Joseph Rencic

In the decade since the National Academies of Sciences, Engineering, and Medicine (NASEM) report Improving Diagnosis in Health Care , substantial progress has been made in understanding and teaching diagnostic reasoning. This manuscript reviews key advancements in the science and theory of clinical reasoning, including the shift from exclusive focus on individual cognitive models to those that embrace context and team-based approaches. Recent innovations in diagnosis education, such as development of formal competencies, use of structured reflection, and approaches to assessment are discussed. Despite these gains, challenges remain in translating theory into practice, particularly in curricular innovation and implementation, faculty development, and assessment. The emergence of generative artificial intelligence presents both opportunities and imperatives for reimagining diagnosis education. The authors call for sustained efforts to embed diagnostic excellence across health professions education, emphasizing interprofessional collaboration, patient engagement, and system-level reform to reduce diagnostic error and improve outcomes.

Illness scripts are mental networks of information about medical diseases and are used by clinicians to efficiently make diagnostic and treatment decisions. The traditional components of an illness script include the epidemiologic risk factors, pathophysiology, and clinical findings of a disease [Feltovich PJ, Barrows HS. Issues of generality in medical problem solving. In: Schmidt HG, De Volder ML, editors. Tutorials in problem-based learning. Assen/Maastricht: Van Gorcum; 1984:128–42 pp]. Here, we coin the term illness script builder templates (ISBTs), which are educational tools to help learners develop their illness scripts. While the actual concept of ISBTs is not new, we aim to explicitly distinguish these discrete learning tools from illness scripts themselves, which are mental frameworks. ISBTs have significant pedagogic value. They are being more widely adopted in clinical curricula in medical school, as they have been well-received by students and educationally effective. ISBTs allow the integration of new knowledge to be intuitive, as they take advantage of our predilection toward story-based learning. Limitations to ISBTs also exist, especially when considering their optimal length and structure as well as complexity for different levels of learners. We additionally explore the specific strengths and limitations of ISBTs and propose strategies to maximize their use in education and clinical practice.

Clinical reasoning is an essential expertise of health care professionals that includes the complex cognitive processes that lead to diagnosis and management decisions. In order to optimally teach, learn, and assess clinical reasoning, it is imperative for teachers and learners to have a shared understanding of the language. Currently, educators use the terms schema and framework interchangeably but they are distinct concepts. In this paper, we offer definitions for schema and framework and use the high-stakes field of aviation to demonstrate the interplay of these concepts. We offer examples of framework and schema in the medical education field and discuss how a clear understanding of these concepts allows for greater intentionality when teaching and assessing clinical 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.

The importance of clinical reasoning in patient care is well-recognized across all health professions. Validity evidence supporting high quality clinical reasoning assessment is essential to ensure health professional schools are graduating learners competent in this domain. However, through the course of a large scoping review, we encountered inconsistent terminology for clinical reasoning and inconsistent reporting of methodology, reflecting a somewhat fractured body of literature on clinical reasoning assessment. These inconsistencies impeded our ability to synthesize across studies and appropriately compare assessment tools. More specifically, we encountered: 1) a wide array of clinical reasoning-like terms that were rarely defined or informed by a conceptual framework, 2) limited details of assessment methodology, and 3) inconsistent reporting of the steps taken to establish validity evidence for clinical reasoning assessments. Consolidating our experience in conducting this review, we provide recommendations on key definitional and methodologic elements to better support the development, description, study, and reporting of clinical reasoning assessments.

Article Ecological psychology: diagnosing and treating patients in complex environments was published on September 1, 2020 in the journal Diagnosis (volume 7, issue 3).

Article Expanding boundaries: a transtheoretical model of clinical reasoning and diagnostic error was published on 24 Jun 2020 in the journal Diagnosis (, Ahead of Print).