The authors trace the intellectual history of evidence-based medicine to its early definition, which emphasized the careful, accurate, and judicious application of the best available evidence to decisions for individual patients. That formulation, they note, placed excessive weight on the published literature as the sole source of clinical knowledge and neglected the vital role of physician experience, an imbalance that drew criticism from practicing clinicians. A more comprehensive redefinition followed in 1996, when the epidemiologists David Sackett and Muir Gray described evidence-based medicine as the conscientious, explicit, and judicious use of current best evidence in decisions about the care of individual patients, insisting that physicians integrate research evidence, clinical expertise, and patient preferences. This three-pillar model, together with the familiar five-step workflow of formulating answerable questions, retrieving evidence, critically appraising it, applying it, and evaluating outcomes, was designed to help clinicians transcend the limits of personal experience by drawing on the collective findings of global research.

But as the framework spread worldwide, its structural weaknesses came into sharper focus. The first is the tension between the average and the individual. Evidence-based medicine relies heavily on population-based studies that reflect average outcomes, yet clinicians treat unique human beings whose biology, comorbidities, and circumstances may differ substantially from any trial population. When average results are applied to individual patients, the clinician’s judgment becomes critical, but those experiential elements were largely underemphasized in the early framework. The second weakness is statistical. Meta-analyses commonly use tools such as Cochran’s Q to assess heterogeneity among studies, yet Q is sensitive to the number of studies included and tends to increase with sample size, potentially generating false-positive conclusions about heterogeneity. The authors also cite publication bias, small-sample distortion, and instability arising from the choice between fixed-effect and random-effects models, all of which can compromise the reliability of pooled analyses that sit at the top of the traditional evidence hierarchy.

Even when the evidence itself is sound, translating it into practice proves difficult. The paper identifies four recurring barriers: delayed access to new knowledge amid information overload and time pressure, limited skills in interpreting and applying evidence, a mismatch between research findings and real-world clinical realities, and communication gaps with patients. The exponential growth of medical knowledge makes it increasingly hard for clinicians burdened with heavy workloads to stay current, and even when up-to-date evidence is available, converting it into individualized care remains a complex task, particularly in primary care. More critically, many studies produce results that are statistically significant yet lack practical clinical relevance, a disconnect captured in the phrase statistical significance does not equal clinical significance. The problem is especially pronounced in chronic disease management and geriatric medicine, where rigid application of the best available evidence frequently fails to deliver optimal benefit to the individual patient.

The evidence system itself, the authors argue, is more fragile than its reputation suggests. Research quality varies widely, and the absence of a unified evaluation framework complicates appraisal. Randomized controlled trials, regarded as the gold standard, are constrained by strict inclusion and exclusion criteria, so their conclusions may have limited applicability in real-world settings marked by population heterogeneity, methodological discrepancies, and implementation biases. Systematic reviews and meta-analyses, though ranked as higher-level evidence, often involve selective inclusion of published positive findings while neglecting substantial bodies of unpublished negative data, a form of confirmatory analysis that can amplify the design flaws of the original studies. The academic evaluation system compounds the problem: studies in high-impact journals tend to prioritize novelty over clinical practicality, leading decision-makers to overlook pragmatic evidence that could genuinely improve patient outcomes.

Against this backdrop, Qin and colleagues present evidence-informed medicine not as a rejection of evidence-based medicine but as its critical and evolutionary advancement. The central move is philosophical: where evidence-based medicine asks whether there is evidence, evidence-informed medicine asks whether that evidence is truly applicable to this specific patient. The framework rejects rigid evidence supremacy in favor of a dynamic, context-sensitive appraisal of the spatial and temporal limits of evidence. Even Level I systematic reviews of randomized trials are constrained by the populations, settings, and technological contexts of the time. A surgical technique validated a decade ago may have been superseded by minimally invasive alternatives, and high-cost interventions recommended by guidelines may be infeasible in under-resourced hospitals. The authors liken this dialectical scrutiny to the reflective corroboration model used in forensic reasoning and to the traditional Chinese medical principle of syndrome differentiation and treatment, which demands contextualized revalidation of knowledge within each patient’s specific circumstances.

