Evidence map›Paper›PMID 41514451›Full record

SynthesisImplementation science : IS2026

Learning health system for implementation, scale-up, and sustainment: a systematic review to consolidate guidance for improvement.

Cassandra Lane, Sam McCrabb, Heidi Turon, Caitlin Bialek, Lucy Couper, Magdalena Wilczynska, Samantha Gray, Courtney Barnes, Madeleine Fee, Tanja Kuchenmüller and 2 more

Abstract readSystematic Review
In one paragraph

Synthesis in Implementation science : IS, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Cassandra Lane *School of Medicine and Public Health, The University of Newcastle, Newcastle, NSW, Australia.
Sam McCrabb *School of Medicine and Public Health, The University of Newcastle, Newcastle, NSW, Australia. sam.mccrabb@newcastle.edu.au.ORCID 0000-0002-4216-0251
Heidi TuronSchool of Medicine and Public Health, The University of Newcastle, Newcastle, NSW, Australia.
Caitlin BialekSchool of Medicine and Public Health, The University of Newcastle, Newcastle, NSW, Australia.
Lucy CouperSchool of Medicine and Public Health, The University of Newcastle, Newcastle, NSW, Australia.
Magdalena WilczynskaSchool of Medicine and Public Health, The University of Newcastle, Newcastle, NSW, Australia.
Samantha GraySchool of Medicine and Public Health, The University of Newcastle, Newcastle, NSW, Australia.
Courtney BarnesSchool of Medicine and Public Health, The University of Newcastle, Newcastle, NSW, Australia.
Madeleine FeeSchool of Medicine and Public Health, The University of Newcastle, Newcastle, NSW, Australia.
Tanja KuchenmüllerResearch for Health Science Division, World Health Organization, Geneva, Switzerland.
Davi Mamblona Marques RomaoResearch for Health Science Division, World Health Organization, Geneva, Switzerland.
Luke WolfendenSchool of Medicine and Public Health, The University of Newcastle, Newcastle, NSW, Australia.

Funding

National Health and Medical Research Council APP1153479National Health and Medical Research Council APP11960419NSW Ministry of Health NSW Health Prevention Research Support ProgramNSW Ministry of Health PRSP FellowshipWorld Health Organization 001
6 · The paper itself

Abstract

backgroundLearning Health Systems (LHSs) link research and health service delivery by generating evidence to guide decision-making and continuous improvement. Although various LHS frameworks exist, there is limited practical guidance for how LHSs can improve implementation. This systematic review aimed to consolidate existing guidance to identify the infrastructure (pillars) and improvement processes (steps) required to support a LHS cycle that improves the implementation (including scale up or sustainment) of health programs, policies, or practices.

methodsWe searched five databases and grey literature for documents describing an LHS model, or a process, or process model, guideline, or tool (i.e., guidance) intended to improve the quality of implementation, scale-up, and/or sustainment of health interventions. Title, abstract, and full-text screening were conducted independently by two reviewers. Data were synthesised separately for pillars and steps. Framework synthesis identified pillars and steps, informed by an existing LHS framework and refined iteratively; thematic synthesis explored patterns within each.

findingsFrom 12,151 records and 25 websites, 96 guidance documents were included. Six Pillars were identified as important to operationalise LHS improvement processes: 1-Interest holder engagement, 2-Workforce development and capacity, 3-Evidence surveillance and synthesis, 4-Data collection and management, 5-Governance and organisational processes, and 6-Cross-cutting infrastructure. The improvement process was comprised of 10 'Steps' across three LHS phases: Phase 1) Knowledge to Practice -Identify and understand the problem; Decide and plan for action; Assess and build capacity; Pilot; Phase 2) Practice to Data-Execute the action; Collect data; Monitor and respond; Phase 3) Data to Knowledge- Analyse and evaluate; Disseminate; and Decide (continue, adapt, or cease improvement efforts). Despite the diversity in purpose and context across included documents, the consolidated steps and pillars were conceptually consistent, suggesting a shared foundation. Some contextual variation in emphasis and operationalisation was noted, particularly among guidance focused on scale-up or sustainment.

conclusionsThis review consolidated LHS pillars and improvement steps to better implement, scale or sustain health interventions. Findings provide a structured yet adaptable approach for operationalising implementation-focused learning cycles within LHSs. It informs forthcoming WHO guidance, and supports more systematic, responsive use of evidence in health systems.

trial registrationThe review protocol was prospectively registered on Open Science Framework ( https://doi.org/10.17605/OSF.IO/V4JRC ).

Indexed as

Learning Health SystemQuality ImprovementHumansImplementation ScienceEvidence SynthesisFramework SynthesisImplementationLearning Health SystemScale-upSustainment

Identifiers

PMID41514451
PMCPMC12910773

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.