How Doctors Think: New Case Exercise Dissects the Hidden Logic of Diagnosis

Every day, in hospitals and clinics around the world, physicians perform one of the most cognitively demanding tasks in any profession: they take fragments of information—a symptom here, a lab value there, an ambiguous shadow on an imaging scan—and weave them into a diagnosis that will determine whether a patient lives or dies. Most of the time this process works so smoothly that it looks effortless. But when it fails, the consequences can be devastating, and diagnostic error remains one of the most underappreciated sources of preventable harm in modern medicine. A new paper published in the Journal of General Internal Medicine by Andrew Sanchez of Beth Israel Deaconess Medical Center and Harvard Medical School, Andrew Parsons of the University of Virginia School of Medicine, and Anna Fretz of UCLA offers readers a rare, structured window into that hidden mental machinery, using a single teaching case to illuminate how expert clinicians actually reason.

The article, titled Reading Between the Lines: An Exercise in Clinical Reasoning, appeared online on 13 October 2026 and belongs to a distinctive genre within medical education literature: the blinded case discussion. In this format, one clinician—the discussant—is presented with a real patient’s case piece by piece, without knowing the eventual diagnosis, and is asked to think aloud as the clinical picture unfolds. Everything the discussant says is recorded and analyzed, and the resulting transcript becomes a teaching document in its own right. Fretz served as the blinded discussant for this exercise, while Sanchez analyzed the discussion and Parsons, a recognized scholar of clinical reasoning, reviewed the work and created a figure tracking how diagnostic uncertainty changed over the course of the encounter. The format’s power lies in its honesty: instead of presenting diagnosis as a tidy retrospective narrative, it exposes the false starts, the anchoring on early hypotheses, and the incremental revisions that characterize real clinical thought.

At the heart of the paper is a concept that has been gaining traction in the medical education literature: entropy. Borrowed from information theory, entropy in this context is a measure of uncertainty—of how many plausible explanations remain on the diagnostic table at any given moment. A recent narrative review by Rohlfsen, Shannon, and Parsons in the same journal framed clinical decision-making through the lens of decision theory and entropy, arguing that skilled diagnosticians can be understood as agents who progressively reduce uncertainty by gathering information that discriminates between competing hypotheses. Each test, each examination finding, and each fragment of history either removes entropy, by eliminating possibilities, or fails to do so, leaving the diagnostic space crowded. The new exercise applies this framework to a live case, and Parsons’s figure visualizes that entropy dropping over time as the discussant’s reasoning converges—or fails to converge—on the correct answer.

This quantitative framing matters because it challenges a long-standing assumption in medical training: that expertise is simply a matter of accumulating pattern recognition. Psychologists and education researchers have described how experienced clinicians build rich mental shortcuts, sometimes called illness scripts, that allow instantaneous recognition of familiar diseases. But the references underpinning the new paper point to a more nuanced picture. A study by McQuade and colleagues in the Journal of Hospital Medicine examined the characteristics that differentiate how novices and experts synthesize what researchers call the problem representation—the compressed summary of a patient’s illness that serves as the hinge between raw data and diagnosis. The finding that experts and novices differ not merely in how much they know but in how they structure what they know has profound implications for how medical students should be taught. Related work by Dreicer, Parsons, and colleagues in Perspectives on Medical Education has argued that educators frequently conflate the terms framework and schema, treating them as synonyms when they describe distinct cognitive structures, and that cleaning up this vocabulary is a prerequisite for teaching reasoning well.

While the full text of the exercise is available to journal subscribers, the paper’s reference list reveals the diagnostic territory the case explores, and it is dramatic territory indeed. Among the cited works are reports on pulmonary alveolar proteinosis triggered by immunosuppressive therapy in a transplant recipient, on lymphangitic carcinomatosis in which cancer cells spread through the lymphatic channels of the lung, and on pulmonary tumor embolism, a rare and notoriously elusive condition in which tumor cells obstruct the pulmonary vasculature. These are not the bread-and-butter diagnoses that announce themselves clearly; they are the great mimics of pulmonary medicine, conditions whose presenting complaint—progressive shortness of breath, or dyspnea—can be caused by dozens of more common entities. Springer’s own subject indexing for the article lists dyspnea, diagnostic markers, radiology, and predictive medicine among its related topics, underscoring that the case turns on the interpretation of imaging and the disciplined narrowing of a wide differential diagnosis.

