Using Baldrige criteria for self-assessment.
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Awards and Prizes↗
Biomedical subjects
V R Prybutok
Publications and source records attributed to V R Prybutok.
Transforming a health care information management system.
The article presents results from a survey of 98 top executives at Baylor Health Care System (BHCS), a large, multifunction health care organization in Dallas, Texas. The survey assessed the executives' perceptions of current BHCS quality practices using the first survey developed for the health care industry based on the Malcolm Baldrige National Quality Award (MBNQA) criteria. Findings regarding the quality of BHCS internal and external data and information include the need for a $50 million information system transformation to achieve seven critical success factors for all business units and improved internal and external data and information for the business process redesign and quality transformation. Results highlight the need for further research investigating the information and analysis MBNQA criteria.
Attitude of Health Personnel↗
A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area.
Prediction of ambient ozone concentrations in urban areas would allow evaluation of such factors as compliance and noncompliance with EPA requirements. Though ozone prediction models exist, there is still a need for more accurate models. Development of these models is difficult because the meteorological variables and photochemical reactions involved in ozone formation are complex. In this study, we developed a neural network model for forecasting daily maximum ozone levels. We then compared the neural network's performance with those of two traditional statistical models, regression, and Box-Jenkins ARIMA. The neural network model for forecasting daily maximum ozone levels is different from the two statistical models because it employs a pattern recognition approach. Such an approach does not require specification of the structural form of the model. The results show that the neural network model is superior to the regression and Box-Jenkins ARIMA models we tested.
Journal Article↗
The role of multiple regression and exploratory data analysis in the development of leukemia incidence risk models for comparison of radionuclide air stack emissions from nuclear and coal power industries.
Risks associated with power generation must be identified to make intelligent choices between alternate power technologies. Radionuclide air stack emissions for a single coal plant and a single nuclear plant are used to compute the single plant leukemia incidence risk and total industry leukemia incidence risk. Leukemia incidence is the response variable as a function of radionuclide bone dose for the six proposed dose response curves considered. During normal operation a coal plant has higher radionuclide emissions than a nuclear plant and the coal industry has a higher leukaemia incidence risk than the nuclear industry, unless a nuclear accident occurs. Variation of nuclear accident size allows quantification of the impact of accidents on the total industry leukemia incidence risk comparison. The leukemia incidence risk is quantified as the number of accidents of a given size for the nuclear industry leukemia incidence risk to equal the coal industry leukemia incidence risk. The general linear model is used to develop equations that relate the accident frequency required for equal industry risks to the magnitude of the nuclear emission. Exploratory data analysis revealed that the relationship between the natural log of accident number versus the natural log of accident size is linear.
Journal Article↗