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Research Analytics

• Data capture.  Import needed data and display.

• Clean data.  Examine data.  Drop incomplete data.  Name data as it will be used for the project.  Fix fixable data.  Plot data iteratively to understand details.

• Prepare data.  Add variables for grouping and filtering.  Add medians and accumulators. continue to plot data iteratively.  

• Analyze data.  Produce plots of the data by factors deemed most important, overall and by specific factors.  

• Prepare secondary analyses.  From prior analyses, refine likely causal factors and more focused analyses.

• Select predictive analytics.  Develop predictive models for the processes.  This may involve models using linear and nonlinear mathematics and elements of machine learning and artificial intelligence.

Representative Clients

•Health Care Finance Administration. Statewide demonstration of DRGs in Georgia prior to DRG implementation nationally.

•Bureau of Health Professions. A Review and Evaluation of Nursing Productivity.

•Health Resources and Services Administration. Use of process measures for long-term care facility certification in Illinois.

•National School Lunch Program. Use of linear programming to plan nutritious and cost-effective diets for school lunches in the USA.

•Department of Health and Human Services. National immunization survey for Secretary Joseph Califano.

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