Volume 10, Issue 1 - Special issue: The Future of Diagnosis; Issue Editors: Mark Graber, Ava L. Liberman, Andrew Olson, Andrew S. Parsons, Verity Schaye

Background The environment in which clinicians provide care and think about their patients is a crucial and undervalued component of the diagnostic process. Content In this paper, we propose a new conceptual model that links work conditions to clinician responses such as stress and burnout, which in turn impacts the quality of the diagnostic process and finally patient diagnostic outcomes. The mechanism for these interactions critically depends on the relationship between working memory (WM) and long-term memory (LTM), and ways WM and LTM interactions are affected by working conditions. Summary We propose a conceptual model to guide interventions to improve work conditions, clinician reactions and ultimately diagnostic process, accuracy and outcomes. Outlook Improving diagnosis can be accomplished if we are able to understand, measure and increase our knowledge of the context of care.

Teaching clinical reasoning has long challenged educators because it requires familiarity with reasoning concepts, experience with describing thinking, and comfort with exposing uncertainty and error. We propose that teachers adopt the cognitive apprenticeship model and a method of disclosing uncertainty known as intellectual streaking. These approaches reflect a shift in the educator’s mindset from transmitting medical knowledge to broadcasting cognition. We provide several examples to guide the adoption of these strategies and make recommendations for teachers and training programs to improve the teaching of clinical reasoning.

As we increasingly acknowledge the ubiquitous nature of uncertainty in clinical practice (Meyer AN, Giardina TD, Khawaja L, Singh H. Patient and clinician experiences of uncertainty in the diagnostic process: current understanding and future directions. Patient Educ Counsel 2021;104:2606–15; Han PK, Klein WM, Arora NK. Varieties of uncertainty in health care: a conceptual taxonomy. Med Decis Making 2011;31:828–38) and strive to better define this entity (Lee C, Hall K, Anakin M, Pinnock R. Towards a new understanding of uncertainty in medical education. J Eval Clin Pract 2020; Bhise V, Rajan SS, Sittig DF, Morgan RO, Chaudhary P, Singh H. Defining and measuring diagnostic uncertainty in medicine: a systematic review. J Gen Intern Med 2018;33:103–15), as educators we should also design, implement, and evaluate curricula addressing clinical uncertainty. Although frequently encountered, uncertainty is often implicitly referred to rather than explicitly discussed (Gärtner J, Berberat PO, Kadmon M, Harendza S. Implicit expression of uncertainty - suggestion of an empirically derived framework. BMC Med Educ 2020;20:83). Increasing explicit discussion of – and comfort with -uncertainty has the potential to improve diagnostic reasoning and accuracy and improve patient care (Dunlop M, Schwartzstein RM. Reducing diagnostic error in the intensive care unit. Engaging. Uncertainty when teaching clinical reasoning. Scholar;1:364–71). Discussion of both diagnostic and prognostic uncertainty with patients is central to shared decision-making in many contexts as well, (Simpkin AL, Armstrong KA. Communicating uncertainty: a narrative review and framework for future research. J Gen Intern Med 2019;34:2586–91) from the outpatient setting to the inpatient setting, and from undergraduate medical education (UME) trainees to graduate medical education (GME) trainees. In this article, we will explore the current status of how the science of uncertainty is taught from the UME curriculum to the GME curriculum, and describe strategies how uncertainty can be explicitly discussed for all levels of trainees.

Management reasoning is distinct from but inextricably linked to diagnostic reasoning in the iterative process that is clinical reasoning. Complex and situated, management reasoning skills are distinct from diagnostic reasoning skills and must be developed in order to promote cogent clinical decisions. While there is growing interest in teaching management reasoning, key educational questions remain regarding when it should be taught, how it can best be taught in the clinical setting, and how it can be taught in a way that helps mitigate implicit bias. Here, we describe several useful tools to structure teaching of management reasoning across learner levels and educational settings. The management script provides a scaffold for organizing knowledge around management and can serve as a springboard for discussion of uncertainty, thresholds, high-value care, and shared decision-making. The management pause reserves space for management discussions and exploration of a learner’s reasoning. Finally, the equity reflection invites learners to examine management decisions from a health equity perspective, promoting the practice of metacognition around implicit bias. These tools are easily deployable, and – when used regularly – foster a learning environment primed for the successful teaching of management reasoning.

