Business Intelligence in Healthcare: Trying to Get it Right the First Time!

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1 Business Intelligence in Healthcare: Trying to Get it Right the First Time! David E. Garets, FHIMSS DISCLAIMER: The views and opinions expressed in this presentation are those of the author and do not necessarily represent official policy or position of HIMSS.

2 Conflict of Interest Disclosure David E. Garets, FHIMSS Has a conflict of interest to report: Board Member, since 2010, of Health Care DataWorks 2013 HIMSS

3 Conflict of Interest Disclosure David E. Garets, FHIMSS Salary: N/A Royalty: N/A Receipt of Intellectual Property Rights/Patent Holder: N/A Consulting Fees (e.g., advisory boards): N/A Fees for Non-CME Services Received Directly from a Commercial Interest or their Agents (e.g., speakers bureau): N/A Contracted Research: N/A Ownership Interest (stocks, stock options or other ownership interest excluding diversified mutual funds): Stock options in Health Care DataWorks Other: Interest from Warrants from Health Care DataWorks 2013 HIMSS

4 Learning Objectives What s driving the peak of inflated expectations push towards business intelligence in healthcare? What an enterprise data warehouse and analytics tools environment should look like Where is BI in healthcare going and what are the things to get right the first time?

5 Lots of data, little information and less knowledge WHAT S DRIVING BI IN HEALTHCARE?

6 Business Intelligence (BI) Definition A set of methodologies, processes, architectures, & technologies that transform raw data into meaningful & useful information used to enable more effective strategic, tactical, & operational insights & decision-making.

7 BI for Healthcare Providers Workflow integrated information which enables healthcare providers to drill from reports into detailed analyses of quality, safety, efficiency, effectiveness, regulatory and financial aspects of care practice to identify poor quality, waste, non standard practices, under or over utilized services, & opportunities for improvement.

8 Healthcare Data Maturity Stages Stage 1: Data Collection Characterized by the expanded adoption of EMRs Stage 2: Data Sharing Characterized by the expanded adoption of HIEs Stage 3: Data Analysis Characterized by the adoption of data warehouses Healthcare 1.0 Healthcare 2.0 Source: Dale Sanders, Health Catalyst

9 Healthcare organizations have questions Fact-based or just gut feel? How are the physicians performing in relation to costs and quality? How many patients are readmitted within 30 days of discharge? Questions lead to more questions What are the revenue implications of dif ferent reimbursement models? How can we identify patients during a stay who are high risk for readmission? And still more questions of increasing complexity and value How can we optimize our performance under a new reimbursement arrangement? How can we best treat subpopulations of patients who are at high risk for readmission? Source: The Advisory Board research and analysis.

10 Our Systems Describe the Past; We Need Them to Guide the Future GOAL Degree of Difficulty TODAY 1 Descriptive What happened? Analyze a past capacity bottleneck Report on patient admissions 2 Predictive What might happen? Help predict capacity bottlenecks Identify high -risk patients 3 Prescriptive What should we do? Identify how to optimize capacity Choose the best therapeutic approach for a particular patient How do we cut costs most effectively? Degree of Importance to the HCO Source: The Advisory Board research and analysis.

11 Source: Group Health Cooperative, 2012.

12 Free Advice: DON T EVEN THINK ABOUT CREATING YOUR OWN DATA MODEL!

13 What should the EDW and analytic tool environment look like? THE OPTIMAL EDW ENVIRONMENT

14 Ideal Conceptual BI Environment/Architecture Source: The Advisory Board research and analysis.

15 Another Way to Look At It Source: Health Care DataWorks

16 Today s Reality = Fragmented = Limited Value DM 1 Inpatient EMR EDW Ambulatory EMR MMIS 2 DM 1 Reporting Subsystem Typical Current IT Environment Mix of EMRs Lack of data standardization Multiple and redundant tools Incomplete aggregation Limited or no longitudinal data Limited access Local priorities No single source of truth CVIS 3 1) DM: Data Mart 2) MMIS: Materials Information System 3) CVIS : Cardiovascular Information System 16 Source: The Advisory Board research and analysis.