A second pillar of the new paradigm is a shift from the pursuit of statistical truth to pragmatic utility. Where evidence-based medicine emphasizes thresholds such as a P value below 0.05, evidence-informed medicine underscores net clinical benefit. The authors offer a concrete example: a hypotensive drug might lower systolic blood pressure by 10 millimeters of mercury with strong statistical significance, yet simultaneously raise hospitalization risk by causing hypokalemia. When trial outcomes diverge from real-world evidence, or when disease phenotypes shift across stages, clinicians must construct what the authors describe as a three-dimensional decision model: dissecting the contextual relevance of statistical findings, weighing trade-offs between biomarker improvements and adverse events, and synthesizing patient-specific features such as basal metabolism, medication adherence, and personal values. Evidence, in this view, is transformed from an absolute truth into decision-making material, bridging the gap between the claim that a treatment is effective and the judgment that it is most beneficial for this patient.

The framework also confronts uncertainty and the doctor-patient relationship directly. Clinical reality is often ambiguous, incomplete, and complex, and decisions must still be made when high-quality evidence is absent; in such cases, accumulated clinical experience may provide the best available, and sometimes the only, evidence. The authors argue that physicians should engage in iterative inquiry and reflection rather than rigidly applying population averages to unique cases. On the relational side, they advocate a therapeutic partnership in which the physician acts not merely as a conduit of evidence but as a listener and interpreter of patient experience, and the patient becomes an active co-participant rather than a passive recipient. They note that implementing evidence-based protocols in China faces distinct sociocultural challenges, including demands for unnecessary tests, family concealment of diagnoses, and hierarchical barriers among medical staff, all of which require genuine therapeutic dialogue rather than mechanistic protocol execution.

To operationalize the synthesis, the authors propose an integrated practice pathway with three levels of transformation. First, data must become facts through critical recontextualization: clinicians must recognize, for example, that trial results based on strict inclusion criteria may not generalize to elderly patients with multiple comorbidities. Second, consensus must be advanced into personalized judgment: when guidelines built on population evidence meet patients with complex comorbidities and diverse values, experienced physicians may, under close monitoring, explore treatments suggested by basic research that lack high-level clinical validation, collecting efficacy data to enrich the evidence base. Third, technology must become medical art: clinicians should translate evidence into understandable options, using absolute risk reduction rather than relative risk reduction to prevent patients from being misled by exaggerated efficacy claims, and adapting to resource constraints when the best available evidence is unaffordable. Shared decision-making, built on understanding the patient’s narrative of illness, is identified as the core practice of the integrated model, exemplified in cancer care where survival statistics must be weighed against quality of life, treatment intensity, and financial burden.

The authors also assign artificial intelligence a supporting role, arguing that its ultimate purpose is not to replace physicians’ inquiry-based wisdom and empathy but to relieve the burden of information processing and provide unprecedented decision support, freeing clinicians to focus on higher-level judgment, communication, and relationship building. They frame the overall shift as a triple leap: in thinking, from passive adherence to evidence toward proactive interrogation of its applicability, timeliness, and cost-effectiveness; in goals, from statistical significance toward net real-world benefit and quality of life; and in evidence cognition, from a static hierarchy toward a dynamic, iterative network integrating basic research, real-world data, and patient-reported experience. Guided by a philosophical triad of truth, goodness, and beauty, the integrated model, in the authors’ metaphor, treats evidence-based medicine as well-crafted bricks and masonry standards, while the new paradigm supplies the architectural blueprint, requiring the physician to consider the whole person, the patient’s preferences, and the local clinical context. Whether the profession embraces that blueprint may determine how medicine balances scientific rigor with humanistic care in the decades ahead.

Subject of Research: A proposed shift from evidence-based medicine to evidence-informed medicine in clinical decision-making

Article Title: From evidence-based medicine to evidence-informed medicine

Article References: Qin, N., Hou, L., Yang, Q., & Yang, Z. (2026). From evidence-based medicine to evidence-informed medicine. Holistic Integrative Oncology, 5(1), Article 42. https://doi.org/10.1007/s44178-026-00262-z

Image Credits: AI Generated

DOI: 10.1007/s44178-026-00262-z

Keywords: evidence-based medicine, evidence-informed medicine, clinical decision-making, randomized controlled trials, meta-analysis, patient values, shared decision-making, holistic integrative medicine, medical humanities, personalized medicine, clinical guidelines, artificial intelligence