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Sutter Health Presentation at the Chief Data Officer Forum

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Welcome to Chief Data Officer in Europe Asia USA Australia New Zealand

In recent years, there has been a significant rise in the appointments of Chief Data Officers (CDOs), in both the public and private sector. This is a result of data becoming an increasingly important strategic asset to businesses and public authorities. It is the CDO’s duty to manage this enterprise-wide data and ensure that it is utilised to drive revenue and new business opportunities and cut unnecessary costs. Essentially, to monetise data: to maximise efficiency, drive an increase in revenue and improve service to clients.

However, the position of the CDO is ill-defined, varied and even controversial. Although it is increasingly apparent that data-driven organisations outperform their competitors, the role of the CDO is still under development. The challenges and complexities faced by the new CDOs of this world are wide-ranging.

Not only is the CDO’s position within the C-Level chain of command varied per organisation and at times, ambiguous, they also face the difficulty of striking the balance between IT and business; technology and strategy. Moreover, CDOs are being readily challenged by the ability to keep data management costs low, whilst adhering to increasingly stringent regulatory demands. As well as producing results and analysis of value to their organisation, and getting their voices heard in the boardroom amid the web of C-Level executives and business politics.

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Sutter Health Presentation at the Chief Data Officer Forum

  1. 1. Enterprise Data Management: Consumer Driven Strategy April, 2015 Prepared for CDO SUMMIT
  2. 2. 11 About Sutter Health 1 Facts at a Glance  100 Communities  5,000 Affiliated Physicians  48,000 Employees  24 Acute Care Hospitals  $9.6 billion in Revenues (2013)  31 Surgery Centers  10 NICUs  9 Cancer Centers  8 Cardiac Centers  5 Acute Rehab Centers  4 Trauma Centers
  3. 3. 22 Mission, Vision & Values Vision: Sutter Health leads the transformation of health care to achieve the highest levels of quality, access and affordability. ValuesMission We enhance the well-being of the people in the communities we serve through a not-for-profit commitment to compassion and excellence in health care services.
  4. 4. 33 Vijay Venkatesan - Enterprise Data Management  Expert in healthcare data, analytics and business intelligence Expertise and experience managing large enterprise data functions  VP, Analytics and Business Intelligence, SPS  VP, Management Information and Analysis, KP  Divisional VP for Enterprise Client Reporting and Informatics, HCSC, Chicago  BS, Computer and Information Science, Cleveland State University  MBA, University of Michigan
  5. 5. 44 EDM Definition EDM includes management, integration and visualization of information from: –Enterprise Transactional (structured and unstructured), –Operational, –Clinical, –Social, –Genomics –Device, and –Machine Data To support internal and external needs of data in support of strategy, operations, financial, performance management, medical networks, clinical and patient experience functions.
  6. 6. 55 Objective and role for Enterprise Data Management Objectives for Enterprise Data Management Serve as a central, system-level hub for enterprise reporting and analytics • Includes retrospective, prospective, real-time and predictive data • Provide data visibility to all business and clinical functions • Govern the use and methods of reports and analytical tools via the Data Governance Council • Eliminate the creation of duplicate reports through EDM processes • Empower rapid decision-making through convergence of disparate analytics activities • Drive variation reduction in analytics approaches and methodologies Curation, preparation, and visualization of enterprise data Interpretation of data and reports End Users (Local functions) Enterprise Data Management (System function) Data Sources (Clinical, financial, transactional, social)
  7. 7. 66 Enterprise Governance Proposed end-state governance structure Business Intelligence & Performance Management Enterprise Content Management Content Delivery (Real Time and Post Transactional) Releasable Data Management Metadata Management Master and Reference Data Management Data Quality Management Data Stewardship Big Data, Enterprise Data Warehouse and Data Marts, Unstructured Content (Information Assets Management) Architecture and Technology Enablement Information Strategy
