard Institute Scorecards 2008 EBI PMDSS Dept DBs Short Cycle 2010-12 Predictive Analytics 2013-15
- 14. OutlineOutline • First Principles •Structure and strategy are intertwined • Data cultures require time and leadership • Align metrics with mission
- 15. Analytics ExecutionAnalytics Execution •Avoid ‘shiny paper’ solutions • Reduced development time and cost • ‘Model factory’ capability • Automated model performance monitoring
- 16. OutlineOutline • First Principles •Structure and strategy are intertwined • Data cultures require time and leadership • Align metrics with mission • Calculation doesn’t equal implementation
- 17. A Cautionary TaleACautionary Tale Presenting a solution What the analyst sees What the audience sees What the audience remembers What the presenter remembers Feedback on the solution
- 18. Successful implementation ismore dependent on the communication plan than on technical functionality.
- 19. Emphasis on Deliveringan Outcome not a Product Emphasis on Delivering an Outcome not a Product Focus should be on what it ‘does’ rather than what it ‘is’
- 20. Clear MessagingClear Messaging •What information or capability gap is being filled? • What value does this have to me? • How does this complement other resources? • What does this replace? • What am I accountable to do? • Wash, rinse, repeat…
- 21. Clear Measurement GoalsClearMeasurement Goals No Established Norms Establish processes to monitor and means to measure it Evolving Norms Reinforce and refine processes; improve measurement Standardized Norms Assures performance; optimize measurement
- 22. Adoption Strategy: Incremental Transition AdoptionStrategy: Incremental Transition Depend on existing tools and refer to the new Use both existing and new tools Use new tools to complete the existing Replace existing tools with the new
- 23. Stakeholder AlignmentStakeholder Alignment •Executive leadership • Clinical service lines • Support services • Analytics resources • Improvement specialists
- 24. Work in ProgressWorkin Progress • Strengthening internal partnerships to align priorities and improve governance • Enhancing infrastructure to expand the capability envelope • Optimizing operations for improved developer productivity and user experience