Evidence map›Paper›PMID 37066100›Full record

ArticleLearning health systems2023

Evolving a national clinical trials learning health system.

Kristian D Stensland, Rachel L Richesson, Randy A Vince, Ted A Skolarus, Anne E Sales

Abstract read
In one paragraph

Article in Learning health systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
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

5 authors.

Kristian D StenslandDepartment of Urology University of Michigan Ann Arbor Michigan USA.ORCID https://orcid.org/0000-0002-3765-3393
Rachel L RichessonDepartment of Learning Health Sciences University of Michigan Ann Arbor Michigan USA.ORCID https://orcid.org/0000-0003-0279-7036
Randy A VinceDepartment of Urology University of Michigan Ann Arbor Michigan USA.
Ted A SkolarusDepartment of Urology University of Michigan Ann Arbor Michigan USA.
Anne E SalesDepartment of Learning Health Sciences University of Michigan Ann Arbor Michigan USA.

Funding

Building capacity for clinical trial improvement through implementation scienceF32CA264874 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI STENSLAND, KRISTIAN DONALD · 2021 to 2021
$84k
NCI NIH HHS F32 CA264874
6 · The paper itself

Abstract

Clinical trials generate key evidence to inform decision making, and also benefit participants directly. However, clinical trials frequently fail, often struggle to enroll participants, and are expensive. Part of the problem with trial conduct may be the disconnected nature of clinical trials, preventing rapid data sharing, generation of insights and targeted improvement interventions, and identification of knowledge gaps. In other areas of healthcare, a learning health system (LHS) has been proposed as a model to facilitate continuous learning and improvement. We propose that an LHS approach could greatly benefit clinical trials, allowing for continuous improvements to trial conduct and efficiency. A robust trial data sharing system, continuous analysis of trial enrollment and other success metrics, and development of targeted trial improvement interventions are potentially key components of a Trials LHS reflecting the learning cycle and allowing for continuous trial improvement. Through the development and use of a Trials LHS, clinical trials could be treated as a system, producing benefits to patients, advancing care, and decreasing costs for stakeholders.

Indexed as

clinical trialscontinuous improvementlearning cyclelearning health system

Identifiers

PMID37066100
PMCPMC10091198

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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 OpenQuestion graph.