March 28, 2003 Computer Science 40th Anniversary Symposium 1

Health Care Data Warehousing:Current and Future Directions

Alan R. Hevnerahevner@coba.usf.edu

Information Systems and Decision Sciences

College of Business Administration

University of South Florida

Tampa, FL

March 28, 2003 Computer Science 40th Anniversary Symposium 2

Outline

Research Portfolio Health Care Data Warehousing Future Directions

Bioterrorism Surveillance Systems Environmental Health Physician/Hospital/Procedure Volume Studies

March 28, 2003 Computer Science 40th Anniversary Symposium 3

Research Portfolio

National Institute for Software Testing and Productivity (NISTP) – DOD Funding

Computational Intelligence (CI) Testing Tools Graduate Courses on Software Testing

Telemedicine Quality Attributes – VA Partner Formal Methods for Network-Centric System Specification E-Commerce Software Development Collaborative Programming and Agile Methods Inspection Techniques for Graphical Models Design Science as a Research Paradigm in IS

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Health Care Data Warehouse Research

Co-Principal Investigators James Studnicki - COPH, USF Don Berndt and Alan Hevner - COBA, USF

Research Staff Center for Health Outcomes Research Staff COBA and COPH Doctoral and Masters Students

Collaboration and Funding U.S. Dept. of Commerce TOP Grant Florida Department of Health Bear Stearns Research Laboratory Florida Communities

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CATCH Data Warehouse

Utilizes over 400 health status indicators.

Fine-Grained and Transaction-OrientedData Warehouse Structures

AggregateData Warehouse

Structures

CATCHReport

Structures

VitalStatistics

HospitalDischarges

CancerRegistry

Demographics

Marketing Data

March 28, 2003 Computer Science 40th Anniversary Symposium 6

Data Dissemination Modes

Effective Presentation of Data Warehouse Information to Decision Makers

Data Dissemination Modes Ad-Hoc Queries and Data Browsing (SQL/QBE) Pre-Defined Report Generation (CATCH Reports) Desktop Data Warehousing (iCAP - MS Excel) Online Analytic Processing (OLAP) Geographic Information Systems (GIS) Web-Enabled Access

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CATCH Workflow

Data Staging Customized for state

data. Indicator Calculation Report Production

State-Specific Data Sources

CATCH DataCATCH DataWarehouseWarehouse

Fine-Grained and Transaction-OrientedData Warehouse Structures

AggregateData Warehouse

Structures

CATCHReport

Structures

National Data National Data SourcesSources

Sto

red

Pro

ce

du

res

OLAP Access

Pre-Defined CATCH Reports

March 28, 2003 Computer Science 40th Anniversary Symposium 8

Research Applications

Bioterrorism Surveillance Systems Environmental Health Impacts – EPA

Project Physician/Hospital/Procedure Volume

Studies

March 28, 2003 Computer Science 40th Anniversary Symposium 9

Bioterrorism Surveillance Systems

Sentinel Networks Throughout Florida Anticipate and prevent (if possible). Sense and provide early warning. React and minimize epidemiological impacts.

Surveillance System Infrastructure Networks connecting sensors and early data indicators. Historical data warehouses for pattern recognition. Intelligent agents to alert populace, disseminate reaction,

and eliminate threat.

March 28, 2003 Computer Science 40th Anniversary Symposium 10

Three Pillars of Threat Surveillance

Real-Time Data Collection Data with minimal lag time.

Data Warehousing & Data Mining Historical context, analysis, and pattern recognition

techniques for threat detection. Communications & Alert Networks

Timely dissemination to response groups.

“A surveillance system includes a functional capacity for data collection, analysis, and dissemination linked to public health programs.” [CDC]

March 28, 2003 Computer Science 40th Anniversary Symposium 11

CATCHHealth Care

Data Warehouse

Pattern RecognitionEngines

FlashData Warehouse

Threat AssessmentDashboards

Shared Metadata

Metadata

StatewideHealth CareDatabases

DW

Sta

gin

g

Fla

sh S

tagin

g

Real-TimeData

Surveillance System

ArchitectureResearch Challenges Flash / Real-Time Data

Warehouses Threat Detection via

Data Mining

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Data Mining & Pattern Recognition

Alarm thresholds are determined on the basis of historical pattern recognition. Does the current real time data constitute an abnormal

pattern? Historical health care data is maintained in a data

warehouse. Sophisticated browsing technologies and/or

intelligent data mining will help recognize when data streams trigger alarm thresholds for a threat.

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Environmental Health Project

Develop a series of indicators that can characterize community exposure to various environmental contaminants or hazards.

Use evolving EPA models of dispersion and exposure. Collect experiential and historical information.

Describe the socioeconomic and demographic characteristics of these communities of interest.

Investigate any association between exposure levels and variation in health status.

Tools include hierarchical linear models and spatial statistics.

All Cancer Mortality

Health Status IndicatorsMerged with EnvironmentalExposure Data

Total TRI Air Emissions

Source: EPA 1999 Toxic Release Inventory

Health Status IndicatorsMerged with EnvironmentalExposure Data

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Clinical Volume Research

Web-based access to volume data on: Procedures Physicians Hospitals

Relationship of volume to positive patient outcomes Applications

Healthcare Organizations Consumers

Compelling research results Paper under review at Peer Refereed Journal

Extract of Research Findings

© 2002 Studnicki et al. Submitted for journal publication. Do not copy without permission of authors.

Table 3. For Selected Surgical Procedures in Florida (1998), Physician Volume Distribution Characteristics

ProceduresTotal

ProceduresTotal

Physicians

Mean Procedures/Physician

Median Procedures/Physician

% Single Procedure Physicians

Maximum Volume

Mean Procedures/Physician

% Total Procedures

Mean Procedures/Physician

% Total Procedures

Operations of Cardiovascular SystemCoronary artery bypass graft 25124 384 65.4 20 37.0% 421 2.2 1.7% 235.0 35.5%Removal coronary artery obstruction 39986 906 44.1 9 29.8% 574 2.0 2.3% 196.1 44.1%Open heart surgery 7052 317 22.2 11 31.2% 210 2.7 6.1% 92.0 40.4%Insertion, removal, replacement, repair of pacemaker leads and devices 14569 1124 13.0 3 36.1% 204 1.4 5.3% 65.7 50.5%Puncture of vessel 21549 4712 4.6 2 46.4% 121 1.1 11.7% 23.1 50.4%Cardiac catherization 48956 1350 36.3 14 31.6% 405 2.4 3.4% 153.6 42.0%

All Physicians with at Least One ProcedureBy Surgical Volume

Lowest 50% PhysiciansBy Surgical Volume

Highest 10% Physicians

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Application Directions

Hospital Use Risk Management

Retrospectively analyze patterns of low hospital/MD combinations Identify variation in volume related processes of care (e.g., based on

payer source) “Real time” risk assessment at scheduling Develop guidelines/algorithms

Referral patterns Privileging considerations

Consumer Use Selection of Doctors and Hospitals Education

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Future Research Directions

Health care data quality at source Motivation for reporting timely and accurate data Data collection audits

Use of health care data in communities National Standards for community health assessments (very limited data

sets required) How are data used to make health care decisions? Different stakeholders use of data

Ability to inform national debate on health care issues Quality of Care to poor, disadvantaged, minorities Comparison of care under different health care programs (Managed Care

Programs)