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Policy decisions may be more likely to represent individual identified by the policy maker's goals rather than societal utility and are more likely to be supported than a policy decision presented as being a rational, societal utility-maximizing choice. Policy decisions related to decision maker's experience linked to individual memories stored in cognition are more likely to be supported than those that are abstract or remote to the decision maker's experiences. Thus, the above propositions suggest that the manner in which policy questions are framed and policy maker experience will influence decision utility assessments and subsequent choices regarding health policies.

With respect to decision making, the influence of affect on individuals has been shown to influence the manner in which individuals perceive situations, the motivation of decision behaviors, the degree of decision risk tolerance, and the level and type of information recall people exhibit [ 6 , 76 - 80 ].

Research has identified both state and trait sources of affect [ 81 - 83 ]. State affect is the transient, short-term mood, while trait affect typically referred to as positive and negative affect is the more global overall mood that tends to be stable over time [ 82 ]. Individuals high in positive affect tend to reflect enthusiasm, alertness, and a positive outlook on life, while individuals high in negative affect tend to experience dissatisfaction and distress and have a poor outlook on life [ 69 , 73 , 82 - 84 ].

State influences are generally less reliable, stable, and predictable than trait influences; thus, they are more resistant to decision-making process improvements [ 81 , 83 ]. While much research into affect and cognition focuses on the influence of induced transitory mood state , we focus here on the long-term effect of one's trait affect on cognitive processing due to the more stable and predictable nature of trait affect [ 83 , 84 ]. The focus on trait affect in behavior and cognitive processing is critical, given that affect has been shown to play a dominant role in both decision making and organizational outcomes [ 68 , 81 , 84 - 86 ].

Trait affect research identifies both positive and negative affect as influences on cognitive processing and decision-making behavior [ 69 , 81 - 84 ]. Individuals with high positive affect are more likely to perceive risky situations as being more certain and are less likely to believe that risky decisions will create negative personal outcomes than negative affect persons. Other studies measuring perceptions of an organization's strategic business environment found high negative affect individuals were more likely to have poor perceptions of the organization's performance, potential industry growth, and industry complexity [ 73 , 87 , 88 ].

Similar results with negative affect individuals have been found with perceptions of job performance and work attitudes [ 69 , 83 , 85 ]. The affect literature supports the conclusion that trait affect is a robust phenomenon that influences the decision-making process. Social information processing models postulate that affect-related concepts are stored in permanent memory bins in much the same fashion as knowledge and experiences [ 10 ].

Affect is labeled and stored as specific representations, such as happy, angry, or sad. These emotions can be labeled in permanent memory as independent feelings or as associations with previous events and experiences. If a goal-directed information process is triggered by affect, it is highly probable that a different memory process will occur than a goal-directed process with no affect. Affect is an important component of deliberative information processing and is likely a key influence in complex cognitive tasks such as deliberative decision making [ 63 , 88 - 90 ].

In general, positive affect has been shown to trigger quicker, more flexible, and more efficient processing strategies. Conversely, negative affect tends to trigger slower, more systematic, and more analytical processing strategies [ 6 , 77 , 79 , 88 - 92 ]. In addition, personal importance mediates the affect-cognitive processing relationship during decision making when greater personal importance encourages decision makers to utilize self-serving judgment strategies [ 93 ].

For example, individuals with high levels of negative affect are more prone to make biased choices when decisions were personally relevant [ 91 ].

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While affect and policy decision making has not been extensively studied, based on the strength of the evidence supporting affect as an influence on cognitive processing, the following exploratory propositions are presented:. Those high in positive affect are more likely to support policies with high risk and high uncertainty, while those high in negative affect are more likely to support policies with minimal risk and minimal uncertainty. Affect can and does serve as a subjective force on policy makers during the health policy decision process. The final area of influence included in the cognitive information processing framework is heuristics.

Cognitive processing research has found that one's repetitive use of specific procedures and knowledge results in automatic ways to process information [ 64 - 66 ]. In complex decision situations, this automatic processing becomes a dominant force in information processing and results in cognitive shortcutting tactics. This behavior has major implications for the rationality assumptions of EBDM. Heuristics are cognitive processes where full information processing requirements are bypassed and mental shortcutting occurs [ 66 , 71 , 73 , 94 ]. Heuristics are mental 'rules of thumb' that make decisions easier by reducing the complexity of information processing.

