Pub Date : 2018-08-16DOI: 10.1177/1555343418790084
L. Militello, M. Weiner
cussing the Role of Best Practices in Health Care Decision Making. The discussion began with a provocative article published in April 2016 in which Devorah Klein, David Woods, Gary Klein, and Shawna Perry raised the question, “Can we trust best practices?” (Klein, Woods, Klein, & Perry, 2016). Klein and her colleagues highlighted some of the challenges associated with using evidence-based medicine (EBM) to support clinical care and suggested naturalistic decision making (NDM) as an important perspective for addressing these challenges. Soon thereafter, Paul Falzer (2018) submitted a manuscript deepening and extending the discussion, pointing out that these challenges are well known in the health care community, and noting that effective solutions remain elusive. In this issue, Falzer’s article provides an in-depth discussion of the concept of decision making in this context and the unintended negative consequences of evidence-based recommendations. This article became the centerpiece for this special issue; commentators were asked to react to Falzer’s article. Because the topic of EBM and its impact on decision making and the quality of health care falls at the intersection of at least two important scientific disciplines (EBM and NDM), perspectives of experts who have been thinking about these issues in various contexts and from various traditions are important. We have been fortunate to obtain commentaries from a range of thought leaders representing both EBM and NDM for this special issue. As we reviewed the commentaries, it became clear that there is sometimes disagreement about what EBM really is and what it implies for health care. “Critics” tend to view EBM narrowly, whereas “proponents” have a broader and more multidimensional view of EBM. It is also worth noting that many commentators draw strong links between EBM and the “best practices regimen” (i.e., initiatives and interventions that define the quality of decision making by conformance to evidence-based practices). Other contributors note that these are distinct concepts, suggesting that while managed care, health services research, implementation research, and the best practices regimen are directly influenced by classical decision theory, EBM might be characterized as restoring decision making to its “rightful place.” In some ways, this confusion rooted in the language can be seen as encouraging: There might not be as much disagreement as it appears on the surface, if one begins to discuss concepts and approaches rather than relying on labels. Nonetheless, important points of divergence are found in the commentaries. Although many would agree that EBM was never intended to constrain clinician discretion and discount expertise, there is little agreement about how EBM should be implemented. We invite you to enjoy the following commentaries. Haynes, a member of the working group that articulated a vision for evidence-based medicine in 1992 (Evidence-Based Medicine Working Group, 1992)
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Pub Date : 2018-08-16DOI: 10.1177/1555343418784372
E. Schneider
Much of the health system’s avoidable spending may be driven by doctors’ decision making. Past studies demonstrated potentially consequential and costly inconsistencies between the actual decisions that clinicians make in daily practice and optimal evidence-based decisions. This commentary examines the “best practices regimen” through the lens of the quality measurement movement. Although quality measures have proliferated via public reporting and pay-for-performance programs, evidence for their impact on quality of care is scant; the cost of care has continued to rise; and the environment for clinical decisions may not have improved. Naturalistic decision making offers a compelling alternative conceptual frame for quality measurement. An alternative quality measurement system could build on insights from naturalistic decision making to optimize doctors’ and patients’ joint decisions, improve patients’ health outcomes, and perhaps slow the growth of health care spending in the future.
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Pub Date : 2018-08-16DOI: 10.1177/1555343418779677
D. E. Klein, David D. Woods, Gary Klein, S. Perry
In 2016, we examined the connection between naturalistic decision making and the trend toward best practice compliance; we used evidence-based medicine (EBM) in health care as an exemplar. Paul Falzer’s lead paper in this issue describes the historical underpinnings of how and why EBM came into vogue in health care. Falzer also highlights the epistemological rationale for EBM. Falzer’s article, like our own, questions the rationale of EBM and reflects on ways that naturalistic decision making can support expertise in the face of attempts to standardize practice and emphasize compliance. Our objectives in this commentary are first to explain the inherent limits of procedural approaches and second to examine ways to help decision makers become more adaptive.
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Pub Date : 2018-08-16DOI: 10.1177/1555343418775288
L. Beach
Invited commentary on “Naturalistic Decision Making and the Practice of Health Care,” by Paul R. Falzer for the Journal of Cognitive Engineering and Decision Making.
