Deliberate reflection and clinical reasoning: Founding ideas and empirical findings

Dual process models of clinical reasoning: The central role of knowledge in diagnostic expertise

RATIONALE

Research on diagnostic reasoning has been conducted for fifty years or more. There is growing consensus that there are two distinct processes involved in human diagnostic reasoning: System 1, a rapid retrieval of possible diagnostic hypotheses, largely automatic and based to a large part on experiential knowledge, and System 2, a slower, analytical, conscious application of formal knowledge to arrive at a diagnostic conclusion. However, within this broad framework, controversy and disagreement abound. In particular, many authors have suggested that the root cause of diagnostic errors is cognitive biases originating in System 1 and propose that educating learners about the types of cognitive biases and their impact on diagnosis would have a major influence on error reduction.

AIMS AND OBJECTIVES

In the present paper, we take issue with these claims.

METHOD

We reviewed the literature to examine the extent to which this theoretical model is supported by the evidence.

RESULTS

We show that evidence derived from fundamental research in human cognition and studies in clinical medicine challenges the basic assumptions of this theory-that errors arise in System 1 processing as a consequence of cognitive biases, and are corrected by slow, deliberative analytical processing. We claim that, to the contrary, errors derive from both System 1 and System 2 reasoning, that they arise from lack of access to the appropriate knowledge, not from errors of processing, and that the two processes are not essential to the process of diagnostic reasoning.

CONCLUSIONS

The two processing modes are better understood as a consequence of the nature of the knowledge retrieved, not as independent processes.

'Walking together': How relationships shape physicians' clinical reasoning

INTRODUCTION

The clinical reasoning literature has increasingly considered context as an important influence on physicians' thinking. Physicians' relationships with patients, and their ongoing efforts to maintain these relationships, are important influences on how clinical reasoning is contextualised. The authors sought to understand how physicians' relationships with patients shaped their clinical reasoning.

METHODS

Drawing from constructivist grounded theory, the authors conducted semi-structured interviews with primary care physicians. Participants were asked to reflect on recent challenging clinical experiences, and probing questions were used to explore how participants attended to or leveraged relationships in conjunction with their clinical reasoning. Using constant comparison, three investigators coded transcripts, organising the data into codes and conceptual categories. The research team drew from these codes and categories to develop theory about the phenomenon of interest.

RESULTS

The authors interviewed 15 primary care physicians with a range of experience in practice and identified patient agency as a central influence on participants' clinical reasoning. Participants drew from and managed relationships with patients while attending to patients' agency in three ways. First, participants described how contextualised illness constructions enabled them to individualise their approaches to diagnosis and management. Second, participants managed tensions between enacting their typical approaches to clinical problems and adapting their approaches to foster ongoing relationships with patients. Finally, participants attended to relationships with patients' caregivers, seeing these individuals' contributions as important influences on how their clinical reasoning could be enacted within patients' unique social contexts.

CONCLUSION

Clinical reasoning is influenced in important ways by physicians' efforts to both draw from, and maintain, their relationships with patients and patients' caregivers. Such efforts create tensions between their professional standards of care and their orientations toward patient-centredness. These influences of relationships on physicians' clinical reasoning have important implications for training and clinical practice.

Effects of interacting with a large language model compared with a human coach on the clinical diagnostic process and outcomes among fourth-year medical students: study protocol for a prospective, randomised experiment using patient vignettes

INTRODUCTION

Versatile large language models (LLMs) have the potential to augment diagnostic decision-making by assisting diagnosticians, thanks to their ability to engage in open-ended, natural conversations and their comprehensive knowledge access. Yet the novelty of LLMs in diagnostic decision-making introduces uncertainties regarding their impact. Clinicians unfamiliar with the use of LLMs in their professional context may rely on general attitudes towards LLMs more broadly, potentially hindering thoughtful use and critical evaluation of their input, leading to either over-reliance and lack of critical thinking or an unwillingness to use LLMs as diagnostic aids. To address these concerns, this study examines the influence on the diagnostic process and outcomes of interacting with an LLM compared with a human coach, and of prior training vs no training for interacting with either of these 'coaches'. Our findings aim to illuminate the potential benefits and risks of employing artificial intelligence (AI) in diagnostic decision-making.

