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Using Clinical Decision Support to Measure Quality for Special Populations

Using Clinical Decision Support to Measure Quality for Special Populations. T. Bruce Ferguson Jr. MD East Carolina Heart Institute Chair, Dept. of Cardiovascular Sciences Brody School of Medicine at ECU. AHRQ THQIT Grant, 2004-2005. PURPOSE.

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Using Clinical Decision Support to Measure Quality for Special Populations

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  1. Using Clinical Decision Support to Measure Quality for Special Populations T. Bruce Ferguson Jr. MD East Carolina Heart Institute Chair, Dept. of Cardiovascular Sciences Brody School of Medicine at ECU

  2. AHRQ THQIT Grant, 2004-2005 PURPOSE • design for implementation a new IT platform to support a Longitudinal CVD Information System (LCIS) • address disparities in CVD, in the unique safety-net Charity population in Louisiana. SCOPE • multi-institutional project within 8-hospital Charity • Hospital system in LA • Collaboration Partnership between: • LSU Schools of Medicine and Public Health • Tulane University Schools of Medicine and Public Health • ARMUS Corporation (IT provider) • LA Department of Health and Hospitals, Office of Public Health

  3. METHODS • Our focus was directed at augmenting existing resources and creating an LCIS for collecting, analyzing, and coupling clinical and financial data to assess the medical and financial care effectiveness in this CHF population. • Technology issues centered on: • longitudinal data collection methodology across system • Integration of clinical longitudinal data into repository/registry for analysis • This LCIS would enable us addressing the significant care-delivery and patient-related disparities within this population and setting.

  4. RESULTS A model prototype of the LCIS was developed, including testing and evaluation components. A patient centric prototype enabled multiple providers to collect longitudinal CHF data from patient encounters within multiple care-delivery settings. Prototype testing and refinement was being undertaken at the time hurricane Katrina devastated LSU and Tulane Schools of Medicine, and disrupted forever the Charity safety-net population and system.

  5. Secured ASP Point of Care Patient Centric Data Collection (Nota Medica) CHF Clinical Registry (Outcomes) NM and Outcomes Integration RESULTS

  6. CLINICAL DECISION SUPPORT In CVD, long history with retrospective analysis of clinical data: IMPROVEMENT IN QUALITY OF CARE Ferguson TB Jr. et al. Ann Thorac Surg 2002; 73:480-490

  7. AHRQ HS 10403-07: CQI in Medicine SUSTAINABILITY Ferguson, TB Jr. JAMA 2004 USED CLINICAL DATA SYSTEMS TO ESTABLISH THE INFRASTRUCTURE FOR CONTINUOUS QUALITY IMPROVEMENT WITH PROCESS AND OUTCOMES DATA

  8. YLL Due To CVD: Eastern North Carolina would rank 50th • Morbid Obesity • Diabetes • Cholesterol • High Blood Pressure • Heart Attacks • Strokes YLL-75 per 10,000 < 75 0.0 – 648.5 648.6 – 740.3 740.4 – 825.8 825.9 – 922.7 922.8 – 1,067.0 1,067.1 – 3,254.3 1998, All-Cause, Age-Adjusted, per 10,000 Population Center for Health Services Research and Development East Carolina University

  9. Care Delivery Organization University Health Systems of Eastern North Carolina Pitt County Memorial Hospital 745 Bed private hospital 95 – 100% Occupied Regional affiliated hospitals (11) Brody School of Medicine at ECU 3rd largest component of UNC System

  10. East Carolina Heart Institute@ PCMH & BSOM

  11. ECHI Clinical Cardiovascular Information System Critical drivers (IOM, payers) to force information into longitudinal, patient-centric, value-based focus Integration of domains of clinical information silos into common platform interfaced with common analytic engine

  12. East Carolina Heart Institute Analytical platform encompasses all major cardiovascular data through common Web Access portal and entry mechanism, agnostic of the individual “Database” or “Registry” Integrated Clinical CV Dataset provides for cross-DB analysis and reporting

  13. CLINICAL DECISION SUPPORT • Technologies: • Data Repository • Data set independent design • Meta data • Flexible and Expandable • Applications • Independent of any data set and database engine • Object Oriented Design • Visual query, data and summary tables • Ad-hoc analysis • Matching Algorithm (99.7%) • Presentation Layer

  14. CLINICAL DECISION SUPPORT Clinical Data (STS, Cath/PCI, ICD, etc) Financial Data (UB 92, Claims, etc) Data Collection Repository Matching PreOP Consultation PreOP Risk Analysis Analysis Presentation Performance Reviews, CQI Measures, Clinical and Financial Reporting

  15. CLINICAL DECISION SUPPORT Analyze Cost, Complications and Mortality by Predicted Risk Groups

  16. CLINICAL DECISION SUPPORT Clinical Outcomes Risk Calculation

  17. Clinical Decision Support in CVD

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