The challenge posed by such cases is precisely why the authors chose the entropy framework. When a patient presents with breathlessness and an abnormal chest image, the initial diagnostic space is enormous: infection, heart failure, clot, malignancy, interstitial lung disease, and many rarer entities all compete. A clinician who orders tests at random will generate data without reducing uncertainty, because much of that data is redundant—what information theorists would call high in entropy overlap. The cited work on entropy removal of medical diagnostics and entropy removal of clinical features, published in Scientific Reports in 2024 and 2025, explores exactly this problem: how to select the sequence of clinical features and tests that maximally shrinks the hypothesis space with each new piece of information. In that light, the expert clinician resembles an efficient search algorithm, and diagnostic excellence becomes a measurable property of the reasoning process rather than an ineffable gift.

The paper also engages with the darker side of diagnostic cognition: the systematic errors to which even experienced physicians succumb. One cited reference, by de Alencar Neto and Santos-Neto in the Journal of General Internal Medicine, examines what the authors call the post hoc pitfall—rethinking how sensitivity and specificity, the statistical properties of diagnostic tests, actually behave in clinical practice when tests are interpreted in sequence rather than in isolation. Another cited work, by Gheihman, Johnson, and Simpkin in Medical Teacher, offers twelve tips for thriving in the face of clinical uncertainty, an acknowledgment that ambiguity is not a failure state to be eliminated but a permanent condition of medical work to be managed. The exercise format makes these hazards visible in real time: readers watch the discussant weigh the possibility of anchoring too early on a plausible but wrong hypothesis, and see how the deliberate articulation of competing explanations serves as a safeguard against premature closure, one of the most frequently documented contributors to diagnostic error.

Why should anyone outside a hospital care about the fine structure of clinical reasoning? The answer lies in the scale of the problem. Diagnostic errors contribute substantially to preventable morbidity and mortality, and most of these errors are cognitive rather than technical—the right test was available, but the wrong conclusion was drawn from it. Teaching programs have traditionally transmitted reasoning skills by osmosis, hoping that trainees would absorb diagnostic acumen from watching skilled mentors. Papers like this one represent a shift toward making the invisible visible: by transcribing and analyzing expert reasoning against a framework of entropy, problem representation, and schema, they give educators concrete material to teach and learners concrete models to imitate. The authors’ explicit division of labor—discussant, analyst, and reasoning scholar—mirrors the collaborative structure that medical education researchers have advocated for turning individual cases into generalizable lessons.

There is also a forward-looking dimension. As artificial intelligence systems move into clinical practice, offering differential diagnoses and flagging imaging findings, a pressing question is whether machines and humans reason in comparable ways. The entropy framework provides a common language: an algorithm that selects the maximally informative next test and a physician who asks the single most discriminating question are, in principle, doing the same thing. Studies of how experts and novices differ in synthesizing problem representations, and of how uncertainty can be quantified and tracked through a diagnostic encounter, will inform how such tools are evaluated and integrated. In the meantime, the humble blinded case discussion remains one of the most powerful instruments available for studying the mind of the diagnostician. Reading between the lines of this exercise, readers find not just the solution to one patient’s puzzle but a demonstration that the process of reaching it can itself be examined, measured, and taught—and that the difference between a good diagnosis and a missed one often lies in the disciplined management of uncertainty, one piece of information at a time.

Subject of Research: Clinical reasoning and diagnostic decision-making using a blinded case exercise and entropy-based analysis of uncertainty

Article Title: Reading Between the Lines: An Exercise in Clinical Reasoning

Article References: Sanchez, A., Parsons, A., & Fretz, A. (2026). Reading Between the Lines: An Exercise in Clinical Reasoning. Journal of General Internal Medicine. https://doi.org/10.1007/s11606-026-10914-5

Image Credits: AI Generated

DOI: 10.1007/s11606-026-10914-5

Keywords: clinical reasoning, diagnosis, diagnostic error, entropy, medical education, dyspnea, pulmonary medicine, problem representation, clinical uncertainty, radiology, Journal of General Internal Medicine, case discussion