Evidence map›Paper›PMID 42170598›Full record

ArticleFrontiers in health services2026

Improvement science as the engine for the next generation of learning health networks.

Gareth J Parry, Lloyd Provost, Peter Margolis

Abstract read
In one paragraph

Article in Frontiers in health services, 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

3 authors.

Gareth J ParryHealth Evaluation Research Lab, Cambridge Health Alliance, Cambridge, MA, United States.
Lloyd ProvostAssociates in Process Improvement, Austin, TX, United States.
Peter MargolisStanford University School of Medicine, Palo Alto, CA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While the evidence-based care movement has traditionally relied on randomized controlled trials (RCTs), these methods often lack the flexibility and external validity required for complex, real-world clinical settings. To overcome these issues, this paper proposes an Improvement Science Informed Learning System (ISILS) for Learning Health Networks. The ISILS operates as a continuous, self-reinforcing cycle. Its design integrates methods for identifying and overcoming unwanted variation in outcomes, determining effective practices, and rapidly implementing those successful practices across network member systems. This actor-oriented organizational structure promotes knowledge creation across diverse sites, enabling patients, clinicians, and researchers to co-produce health. Using the management of pediatric Crohn's disease as an illustrative case, this paper illustrates how the ISILS can identify superior therapeutic drug monitoring strategies and improve network-wide remission rates. Ultimately, this integrated approach ensures that best-practice care is a predictable output of a designed and active learning system.

Indexed as

causal inferenceimplementation science (MeSH)improvement sciencelearning health networklearning health systemsquality improvement

Identifiers

PMID42170598
PMCPMC13187945

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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 OpenQuestion graph.