Traditional Analytics and Beyond:

Traditional Analytics and Beyond: Intermountain Healthcare's Continuing Journey to Analytic Excellence Lee Pierce AVP, Business Intelligence & Analytics Lee.Pierce@imail.org

Agenda Intermountain Healthcare Overview Intermountain s Journey to Build Analytic Capabilities BI/Data Governance at Intermountain Healthcare Big Data at Intermountain Healthcare

Intermountain Healthcare Profile An Integrated Health System 22 hospitals 33,000 employees 600,000 members 25% market share 200 clinics 1,000 employed physicians 1975 1983 1994 3

Intermountain Healthcare Profile Utah and Southeast Idaho Strategic decision to limit expansion Focus on markets that funnel to Salt Lake City Clinical integration in a defined geographic area 4

Achieving Intermountain s Vision Mission Excellence in the provision of healthcare services to communities in the Intermountain region. Vision To be a model healthcare system by continually learning and providing extraordinary care in all its dimensions. Clinical Excellence The best clinical practice delivered in a consistent and integrated way at the lowest appropriate cost to the population we serve

Intermountain s Core Business Perfecting the Clinical Work Process

Our approach requires sophisticated information systems to track outcomes and prompt best practices

Intermountain s Clinical Programs Aligns care team in the process of care Physician specialists Nurses Data experts Administrators Other caregivers Develops, implements, and sustains evidencebased care

EDW Integration in Clinical Programs Clinical Program Team Business/Clinical Leader Determines vision priorities Outcomes Analyst/Statistician Develops the analytical processes Performs advanced statistical analysis Data Manager Serves as liaison between EDW and Clinical Program Leads requirements analysis effort Improves data quality Facilitates data capture of the operational systems EDW Team Data Architect Designs, develops, and maintains data infrastructure Provides software project management BI Developer Assists with ETL Develops reports and reporting applications

Clinical Program Philosophy Goal is to deliver the best care to every patient every time Promote clinical research Enhance the business-clinical partnership to optimize efficiency Develops and improves processes using information systems and data

Data Drives Intermountain s Clinical Programs 1. Behavioral Health 2. Cardiovascular 3. Intensive Medicine 4. Oncology 5. Pediatric Specialty 6. Primary Care 7. Surgical Services 8. Women and Newborns

Foundational Leaders Dr. Homer Warner Medical informatics founder 1950s computer assisted CV decision support 1970s HELP system developed Dr. Brent James CQI - Standardization of clinical care through data analysis

Outcomes Improvement Approach Pareto Analysis Determine Outcomes Disseminate Results The best clinical practice delivered in a consistent and integrated way Define Best Practice Measure Performance And Compliance

Value Cycle Research Decision Support Patient Care Data Ideas EDW Improved Care Insight Action 14

Case Studies

Percent <39 Weeks Elective Delivers <39 Weeks

Timing of Elective Inductions 1.20% Increased risk of newborns on ventilators Percent on Ventilator 1.00% 0.80% 0.60% 0.40% 0.20% 0.00% 1.12% 0.45% 0.21% 37 th Week 38 th Week 39 th Week

Elective Delivers <39 Weeks 35% 30% Percent <39 Weeks 25% 20% 15% 10% 5% 0% JFMAMJJASONDJ FMAMJJASONDJ FMAMJJASONDJ FMAMJJASONDJ FMAMJJASONDJ FMAMJJASONDJ FMAMJJASONDJ FMAMJJASONDJ FMAMJJASONDJ FMAMJJASONDJ FMAMJJAS 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Month

Colon Surgery: Evidence Based Interventions and Associated Measures Intervention Patient Education Measure Enrollment Early Mobilization After Surgery Activity PT / Nursing walking, transfers, etc. Appropriate IV Fluid Admin Narcotic Sparing Analgesia Early enteral nutrition Fluid Administration Med Administration, Morphine Equivalence Diet Administration Bowel/Emesis/Flatus Financial Measures

Colon Surgery Care Process Report 20

Colon Surgery Care Process - Financials 21

Colon Surgery Results: $1.2 million annual savings, LOS decreased from 8.44 to 6.75, while maintaining or improving clinical quality 2010 Computerworld Business Intelligence Award Driving Process Change with BI 22

Many Other Successes Over the Years Clinical Quality Improvement Examples include: Blood Utilization Heart Failure, CV Discharge Medications Diabetes, Asthma, CAP Healthcare Operations Lab, OR, Hospital Operations, Patient Satisfaction, Core Measures, Meaningful Use Research Office

Data Warehouse Profile 15 TB Oracle Data Warehouse ~9000 queriable tables ~150,000,000 queries per month 95,000,000,000 rows of data 40 FTEs 29 data architects (data input) 11 business intelligence developers (data for reports) Coordination with clinical and business areas

Our Approach to Building an EDW Started in 1997 Built but did not use an enterprise data model Understood users needs Balanced quality, speed, cost, scope Access for primary analytic producers power users

EDW Conceptual Architecture Data Sources Internal Enterprise Data Warehouse Data Access Finance External Lab Claims Pharmacy EMR OTHERS SOURCE Data Marts EMR Patient Acct Claims Supply Chain Patient Sat. Cardiovascular Primary Care Women & Newborn AHR Surgical Serv SUBJECT Data Marts BI Tools State/Federal Master Reference Data

