Evidence map›Paper›PMID 29805921›Full record

ArticleIEEE journal of translational engineering in health and medicine2018

Developing a Policy Flight Simulator to Facilitate the Adoption of an Evidence-Based Intervention.

Michael J Pennock, Zhongyuan Yu, Karen B Hirschman, Kara Pepe, Mark V Pauly, Mary D Naylor, William B Rouse

Abstract read
In one paragraph

Article in IEEE journal of translational engineering in health and medicine, 2018. 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
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.

  1. Review
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

7 authors.

Michael J PennockSchool of Systems and EnterprisesStevens Institute of TechnologyHobokenNJ07030USA.
Zhongyuan YuSchool of Systems and EnterprisesStevens Institute of TechnologyHobokenNJ07030USA.
Karen B HirschmanSchool of NursingUniversity of PennsylvaniaPhiladelphiaPA19104USA.
Kara PepeSchool of Systems and EnterprisesStevens Institute of TechnologyHobokenNJ07030USA.
Mark V PaulyWharton SchoolUniversity of PennsylvaniaPhiladelphiaPA19104USA.
Mary D NaylorSchool of NursingUniversity of PennsylvaniaPhiladelphiaPA19104USA.
William B RouseSchool of Systems and EnterprisesStevens Institute of TechnologyHobokenNJ07030USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While the use of evidence-based interventions (EBIs) has been advocated by the medical research community for quite some time, uptake of these interventions by healthcare providers has been slow. One possible explanation is that it is challenging for providers to estimate impacts of a specific EBI on their particular organization. To address that concern, we developed and evaluated a type of simulation called a policy flight simulator to determine if it could improve the adoption decision about a specific EBI, the transitional care model (TCM). The TCM uses an advanced practice nurse-led model of care to transition older adults with multiple chronic conditions from a hospitalization to home. An evaluation by a National Advisory Committee, made up of senior representatives from various stakeholders in the U.S. healthcare system, found the policy flight simulator to be a useful tool that has the potential to better inform adoption decisions. This paper describes the simulation development effort and documents lessons learned that may be useful to the healthcare modeling community and those interested in using simulation to support decisions based on EBIs.

Indexed as

Evidence based interventionmodeling and simulationmulti-level model

Identifiers

PMID29805921
PMCPMC5957263

What OpenQuestion holds

Textmetadata
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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.