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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. - PowerPoint PPT Presentation
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Using Clinical Decision Support to Measure Quality for Special
Populations
T. Bruce Ferguson Jr. MDEast Carolina Heart Institute
Chair, Dept. of Cardiovascular SciencesBrody 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.
PURPOSEAHRQ THQIT Grant, 2004-2005
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.
RESULTSSecured ASP
CHF Clinical Registry
(Outcomes)
Point of Care Patient Centric Data Collection(Nota Medica)
NM and Outcomes Integration
CLINICAL DECISION SUPPORTIn CVD, long history with retrospective analysis of clinical data:
Ferguson TB Jr. et al. Ann Thorac Surg 2002; 73:480-490
IMPROVEMENTIN
QUALITYOF
CARE
AHRQ HS 10403-07: 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 DevelopmentEast Carolina University
1998, All-Cause, Age-Adjusted, per 10,000 Population
YLL Due To CVD:YLL Due To CVD: Eastern North Carolina would rank Eastern North Carolina would rank
50th50th
825.9 – 922.7
648.6 – 740.3740.4 – 825.8
1,067.1 – 3,254.3
0.0 – 648.5
YLL-75 per 10,000 < 75
922.8 – 1,067.0
• Morbid ObesityMorbid Obesity• DiabetesDiabetes• CholesterolCholesterol• High Blood PressureHigh Blood Pressure• Heart AttacksHeart Attacks• StrokesStrokes
Care Delivery OrganizationUniversity 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
East Carolina Heart Institute@ PCMH & BSOM
ECHI Clinical Cardiovascular Information System
Critical drivers (IOM, payers) to force information into longitudinal, patient-centric, value-based focus
CV Information – Current Statusat BSOM and PCMH
CrusadeDX
STS NCD ACC/PCI ACC/ICD Vascular CHF Others
Web SMS EncompassQMI NRMI
Clinical Databases
Hospital Information Systems Registries
Logician
Outpatient
Stand-alone systems, Requires local IT Support and Backup, Limited analytical, reporting capabilities
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 SUPPORTTechnologies:
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)
PreOPRisk Analysis
PreOPConsultation
Performance Reviews, CQI Measures, Clinical and Financial Reporting
Data C
ollectionA
nalysisPresentation
Matching
CLINICAL DECISION SUPPORT
CLINICAL DECISION SUPPORTAnalyze Cost, Complications and Mortality by Predicted Risk Groups
CLINICAL DECISION SUPPORT Clinical Outcomes Risk Calculation
Clinical Decision Support in CVD