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

Research Article

Ann Nurs Res Pract. 2016; 1(1): 1005.

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

Jones J¹*, Lee D¹ and Bayhi L²

¹Department of Nursing, Southeastern Louisiana University, USA

²Carpenter Health network, Baton Rouge, Louisiana, USA

*Corresponding author: Janet Jones, Department of Nursing, Southeastern Louisiana University, SLU 10835 Hammond, Louisiana 70402, USA

Received: June 03, 2016; Accepted: July 20, 2016; Published: July 22, 2016


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:

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.

Keywords: Data-driven policy-making model; Data-driven decision making; Policy analysis; Healthcare policy analysis


AHRQ: Agency for Healthcare Research And Quality; CDC: Centers for Disease Control And Prevention; HIFA: Health Insurance Flexibility and Accountability; US: United States; WHO: World Health Organization


Healthcare policy analysis is often necessary to clarify practice, evaluate complex publically or privately supported programs, determine the distribution of scarce resources, and propose future policy formulation. Often such policy analysis is conducted by an inter-professional team. A policy analysis may be conducted by a variety of healthcare providers, policymakers, planners, or analysts [1].

Policy analysis is a reproducible systematic description and explanation of the causes and consequences of political action or inaction [2]. In addition policy analysis assists healthcare providers to be more effective change agents and advocates [3]. Stakeholders can be identified and partnerships formed to enhance lobbying efforts. The stakeholders are individuals that have an interest in the issue for a variety of reasons [4].

Emphasis has been placed on the incorporation of evidencebased practice which integrates research findings into the clinical setting. A similar practice has been emphasized by using data and policy analysis to understand policy failures and successes, as well as future policy implementation. The Data-driven Policy Model involves explicit priorities and guiding questions to conduct a policy analysis. Data is used to support the process of developing policy options for numerous situations.

Despite the potential impact of policy analysis, limited education and training is devoted to this topic. Multiple models, frameworks, and theories have been used to guide policy analyses. Policy analysis is complex. The analyst must have an understanding of the content, the context in which the policy was developed, the stakeholders, and intended/unintended consequences. A review of legislative policy, rules, and regulations can be overwhelming due to the enormity of information. A framework or theory adds credibility to the policy analysis and guides the analyst through the maze of related information.

Walt et al [1] reviewed various frameworks and theories used in policy analysis with recommendations for advancing the field of policy analysis. The authors claim that one of the oldest and best known frameworks is the stages heuristic. In 1956 Lasswell (as cited by 1, p310) presented the four stages of heuristic as agenda setting, formulation, implementation, and evaluation. Agenda setting is essentially defining the issue of interest. In the formulation stage legislation, rules and regulations are developed. Once formulated the policies are implemented and evaluated for effectiveness. As with many policy process frameworks the heuristic stages have been criticized for being too linear.

Historical Literature Review

The policy triangle frameworks were proposed for healthcare policy analysis [2]. These frameworks incorporate a political economy perspective. Subsequently this framework has been used to analyze a variety of policies in different countries. Currently economics and scarce resources are often considered in many policy analysis frameworks.

Network frameworks have been used to evaluate policies that involve multiple organizations or systems. The emphasis is on the inter-connections between the groups. This aspect is crucial to evaluating the effectiveness of global health initiatives. Often non-governmental and governmental organizations team together based on their common values and shared resources. The network framework analyses the shared decision-making and exchange of resources to achieve these goals.

In 1984 Kingdon proposed the multiple streams theory (as cited by 2, p311). This theory argues that the public policy process has random characteristics. Problems, policies, and politics flow in independent streams. Laraway and Jennings [5] used Kingdon’s theory to describe the Health Insurance Flexibility and Accountability (HIFA) Initiative. The initiative’s goal was to assist states in expanding and increasing healthcare access to low-income individuals. At the time of initiation, the economy was strong. In addition a large number of uninsured individuals had not been affected by September 1, 2001. So a variety of events occurred in different streams but contributed to the failure of this initiative.

Baumgartner and Jones [6] introduced the punctuated equilibrium theory. Policy-making is characterized by periods of stability in which minimal or small incremental changes are made. Periodically disruption occurs resulting in bursts of rapid and transformational change. Thus the policy process is cyclical in nature.

Walt et al [2] describe multiple implementation theories. These theories focus on the top-down nature of policy development versus bottom-up policy implementation. Most policies are formulated in a top-down manner based on the decision-making of legislators that are far removed from the issue. Often the policy implementation is based on the bottom-up nature by having the grass roots people that are directly involved with the issue performing the implementation components. This disconnect is hypothesized as being the reason for incongruence between policy development, implementation, and outcomes.

Lessa et al [7] examined policy analyses published from 2008 through 2013. These investigators found that the policy cycle or policy process theory was the most common used for analyzing policy. The policy development process is viewed more as a political activity not necessarily a scientific process [8]. This theory suggests that advocates should match their strategies with the policy process stage. Consequently the probability of the advocated policy reform will be adopted.

