The threshold approach rests on a deceptively simple piece of decision theory. For any suspected diagnosis, a clinician can compare the expected benefit of treatment against the expected harm. If the benefit-to-harm ratio is high, treatment is justified even at low probability of disease; if the ratio is low, treatment is warranted only at high probability. These two probabilities define the testing threshold and the treatment threshold. Below the testing threshold, the diagnosis is too improbable to pursue, and further testing adds more risk than information. Above the treatment threshold, the diagnosis is probable enough that the benefits of therapy outweigh the harms of being wrong. Between the two thresholds lies the zone of diagnostic uncertainty, where additional tests are genuinely useful because their results can move the probability across a boundary that changes management.

Rohlfsen and Parsons contend that this elegant structure is being eroded not by new evidence but by the sheer volume of modern clinical information. They invoke entropy in its information-theoretic sense, the average uncertainty contained in a probability distribution. Every laboratory result, imaging study, and vital sign carries information, but the diagnostic value of any single result depends on what the clinician already believes. As the number of available data points multiplies, the average uncertainty in the aggregate picture can rise even when each individual result seems informative. The authors suggest that clinicians and educators have begun to mistake the accumulation of data for the reduction of uncertainty, a confusion that the threshold approach was designed to prevent.

The letter draws on a substantial body of empirical work on diagnostic reasoning. Peter Han, William Klein, and Nikhil Arora, writing in Medical Decision Making in 2011, developed a conceptual taxonomy of uncertainty in health care, distinguishing between the uncertainty of probability, the ambiguity of evidence, and the complexity of the clinical situation. Their framework makes clear that not all uncertainty is statistical. A clinician may know the sensitivity and specificity of a test with precision yet remain uncertain about how those parameters apply to a particular patient whose presentation does not match the populations in which the test was validated. The threshold approach addresses only the probabilistic component, and Rohlfsen and Parsons argue that medical education has overemphasized that component at the expense of the others.

Empirical studies have documented the consequences. Andrew Meyer, Vishal Payne, David Meeks, Rakesh Rao, and Hardeep Singh, in a 2013 vignette study published in JAMA Internal Medicine, examined physicians’ diagnostic accuracy, confidence, and resource requests. They found that diagnostic confidence was often poorly calibrated with respect to actual accuracy, and that physicians frequently requested additional resources even when their stated confidence in a diagnosis was high. The finding is difficult to reconcile with a purely rational threshold model, in which resource requests should scale with the distance between the current probability and the relevant threshold. The authors of the letter suggest that the discrepancy reflects a failure to teach clinicians where their thresholds actually lie, leaving them to set boundaries by intuition rather than by explicit reasoning.

Jeffrey Stojan, Mahan Daniel, Sara Hartley, and Larry Gruppen, writing in Medical Education in 2022, developed a threshold model of dealing with uncertainty in clinical reasoning, examining the roles of experience and task framing. Their work suggests that experienced clinicians do not simply have better-calibrated thresholds; they also frame diagnostic tasks differently, sometimes treating a case as a learning exercise and sometimes as a high-stakes decision, with corresponding shifts in how much uncertainty they are willing to tolerate before acting. The letter argues that this variability is not a defect but a feature of expert judgment, and that pedagogy should make it explicit rather than pretend that a single threshold applies to every situation.

Rohlfsen and Parsons also reference the 5L framework of diagnostic reasoning, developed by Olivia Brumfield, Ahmed Mohamed, Sheng He, and Cory Rohlfsen and published in the Journal of Hospital Medicine in 2026. The framework provides a stepwise scaffold to support clinician educators, breaking diagnostic reasoning into discrete stages that can be taught and assessed. The letter situates the threshold approach within this pedagogical structure, suggesting that the 5L framework gives educators a language for discussing where a diagnostic probability stands relative to a decision threshold, and what kind of evidence would move it. Without such a language, the authors contend, discussions of diagnostic reasoning tend to collapse into vague appeals to clinical gestalt, which students cannot replicate and which resist assessment.

The letter also invokes the ADEPT study protocol, a multicenter prospective quality and safety program described by Jeffrey Schnipper and colleagues in the Journal of Hospital Medicine in 2023, designed to improve diagnostic processes in medical inpatients through prevention and teamwork. The letter suggests that programs like ADEPT represent the systems-level counterpart to the threshold approach, embedding diagnostic reasoning into the structure of hospital care rather than leaving it to the individual clinician. The authors argue that diagnostic excellence requires both the individual judgment of the threshold approach and the organizational support of programs that make diagnostic processes visible, auditable, and improvable.

The implications extend beyond internal medicine. Rohlfsen and Parsons write in the context of predictive medicine and the philosophy of medicine, subjects that the journal’s machine-learning-driven related-articles system associates with the letter. The authors suggest that as diagnostic algorithms become more prevalent, the threshold approach may need to be renegotiated. An algorithm can output a probability, but it cannot set a threshold, because threshold setting depends on the benefit-to-harm ratio of treatment, which is a value judgment about what matters to a particular patient. The letter implies that clinicians who delegate probability estimation to algorithms must retain the judgment required to set thresholds, and that medical education should teach that judgment explicitly rather than assume it emerges from exposure to clinical work.

The letter, published on 8 September 2026, received no funding sources and reports no conflicts of interest. Rohlfsen wrote the initial draft and Parsons reviewed and edited it, with both contributing to the conceptualization of the submission. The work is situated within a tradition of clinical decision theory that stretches back to the Pauker and Kassirer paper, and the authors suggest that the tradition remains vital precisely because the problems it addresses have not been solved by technology. The entropy of modern clinical data has increased, the authors argue, but the fundamental question of when a diagnosis is probable enough to act upon remains a matter of judgment, and judgment is something that must be taught.

Subject of Research: Beyond Entropy: Decision Threshold, Judgment, and Pedagogy

Article Title: Beyond Entropy: Decision Threshold, Judgment, and Pedagogy

Article References: Rohlfsen, C., & Parsons, A. S. (2026). Beyond Entropy: Decision Threshold, Judgment, and Pedagogy. Journal of General Internal Medicine. https://doi.org/10.1007/s11606-026-10753-4

Image Credits: AI Generated

DOI: 10.1007/s11606-026-10753-4

Keywords: Beyond, Entropy, Decision, Threshold, Judgment, Pedagogy, scientific research