The research team conducted individual semi-structured interviews with thirty-four clinicians spanning the entire training continuum, from medical students to postgraduate year one through five residents in internal medicine, and on to attending physicians in internal medicine and critical care medicine at McGill University-affiliated academic hospitals. The interviews were interpreted using applied thematic analysis, a rigorous qualitative method that allows researchers to identify recurring patterns in how participants describe their experiences. What emerged was a developmental arc, a story of how understanding of goals-of-care discussions transforms as clinicians gain experience and seniority.

The analysis organized its findings into four central themes that map the trajectory from novice to expert. The first traces a shift from concrete to abstract conceptualizations of what a goals-of-care discussion actually is. Less experienced trainees frequently conflated goals-of-care conversations with code status discussions, reducing a rich, patient-centered dialogue to a narrow question about resuscitation preferences. For these novices, the task was often perceived as a form to be completed, a checkbox in the admission paperwork rather than an exploration of what matters most to a patient facing serious illness. More senior clinicians, by contrast, described goals of care in abstract and contextual terms, framing these conversations as ongoing negotiations that integrate prognosis, patient values, family dynamics, and clinical uncertainty.

The second theme captures a parallel movement from performative to organic approaches. Early trainees described following scripted frameworks, reciting standardized phrases, and treating the conversation as a performance to be evaluated by a supervising attending. This performative orientation, while a reasonable starting point, left them rigid and easily thrown when a patient or family member deviated from the expected script. Experienced physicians described something fundamentally different: an organic, responsive conversation that flexes to the emotional and informational needs of the moment. They spoke of reading the room, pacing the disclosure of difficult information, and weaving goals-of-care discussions naturally into the fabric of clinical care rather than isolating them as discrete, formulaic events.

The third theme, from follower to leader in shared decision-making, addresses one of the most consequential differences between junior and senior clinicians. Novices were markedly less comfortable offering guidance and tended to position themselves as passive facilitators, presenting options without direction and hoping the patient would choose. This hesitation, the researchers suggest, may reflect both a lack of clinical confidence and an incomplete understanding of prognosis. Attending physicians, drawing on years of accumulated judgment, demonstrated a greater willingness to make recommendations, to share their expert opinion while still honoring patient autonomy, and to steer the conversation toward medically appropriate and patient-aligned decisions. This evolution from deference to directed guidance represents a core component of what experts mean by clinical judgment in serious illness communication.

The fourth theme concerns how learning itself happens, described as scaffolding between formal and informal development. Participants agreed almost universally that goals-of-care skills develop as a career-long endeavor, shaped far more by observation, feedback, and lived clinical experience than by structured instruction. Formal training on this topic was described as limited, fragmented, and often arriving too late. Trainees reported watching senior colleagues conduct these conversations, debriefing afterward with attendings when circumstances allowed, and gradually building competence through repetition and reflection. Yet the study found that many trainees were expected to lead goals-of-care discussions independently early in their training, before adequate scaffolding had been established, raising pointed concerns about patient care and trainee distress.

The implications of this developmental mismatch are significant. Internal medicine is, by the nature of its patient population, a specialty where goals-of-care conversations are frequently required. Hospitalized patients with advanced illness, uncertain prognoses, and complex family situations present these dilemmas daily. If junior physicians are being sent into these encounters with a conceptualization limited to code status and a performative, script-dependent approach, both patients and trainees are being underserved. Prior research cited by the authors, including studies of residents’ code status discussion skills and randomized trials of simulation-based communication training, has shown that these skills can be taught, but the new findings suggest that isolated interventions may not be enough. What is needed, the authors argue, is a structured, stage-specific, longitudinal curriculum that meets learners where they are in their development.

Such a curriculum would look markedly different at each stage. For medical students and early residents, the priority would be building accurate conceptual foundations, decoupling goals of care from code status, and introducing frameworks that emphasize eliciting patient values before discussing interventions. For mid-level residents, deliberate practice with simulated patients and observed conversations with structured feedback could accelerate the shift from performative to organic approaches. For senior residents and early attendings, coaching on recommendation-giving and prognostic communication would support the transition to leadership in shared decision-making. The study’s participants themselves called for exactly this kind of sequenced, longitudinal design, echoing broader movements in medical education such as competency-based frameworks and longitudinal coaching programs that have been adopted in Canada and elsewhere.

