Leveraging EDW for Information- Driven Decision Making. Gregory A. Spencer, MD, FACP Louis Cervone, Jr. Crystal Run Healthcare

Transcription

1 Leveraging EDW for Information- Driven Decision Making Gregory A. Spencer, MD, FACP Louis Cervone, Jr. Crystal Run Healthcare

2 Agenda Introduction Enterprise Data Warehouse Business Intelligence Past and Present Data-Driven Decision Support Physician Compensation Growth and Practice Expansion Quality Improvement and Clinical Best Practices Annual Budgeting Process Risk-Based Contracting

3 Crystal Run Healthcare Physician owned MSG in NY State, founded providers, 32 locations Joint Venture ASC, Urgent Care, Diagnostic Imaging, Sleep Center, High Complexity Lab, Pathology Early adopter EHR (NextGen ) 1999 Accredited by Joint Commission 2006 Level 3 NCQA PCMH Recognition 2009, 2012 Health Plan

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6 Enterprise Data Warehouse A data warehouse is a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of management s decision-making process. -Bill Inmon (1992)

7 Business Intelligence Past Initially BI = business only, reports Quality, safety measures and clinical performance later Basic tools: SQL, SSRS, Excel Manual and time consuming Report generation > analysis Lack of scalability and extensibility Mostly tabular / numeric

8 Business Intelligence Present Central EDW- many sources, fewer joins Scalable More analysis, less reporting Self-service and drill down Consume and deliver information Visual

9 Culture of Data We need to overcome hunch and intuition with empirical evidence.... We can start with a hunch or strong belief, but we act on it through experiment. We want evidence. We ve gone from the introduction of experimentation as a technique to a culture of experimentation as a business discipline. - Gary Loveman, CEO Ceasars Entertainment

10 Metadata: EDW Atlas Security and Auditing FINANCIAL SOURCES (NextGen EPM) Common, Linkable Vocabulary DEPARTMENTAL SOURCES (IOS, Great Plains, etc.) Financial Source Marts Departmental Source Marts Clinical ADMINISTRATIVE SOURCES (Claims) Administrative Source Marts Financial Analytics Patient Source Marts PATIENT SATISFACTION SOURCES (Sullivan Luellin, Press Ganey) MANY more! EMR Source Marts HR Source Mart CLINICAL SOURCE (NextGen, RIS, LIS, ASC) Human Resources (Empath, HRIS)

11 Data-Driven Decision Support

12 Physician Compensation Not just based on RVUs and patients Site of service Panel size increasingly important Internal models Specialty sometimes is not obvious Volumes of certain patient types change over time

13 Attribution/ Panel Size

14 General surgeon or specialty breast surgeon?

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16 Who is or has become a geriatrician? Name % Patients > 60 A % B 88.00% C 66.67% D 65.47% E 58.00% F 56.20% G 52.20% H 47.68% I 47.36% J 47.16% K 46.77% L 46.14% M 43.83% N 43.10% O 42.35% P 41.56% Q 40.67% R 39.99% S 39.49%

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18 Growth and Practice Expansion

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20 Growth and Expansion: Geoanalytics and mapping Siting new facilities- where Specialties needed in an area How many Where were patients coming from What practice sites were they going to

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23 Quality Improvement and Clinical Best Practices

24 Q Q Q Q Q Q Q Q Q Q Q Q Q Q st quarter 2nd quarter 3rd quarter 4th quarter 1st quarter 2nd quarter 3rd quarter 4th quarter 1st quarter 2nd quarter 3rd quarter 4th quarter 1st quarter 2nd quarter Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q % Hgb A1c > 9 78% BP <140/90 25% 76% 20% 74% 15% 10% 5% 0% CRHC Results NCQA Goal 72% 70% 68% 66% 64% CRHC Results NCQA Goal LDL Cholesterol <100 Mammography 62% 60% 58% 56% 54% 52% 50% 48% 46% CRHC Results NCQA Goal 78% 76% 74% 72% 70% 68% 66% 64% Score NCQA Goal

25 Percent Decreasing Readmissions Q Q Q Q Q2 Q Quarter Q Q Q Q3 2014

26 Provider Scorecard

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29 Variation Reduction

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31 Annual Budgeting Process

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33 Snow Day Visits 2014 Date Day Actual Visits Expected Visits Loss December 17, 2013 Tuesday January 2, 2014 Thursday January 3, 2014 Friday January 7, 2014 Tuesday January 8, 2014 Wednesday January 21, 2014 Tuesday February 3, 2014 Monday February 5, 2014 Wednesday February 13, 2014 Thursday February 14, 2014 Friday February 18, 2014 Tuesday March 3, 2014 Monday Total 14208

34 Snow Effect Light snow- 8% (SD 9%) Storm- 33% (SD19%) Nor Easter 50% each day for 2 days (SD 28%) Cumulative effect of snow events for the season was 70 patients per provider

35 Snowstorm Mitigation Strategies Banking visits- (About 70 per provider for the season) Access Saturdays Pay it Forward Planning for the storm Personal plans with universal application Snow day core teams at each building Careful use of preemptive moves Ongoing administrative work

36 Risk Based Contracting MSSP ACO Next generation ACO Payers Shared savings More risk

37 Payer Negotiations Demonstrate you know at least as much as the You re not like other providers Population Comorbidities/ high-risk patients Care management PMPM Medical Homes Quality- we re going to improve it for you

38 Risk Management Risk partitioning of the population Utilization, as close to real time as possible Spend, benchmarks, risk-adjustment Hot spot areas Getting credit for risk: HCC

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45 Questions Gregory A. Spencer MD FACP Louis Cervone, Director of Business Intelligence