They operate through the use of categorization to interpret information. New information is categorized based on familiar knowledge drawn from memory bins and results in more automatic processing than would normally be required [ 10 ]. Although there are many different heuristics, they are categorized based on the similarity of types of cognitive processing being utilized [ 66 ]. The three main categories of heuristics include availability, representativeness, and anchoring and adjustment [ 10 , 66 ]. The availability heuristic is the tendency for a decision maker to assess the frequency, probability, or likely cause of an event based on similar occurrences readily accessible in one's memory bins.

Availability exerts a strong influence when the event evokes vivid emotions and is easily recalled [ 66 ]. Many media reports tend to exhibit a certain degree of sensationalism or priming that helps foster an availability heuristic [ 95 ]. For example, a health policy decision regarding the distribution, labeling, and storage restrictions of lethal drugs in hospitals will likely be strongly influenced if the media has recently presented a story about recent deaths that have occurred in emergency rooms from a mix-up between sodium chloride and potassium chloride. This example highlights the observation that decision makers spend considerable time and energy on a policy decision when linked to recent dramatic events profiled in the media [ 2 , 3 , 5 ].

While serious drug interactions or mix-ups are a rare occurrence, many media stories about healthcare system efficacy include a dramatic, emotional component that can easily trigger an availability heuristic in related decision situations. In doing this, statistical probabilities are erroneously discounting [ 66 ]. For example, a policy maker may decide in favor of a health policy supporting mandatory immunizations for meningitis based on the successful implementation of other childhood immunization policies that have helped minimize the spread of contagious diseases among children e.

The policy maker may then fail to account for the risk factors associated with contracting meningitis, which are statistically less probable than risks associated with contracting other contagious diseases such as measles [ 96 ]. Using the representativeness heuristic, the policy maker's decision is influenced by a simplistic cognitive shortcut that fails to consider relevant and potentially critical evidence.

Finally, the third heuristic, anchoring and adjustment, involves a decision maker's utilization of a personally relevant initial value derived from memory as an initial determination point about the value of a decision assessment [ 66 ]. Subsequent assessment of each decision option's value is adjusted based on the initial anchor point that the decision maker identified.

For example, a policy maker determines amounts of financial support for a regional health authority using the previous budget to set the current financial budget irrespective of need, extenuating circumstances, or technological requirements. This results in potentially irrelevant data being used to determine the value and outcome of a key decision alternative, such as future budgeting and healthcare resource spending.

The utilization of heuristics in decision making has been shown to be a robust source of influence in the assessment and judgment of decision options, such as the likelihood of contracting a disease, identifying probabilities of accidental fatalities, information identification, and pharmaceutical risk [ 66 , 71 , 73 , 75 ]. This linkage of decision-making heuristics to experiences during cognitive information processing supports the following proposition:.

Policy makers who are presented with cognitively difficult policy information and who have available in their memory a relevant heuristic will utilize that specific cognitive shortcut to support the presented policy, while those individuals who do not have an available relevant cognitive heuristic will be less likely to use a heuristic in support of the presented policy.

The purpose of discussing information processing is to comprehend how incoming information and cognitive shortcutting are common occurrences that simplify cognitive processing demands [ 9 , 10 , 32 , 44 , 48 , 64 , 73 ]. Given the complexity of most nations' health system challenges, cognitive shortcutting by policy makers is to be expected. However, one must be mindful that cognitive shortcuts do not ensure that the final decision best resolves a problem, and cognitive shortcutting fails to follow the expectations of EBDM [ 66 ].

Evidence-based health policy can alter the manner in which healthcare policy is presently administered, and its growing prominence in many healthcare systems warrants examination. However, the policy process, irrespective of the nation or health system, is not a linear, rational model in which an idealized solution for a public problem can be ascertained and optimally implemented [ 13 , 19 , 30 ]. In this era of increasing prevalence of EBDM, the rationality assumptions in EBDM must be challenged to ensure effective policy decision making and high quality care for all citizens.

This paper has argued that cognitive information processing is fraught with many opportunities for subtle factors to influence policy makers' assessment of decision options. These factors are then likely to influence the resulting policy decision in a manner that is inconsistent with many of the evidence-processing expectations of EBDM. Given consideration of the complexity of cognitive information processing and the role of individual goals in how information is being processed, it is not surprising that health policy makers would readily adopt cognitive processes that simplify decisions.

Even when policy makers do not make decisions in isolation, individual subjectivity and potential biases enter the group decision process, thus influencing the outcomes. The multi-billion dollar question is how can cognitive information processing be improved in order to ultimately lead to better health policy decisions?

