Quadramed User s Conference July 30, EMPI Links Hospitals to Transform Data Exchange

Transcription

1 Quadramed User s Conference July 30, 2011 EMPI Links Hospitals to Transform Data Exchange

2 AGENDA Introduction of Foundation/IQSC Program Implementation for EMPI REMPI Reporter Research Questions

3 DFWHC Foundation 501c(3) Charitable Organization Affiliated with Dallas-Fort Worth Hospital Council Mission Continually improve the community s health by promoting safe, high quality, cost effective, accessible and equitable healthcare and by strengthening the healthcare workforce through education, research and collaboration. Vision Become the community resource to create new knowledge, insight and wisdom for the continuous improvement of healthcare. 3

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6 Who Are the Data Submitters? 77 hospitals 63 Acute 4 Psychiatric 7 Rehab/LTC 3 AmbSurg 17 counties 6

7 General Description of Information Submitted Claims from all participating hospitals with no blinding of any data elements Claims for all payers, including self-pay patients, for all patient encounters except outpatient lab or any hospital-based outpatient clinic 7

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9 Scope PROGRAM IMPLEMENTATION OF EMPI Page 9

10 EMPI Linkage Project Goal Apply advanced probabilistic matching algorithms to accurately link disparate person / patient records within and across healthcare organizations for improving patient safety, quality & health. Page 10

11 Linkage Project Overview Evaluated vendors GUI and out of the box readiness Operating System Support/Training Security Data Integrity Linkage Method What type of control do we have over the linking Etc. Narrowed to three vendors Requested proof of concept Two vendors agreed to proof of concept Selected QuadraMedas a development partner for our Record Linkage project Page 11

12 Getting Started: The Partnership Plan QuadraMed Perform SmartScanWeight File Generation Install Ensemble Implement Smart Identity exchange Complete SmartScanand load data Install SmartMerge Provide Training on QuadraMed Products DFWHC Foundation Purchase Dedicated Windows Server Provide Remote Access to Smart Identity exchange InterSystemsEnsemble Training Cooperatively Define Initial Auto-Linking Process Rules Page 12

13 Quadramed spart of the HIE Landscape Page 13

14 Configurability & Scalability Page 14

15 Initial Process Data extracted from DFWHC Foundation Warehouse Data runs through Smart I/X and then extracted for SmartScan Analysis SmartScanscientifically computes the probabilistic weights for all demographic database record elements and performs the initial detection of duplicates Approximately 45% of the records in the database were identified as potential linkages, as expected in a regional database Auto-link of High Probability patient matches will occur based on linkage weight and predefined deterministic matching criteria Moderate to Low Probability duplicate records will be output to SmartMerge for review Examine patterns that may result in further auto-linking rules Amplify auto-linking as DFWHC Foundation tolerance for error is higher than typical hospital Page 15

16 SmartScanResults Continued What a Hospital or System would review manually $$$ Cost Prohibitive for our purpose! Page 16

17 Auto-Link Rules: Ten Auto-Link rules were created and tested by the Foundation and are currently being used. The rules are a combination of probabilistic elements (e.g. a match weight threshold of 12) and deterministic elements (e.g. the SSN if present on both records, must match exactly) that were jointly determined by the Foundation and QuadraMed. Duplicate Distribution by Auto-Link Status Duplicates Total pairs with auto-link status of 0 163,803 Total pairs with auto-link status of 1 311,568 Total pairs with auto-link status of 2 4,220 Total pairs with auto-link status of Total pairs with auto-link status of 4 18,462 Total pairs with auto-link status of Total pairs with auto-link status of 6 16,804 Total pairs with auto-link status of 7 6,704 Total pairs with auto-link status of Total pairs with auto-link status of 9 1,726 Count of pairs with an auto-link status 360,587 Page 17

