NDM and Healthcare Guidelines: More Attention to the Current Status, Complexity, and Context Is Needed

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 One weakness of this paper is that it appears to be based on an outdated understanding of implementation science.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