Algorithms for Health Care Quality Management and Outcomes Assessment

Award Information
Agency: Department of Commerce
Branch: N/A
Contract: N/A
Agency Tracking Number: 37769
Amount: $199,133.00
Phase: Phase II
Program: SBIR
Awards Year: 1998
Solicitation Year: N/A
Solicitation Topic Code: N/A
Solicitation Number: N/A
Small Business Information
3046A Berkmar Drive, Charlottesville, VA, 22901
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 B. Eugene Parker
 () -
Business Contact
Phone: (804) 973-1215
Research Institution
American health care is in the midst of momentous change. Quality improvement principles are being used increasingly to enhance productivity and efficiency of health care delivery and to help contain costs. In producing patient outcomes and resource utilization measures, it is vital to control appropriately for case mix (i.e., differences in patients due, e.g., to illness severity) in the predictive model. If the severity of illness among patients can be accounted for accurately, differences in patient outcomes--mortality, resource utilization, morbidity, and patient function (both at discharge and over the long term)--will reflect, in large measure, differences in the quality-of-care received. With an accurate patient outcomes model, trained and validated using high-integrity hospital patient records, control for illness severity is explicit: the predictive model captures the relationship between observables and outcomes. Quality outcomes differing significantly from the expected values, as predicted by the model, reflect differences in quality of care. Static and dynamic polynomial neural networks (PNNs) will be used to synthesize accurate prediction of patient outcomes based on the most cost-effective patient observables. For categorical variables, PNNs are trained using a minimum logistic-loss fitting criterion; for continuous variables, a minimum squared- error criterion is used.

* Information listed above is at the time of submission. *

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