18 Due to the nature of the warehouse, many patients have more than two records in the warehouse. Auto-linking can be used to aggregate these individual records under one EMPI number in order to provide a person-centric view of the data. Multiple Distribution by Auto-Link Status Multiples Retirees with auto-link status of 1 401,153 Retirees with auto-link status of 2 4,861 Retirees with auto-link status of 3 83 Retirees with auto-link status of 4 16,456 Retirees with auto-link status of Retirees with auto-link status of 6 10,468 Retirees with auto-link status of 7 8,943 Retirees with auto-link status of Retirees with auto-link status of 9 2,305 Count of retirees in multiples with auto-link status 444,830 Count of survivors in multiples with auto-link status 199,603 Total multiple records involved in an auto-link 644,433 68% of the Multiples meet an auto-linking rule. The existing multiples can be programmatically merged based on the auto-link status. By applying the auto-linking rules as each new file is loaded, fewer and fewer multiple groups will be formed. Page 18

19 WISDOM Readmissions in North Texas Page 19

20 Regional Enterprise Master Patient Index (REMPI) Probabilistic electronic tool that matches patient encounters across hospitals and systems when applied to the Information and Quality Services Center Data Set Allows identification and analysis of patient activity regardless of encounter location or payer Readmissions ER utilization Imaging utilization 20

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22 Minimize Negative Revenue Impact Analysis of regional readmission profiles Implementation of operational and treatment modifications for patients at highest risk of readmission 12% of customers are generating 46% of the Business 22

23 How much data can be Gained? Chart shows one year of data for Hospital A with all cause readmissions of less than 30 days 23

24 Readmits by Product Line Hospital A All Cause Readmissions 24

25 REMPI Value Provide hospitals with data currently only available to Medicare and private health insurers such as Aetna and United. Seeing the whole episode of care. Explore characteristics of those patients who demonstrated more than the average frequency of re-admission pattern. count REMPI Hospital Affected Top DRG Avg days between hospitalizations Baylor Plano = 5, Medical Center Plano = 2, Methodist Dallas = 2, Presbyterian Red blood cell Dallas = 6, Presbyterian Plano = 6, disorders w/o Richardson Regional = 6, Trinity MCC Medical Center = 7 Red blood cell disorders w MCC Other disorders of nervous system w MCC Menstrual & other female reproductive system disorders w/o CC/MCC 3 Baylor All Saints FW=8, Baylor Southwest FW=3, Dallas Regional = 4, Harris Methodist SW = 4, Harris Methodist FW = 3, Huguley= 11, JPS = 1, Parkland = 1, Med Center Arlington = 1 Cranial & peripheral nerve disorders w/o MCC Chest pain Cardiac arrhythmia & conduction disorders w/o CC/MCC Cardiac arrhythmia & conduction disorders w CC 9 25

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27 Tracking Readmissions Emergency Room Visits for 1 year Frequent Flyers 27

28 Blinded Physician Names 28

29 Current Research in Progress A Model of Patient Readmissions Related to Congestive Heart Failure with University of Texas at Denton and University Texas Southwest Medical Center Exploratory study linking transitional care processes to 30-day readmissions with Texas A&M Health Science Center Comparative Effectiveness of Clinical Care Processes in Resuscitation and Management of Moderate to Severe Traumatic Injuries with the Baylor Research Institute Dallas Air Pollution Epidemiology Study with Rollins School of Public Health and Emory University Harnessing EMR Data to Reduce Readmissions: Developing and Validating a Real Time Predictive Model with Parkland Health and Hospital System, Texas Health Resources, and University Texas Southwest Medical Center Abdominal Aortic Aneurysms with Baylor Research Institute Diabetes Study with sanofi aventis Page 29

30 Plans for the Future Develop episodic metrics and analytic capability to evaluate chronic illness models Report on top DRG s for readmission with regional comparisons Analyze frequent flyer patients in Emergency Departments and their characteristics Track infections and other complication rates Track distance from the hospital to the patient s home address Track survival via Medicare Death Master file Trend admitting and operating physician by name Page 30

31 Contacts Theresa Mendoza (Director IQSC) or Dallas-Fort Worth Hospital Council Education and Research Foundation 31