The study also carries a broader message about the nature of expertise in medicine. Communication skills are often treated as soft skills, assumed to be absorbed along the way rather than rigorously taught. This research demonstrates that the growth from novice to expert in goals-of-care discussions follows a describable, predictable developmental progression, one that can be anticipated and supported rather than left to chance. The differences in conceptualization, approach, and decision-making role between junior and senior clinicians are not merely matters of personality or confidence. They reflect distinct cognitive and professional stages, each with its own learning needs. Recognizing this progression allows educators to design training that is developmentally attuned, rather than one-size-fits-all lectures delivered at a single point in training.

As populations age and chronic serious illness becomes an ever-larger share of medical practice, the ability to conduct skillful, compassionate goals-of-care conversations will only grow in importance. This study, funded by the Fédération des Médecins Résidents du Québec Research Grant and presented in preliminary form at the International Conference on Residency Education in Ottawa, provides a roadmap for how the medical education community might respond. By treating goals-of-care communication as a career-long developmental arc with structured support at every stage, training programs can ensure that physicians arrive at these pivotal bedside moments not as anxious novices clutching a script, but as confident, nuanced clinicians capable of guiding patients and families through the most consequential decisions of their lives.

Subject of Research: How internal medicine physicians develop goals-of-care discussion skills across the training continuum

Article Title: How Internal Medicine Physicians Learn to Conduct Goals-of-Care Discussions: A Qualitative Study Across the Training Continuum

Article References: Lee, C. B., Snell, L., Li, K. X., Jayaraman, D., & Nugus, P. (2026). How Internal Medicine Physicians Learn to Conduct Goals-of-Care Discussions: A Qualitative Study Across the Training Continuum. Journal of General Internal Medicine. https://doi.org/10.1007/s11606-026-10696-w

Image Credits: AI Generated

DOI: 10.1007/s11606-026-10696-w

Keywords: goals of care, internal medicine, medical education, communication skills, qualitative study, shared decision-making, code status, residency training, palliative care, clinical judgment, longitudinal curriculum, physician development

The original review had presented entropy as a way to quantify uncertainty in medical reasoning, describing it as offering a concise summary of uncertainty that nonetheless lacks a built-in mechanism for action. That admission, the Singapore authors contend, is precisely where the trouble begins. In clinical practice, uncertainty is not merely a quantity to be measured; it is a condition to be navigated, weighed against risks, benefits, and patient values, and ultimately resolved into a decision: treat, test, observe, or reassure. A framework that summarizes uncertainty without specifying how to act on it, they argue, risks creating what they call an illusion of precision, presenting clinicians with a single descriptive number that feels rigorous but resists translation into a concrete clinical act.

The technical heart of the critique lies in a comparison between entropy and Bayesian inference, the dominant framework for reasoning under uncertainty in medicine and statistics. Bayesian models produce state-specific probabilities: the probability, for instance, that a patient with chest pain is having a myocardial infarction versus a benign cause. These actionable probabilities can then be compared against established decision thresholds, most famously formalized by Pauker and Kassirer in the New England Journal of Medicine in 1980. The threshold approach defines a testing threshold and a treatment threshold; if the probability of disease falls below the former, the clinician forgoes testing, and if it rises above the latter, treatment proceeds without further diagnostic workup. This architecture converts probability directly into action, providing a rational bridge between belief and behavior.

Entropy, by contrast, collapses an entire probability distribution into a single scalar. In information theory, the Shannon entropy of a diagnostic hypothesis set is maximal when all possibilities are equally likely and minimal when one diagnosis dominates. A high-entropy differential diagnosis tells the clinician that the situation is genuinely uncertain, but it does not say which diagnosis is most probable, what test would most efficiently reduce the uncertainty, or whether further investigation is even warranted given the stakes. Two patients could carry identical entropy values while demanding radically different management: one with a high-mortality condition hovering near a treatment threshold, the other with a trivial condition with little actionable consequence. The letter’s authors argue that this loss of state-specific information is not a minor technicality but an ontological mismatch between the descriptive reach of entropy and the prescriptive demands of clinical judgment.

The critique also engages with the literature on value of information, a family of methods for prioritizing research and testing by quantifying how much a new piece of information would be worth in terms of improved outcomes. Value of information analysis, as codified by Jackson and colleagues in Epidemiologic Methods in 2021, builds explicitly on decision-theoretic foundations, linking the acquisition of information to expected gains in health. Bayesian probability combined with threshold logic naturally accommodates these calculations: knowing a probability and the payoff matrix of actions allows one to compute the expected value of perfect or sample information. Entropy alone, stripped of state-specific probabilities and payoff structures, cannot perform this function. A clinician told that a case has an entropy of 1.7 bits has learned little about what to do next, whereas a clinician told that the probability of disease is 45 percent against a testing threshold of 30 percent knows immediately that more information is worth acquiring.