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The information presented and the propositions presented highlight weaknesses in the decision-making process. Many organizations and agencies have policy enhancement strategies already in place [ 13 ], so the comments here are directed towards two overarching components of EBDM and, ideally, will aid in improving current decision-making practices. The first component, what is the nature of the evidence being created by researchers to be utilized in EDBM, and the second component, what practices can foster better decision making on the part of the policy makers:.

Within the first component, an initial challenge arises around the manner in which health services research is conducted.

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As healthcare is a multi-sector industry, it draws health services researchers from a wide variety of health and social science disciplines e. Deriving from these various epistemologies, research is theorized, conducted, analyzed, and evaluated using many different methods [ 97 , 98 ].

As a result, studies, methods, and subsequent findings may or may not be accepted as valid based upon one's philosophical and theoretical orientation regarding science [ 97 , 99 ]. This compounds the dilemma of defining evidence and identifying superior evidence to be used in EBDM [ 13 ]. Evidence, as we know, is a major element of EBM the precursor to EBDM , and the hierarchical evidence spectrum argued by Sackett and others highlight Randomized controlled trials RCTs and meta-analyses as the gold standard of evidence [ ].

This EBM foundation privileges positivist science and diminishes research conducted outside the empirical, quantitative perspective to being of lesser value, an unfair and unfounded position. The outcomes of this imperative academic exercise should see health services researchers embrace various research methods and the validity of findings across the research spectrum, thereby minimizing some of the existing confusion surrounding the question of what is good evidence and what evidence should be used. Continuing within the first component, the second challenge derives directly from the first--translating research findings into evidence that is amenable to the end-users.

In this call, we define the end-users of health services research to be decision makers, managers, politicians and others rather than the practitioners who utilize research for clinical practice from such sources as the Cochrane Collaboration [ 13 ]. Many researchers have highlighted the myriad of difficulties translating health services research into information readily understood and useable by the health services community [ 13 , , ]. As such, it becomes vital that health services researchers pursue improvements in how they prepare and report research for the end-users, including actions such as:.

This will aid in articulating the context of the research, identifying the relationship and purpose of the research to key stakeholders, and explicate how the findings can translate into meaningful policy achievements. These actions should then serve to create a mutually beneficial relationship with both parties having an investment in seeing the research findings utilized. Preparing research findings for dissemination with sensitivity to language, inferences, and assumptions typically found in academic writings.

Expecting end-users to have a full comprehension of 'research speak' sets up the dissemination mode for ineffective translation as certainly as would it be if health services researchers were expected to have full comprehension of the language, jargon, and acronyms commonly used in 'med speak'. The ability to ensure data, findings, and reports are expressed in commonly used language will aid decision makers to use the available evidence. Additionally, this may help alleviate situations in which decision makers are attempting to utilize evidence with conflicting information and conclusions.

Within the second component, fostering improved decision making, the next challenge is finding a balance between individual utility assessments and stakeholder utilities. To improve decision making, there are a number of suggestions and improvements to pursue including:. Given that policy making does not occur in isolation, it is important to identify the components of the network that are relevant and require consideration e.

Within that, coordination of information gathering and clarification of policy objectives that articulate the goals and objectives of the various stakeholders will help to define the utility objectives of a given policy. Using this information, policy direction can then be orientated to achieve the desired outcomes for the various stakeholders. Assessment of the policy alternatives by stakeholder groups with diverse interests and objectives. Independent reviews will assist with critical review of government policy and help to promote policy that best meets public needs and maximizes the utility of broader stakeholder groups.

Policy implementation and subsequent outcomes require in-depth scrutiny and evaluation to ensure the policy is meeting its initial objectives. While 'policy evaluation' modes are often found in many policy models, the consistency of evaluation and response to such evaluations are often cursory and, many times, ineffective [ 13 , 19 , 25 ].

Involving stakeholders to become part of the policy creation process naturally leads to their participation in the evaluation process. Having this added element will help to ensure that thorough evaluation does occur, reflects the outcomes attained, and maximizes stakeholder utility. Continuing within the second component improved decision making , another challenge involves the actual decision-making process when groups are involved [ 13 , 19 , 25 , ]. Group decision making has its own limitations see Bazerman, , for in-depth discussion and decision processes need to be balanced with effective group decision making tools [ 58 , ].

Decision-making processes within groups often involve either a process of inquiry collaborative problem solving or a process of advocacy a function of persuasion and opinion influencing. Clearly identifying the nature of the policy decision will help direct the roles of the participants toward seeking ideas and solutions versus efforts to polarize the group toward one or two outcomes. Specific goals and direction must be spelled out to the involved group s in order to ensure the decision process, whether problem solving or persuasion, fulfills the overarching policy objectives [ ].