Diagnostic reasoning is a foundational ability of health professionals. The goal of enhancing clinical reasoning education is improved diagnostic accuracy and reduced diagnostic error. In order to do so, health professions educators need not only help learners improve their clinical reasoning, but teach them how to develop expert performance. An evidence-based learning strategy that is strongly associated with expert performance is self-regulated learning (SRL). SRL is the modulation of “self-generated thoughts, feelings, and actions that are planned and cyclically adapted to the attainment of personal goals”. At this time, there is little data on the use of SRL to improve diagnostic reasoning. However, there appear to be numerous opportunities to utilize SRL in novel ways to improve diagnostic reasoning given what is already known about this competency. Examples that are discussed include the role SRL can play in simulation, clinical experiences, assessment, and novel technologies such as virtual reality, artificial intelligence, and machine learning. SRL is central to the philosophy that health professionals are life-long learners, as it teaches learners “how to learn”. SRL has the potential to help achieve the goal of improved diagnostic accuracy and reduced diagnostic error.

Diagnostic performance is uniquely challenging to measure, and providing feedback on diagnostic performance to catalyze diagnostic recalibration remains the exception to the rule in healthcare. Diagnostic accuracy, timeliness, and explanation to the patient are essential dimensions of diagnostic performance that each intersect with a variety of technical, contextual, cultural, and policy barriers. Setting aside assumptions about current constraints, we explore the future of diagnostic performance feedback by describing the “minimum viable products” and the “ideal state” solutions that can be envisioned for each of several important barriers. Only through deliberate and iterative approaches to breaking down these barriers can we improve recalibration and continuously drive the healthcare ecosystem towards diagnostic excellence.

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.

Objectives A first step in studying diagnostic delays is to select the signs, symptoms and alternative diseases that represent missed diagnostic opportunities. Because this step is labor intensive requiring exhaustive literature reviews, we developed machine learning approaches to mine administrative data sources and recommend conditions for consideration. We propose a methodological approach to find diagnostic codes that exhibit known patterns of diagnostic delays and apply this to the diseases of tuberculosis and appendicitis. Methods We used the IBM MarketScan Research Databases, and consider the initial symptoms of cough before tuberculosis and abdominal pain before appendicitis. We analyze diagnosis codes during healthcare visits before the index diagnosis, and use k-means clustering to recommend conditions that exhibit similar trends to the initial symptoms provided. We evaluate the clinical plausibility of the recommended conditions and the corresponding number of possible diagnostic delays based on these diseases. Results For both diseases of interest, the clustering approach suggested a large number of clinically-plausible conditions to consider (e.g., fever, hemoptysis, and pneumonia before tuberculosis). The recommended conditions had a high degree of precision in terms of clinical plausibility: >70% for tuberculosis and >90% for appendicitis. Including these additional clinically-plausible conditions resulted in more than twice the number of possible diagnostic delays identified. Conclusions Our approach can mine administrative datasets to detect patterns of diagnostic delay and help investigators avoid under-identifying potential missed diagnostic opportunities. In addition, the methods we describe can be used to discover less-common presentations of diseases that are frequently misdiagnosed.

In this op-ed, we discuss the advantages of leveraging natural language processing (NLP) in the assessment of clinical reasoning. Clinical reasoning is a complex competency that cannot be easily assessed using multiple-choice questions. Constructed-response assessments can more directly measure important aspects of a learner’s clinical reasoning ability, but substantial resources are necessary for their use. We provide an overview of INCITE, the Intelligent Clinical Text Evaluator, a scalable NLP-based computer-assisted scoring system that was developed to measure clinical reasoning ability as assessed in the written documentation portion of the now-discontinued USMLE Step 2 Clinical Skills examination. We provide the rationale for building a computer-assisted scoring system that is aligned with the intended use of an assessment. We show how INCITE’s NLP pipeline was designed with transparency and interpretability in mind, so that every score produced by the computer-assisted system could be traced back to the text segment it evaluated. We next suggest that, as a consequence of INCITE’s transparency and interpretability features, the system may easily be repurposed for formative assessment of clinical reasoning. Finally, we provide the reader with the resources to consider in building their own NLP-based assessment tools.