Diagnosing, fast and slow

The role of information systems in emergency department decision-making-a literature review

OBJECTIVES

Healthcare providers employ heuristic and analytical decision-making to navigate the high-stakes environment of the emergency department (ED). Despite the increasing integration of information systems (ISs), research on their efficacy is conflicting. Drawing on related fields, we investigate how timing and mode of delivery influence IS effectiveness. Our objective is to reconcile previous contradictory findings, shedding light on optimal IS design in the ED.

MATERIALS AND METHODS

We conducted a systematic review following PRISMA across PubMed, Scopus, and Web of Science. We coded the ISs' timing as heuristic or analytical, their mode of delivery as active for automatic alerts and passive when requiring user-initiated information retrieval, and their effect on process, economic, and clinical outcomes.

RESULTS

Our analysis included 83 studies. During early heuristic decision-making, most active interventions were ineffective, while passive interventions generally improved outcomes. In the analytical phase, the effects were reversed. Passive interventions that facilitate information extraction consistently improved outcomes.

DISCUSSION

Our findings suggest that the effectiveness of active interventions negatively correlates with the amount of information received during delivery. During early heuristic decision-making, when information overload is high, physicians are unresponsive to alerts and proactively consult passive resources. In the later analytical phases, physicians show increased receptivity to alerts due to decreased diagnostic uncertainty and information quantity. Interventions that limit information lead to positive outcomes, supporting our interpretation.

CONCLUSION

We synthesize our findings into an integrated model that reveals the underlying reasons for conflicting findings from previous reviews and can guide practitioners in designing ISs in the ED.

Cognitive biases in internal medicine: a scoping review

BACKGROUND

Medical errors account for up to 440,000 deaths annually, and cognitive errors outpace knowledge deficits as causes of error. Cognitive biases are predispositions to respond in predictable ways, and they don't always result in error. We conducted a scoping review exploring which biases are most prevalent in Internal Medicine (IM), if and how they influence patient outcomes, and what, if any, debiasing strategies are effective.

CONTENT

We searched PubMed, OVID, ERIC, SCOPUS, PsychINFO, and CINAHL. Search terms included variations of "bias", "clinical reasoning", and IM subspecialties. Inclusion criteria were: discussing bias, clinical reasoning, and physician participants.

SUMMARY

Fifteen of 334 identified papers were included. Two papers looked beyond general IM: one each in Infectious Diseases and Critical Care. Nine papers distinguished bias from error, whereas four referenced error in their definition of bias. The most commonly studied outcomes were diagnosis, treatment, and physician impact in 47 % (7), 33 % (5), and 27 % (4) of studies, respectively. Three studies directly assessed patient outcomes. The most commonly cited biases were availability bias (60 %, 9), confirmation bias (40 %, 6), anchoring (40 %, 6), and premature closure (33 %, 5). Proposed contributing features were years of practice, stressors, and practice setting. One study found that years of practice negatively correlated with susceptibility to bias. Ten studies discussed debiasing; all reported weak or equivocal efficacy.

OUTLOOK

We found 41 biases in IM and 22 features that may predispose physicians to bias. We found little evidence directly linking biases to error, which could account for the weak evidence of bias countermeasure efficacy. Future study clearly delineating bias from error and directly assessing clinical outcomes would be insightful.

A new model for categorizing cognitive biases and debiasing strategies in dermatology

Cognitive biases are a significant cause of medical error. They arise from "system 1" thinking, which depends on heuristics to make quick decisions in complex situations. Heuristics make us "predictably irrational," distorting our ability to accurately assess probabilities in clinical scenarios. It is well reported in the literature that metacognition, the art of reflecting on one's thought processes, is the optimal way to deal with cognitive biases. However, it is unclear how this can be consistently implemented in dermatological practice. Our debiasing attempts thus far have been sporadic at best. This article categorizes important cognitive biases according to each stage of the doctor-patient interaction (history taking, clinical examination, investigations, diagnosis, and management). We hope that providing this clinically relevant framework can foster metacognition and a platform for algorithmic debiasing. This will enable us to engage "system 2" (analytical thinking) in a targeted way, thereby avoiding excessive cognitive load. Organization-level interventions should also be implemented to free up the cognitive capacity of an individual and to enable them to employ system 2 thinking more regularly.

Cognitive Bias and Diagnostic Errors among Physicians in Japan: A Self-Reflection Survey

This cross-sectional study aimed to clarify how cognitive biases and situational factors related to diagnostic errors among physicians. A self-reflection questionnaire survey on physicians’ most memorable diagnostic error cases was conducted at seven conferences: one each in Okayama, Hiroshima, Matsue, Izumo City, and Osaka, and two in Tokyo. Among the 147 recruited participants, 130 completed and returned the questionnaires. We recruited primary care physicians working in various specialty areas and settings (e.g., clinics and hospitals). Results indicated that the emergency department was the most common setting (47.7%), and the highest frequency of errors occurred during night-time work. An average of 3.08 cognitive biases was attributed to each error. The participants reported anchoring bias (60.0%), premature closure (58.5%), availability bias (46.2%), and hassle bias (33.1%), with the first three being most frequent. Further, multivariate logistic regression analysis for cognitive bias showed that emergency room care can easily induce cognitive bias (adjusted odds ratio 3.96, 95% CI 1.16−13.6, p-value = 0.028). Although limited to a certain extent by its sample collection, due to the sensitive nature of information regarding physicians’ diagnostic errors, this study nonetheless shows correlations with environmental factors (emergency room care situations) that induce cognitive biases which, in turn, cause diagnostic errors.

Efficacy of Artificial-Intelligence-Driven Differential-Diagnosis List on the Diagnostic Accuracy of Physicians: An Open-Label Randomized Controlled Study

Background: The efficacy of artificial intelligence (AI)-driven automated medical-history-taking systems with AI-driven differential-diagnosis lists on physicians’ diagnostic accuracy was shown. However, considering the negative effects of AI-driven differential-diagnosis lists such as omission (physicians reject a correct diagnosis suggested by AI) and commission (physicians accept an incorrect diagnosis suggested by AI) errors, the efficacy of AI-driven automated medical-history-taking systems without AI-driven differential-diagnosis lists on physicians’ diagnostic accuracy should be evaluated. Objective: The present study was conducted to evaluate the efficacy of AI-driven automated medical-history-taking systems with or without AI-driven differential-diagnosis lists on physicians’ diagnostic accuracy. Methods: This randomized controlled study was conducted in January 2021 and included 22 physicians working at a university hospital. Participants were required to read 16 clinical vignettes in which the AI-driven medical history of real patients generated up to three differential diagnoses per case. Participants were divided into two groups: with and without an AI-driven differential-diagnosis list. Results: There was no significant difference in diagnostic accuracy between the two groups (57.4% vs. 56.3%, respectively; p = 0.91). Vignettes that included a correct diagnosis in the AI-generated list showed the greatest positive effect on physicians’ diagnostic accuracy (adjusted odds ratio 7.68; 95% CI 4.68–12.58; p < 0.001). In the group with AI-driven differential-diagnosis lists, 15.9% of diagnoses were omission errors and 14.8% were commission errors. Conclusions: Physicians’ diagnostic accuracy using AI-driven automated medical history did not differ between the groups with and without AI-driven differential-diagnosis lists.