METHODS AND ANALYSIS

We are conducting a prospective, randomised experiment with N=158 fourth-year medical students from Charité Medical School, Berlin, Germany. Participants are asked to diagnose patient vignettes after being assigned to either a human coach or ChatGPT and after either training or no training (both between-subject factors). We are specifically collecting data on the effects of using either of these 'coaches' and of additional training on information search, number of hypotheses entertained, diagnostic accuracy and confidence. Statistical methods will include linear mixed effects models. Exploratory analyses of the interaction patterns and attitudes towards AI will also generate more generalisable knowledge about the role of AI in medicine.

ETHICS AND DISSEMINATION

The Bern Cantonal Ethics Committee considered the study exempt from full ethical review (BASEC No: Req-2023-01396). All methods will be conducted in accordance with relevant guidelines and regulations. Participation is voluntary and informed consent will be obtained. Results will be published in peer-reviewed scientific medical journals. Authorship will be determined according to the International Committee of Medical Journal Editors guidelines.

Beyond thinking fast and slow: Implications of a transtheoretical model of clinical reasoning and error on teaching, assessment, and research

From dual process to a family of theories known collectively as situativity, both micro and macro theories of cognition inform our current understanding of clinical reasoning (CR) and error. CR is a complex process that occurs in a complex environment, and a nuanced, expansive, integrated model of these theories is necessary to fully understand how CR is performed in the present day and in the future. In this perspective, we present these individual theories along with figures and descriptive cases for purposes of comparison before exploring the implications of a transtheoretical model of these theories for teaching, assessment, and research in CR and error.

Lessons in clinical reasoning - pitfalls, myths, and pearls: a woman brought to a halt

OBJECTIVES

Limitations in human cognition commonly result in clinical reasoning failures that can lead to diagnostic errors. A metacognitive structured reflection on what clinical findings fit and/or do not fit with a diagnosis, as well as how discordance of data can help advance the reasoning process, may reduce such errors.

CASE PRESENTATION

A 60-year-old woman with Hashimoto thyroiditis, diabetes, and generalized anxiety disorder presented with diffuse arthralgias and myalgias. She had been evaluated by physicians of various specialties and undergone multiple modalities of imaging, as well as a electromyography/nerve conduction study (EMG/NCS), leading to diagnoses of fibromyalgia, osteoarthritis, and lumbosacral plexopathy. Despite treatment for these conditions, she experienced persistent functional decline. The only definitive alleviation of her symptoms identified was in the few days following intra-articular steroid injections for osteoarthritis. On presentation to our institution, she appeared fit with a normal BMI. She was a long-time athlete and had been training consistently until her symptoms began. Prediabetes had been diagnosed the year prior and her A1c progressed despite lifestyle modifications and 10 pounds of intentional weight loss. She reported fatigue, intermittent nausea without emesis, and reduced appetite. Examination revealed intact strength and range of motion in both the shoulders and hips, though testing elicited pain. She had symmetric hyperreflexia as well as a slowed, rigid gait. Autoantibody testing revealed strongly positive serum GAD-65 antibodies which were confirmed in the CSF. A diagnosis of stiff-person syndrome was made. She had an incomplete response to first-line therapy with high-dose benzodiazepines. IVIg was initiated with excellent response and symptom resolution.

CONCLUSIONS

Through integrated commentary on the diagnostic reasoning process from clinical reasoning experts, this case underscores the importance of frequent assessment of fit along with explicit explanation of dissonant features in order to avoid misdiagnosis and halt diagnostic inertia. A fishbone diagram is provided to visually demonstrate the major factors that contributed to the diagnostic error. The case discussant demonstrates the power of iterative reasoning, case progression without commitment to a single diagnosis, and the dangers of both explicit and implicit bias. Finally, this case provides clinical teaching points in addition to a pitfall, myth, and pearl specific to overcoming diagnostic inertia.

