The study, led by Jerome Ndayisenga of the National Reference Laboratory at Biomedical Centre in Kigali, took the form of a secondary qualitative analysis drawing on semi-structured interviews conducted at two secondary-level hospitals: Nyamata Level Two Teaching Hospital and Nyarugenge District Hospital. The researchers recruited physicians involved in antibiotic prescribing through convenience sampling, continuing until data saturation was achieved. In the end, 24 physicians participated, all of them general practitioners under the age of 35. That demographic detail matters: these are the front-line clinicians who handle the bulk of patient encounters in district and teaching hospitals across Rwanda and much of sub-Saharan Africa, often with limited access to laboratory confirmation and without the safety net of specialist backup.

Using an inductive thematic analysis, the team treated individual codes as units of meaning and grouped them into subthemes and overarching themes. Three major themes emerged from the data, and together they sketch a portrait of clinicians caught between competing risks. The first theme, which the authors labeled physician uncertainty tolerance, captured the participants’ concerns about disease progression and the dangers of withholding antibiotics when a bacterial infection could not be ruled out. For a junior physician watching a febrile patient whose condition might deteriorate overnight, the fear of doing nothing can outweigh the abstract, population-level harm of an unnecessary prescription.

The second theme, avoiding unjustified prescription, revealed the defensive strategies some physicians deploy to resist that pressure. Participants described practicing watchful waiting, ordering additional laboratory investigations such as full blood counts and C-reactive protein tests, and consulting senior colleagues before committing to antimicrobial therapy. These behaviors represent deliberate attempts to buy time and gather evidence, converting an intolerable moment of uncertainty into a manageable, staged decision. Yet the study makes clear that such strategies are unevenly applied, depending on the individual clinician’s risk tolerance, the patient’s presentation, and the practical constraints of the hospital in question.

The third theme proved to be the most consequential for antimicrobial stewardship. Participants described feeling safer to treat with antibiotics, adopting an approach the authors characterized as treat first, adjust later. In this mode, physicians initiate definitive antibiotic treatment despite diagnostic uncertainty, planning to revisit the decision once laboratory results arrive or the clinical picture clarifies. The logic is clinically defensible on an individual basis, sepsis and other bacterial infections can kill within hours, but multiplied across thousands of encounters, it becomes a systematic engine of unnecessary antibiotic exposure.

What unites the three themes, the authors argue, is a form of systematic pressure: the urgency to decide when the evidence is not definitive. In secondary-level hospitals, where microbiology capacity is often limited and antimicrobial susceptibility tests may be unavailable or slow, the clinician’s uncertainty cannot simply be resolved by better data. Laboratory investigations such as full blood counts and C-reactive protein measurements provide probabilistic signals rather than definitive answers, and turnaround times may stretch beyond the window in which a treatment decision must be made. The result is a clinical environment in which prescribing becomes the default escape route from ambiguity.

The study’s grounding in Rwanda gives it particular weight in the global conversation about antimicrobial resistance. Low- and middle-income countries bear a disproportionate share of the AMR burden, driven by a combination of high infectious disease prevalence, constrained diagnostic infrastructure, and, in some settings, over-the-counter antibiotic availability. Secondary-level hospitals occupy a critical position in this landscape: they are the first point of formal medical contact for many patients, staffed largely by junior general practitioners, and equipped with laboratories that fall short of the reference facilities found in national hospitals. Understanding how prescribing decisions are made at this level is therefore essential to any realistic stewardship strategy.

The variability the researchers documented is itself a finding. While some physicians prioritized minimizing unnecessary antibiotic exposure, employing watchful waiting and repeated consultation to hold the line, others focused on preventing potential clinical deterioration, treating early and adjusting later. This heterogeneity means that two patients with similar presentations at the same hospital may receive markedly different antibiotic decisions depending on which clinician sees them. From a stewardship perspective, such inconsistency is a problem in its own right, because it undermines guideline adherence and makes prescribing patterns difficult to predict, monitor, or correct.

The authors’ central recommendation follows directly from their data: treatment guidelines should incorporate clearer, more explicit guidance on managing diagnostic uncertainty. Current guidelines tend to assume a level of diagnostic clarity that often does not exist at the point of care, leaving junior physicians to improvise their own risk calculations. Embedding structured decision pathways, for instance, specifying when watchful waiting is safe, which investigations should precede treatment, and how to sequence empirical therapy with planned review, could standardize prescribing practices and reduce the discretionary variability that the study observed. Such guidance would function as a cognitive aid, redistributing some of the decision-making burden from the individual clinician to the institutional protocol.

Beyond the Rwandan context, the study speaks to a growing recognition within antimicrobial stewardship research that prescribing behavior cannot be changed by guidelines and audit alone. Diagnostic uncertainty is a psychological and structural phenomenon, and it demands interventions that operate at both levels: rapid diagnostics that shrink the uncertainty itself, and training and protocols that help clinicians tolerate the residual uncertainty without defaulting to antibiotics. For the junior physicians at the center of this study, every ambiguous fever is a small ethical dilemma between the patient in front of them and the future patients who will inherit the consequences of resistance. The research suggests that the most effective stewardship programs will be those that acknowledge that dilemma, rather than pretending it can be resolved by willpower alone.

Subject of Research: Diagnostic uncertainty and antibiotic prescribing practices among junior physicians in low-resource settings

Article Title: Diagnostic uncertainties as driver of antibiotic prescription in low-resource settings: secondary qualitative analysis of junior physicians’ perspectives

Article References: Ndayisenga, J., Mugabo, E., Ntiyamira, S., Bugingo, J. P., Musabeyesu, G., Sibomana, O., Umuhoza, A., Bimenyimana, P. C., Igizeneza, A., Gahamanyi, N., Gashegu, M., Mukagatare, I., Ngabonziza, J. C. S., & Fitch, M. I. (2026). Diagnostic uncertainties as driver of antibiotic prescription in low-resource settings: secondary qualitative analysis of junior physicians’ perspectives. BMC Health Services Research. https://doi.org/10.1186/s12913-026-15709-2

Image Credits: AI Generated

DOI: 10.1186/s12913-026-15709-2

Keywords: antimicrobial resistance, diagnostic uncertainty, antibiotic prescribing, Rwanda, qualitative research, antimicrobial stewardship, junior physicians, secondary-level hospitals, low-resource settings, clinical decision-making, Diagnostic, uncertainties

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