Leveraging EDW for Information- Driven Decision Making. Gregory A. Spencer, MD, FACP Crystal Run Healthcare March 2, 2016

Transcription

1 Leveraging EDW for Information- Driven Decision Making Gregory A. Spencer, MD, FACP Crystal Run Healthcare March 2, 2016

2 Conflict of Interest Gregory A. Spencer MD FACP Has no real or apparent conflicts of interest to report.

3 Agenda Introduction Enterprise Data Warehouse Business Intelligence Past and Present Risk Management Payer Negotiations Population Health Growth and Expansion: Geo-analytics Operations Example

4 Learning Objectives Evaluate the versatility of an enterprise data warehouse in creating a healthcare ecosystem that is patient-centric and value-based Demonstrate how an enterprise data warehouse, in concert with other data sets, provide critical information to organizations in determining the location of and services offered at new office facilities Evaluate the efficacy of the enterprise data warehouse in improving operational efficiency and risk management Summarize how an enterprise data warehouse supports informed decisions by giving clinical and operational decision-makers efficient and effective access to all necessary information

5 Benefits Realized for the Value of Health IT Office Expansion- Analysis speed improved by 88% Medicaid Grant Petitioning- Analysis speed improved 98% Risk-based Contracting - Improved prioritization of clinical variation initiatives

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8 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

9 Risk Based Arrangements MSSP ACO Next generation ACO Payers Shared savings More risk

10 Enterprise Data Warehouse A data warehouse is a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of management s decisionmaking process. -Bill Inmon (1992) A data warehouse is database made up of information from your other databases that is optimized for analysis and reporting rather than transactions. -Me (recently)

11 EDW FINANCIAL SOURCES (e.g. Lawson) DEPARTMENTAL SOURCES (e.g. Apollo) Patient Provider Provider Bad Debt ADMINISTRATIVE SOURCES (e.g. API Time Tracking) Charge Cost Encounter Survey Census Enterprise data model Facility House Keeping Diagnosis Procedure Employee EMR SOURCE (e.g. NextGen, Epic) Time Keeping Cath Lab PATIENT SATISFACTION SOURCES (e.g. Press/Ganey) Enforced Referential Integrity More Transformation Less Transformation

12 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)

13 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

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

15 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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18 Payer Negotiations Demonstrate you know at least as much as the payer 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

19 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

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

21 Population Health Care gaps/ quality (old news) Best practices Variation reduction Risk partitioning Charlson Milliman High risk known groups- CHF, COPD, high A1cs

22 Provider Scorecard

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25 Variation Reduction

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30 Growth and Expansion: Geo-Analytics 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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38 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

39 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

40 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

41 Benefits Realized for the Value of Health IT Improved in multiple domains within the practice Scalability and standardization within organization to help ensure growth and survival of the organization Clinical process improvements

42 Questions Gregory A. Spencer MD FACP