Utilizing structured group decision-making processes will assist in minimizing the common traps of group decisions, such as non-rational escalation of commitment and the groupthink phenomenon [ 58 , 96 , ]. For example, establishing a set time for problem identification, solutions, and discussion, utilizing actions to combat the groupthink, such as designating specific individuals to function as 'devil's advocate', encouraging dissent and debate to optimize productivity, identifying and curtailing pressure for conformity, and recognizing the political vulnerabilities with the group s.

Controlling the structure of the group and the individuals who comprise the decision-making body will help ensure diversity of utility, needs, experience, knowledge, skills, and abilities. Diverse groups are known to be more creative in their decision processes as a result of their diversity and tend to attain more creative solutions to issues being addressed [ 59 , 66 ].

Therefore, advocates of various positions and backgrounds can be appointed in order to ensure a multitude of perspectives are brought into the policy-making decision process. This will also help to balance out the challenge of overcoming the influence of individual affect. Decision processes involving numerous people are more likely to strike a balance among affect states, thereby minimizing a dominant affect influence and balance risk taking.

The final strategy to counter factors that impede optimal policy decision making, such as satisficing and heuristic use, links back to point two translating research findings into evidence that is amenable to the end-users and the way in which research evidence is compiled for end-users. To utilize evidence and minimize cognitive shortcutting, the following steps will be useful:. As noted, health services research, aggregated across studies and translated into reliable and valid findings, is a key to evidence-based decisions. This information needs to be readily available to decision makers in the policy formulation process.

Availability of translatable data would expand the individual experience factor and become part of the information basis that influence decision making. The three heuristics discussed were availability, representativeness, and anchoring and adjustment. Policy research papers and briefs should recognize these heuristics and focus on summaries that increase availability of relevant information, articulate data that clarifies best practice of similar problems and issues, and provide data on relevant anchors, baseline, and tracked performance indicators such as the scorecards used by many agencies and organizations.

Finally, organizational commitment to educating and training key decision makers in decision-making processes will help provide the foundation and knowledge to assist individuals in recognizing when heuristics are being used and providing the opportunity for intervention if the heuristics are detrimental to the policy decision. Training key individuals in decision-making skills is as valuable to policy making as teaching negotiation skills is to those who participate in workplace negotiation, union contracts, and conflict resolution.

All of the above suggestions were made to encourage and support the discussion of alternatives to improve the health policy decision process and, ultimately, the delivery of health services across the globe. Increased recognition of the inherent biases and individual decision-making flaws is a first step of aligning policy goals with decision utilities. Additional alignment may be achieved by dedicated efforts to improve the cognitive information process and the information available to policy makers. In presenting our cognitive information-processing framework, we contribute to the health policy decision literature by developing a framework that captures a wide variety of factors influencing decision-making situations.

Furthermore, we argue that these considerations are globally relevant and that a comprehensive understanding of the mechanisms of cognitive processing aids decision makers in developing awareness of how they process relevant decision information and how they may be subtly influenced while discounting actual evidence. In addition, empirical studies could be designed to test the degree to which these issues impact health policy decision in various settings. Identifying and better understanding these influences will empower both health policy makers and managers to enhance their decision-making.

The mechanics of decision making and how individual cognitively process information when evaluating decision alternatives must become explicit knowledge that is utilized to aid the EBDM goals of policy makers. We posit that a greater awareness of the reasons behind policy makers' actions will promote better and more informed decisions. DM conceived and drafted the original manuscript.

Both authors DM and NSB contributed in further drafting of the manuscript for publication, and both authors have participated in revisions for intellectual content. DM was responsible for all formatting, literature updating, responding to reviewers, and the submission process. DM and NSB have given final approval of the version of the manuscript to be submitted.

An earlier draft of this paper was presented at the Academy of Management's annual meeting, August , and was published in the Best Paper Proceedings of the Academy of Management Meeting. The authors gratefully thank the anonymous reviewers at the Academy of Management, Gwen McGhan, Diane Brannon, and Tom Knarr for their helpful comments and suggestions on earlier drafts of this manuscript. National Center for Biotechnology Information , U. Journal List Implement Sci v. Published online May Nealia S Bruning 2 I. Received Sep 25; Accepted May This article has been cited by other articles in PMC.

Abstract Background Current healthcare systems have extended the evidence-based medicine EBM approach to health policy and delivery decisions, such as access-to-care, healthcare funding and health program continuance, through attempts to integrate valid and reliable evidence into the decision making process. Discussion We contend that health policy decision makers are generally unable to attain the basic goals of evidence-based decision making EBDM and evidence-based policy making EBPM because humans make decisions with their naturally limited, faulty, and biased decision-making processes.