17 BI Maturity Model Fragmented Enterprise Perspective Advanced Analytics Big Data BI architecture Data sources / data currency Types of analysis / use of analytics Data models None or several point solutions Transaction application from one system or BI tool specific from limited number of internal source systems Automated internal reporting Departmental Central infrastructure basics implemented ETL established for primary data sources Enterprise KPIs and automated external reporting Common vocabulary, star schema, dimensional BI core and self service infrastructure in place ETL established for secondary data sources Predictive and prescriptive analytics and evidence based analytics Multiple data models Optimized infrastructure (e.g., data marts, ODS) Web, patients, genomics, and other external sources Analytics combining multiple and complex data sources No schema Data governance Independent and departmental Common policies and standards, centrally managed KPIs, and security management Agreed upon agenda and priorities, data normalization, and initiate source system changes Stewards of internal and external data, complex analysis review, and sophisticated delivery methods Tools Skills Culture / enterprise data literacy BI governance / org structure Redundant toolsets SQL, Excel, light data modeling, light visualization Value of data underappreciated and gut feel decisions Local control Consolidated data management tools In depth knowledge of physical and logical data modeling, and light statistics Champions emerging and growing emphasis on factbased decisions Central agenda and central funding Extended analytic capabilities In depth knowledge of statistics and operations analysis, procedural programming Training on data literacy, identifying BI opportunities, and making changes Coordinated resources Specialized, targeted capabilities NLP, genomics, and rules engine programming Engrained understanding of BI capabilities and limitations Includes relevant, external resources Source: The Advisory Board research and analysis.

18 Things to get right the first time WHERE BI IS GOING

19 Data Governance Overview Data Quality Data Architecture Data Development Meta Data Document & Content Data Governance Data Operations Data Security Data Warehousing & Business Intelligence Reference & Master Data Source: The Advisory Board research and analysis.

20 20 Define process for resolving data quality problems Narrative descriptions from data stewards One Step At A Time Meta Data Data Quality Data Architecture Data Development Data Governance Enterprise data model strategy Data Operations Choose core data model strategy Choose RDB, OS, ETL tools, and metadata repository Define backup and DR for the EDW Document & Content Data Warehousing & Business Intelligence Reference & Master Data Data Security Access-security Roles Define data security classification Choose core data analysis and visualization tools Patient ID, provider ID, ICD, CPT, facility ID, and department ID Source: The Advisory Board research and analysis.

21 BI is an Essential Capability for an ACO Quality Risk Performance Risk Utilization Risk Descriptive Service line or physician performance Quality measures performance Identify variations in practice Patient chart scanner Patient satisfaction Bundled payments scorecard Evidence-based guidelines compliance PCP attribution Disease dashboards Contract performance scorecards Benchmarks comparisons Ambulatory care sensitive admissions Predictive Readmission risk Fraud detection Financial modeling Patientcompliance Population risk Complications risk (e.g., admissions or HAIs) Estimate demand destruction Prescriptive Inventory optimization Nurse scheduling Care pathway optimization Patient discharge planning Patient engagement approaches Throughput optimization Facility and provider network planning Cognitive support for clinical decision-making Source: The Advisory Board research and analysis.

22 IT Should NOT Drive the Bus IT Governance Executive Steering EMR BI IT Governance BI Governance Data Governance EDW Data Governance BI Strategy Source: The Advisory Board research and analysis.

23 Shifts in Provider Business and Clinical Models Source: John Glaser, Siemens Medical Group.

24 Now What? Gain a deep understanding of your enterprise s strategic direction for the next one, two, and three years to align the BI plan. Form an ACO? Add value-based contracting? Develop centers of excellence? Pursue quality and cost initiatives? Make (or update) your architecture, data model, and tool choices based on your planned BI deliverables. Standalone EDW, bus architecture, or EMR bundled analytics, or? Pre-packaged data model f or descriptive analytics? Specialized tools for research? Re-evaluate your enterprise s engagement with and readiness for your BI agenda. Are key business and clinical execs engaged in leadership roles? Can the silos of federated models be swayed to an enterprise approach? Is the enterprise ready to make hard changes based on the story told by the data?! Get started on building your next phase of the BI maturity model: your enterprise s needs are accelerating. Source: The Advisory Board research and analysis.

25 Thank You! Dave Garets, FHIMSS Senior Advisor, The Advisory Board Company

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