Business Intelligence Users Enable these primary users/producers Authoring Data mining SQL Power Analysts Interactive analysis Business Users Managers Emailed report Dashboard Scorecard Casual Users Executives

EDW Then and Now 1997 95 billion records 15 TB Seen as a critical system Thousands of users 40 FTE s 5,000,000 queries/day 1 million records Academic prototype 10 users willing to take a risk 1.5 FTE s 100 queries per day Today

Data Warehouse Technologies RDBMS: Oracle 11g ETL: IBM DataStage BI Suite: IBM Cognos, Tableau Cube Engine: Microsoft SQL Server Analysis Services (2008) Other tools: Visual Mining NetCharts, Corda PopCharts, SAS, Statit

I have way too many projects and not enough staff I wish the business would help prioritize requests

Purchase our product and your data and BI needs will be solved -BI Solution Vendor

Wow, that s a lot of analysts throughout our company. What are they all working on?

Data Governance? What s that?

You have an EDW in your department, too?

Alignment With Strategy Establish business intelligence & data governance Provide vision Drive strategic initiatives and decisions Align with business and clinical leadership Prioritize work Address BI challenges Manage data governance processes, tools

Core BI/Data Governance Concepts Develop new BI solutions Data Warehouse subject area ETL Data Mart and ETL processes Support production BI solutions Support of Data Load processes Help end users Support of BI platform Cube Reports and OLAP Views Deliver to users Doing Lead the activities above WHAT Plan, prioritize and fund the projects WHO Organize people in order to effectively HOW Optimize the way we achieve success Leading Source: Hitachi Consulting

Gartner BI Maturity Model Level 1 Unaware Level 2 Tactical Level 3 Focused Level 4 Strategic Level 5 Pervasive Successful focus on a specific business need Business objectives drive BI and performance management strategies Information is trusted across the company Total lack of awareness Spreadsheet and information anarchy One - off report requests No business sponsor; IT executive in charge Limited users Data inconsistency and stovepiped systems Funding from business units on a project - by - project basis Specific set of users are realizing value BICC in place Hybrid technologies Effective use by users driving business strategy Governance policies are defined and enforced Use of BI is extended to suppliers, customers and business partners BI integrated into enterprise architecture and application development processes 3-6m 6-12m 12-36m 36+m Small investment in tools and staff required Typical of where most large mid sized companies have landed Result of a significant commitment and investment Journey over time to operational - izing BI Source: Gartner (April 2007)

Information Governance Organization CIO Information Management Executive Committee Information Management Council EDW Team Data Owners = BI Governance = Data Governance Analytic Center of Excellence (Analyst Representatives from key areas of the organization) Data Stewards Data Stewards Data Stewards

What Information Governance Has Accomplished at Intermountain BI Governance Project Prioritization Strategic Initiatives BI Build vs. Buy Process Dashboard Standardization BI Tool Rationalization Analyst Leadership Team Analyst Training Overhaul Data Governance Data Stewardship Roles Definition Data Stewardship Inventory, Tools Evaluation Data Governance Training

Future Efforts Shared Data Structures/Analytic Health Repository Formalize/Evolve Data Governance Big Data Initiatives Operationalize Predictive Analytics Genomics Analytics

The Analytic Health Repository (AHR) AHR Data are: Standardized and cleaned Integrated across systems Optimized for population analysis Definition re-use Rules engine & sophisticated logic Specialized tools = rapid research Collaborative knowledge & effort Shared cohort and measures data structures National and International Terminology Standards Goal: Trusted source for clinical information, leverage all data assets and expertise Research Ideas Decision Support Improved Care

Data Governance Areas of Focus Data Governance Leadership Roles Data Stewardship Inventory, Education Data Quality Tools and Standard Processes Standard Metrics / Business Glossary Master Data Management (locations) Alignment with other teams Transparency Committee, Office of Research, etc.

BIG Data CHALLENGING Data

Big Data - It s All About Analytics Need to store and process all data Includes structured, unstructured, machine, sensor, social, text Must drive value into business What business are we in? What information do we have that isn t in traditional (structured) forms? Can we leverage that to drive better decisions?

Big Data Doesn t necessarily mean lots of data Ability and capacity to: Leverage all of your data (volume and variety) Speed (velocity) Better decision making (value and veracity) Better decisions & achieve organizational goals

Velocity Low Latency Data Germwatch Application Sensor data NICU Monitors Social Media Trending symptoms, illnesses, sentiment Financial Services Fraud detection, credit approval, customer service

Volume Text data Visit summaries Dictation Notes PDF Machine or sensor data All data points, not just snapshots Genetics Detailed sequencing data

Our Next Steps for Big Data Governance Organized workgroups (technical and business) to understand technology and use cases Opportunities at Intermountain Clinical Notes text analytics, NLP Patient Engagement Surveys patient comments Marketing/Communications - social media, sentiment analysis, patient profiles Vendors/Accelerators Talking to many, will pilot in Intermountain s Transformations Lab

Summary It s all about the patient, perfecting the clinical work process analytics is key Governance = alignment with strategy, enables an enterprise approach Big/Challenging Data start with traditional data/analytics, augment with new sources and technologies

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