Paul R.Falzer为《认知工程与决策杂志》撰写的“自然主义决策与医疗保健实践”特邀评论。
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Pub Date : 2018-08-16DOI: 10.1177/1555343418782850
R. Hamm, Zsolt J. Nagykaldi
Decision Making and the Practice of Health Care” by Paul R. Falzer in this special issue. Falzer (2018) and Klein, Woods, Klein, and Perry (2016) have called attention to the fact that external payers (insurance companies, government programs) may reward or penalize individual physicians or clinical groups depending on whether their behavior accords with the recommendation of an evidence-based clinical practice guideline (EB-CPG). (Klein et al. [2016] call this the “best practices regimen,” but to avoid confusion with a methodology focused on design of exemplary and successful approaches for accomplishing clinic tasks [Mold & Gregory, 2003], we will refer to “enforced conformance” to EB-CPG). Clinic administration may then institute a system to measure and reward individual physician performance to assure the group practice overall meets the stated standard. Falzer identifies flaws in the logic behind making reward contingent on meeting guideline-related standards and reviews the varied responses physicians have to enforced conformance. Although we concur with many aspects of the critique, we value the science and wisdom embodied in the guidelines and recognize that at times physicians may need external motivation to heed them. The focus is CPGs that in good faith address the health needs of patients and society. There are three frameworks for these. Guidelines can be based on the judgments of expert clinicians and other stakeholders (Crownover & Unwin, 2005), on studies providing justified evidence that clinical practices are likely to have beneficial effects (Alonso-Coello et al., 2016), or on broader analyses that consider the money needed to produce those beneficial effects so that society’s resources may be allocated to the most cost-effective practices (Mandelblatt, Fryback, Weinstein, Russell, & Gold, 1997; Pandya, 2018). Interestingly, the expert judgment recommendations are correlated with the cost-effectiveness analysis recommendations (Kuntz, Tsevat, Weinstein, & Goldman, 1999). CPGs from any of these frameworks can experience the problems Falzer (2018) identifies when recommendations become requirements and nonconformance with them is penalized. However, physicians cannot simply be ordered to behave in a way that maximizes rewards or optimizes outcomes. The way they manage patients is based on long-established habit (Hamm, 2009a). Hearing about, reading, or studying an EB-CPG endorsed by a respected authority does not make physicians immediately change behavior, even if they intend to. They have to learn the recommended alternative behavior, recognize when the guideline describes something different from what they usually do, and make the conscious choice to change behavior. This must be done consciously until it can become a new habit (Abernathy & Hamm, 1995). This situation is more complex when the current habitual practice involves multiple actors (Ackerman, Gonzales, Stahl, & Metlay, 2013; Gonzales, Steiner, Lum, & Barrett, 1999),
Paul R.Falzer在本期特刊中的《决策与医疗实践》。Falzer（2018）和Klein、Woods、Klein和Perry（2016）呼吁注意这样一个事实，即外部付款人（保险公司、政府项目）可能会根据个人医生或临床团体的行为是否符合循证临床实践指南（EB-CPG）的建议来奖励或惩罚他们。（Klein等人[2016]称之为“最佳实践方案”，但为了避免与专注于设计完成临床任务的示范性和成功方法的方法相混淆[Mold&Gregory，2003]，我们将提及EB-CPG的“强制合规性”）。然后，诊所管理部门可以建立一个系统来衡量和奖励个别医生的表现，以确保团队实践总体符合规定的标准。Falzer发现了将奖励视为符合指南相关标准的逻辑中的缺陷，并审查了医生对强制遵守的各种反应。尽管我们同意批评的许多方面，但我们重视指导方针中体现的科学和智慧，并认识到有时医生可能需要外部动机来关注它们。重点是本着诚意满足患者和社会健康需求的CPG。有三个框架。指南可以基于临床医生专家和其他利益相关者的判断（Crownover&Unwin，2005），基于提供合理证据证明临床实践可能具有有益效果的研究（Alonso-Coello等人，2016），或者基于更广泛的分析，考虑产生这些有益效果所需的资金，以便将社会资源分配给最具成本效益的做法（Mandelblatt，Fryback，Weinstein，Russell，&Gold，1997；Pandya，2018）。有趣的是，专家判断建议与成本效益分析建议相关（Kuntz，Tsevat，Weinstein，&Goldman，1999）。当建议成为要求时，来自任何这些框架的CPG都可能遇到Falzer（2018）发现的问题，不符合这些建议的情况将受到惩罚。然而，不能简单地命令医生以最大化奖励或优化结果的方式行事。他们管理病人的方式是基于长期养成的习惯（Hamm，2009a）。听到、阅读或研究由受人尊敬的权威机构认可的EB-CPG并不能让医生立即改变行为，即使他们打算这样做。他们必须学习推荐的替代行为，识别指南何时描述了与他们通常做的不同的事情，并有意识地选择改变行为。这必须有意识地进行，直到它成为一种新习惯（Abernathy&Ham，1995）。当当前的习惯性做法涉及多个参与者时，这种情况会更加复杂（Ackerman，Gonzales，Stahl，&Metlay，2013；Gonzales、Steiner、Lum和Barrett，1999），而不仅仅是单个医生，当当前的做法效果更好时（Hamm，2009b）。因此，个体医生可能需要强烈的动机来遵守指导方针，即使他或她在理智上接受它是正确的。782850 EDMXX10.1177/1555334341878250Journal of Cognitive Engineering and Decision MakingJudge and Guidelines 2018
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Pub Date : 2018-08-16DOI: 10.1177/1555343418790715
P. Falzer
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Pub Date : 2018-08-16DOI: 10.1177/1555343418789831
R. Haynes
Expert and informed decision making is an essential process in all of health care. Evidence-Based Medicine (EBM) purports to support and enhance this process by the timely infusion of high-quality, pertinent evidence from health research, tailored as closely as possible to the individual and their health problem. Doing so is not an easy task for many reasons, beginning with imperfections and incompleteness in the evidence and ending with the complexities of the dual decision making required by individuals and their care providers. EBM needs a lot of help supporting decision-making processes and welcomes further interdisciplinary collaboration. The “conformist principle,” “best practice regimens,” and “transductive models” should not be considered as barriers to such collaboration: These are not part of EBM. Rather, EBM has always seen evidence from health research as but one of many inputs to decision making by providers and patients. An overarching problem for collaboration to address is understanding the decision-making process well enough to develop effective means to bolster it, so that people are consistently offered the current best options for their problems in a way that fits their circumstances and that they can understand and judge.
