The Data-Driven Policy Analysis Framework as a Template for Healthcare Policy Analysis

The purpose of this abstract is to illustrate how the Data-Driven Policy making Model can be used as a template and guide in analyzing and evaluating healthcare policy. The objectives include: 1) Discuss the Data-Driven Policymaking Model, 2) Analyze the connection of evidence-based practice and healthcare policy analysis and 3) Evaluate the utilization of data-driven policymaking and the development of advocacy plans to revise and develop healthcare policy. The Data-Driven Policymaking Model consists of four stages: Stage One – Define the definitions and priorities of the healthcare policy analysis. Identify the healthcare policy problem. Stage Two – Examine available data to aid in policy development. Identify data matrixes and sources. Stage Three – Analyze, clarify, and disseminate the data. Utilize descriptive statistics for quantitative data. Outline discourse analysis principles for qualitative data from legislative session minutes, bills, and public forums. Stage Four – The action phase where policy options supported by data are explored and advanced. Data-driven policymaking findings can be outlined in contingency tables with frequencies, percentages, and risk indices to provide the context of healthcare policy analysis. Qualitative data can be analyzed using discourse analysis and presented in timelines and thematic grouping into categories. Theme analysis can identify effective strategies to be utilized in advocacy plans. Thus, the data can drive policy making inclusive of revisions and development. The healthcare policy analysis should result in an evidence-based white paper outlining an effective advocacy plan. Data supports policy development, reformulation, alternatives or termination.