  8. 8. 77 External Paradigm Shifts CHANGING ECOMONIC INCENTIVES Payment Reform Total Care Accountability (Shared Risks and Shared Savings) CHANGING TECHNOLOGIES Smart phone applications  Devices, Sensors and monitors  Social media and digital content CHANGING THE GAME Transparency Coordination and prevention Increased access Personalization anywhere everywhere Affordability
  9. 9. 88 Is Creating Data Explosion and Liquidity Currently physical world and software worlds are detached Internet of things promises to bridge this – It is about sensors and actuators everywhere
  10. 10. 99 And generating new demands for Insight from this data……DataVolume Customer Data Point of Sale Mobile Structured Data Click Stream Social Network Location- based Data Text Data IMHO, it’s great! Future20152011 Collect, process, and deliver insight to support real-time business Access relevant information from structured and unstructured dataDocuments Internet Genomic Data
  11. 11. 1010 Dealing with Volume and Variety of Data……..
  12. 12. 1111 Requires a Paradigm Shift…………..
  13. 13. 1212 Current State of Data and Analytics…. Situation Widely-dispersed, non-integrated disparate data sources Departmental analytics Problem Data inconsistency – No Single Source of Truth Time and costs associated with data gathering/information Speed of diligence incorporating new data sources Desired State Sub-optimal business impact enterprise wide Optimized business impact enterprise wide Departmental Analytics Vendor driven projects Decentralized model (Silos) Federated with strong governance Centralized Fragmented pockets of analytics capabilities Non-standard approaches of conducting the same analytics Greater redundancy of information and analytics Lack of information sharing across the enterprise Common approach to data integration and management, governance and stewardship Integrated predictive models which support a wide array of decision making Repeatable, sustainable and credible process that can promote enterprise adoption Process and models adapt to the changing business model. Faster access to Data to drive insights that create actions and decisions
  14. 14. 1313 Current State of Data – Technical View DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DBDB DB DB DB DBDB DB DB DB DBDB DB DB DB DBDB DB DB DB DBDB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB DB
  15. 15. 1414 Our Strategic Objectives Clinical Excellence and Quality-Driven  Evidence-based best practices to enhance quality of care  High impact, high value for patients, populations, and communities at-large  Expand access to high quality, cost effective health care Operational Excellence and Financial Strength  Improve data transparency and accountability across stakeholder groups  Diversification of revenues and to strengthen brand and market position  Adapt to changing regulatory environment Employer Excellence  Maintain market position as the best-in-class organization  Embrace structures that are sharing the risks and rewards of delivering the highest quality care  Enable employees with the right tools, technology, and efficient processes Commitment to Industry Innovation  Enhance and transform the approach to personal health, patient health, population health  Innovate delivery models that improve outcomes for all stakeholders  Contribute life-saving best practices to the larger healthcare industry
  16. 16. 1515 Our Opportunities
  17. 17. 1616 Target State – From Data to Action The end-to-end process view across data-to-insight and insight-to-action can help design effective analytics solutions and enable targeted change management to embed them into business processes. In many respects, this is the real power of Enterprise Performance Management (EPM)
  18. 18. 1717 Vision – Imagine iTunes for Sutter Health – WeConnectYou Operational and Ready for Enterprise Access Current Initiatives • Advanced Illness Management (In Production) • ACO Shared Savings (4/3/2015) • Clinical Documentation Initiative (WIP) • West Bay Operating Unit (WIP) Other pilots underway • SHEMS Resiliency • EMIM Exceptions • ASC • Finance System Dashboard • S3 CBO Dashboard • Clinical Use Cases: • Predictive Model for Readmissions • PCOC Models • …..
  19. 19. 1818 Benefits Information Islands Transparency Information Independence Information Interdependence Accumulation of Data Insight and Exceptions Regional Ownership Enterprise Assets Technical Reporting Teams Real-time action & Decision Support Next generation tools enabling forward looking insight and modeling New projects follow an established set of guiding principles Business owners will be notified of unfavorable trends or conditions Shared information supported by common data definitions, tools, and access methods Data Manipulation Actionable Decisions Business users receive timely information that is tailored to their needs Execution will position us to exceed customer needs and become a industry leader