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Pub Date : 2018-08-16DOI: 10.1177/1555343418778703
D. Matlock, R. Glasgow
Evidence-based medicine and guidelines cannot solve all problems in healthcare (Kemm, 2006). Indeed, it can be exceedingly frustrating for a clinician when the quality of his or her care gets measured based on adherence to guidelines that do not apply to a given patient (Boyd et al., 2005). Common examples of this are blood pressure and diabetes treatments for older adults. Lowering both blood pressure and glucose levels are important but can also be quite harmful for individual patients who are at higher risk for falls, incontinence, hypoglycemia, and cognitive impairment if either is controlled too aggressively. In this issue, Dr. Falzer (2018) contributes an article titled “Naturalistic Decision Making (NDM) and the Practice of Health Care.” He argues that the “best practices regimen”—an approach based on evidence and guidelines—has not worked due to a fundamental fallacy that they are overly simplistic and do not account for the nuances of modern medicine in the way that NDM could. He further asserts that implementation science approaches have not helped because they only serve to support and perpetuate the flawed “best practices regimen” approach. His point is well taken that some of the evidence and some (generally older) guidelines fall far short of providing guidance for the complex patient. However, the argument has important weaknesses. The reasoning seems to begin with a conclusion that is supported by an argument based on older thinking about implementation science and guidelines that support the a priori conclusion. This type of reasoning is a classic example of confirmation bias— a common risk when people are left to NDM approaches (Nickerson, 1998). One weakness of this paper is that it appears to be based on an outdated understanding of implementation science. Since the Lomas (Lomas et al., 1989) definition, the field of implementation science has evolved extensively and includes an understanding of how treatments reach the maximum number of eligible patients, how they are adapted to fit into different clinical contexts, how they are sustained, and how both changes in context and potential unintended consequences can be anticipated and avoided (Brownson, Colditz, & Proctor, 2017; Chambers, Glasgow, & Stange, 2013; Glasgow et al., 2012; Stirman et al., 2012). Since the articles referenced within the manuscript, there have been multiple advances in our understanding of both how to disseminate 778703 EDMXXX10.1177/1555343418778703Journal of Cognitive Engineering and Decision MakingNdm and Healthcare Guidelines 2018
循证医学和指南不能解决医疗保健中的所有问题（Kemm，2006）。事实上，当临床医生根据不适用于特定患者的指南来衡量其护理质量时，这可能会让他们非常沮丧（Boyd等人，2005）。常见的例子是老年人的血压和糖尿病治疗。降低血压和血糖水平很重要，但如果控制得过于激进，对跌倒、失禁、低血糖和认知障碍风险较高的个别患者来说也会非常有害。在本期文章中，Falzer博士（2018）发表了一篇题为《自然主义决策与医疗保健实践》的文章。他认为，“最佳实践方案”——一种基于证据和指南的方法——没有奏效，因为存在一种根本的谬论，即它们过于简单化，没有像NDM那样考虑到现代医学的细微差别。他进一步断言，实施科学方法没有帮助，因为它们只会支持和延续有缺陷的“最佳实践方案”方法。他的观点得到了很好的理解，一些证据和一些（通常是旧的）指南远远不能为复杂的患者提供指导。然而，这一论点有重要的弱点。推理似乎始于一个结论，该结论得到了基于对实现科学和支持先验结论的指导方针的旧思想的论点的支持。这种类型的推理是确认偏见的一个经典例子——当人们被NDM方法所左右时，这是一种常见的风险（Nickerson，1998）。这篇论文的一个弱点是，它似乎是基于对实现科学的过时理解。自Lomas（Lomas et al.，1989）定义以来，实施科学领域已经发生了广泛的发展，包括了解治疗如何达到符合条件的患者的最大数量，如何适应不同的临床环境，如何持续，以及如何预测和避免背景变化和潜在的意外后果（Brownson，Colditz，&Proctor，2017；Chambers，Glasgow，&Stange，2013；Glasgow等人，2012年；Stirman等人，2012）。自手稿中引用的文章以来，我们对如何传播778703 EDMXX10.1177/155533418778703《认知工程与决策杂志》和《2018年医疗保健指南》的理解取得了多项进展
{"title":"NDM and Healthcare Guidelines: More Attention to the Current Status, Complexity, and Context Is Needed","authors":"D. Matlock, R. Glasgow","doi":"10.1177/1555343418778703","DOIUrl":"https://doi.org/10.1177/1555343418778703","url":null,"abstract":"Evidence-based medicine and guidelines cannot solve all problems in healthcare (Kemm, 2006). Indeed, it can be exceedingly frustrating for a clinician when the quality of his or her care gets measured based on adherence to guidelines that do not apply to a given patient (Boyd et al., 2005). Common examples of this are blood pressure and diabetes treatments for older adults. Lowering