What makes the letter particularly provocative is its pivot from decision theory to pedagogy. The authors acknowledge that the original review rightly locates entropy’s true promise in standardization and scalability, especially for artificial intelligence systems trained on vast clinical datasets. In that context, entropy can serve as a useful computational statistic, a way for machine learning systems to flag cases of high diagnostic ambiguity, route them to specialists, or measure model confidence. But the authors warn that the vision of an “entropy-based medicine” must be weighed against its potential educational consequences. Medicine has long oscillated between the aspiration to quantify everything and the recognition that its core practice remains an interpretive, human activity. If trainees learn that good clinical reasoning means minimizing a calculated uncertainty value, the letter suggests, they may lose sight of a more important competency: the cultivated ability to tolerate uncertainty and still act responsibly.

That argument draws on a growing body of medical education scholarship, most prominently the 2016 New England Journal of Medicine perspective by Simpkin and Schwartzstein titled “Tolerating uncertainty — the next medical revolution?” That piece argued that discomfort with uncertainty drives a range of pathology in modern medicine, from excessive diagnostic testing and defensive medicine to communication failures and burnout. Uncertainty tolerance, far from being a soft skill, is framed as a professional capacity intimately linked to clinical judgment, effective patient communication, and patient safety. The Singapore authors build directly on this framing: an “entropy-minimization” mindset, they caution, could distract trainees from the deeper goal of becoming comfortable living with ambiguity. In a busy clinical environment, the temptation to chase a single number that promises clarity is strong, and a pedagogy built around minimizing entropy could reinforce precisely the reflexive, test-driven behavior that educators have spent years trying to moderate.

The debate also carries implications for how artificial intelligence tools will be explained and governed in medicine. As machine learning systems become embedded in triage, imaging interpretation, and predictive analytics, measures of model uncertainty such as entropy will increasingly be surfaced to clinicians, perhaps as confidence scores or risk flags. The letter’s warning suggests that how these numbers are taught, contextualized, and displayed will matter enormously. A confidence metric presented without a decision threshold or a treatment implication invites either blind deference or reflexive dismissal. Used well, however, uncertainty quantification can prompt exactly the right kind of reflection: a pause before acting on a low-confidence prediction, a request for a second opinion, or a conversation with the patient about the limits of what is known. The difference lies not in the mathematics but in the professional culture that surrounds it.

None of this amounts to a rejection of information theory in medicine. The letter is explicit in crediting the original review with a valuable service: introducing a complex concept to a general medical audience and sparking a necessary dialogue on the nature of clinical uncertainty. Its authors position their critique as a call for deeper conversation rather than a dismissal, insisting that before the profession embraces new quantitative tools, it must clarify their proper place in a practice that remains both a science and an art. The historical parallel is instructive. Bayesian reasoning took decades to move from statistical journals into bedside teaching, and only became genuinely useful to clinicians once it was paired with threshold frameworks, likelihood ratios, and pretest probability estimation. Entropy, if it follows a similar path, will need its own translation layer: ways of connecting a global uncertainty measure to the specific probabilities, stakes, and values that drive individual decisions.

For now, the Singapore letter stands as a compact but pointed intervention in one of the most consequential conversations in contemporary medicine: how a profession built on judgment should metabolize the quantitative machinery of the information age. Its message resonates well beyond internal medicine, touching any field wrestling with the promise of AI-assisted uncertainty quantification, from radiology to public health modeling. The core claim is deceptively simple. Measuring uncertainty is not the same as managing it, and a number that summarizes doubt without pointing toward action may, in the hands of an overburdened clinician or a trainee still forming professional habits, do more to obscure good judgment than to support it. As hospitals and developers race to embed uncertainty metrics in clinical workflows, this letter insists that the decisive questions are not computational but philosophical and pedagogical: what do we want clinicians to learn when we teach them to measure what they do not know?

Subject of Research: The limitations of entropy as a quantitative measure of clinical uncertainty in medical decision-making, judgment, and education.

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

Article References: Sadasiv, M. S., & Chow, M. (2026). Beyond Entropy: Decision Thresholds, Judgment, and Pedagogy. Journal of General Internal Medicine. https://doi.org/10.1007/s11606-026-10746-3

Image Credits: AI Generated

DOI: 10.1007/s11606-026-10746-3

Keywords: entropy, clinical decision-making, uncertainty, Bayesian inference, decision thresholds, medical education, clinical judgment, artificial intelligence, decision theory, value of information, diagnostic uncertainty, internal medicine