Using Clinical Decision Support to Measure Quality for Special Populations T. Bruce Ferguson Jr. MD East Carolina Heart Institute Chair, Dept. of Cardiovascular.

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Presentation transcript:

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

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. PURPOSE AHRQ THQIT Grant, 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

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.

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.

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

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

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

Center for Health Services Research and Development East Carolina University 1998, All-Cause, Age-Adjusted, per 10,000 Population YLL Due To CVD: Eastern North Carolina would rank 50th Eastern North Carolina would rank 50th – – – ,067.1 – 3, – YLL-75 per 10,000 < – 1,067.0 Morbid ObesityMorbid Obesity DiabetesDiabetes CholesterolCholesterol High Blood PressureHigh Blood Pressure Heart AttacksHeart Attacks StrokesStrokes

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 3 rd largest component of UNC System

East Carolina Heart PCMH & BSOM

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

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

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

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

Analyze Cost, Complications and Mortality by Predicted Risk Groups

CLINICAL DECISION SUPPORT Clinical Outcomes Risk Calculation

Clinical Decision Support in CVD