both blood pressure and glucose levels are important but can also be quite harmful for individual patients who are at higher risk for falls, incontinence, hypoglycemia, and cognitive impairment if either is controlled too aggressively. In this issue, Dr. Falzer (2018) contributes an article titled “Naturalistic Decision Making (NDM) and the Practice of Health Care.” He argues that the “best practices regimen”—an approach based on evidence and guidelines—has not worked due to a fundamental fallacy that they are overly simplistic and do not account for the nuances of modern medicine in the way that NDM could. He further asserts that implementation science approaches have not helped because they only serve to support and perpetuate the flawed “best practices regimen” approach. His point is well taken that some of the evidence and some (generally older) guidelines fall far short of providing guidance for the complex patient. However, the argument has important weaknesses. The reasoning seems to begin with a conclusion that is supported by an argument based on older thinking about implementation science and guidelines that support the a priori conclusion. This type of reasoning is a classic example of confirmation bias— a common risk when people are left to NDM approaches (Nickerson, 1998). One weakness of this paper is that it appears to be based on an outdated understanding of implementation science. Since the Lomas (Lomas et al., 1989) definition, the field of implementation science has evolved extensively and includes an understanding of how treatments reach the maximum number of eligible patients, how they are adapted to fit into different clinical contexts, how they are sustained, and how both changes in context and potential unintended consequences can be anticipated and avoided (Brownson, Colditz, & Proctor, 2017; Chambers, Glasgow, & Stange, 2013; Glasgow et al., 2012; Stirman et al., 2012). Since the articles referenced within the manuscript, there have been multiple advances in our understanding of both how to disseminate 778703 EDMXXX10.1177/1555343418778703Journal of Cognitive Engineering and Decision MakingNdm and Healthcare Guidelines 2018","PeriodicalId":46342,"journal":{"name":"Journal of Cognitive Engineering and Decision Making","volume":"12 1","pages":"202 - 205"},"PeriodicalIF":2.0,"publicationDate":"2018-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1555343418778703","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49605475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-08-16DOI: 10.1177/1555343418777342
Yan Xiao, P. Gorman
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Pub Date : 2018-08-16DOI: 10.1177/1555343418774661
K. Catchpole, Myrtede C. Alfred
Quality and safety concerns in health care over the past 20 years precipitated the need to move beyond the traditional view of health care as an artisanal process toward a sociotechnical systems view of performance. The adoption of industrial approaches placed a greater emphasis on standardization of processes and outcomes, often treating humans as the “weak” part of the system rather than valuing their role in holding together complex, opaque, and unpredictable clinical systems. Although some health care tasks can be modeled linearly, others are much more complex. Efforts to reduce variation in clinical reasoning through evidence-based practices have proven problematic by failing to provide a means for context-specific adaptation or to account for the complex and adaptive nature of clinical work. We argue that the current, highly empirical approach to clinical decision making reflects clinical reasoning “as imagined,” whereas the application of the naturalistic decision-making (NDM) paradigm can help reveal clinical reasoning “as done.” This approach will have benefits for improving the conditions for diagnosis; the design of acute, time-pressured clinical work; the identification of deteriorating patients; the development of clinical decision support systems; and many more clinical tasks. Health care seems ready to accept NDM approaches.
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