Summary In this era of increasing adoption of evidence-based healthcare models, the rational choice, utility maximizing assumptions in EBDM and EBPM, must be critically evaluated to ensure effective and high-quality health policy decisions. Background High expenditures in healthcare have stimulated healthcare policy makers to explore more effective and efficient healthcare delivery options. Open in a separate window. The challenges of rational choice Numerous healthcare systems exist globally, yet many of the same factors influence the direction of health policy regardless of national boundaries.

Cognitive information processing framework Social information processing models view cognitive processing as occurring in two stages [ 9 , 10 , 63 - 65 ]. Cognitively generated decision-making influences Research into cognitive processing has identified three major sources of influence on how information is processed and evaluated: Decision maker utility Many policy theorists call for policy making to focus more on understanding the decision process rather than on making decisions that seek maximization of societal utility [ 30 , 31 , 54 ].

The evidence supporting utility as a subjective factor and its amenability to manipulation leads to the following proposition: Affect With respect to decision making, the influence of affect on individuals has been shown to influence the manner in which individuals perceive situations, the motivation of decision behaviors, the degree of decision risk tolerance, and the level and type of information recall people exhibit [ 6 , 76 - 80 ].

While affect and policy decision making has not been extensively studied, based on the strength of the evidence supporting affect as an influence on cognitive processing, the following exploratory propositions are presented: Heuristics The final area of influence included in the cognitive information processing framework is heuristics.

This linkage of decision-making heuristics to experiences during cognitive information processing supports the following proposition: Conclusions Evidence-based health policy can alter the manner in which healthcare policy is presently administered, and its growing prominence in many healthcare systems warrants examination.

The first component, what is the nature of the evidence being created by researchers to be utilized in EDBM, and the second component, what practices can foster better decision making on the part of the policy makers: As such, it becomes vital that health services researchers pursue improvements in how they prepare and report research for the end-users, including actions such as: To improve decision making, there are a number of suggestions and improvements to pursue including: To utilize evidence and minimize cognitive shortcutting, the following steps will be useful: Competing interests The authors declare that they have no competing interests.

Authors' contributions DM conceived and drafted the original manuscript. Acknowledgements An earlier draft of this paper was presented at the Academy of Management's annual meeting, August , and was published in the Best Paper Proceedings of the Academy of Management Meeting.

Journal of Conflict Resolution. Making Policy When the Evidence is in Dispute. Global Evidence, Local Decisions. Evidence of Evidence-Based Health Policy: The Guilford Press; Positive Affect and Decision Making; pp. Kuvaas B, Selart M. Organizational Behavior and Human Decision Processes. Moderating Effects of Differences and Cognitive Processing. Journal of Behavioral Decision Making. The Cognitive Basis of Social Perception.

Lawrence Erlbaum Associates; The Processing of Social Stimulus Information: A Conceptual Integration; pp. Human Cognition in its Social Context. Clinical Decision-Making and Judgment in Nursing. Thompson C, Dowding D, editor. John Wiley and Sons; The Policy Press; Making Sense of 'What Works': Public Policy and Administration.

Does Evidence Affect Policy? Understanding the Growing Challenges to Health Care. Harry and Louise and Health Care Reform: Journal of Health Politics, Policy, and Law.


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The Politics of Evidence-Based Medicine. Policy Choices and Policy Subsystems. Oxford University Press; On Being a Good Listener: Setting priorities for Applied Health Services Research. Courts and Health Policy: Knowledge Transfer Study Group. The Framing of Decisions and the Psychology of Choice. Social Science Research and Decision Making. Columbia University Press; Politics and the Architecture of Choice: Bounded Rationality and Governance.

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University of Chicago Press; Theories of the Policy Process. Fostering the Development of Policy Theory; pp. Alternatives to Rational Choice. Decision Making and Problem Solving; pp. Editor's Note - Evidence: Its Meanings in Health Care and Law. Diffusion of Innovations in Service Organizations: Salmon's influential standing in the field ensures that this volume will be of interest to both undergraduates and professional philosophers, primarily in the philosophy of science.

Read more Read less. Review "Salmon was an important contributor to various debates in the area and the lucidity of his prose makes the great majority of the essays accessible to the full spectrum of readers, from undergraduates to professional philosophers. Oxford University Press; 1 edition June 9, Language: Related Video Shorts 0 Upload your video. Try the Kindle edition and experience these great reading features: Share your thoughts with other customers. Write a customer review. There was a problem filtering reviews right now.

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