Influence of a Large Language Model on Diagnostic Reasoning: A Randomized Clinical Vignette Study

Importance

Diagnostic errors are common and cause significant morbidity. Large language models (LLMs) have shown promise in their performance on both multiple-choice and open-ended medical reasoning examinations, but it remains unknown whether the use of such tools improves diagnostic reasoning.

Objective

To assess the impact of the GPT-4 LLM on physicians' diagnostic reasoning compared to conventional resources.

Design

Multi-center, randomized clinical vignette study.

Setting

The study was conducted using remote video conferencing with physicians across the country and in-person participation across multiple academic medical institutions.

Participants

Resident and attending physicians with training in family medicine, internal medicine, or emergency medicine.

Interventions

Participants were randomized to access GPT-4 in addition to conventional diagnostic resources or to just conventional resources. They were allocated 60 minutes to review up to six clinical vignettes adapted from established diagnostic reasoning exams.

Main Outcomes and Measures

The primary outcome was diagnostic performance based on differential diagnosis accuracy, appropriateness of supporting and opposing factors, and next diagnostic evaluation steps. Secondary outcomes included time spent per case and final diagnosis.

Results

50 physicians (26 attendings, 24 residents) participated, with an average of 5.2 cases completed per participant. The median diagnostic reasoning score per case was 76.3 percent (IQR 65.8 to 86.8) for the GPT-4 group and 73.7 percent (IQR 63.2 to 84.2) for the conventional resources group, with an adjusted difference of 1.6 percentage points (95% CI -4.4 to 7.6; p=0.60). The median time spent on cases for the GPT-4 group was 519 seconds (IQR 371 to 668 seconds), compared to 565 seconds (IQR 456 to 788 seconds) for the conventional resources group, with a time difference of -82 seconds (95% CI -195 to 31; p=0.20). GPT-4 alone scored 15.5 percentage points (95% CI 1.5 to 29, p=0.03) higher than the conventional resources group.

Conclusions and Relevance

In a clinical vignette-based study, the availability of GPT-4 to physicians as a diagnostic aid did not significantly improve clinical reasoning compared to conventional resources, although it may improve components of clinical reasoning such as efficiency. GPT-4 alone demonstrated higher performance than both physician groups, suggesting opportunities for further improvement in physician-AI collaboration in clinical practice.

Using an Equity in Research Framework to Develop a Community-Engaged Intervention to Improve Preexposure Uptake Among Black Women Living in the United States South

ABSTRACT

In the U.S. South, over half of new HIV diagnoses occur among Black Americans with research lagging for women who face increased HIV rates and low PrEP uptake, among other health inequities. Community engaged research is a promising method for reversing these trends with established best practices for building infrastructure, implementing research, and translating evidence-based interventions into clinical and community settings. Using the 5Ws of Racial Equity in Research Framework (5Ws) as a racial equity lens, the following paper models a review of a salon-based intervention to improve PrEP awareness and uptake among Black women that was co-developed with beauty salons, stylists, and Black women through an established community advisory council. In this paper we demonstrate how the 5Ws framework was applied to review processes, practices, and outcomes from a community-engaged research approach. The benefits of and challenges to successful collaboration are discussed with insights for future research and community impact.

The development of clinical reasoning throughout the training and career of psychiatrists in Singapore

Objectives

The current study sought to explain how different professional experiences led Singaporean psychiatrists to alter their clinical reasoning processes as their careers evolved from psychiatry residents to senior consultant psychiatrists.

Methods

The current qualitative study interviewed 26 clinicians at various stages of their psychiatric career, spanning residents to senior psychiatrists.  The authors used a constructivist grounded theory approach to structure the collection and analysis of data. Analyses produced a dense theoretical explanation rooted in the experiences of participants.

