Abstract
BackgroundClinical decision-making has been conceptualized as a sequence of two separate processes: assessment of patients’ functioning and application of a decision threshold to determine whether the evidence is sufficient to justify a given decision. A range of factors, including use of evidence-based screening instruments, has the potential to influence either or both processes. However, implementation studies seldom specify or assess the mechanism by which screening is hypothesized to influence clinical decision-making, thus limiting their ability to address unexpected findings regarding clinicians’ behavior. Building on prior theory and empirical evidence, we created a system dynamics (SD) model of how physicians’ clinical decisions are influenced by their assessments of patients and by factors that may influence decision thresholds, such as knowledge of past patient outcomes. Using developmental-behavioral disorders as a case example, we then explore how referral decisions may be influenced by changes in context. Specifically, we compare predictions from the SD model to published implementation trials of evidence-based screening to understand physicians’ management of positive screening results and changes in referral rates. We also conduct virtual experiments regarding the influence of a variety of interventions that may influence physicians’ thresholds, including improved access to co-located mental health care and improved feedback systems regarding patient outcomes.ResultsResults of the SD model were consistent with recent implementation trials. For example, the SD model suggests that if screening improves physicians’ accuracy of assessment without also influencing decision thresholds, then a significant proportion of children with positive screens will not be referred and the effect of screening implementation on referral rates will be modest—results that are consistent with a large proportion of published screening trials. Consistent with prior theory, virtual experiments suggest that physicians’ decision thresholds can be influenced and detection of disabilities improved by increasing access to referral sources and enhancing feedback regarding false negative cases.ConclusionsThe SD model of clinical decision-making offers a theoretically based framework to improve understanding of physicians’ behavior and the results of screening implementation trials. The SD model is also useful for initial testing of hypothesized strategies to increase detection of under-identified medical conditions.Electronic supplementary materialThe online version of this article (doi:10.1186/s13012-016-0517-0) contains supplementary material, which is available to authorized users.
Highlights
Clinical decision-making has been conceptualized as a sequence of two separate processes: assessment of patients’ functioning and application of a decision threshold to determine whether the evidence is sufficient to justify a given decision
Among the nine studies with sufficient data, meta-analysis indicated that an average pooled proportion of 60% of children with positive screens were referred
system dynamics (SD) model predictions fell toward the lower end of the range of results of published trials, as expected
Summary
Clinical decision-making has been conceptualized as a sequence of two separate processes: assessment of patients’ functioning and application of a decision threshold to determine whether the evidence is sufficient to justify a given decision. We compare predictions from the SD model to published implementation trials of evidence-based screening to understand physicians’ management of positive screening results and changes in referral rates. Evidence suggests that the profusion of clinical practice guidelines has had variable effects on the behavior of physicians [1–3] and that the field of medicine is marked by high levels of practice variation, which is often cited as a sign of waste [4, 5]. From their perspective, physicians report that standardized guidelines are often too rigid to apply to the complex presentations of individual patients [6, 7], and the Institute of Medicine/National Academy of Medicine concurs that clinical guidelines are not a “one-size-fits-all approach” [3, 6, 7].
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