ReviewJournal of evaluation in clinical practice2026
From Evidence to Value: Integrating Evidence-Based Medicine, Patient Safety, Quality Improvement, Learning Health Systems, and Value-Based Healthcare.
Review in Journal of evaluation in clinical practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
rationaleEvidence-Based Medicine (EBM) has strengthened clinical decision-making, but evidence alone cannot ensure that care is delivered safely, consistently, or in a manner that produces outcomes meaningful to patients. Patient safety, Quality Improvement (QI), Learning Health Systems (LHS), and Value-Based Healthcare (VBHC) have emerged to address these limitations. Understanding their relationships is important for healthcare systems seeking improvement and patient-centred care. AIMS AND
objectivesTo examine the historical development and conceptual relationships among EBM, patient safety, QI, LHS, and VBHC, and to propose an integrated framework for understanding their complementary roles in healthcare quality.
methodA narrative review was conducted using PubMed as a database, supplemented by reference-list searching. Literature published from approximately 1990 to June 2026 was considered. Searches combined terms related to EBM, patient safety, QI, LHS, VBHC, implementation science, systems thinking, shared decision-making, quality of care, and healthcare transformation. Landmark publications, conceptual papers, guidelines, consensus statements, systematic reviews, and original studies were narratively synthesized.
resultsThe review identifies a Five-Stage Evolution Framework: EBM establishes what works; patient safety addresses how effective care can be delivered safely; QI enables reliable and continuous improvement; LHS creates feedback loops that allow healthcare systems to learn from routine clinical practice; and VBHC evaluates whether these efforts generate outcomes that matter to patients relative to the resources required. These paradigms are complementary rather than competing approaches. Their integration also requires systems thinking, data literacy, interprofessional collaboration, shared decision-making, and attention to patient values and equity. Challenges include the evidence-to-practice gap, organizational barriers, data quality, algorithmic bias, privacy, and the difficulty of measuring individualized value.
conclusionModern healthcare should move beyond isolated quality initiatives toward an integrated system that generates evidence, delivers care safely, continuously learns and improves, and creates meaningful value for patients. The proposed framework is conceptual and requires empirical evaluation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.