  20. 20. 1919 Approach to Transformation HINDSIGHT FORESIGHTRemediate and stabilize current environment • Develop roadmap to address completeness and timeliness of data • Assess current architecture; rationalize redundancies • Accelerate services to customers Enable key business functions • Execute program roadmap for quick hits, medium, and long- term business capabilities • Align and rationalize in-flight projects • Deliver critical initiatives Advance governance and stewardship • Implement governance model to align priorities • Establish alignment objectives and execution plan • Establish communication plan on roles and responsibilities Enhance and build upon current information foundation • Implement near-term information architecture • Implement information prioritization council • Develop metadata and taxonomy Focused on delivering value now KNOW WHERE TO START DON’T BOIL THE OCEAN CREATE DEMAND ITERATE
  21. 21. 2020 F U N C T I O N T O O L S U S E C A S E S Clinical Quality & Care – Readmissions, AIM, ACO, Sepsis etc. Patient Experience – Personalized Continuum of Care, Sutter Health Plus Information Services Human Resources Financial Supply Chain Operations – Care Provider Allocation Epic - Info View & Reporting Workbench ETL For Data Warehouse and Data Marts MDM Data Quality Data Governance Big Data Business Intelligence Reporting Visualization Advanced Analytics EDM Enterprise Solutions
  22. 22. Big Data – Success Criteria for Enterprise Adoption People  Analytical talent is respected and leveraged across functions  A central pool of experts enables cross-learning Shifting from analysis to discovery Technology Relevant technologies are leveraged consistently across the organization— technologies related to (1) data and infrastructure, (2) Business Intelligence and reporting, (3) advanced and predictive analytics, (4) visualization, (5) Streaming and Machine Learning, etc. Governance Standardization of processes and cross-leverage.  The ongoing program for insights includes prioritization of areas to build predictive analytics, review of the impact delivered, and a test-and-learn environment for continuous improvement leveraging Lean Strategic alignment and funding models Data: Data assets across the organization are understood, and there is a plan to integrate data across functional silos; Integrity of information across the organization is maintained, such that there is a “single truth“. Quality of the data is established Compliance, Privacy and Security principles are adhered
  23. 23. 2222 Sutter’s current progress in Enterprise Data Management Level 4 Enterprise Adoption Level 3 Business Adoption Level 2 Technical Adoption Level 1 Infancy Level 5 Data & Analytics as a Service • Thinking about it • Initial Big Data environment in place • Proof-of-Concept / Pilot • Using Big Data mostly for storage / transform • Usage primarily by IT • Some Big Data explanatory analytics • Leveraging the discrete LOB use cases • Structured and unstructured analysis • Predictive analytics applied to Big Data • Leveraging use cases for multiple LOBs • Integrated metadata, quality and governance across Big Data • Predictive insights integrated into business operations • Operating as a “data service provider” • Self-serve data • Collaboration and sharing analytics across the enterprise • Socialization of Big Data Strategy underway • Completed pilots on Big Data Platform • Established a Big Data Ecosystem for exploration • Exploratory analytics on “Readmissions” completed • Integration into EHR proof- of-concept underway Sutter is here
  24. 24. 2323 Critical Success Factors – “You cannot discover new oceans unless you have courage to lose sight of the shore” Establish a narrative for Data – Shrink the change Engage Legal, Strategic Sourcing, Stakeholders, Leadership, Competition etc. Engage IS Technical and Infrastructure teams Identify uses cases that improve foundational and enable business value creation Strategic vendor partnerships to create proof-of-concept Keep the narrative relevant at the executive and grass roots level Don’t compete for table stakes – Forget Reports – Talk about improvements in quality and access to timely and relevant data Show capabilities that are business impacting Change the paradigm from reporting to self-service Enable business value within 90 days
  25. 25. Questions? Thank You

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