Ryder [8] describes eight stages within the policy process model. These stages are not necessarily linear in nature. Two different stages can be in progress simultaneously. At times the stages are difficult to differentiate. The stages are 1) Agenda setting – the action to be taken, 2) Issue filtration/definition – essentially the government decides what should be done, 3) Definitions - clarifying the problem and opportunities, 4) Forecasting - consideration of how the situation will develop with policy options, 5) Options analysis – analysis of costs, benefits, and why the policy should be adopted or not adopted, 6) Objective setting – policy objectives as viewed by different stakeholders, 7) Monitoring –measuring the outcomes, and 8) Policy maintenance – decisions to continue with the policy, reformulate the policy, or terminate the policy. The purpose of a policy analysis using this framework is to analyze the policy retrospectively and prospectively. In retrospect the means in which the policy succeeded or failed could influence policy reformulation. Prospectively the analyst determines what the policy is currently and what it should be.

In a recent literature review conducted in 2016 by the authors, social policy analysis, critical analysis, exploratory analysis, stakeholder analysis and comparative analysis seem to be the predominant frameworks. Social policy analysis analyzes the connection between ethical aspirations of eliminating complex social problems with human rights, justice, and utility [9]. Social policy analysis looks at core principles such as safety, trustworthiness, outcomes, transparency, collaboration, peer support, empowerment, and choice [10]. Social policy analysis consists of quantitative and qualitative data. The emphasis is the impact on people rather than the means in which to implement the policy. Social Policy methods address program outcomes, impact evaluation, cost-benefit analysis, and an assessment of political influences on implementation. The social determinants of health drive public health policy to promote health through policy implementation.

The critical analysis was described as having four components: 1) colleagueship, 2) evidence based analysis, 3) policy development, and 4) final analysis [11]. Exploratory analysis evaluates characteristics or factors affecting behaviors to inform policy [11,12]. Often it is more descriptive in nature. A stakeholder analysis focuses on the motivations, information, and power of key stakeholders [12]. This type of analysis is composed primarily of qualitative data obtained through interviews. Common stakeholders include payers, professional associations, individual healthcare providers, manufacturers, legislators, and patients.

Ritter et al. [13] reviewed a variety of comparative analyses. These analyses seek to determine the extent that a policy has produced the desired effects. Often cross-national or cross-state comparisons can answer this question. Comparative analyses lack uniformity. Only two criteria were identified. First two or more geographic locations are utilized. Secondly the study focuses on a policy. Descriptive epidemiology or a single state or country analysis are not considered to be a comparative analysis. Quantitative and qualitative data are included. The analysis looks at the policy as proposed and compared to the policy implementation.

Currently more authors [14,15] are appealing for inclusion of qualitative data in policy analyses. Qualitative methods include the public’s voice and experiences with healthcare services, research, and policy-making. Often the affected people are not included in policy analysis. The debate of the strengths and limitations of qualitative data is often interjected. Yet secondary analysis of qualitative data can be an effective means to inform policy decisionmaking. Unfortunately qualitative research can be time-consuming and expensive if trying to conduct in-depth interviews with diverse samples [14]. Consequently secondary analysis can be more efficient. Such information can enlighten policy makers on topics like access, waiting times, communication of information, dignity and respect in regards to different health conditions or social groups.

Discourse analysis can be used as a secondary analysis tool to obtain qualitative data. Discourse is a group of ideas or thinking that emerges through textual and verbal communication [16]. By reviewing the legislation, regulations, rules, public forums, and debates; the dominant meanings, assumptions, words, and ideologies can emerge. The public perception of health matters can be demonstrated. Discourse analysis is less used in healthcare policy analysis than in other areas. A timeline or topic map can be developed to identify recurrent themes, language use, ambiguous terms, and stakeholder representation. The discourse is processed, categorized, and coded into semantic networks, themes, or indexes of topic occurrences. Silence is just as important. Who is not speaking can be a glaring message.

There is an array of models, frameworks, and theories to guide policy analysis. They are not necessarily contradictory. Most frameworks focus on specific aspects of policy decision-making. These aspects include social health determinants, the policy process, stakeholder views, the public’s voice, and comparative effectiveness. Some of the frameworks and theories have similar components. Most of them include a question, aim, or goal. The analyses center on policy formulation or development; as well as implementation and evaluation. Unfortunately they are often ambiguous. Little direction is given on how to organize or analyze the vast information encountered. Certainly the analyst can get lost in the myriad of documents, forums, debates and vital statistics. Sonier [17] said that if data is present it will be used in policy-making. However if data is not present, policy-making will move forward without the facts.

Materials and Methods

The Data-Driven Policy-Making Model is more explicit in describing how to conduct a policy analysis. The model incorporates many aspects of the previously discussed frameworks and theories. In 2003 the Agency for Healthcare Research and Quality (AHRQ) presented the model to assist in the data-driven decision-making of four states that were analyzing the impact of their Health Care Safety Nets. Figure 1 Developing Data-Driven Capabilities to Support Policymaking outlines the four stages of the model. These stages are: 1) Definitions and priorities, 2) Data, 3) Assessment, and 4) Action.