Evidence map›Paper›PMID 38269991›Full record

ArticleStudies in health technology and informatics2024

Building Research Infrastructure to Develop Greater Learning Efficiencies (BRIDGE).

Danne C Elbers, Nathanael R Fillmore, Jennifer La, Hannah M Tosi, Samuel Ajjarapu, Rupali Dhond, Karen Murray, Danielle Valley, Colleen Shannon, Mary T Brophy and 1 more

Open access · hybridAbstract read
In one paragraph

Article in Studies in health technology and informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 97% of its field
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

1 citing paper in PubMed, 0 citations in OpenAlex.

  1. 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

11 authors at 3 institutions in 1 country.

Danne C ElbersVA Boston Healthcare System, Boston MA, USA.
Nathanael R FillmoreVA Boston Healthcare System, Boston MA, USA.
Jennifer LaVA Boston Healthcare System, Boston MA, USA.
Hannah M TosiVA Boston Healthcare System, Boston MA, USA.
Samuel AjjarapuVA Boston Healthcare System, Boston MA, USA.
Rupali DhondVA Boston Healthcare System, Boston MA, USA.
Karen MurrayVA Boston Healthcare System, Boston MA, USA.
Danielle ValleyVA Boston Healthcare System, Boston MA, USA.
Colleen ShannonVA Boston Healthcare System, Boston MA, USA.
Mary T BrophyVA Boston Healthcare System, Boston MA, USA.
Nhan V DoVA Boston Healthcare System, Boston MA, USA.
VA Boston Healthcare System · USBoston University · USHarvard University · US

Funding

Intramural VA VA999999
6 · The paper itself

Abstract

In this manuscript, we outline our developed version of a Learning Health System (LHS) in oncology implemented at the Department of Veterans Affairs (VA). Transferring healthcare into an LHS framework has been one of the spearpoints of VA's Central Office and given the general lack of evidence generated through randomized control clinical trials to guide medical decisions in oncology, this domain is one of the most suitable for this change. We describe our technical solution, which includes a large real-world data repository, a data science and algorithm development framework, and the mechanism by which results are brought back to the clinic and to the patient. Additionally, we propose the need for a bridging framework that requires collaboration between informatics specialists and medical professionals to integrate knowledge generation into the clinical workflow at the point of care.

Indexed as

AlgorithmsLearningAmbulatory Care FacilitiesData ScienceHumansKnowledgeUnited StatesLearning health systemoncology

Identifiers

PMID38269991
PMCPMC11166042
OpenAlexW4391224166

What OpenQuestion 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 OpenQuestion graph.