Results

Several differences emerged between the way psychiatry residents and senior psychiatrists explained their reasoning process and the experiences on which they based their preference. Residents preferred using deductive logic-driven frameworks that were diagnosis-centric, because of the pressures they experienced during their training and assessments. Senior psychiatrists emphasized a more holistic and problem-centric approach. Participants attributed the changes that occurred over time to practical experiences, such as their greater clinical responsibility and independence, and individual experiences, such as growing sensitivity to the clinical reasoning process or their growing propensity for professional reflectiveness. These changes manifest as an increase in repertoire and flexibility in deployment of different clinical reasoning strategies.

Conclusions

It is important for trainees to be aware of the deductive and inductive modes of clinical reasoning during supervision and to be comfortable with shifting clinical focus from diagnoses to specific individual problems. Training programs should provide and plan adequate longitudinal clinical exposure to develop clinical reasoning abilities in a way that allows consequences of decisions to be explored. Continued faculty development to ease the diversification of clinical reasoning skills should be encouraged, as should reflectivity in the learners during clinical supervision.

Twelve tips for physicians’ mastering expertise in diagnostic excellence

Diagnostic errors, which account for a large proportion of medical errors, are a global medical challenge. The slogan of reducing diagnostic errors has recently shifted to a new strategy of diagnostic excellence, the core of which is the importance of improving the multidisciplinary diagnostic process. Many of the elements and strategies necessary for diagnostic excellence have been presented. In the context of this diagnostic improvement, some reports have been structured to improve the quality of performance of individual physicians as players. Still, surprisingly, only a few reports have focused on specific day-to-day training strategies for the diagnostic thinking process as expertise. This paper focuses on this point and proposes strategies for refining the diagnostic thinking expertise of frontline physicians in the new era, based on the following four elements: knowledge and experience, diagnostic thinking strategies, information management skills, and calibration and reflection.

Improving diagnostic decision support through deliberate reflection: a proposal

Digital decision support (DDS) is expected to play an important role in improving a physician's diagnostic performance and reducing the burden of diagnostic error. Studies with currently available DDS systems indicate that they lead to modest gains in diagnostic accuracy, and these systems are expected to evolve to become more effective and user-friendly in the future. In this position paper, we propose that a way towards this future is to rethink DDS systems based on deliberate reflection, a strategy by which physicians systematically review the clinical findings observed in a patient in the light of an initial diagnosis. Deliberate reflection has been demonstrated to improve diagnostic accuracy in several contexts. In this paper, we first describe the deliberate reflection strategy, including the crucial element that would make it useful in the interaction with a DDS system. We examine the nature of conventional DDS systems and their shortcomings. Finally, we propose what DDS based on deliberate reflection might look like, and consider why it would overcome downsides of conventional DDS.

Harnessing a knowledge translation framework to implement an undergraduate medical education intervention: A longitudinal study

INTRODUCTION

Implementation of evidence-informed educational interventions (EEI) involves applying and adapting theoretical and scientific knowledge to a specific context. Knowledge translation (KT) approaches can both facilitate and structure the process. The purpose of this paper is to describe lessons learned from applying a KT approach to help implement an EEI for clinical reasoning in medical students.

METHODS

Using the Knowledge to Action framework, we designed and implemented an EEI intended to support the development of students' clinical reasoning skills in a renewed medical curriculum. Using mixed-methods design, we monitored students' engagement with the EEI longitudinally through a platform log; we conducted focus groups with students and stakeholders, and observed the unfolding of the implementation and its continuation. Data are reported according to six implementation outcomes: Fidelity, Feasibility, Appropriateness, Acceptability, Adoption, and Penetration.

RESULTS

Students spent a mean of 24 min on the activity (fidelity outcome) with a high completion rate (between 75% and 95%; feasibility outcome) of the entire activity each time it was done. Focus group data from students and stakeholders suggest that the activity was acceptable, appropriate, feasible, adopted and well-integrated into the curriculum.

DISCUSSION

Through the process we observed the importance of having a structuring framework, of working closely and deliberatively with stakeholders and students, of building upon concurrent evaluations in order to adapt iteratively the EEI to the local context and, while taking students' needs into consideration, of upholding the EEI's core educational principles.