Evidence map›Paper›PMID 31630113›Full record

Trial reportBMJ health & care informatics2019

Using normalisation process theory to understand workflow implications of decision support implementation across diverse primary care settings.

Rebecca G Mishuris, Joseph Palmisano, Lauren McCullagh, Rachel Hess, David A Feldstein, Paul D Smith, Thomas McGinn, Devin M Mann

Registry-linked trialAbstract readRandomized Controlled Trial
In one paragraph

Trial report in BMJ health & care informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02534987 (Integrated Clinical Prediction Rules), which is not on this map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–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.

NCT02534987 nacompletednot on this map

Integrated Clinical Prediction Rules: Bringing Evidence to Diverse Primary Care Settings

TypeinterventionalSponsorNYU Langone HealthRan2015 to 2018Enrolled33ConditionsStrep Throat, PneumoniaArmsiCPR2
3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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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

8 authors.

Rebecca G MishurisBoston University School of Medicine, Boston, Massachusetts, USA rgrochow@bu.edu.ORCID http://orcid.org/0000-0002-4804-3128
Joseph PalmisanoBoston University School of Medicine, Boston, Massachusetts, USA.
Lauren McCullaghNorthwell Health and Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York, USA.
Rachel HessUniversity of Utah School of Medicine, Salt Lake City, Utah, USA.
David A FeldsteinUniversity of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
Paul D SmithUniversity of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
Thomas McGinnNorthwell Health and Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York, USA.
Devin M MannNew York University School of Medicine, New York City, New York, USA.

Funding

Integrated Clinical Prediction Rules: Bringing Evidence to Diverse Primary Care SettingsR01AI108680 · NIAID · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI FELDSTEIN, DAVID ALAN, MANN, DEVIN M · 2014 to 2024
$7.9M
NIAID NIH HHS R01 AI108680
6 · The paper itself

Abstract

backgroundEffective implementation of technologies into clinical workflow is hampered by lack of integration into daily activities. Normalisation process theory (NPT) can be used to describe the kinds of 'work' necessary to implement and embed complex new practices. We determined the suitability of NPT to assess the facilitators, barriers and 'work' of implementation of two clinical decision support (CDS) tools across diverse care settings.

methodsWe conducted baseline and 6-month follow-up quantitative surveys of clinic leadership at two academic institutions' primary care clinics randomised to the intervention arm of a larger study. The survey was adapted from the NPT toolkit, analysing four implementation domains: sense-making, participation, action, monitoring. Domains were summarised among completed responses (n=60) and examined by role, institution, and time.

resultsThe median score for each NPT domain was the same across roles and institutions at baseline, and decreased at 6 months. At 6 months, clinic managers' participation domain (p=0.003), and all domains for medical directors (p<0.003) declined. At 6 months, the action domain decreased among Utah respondents (p=0.03), and all domains decreased among Wisconsin respondents (p≤0.008).

conclusionsThis study employed NPT to longitudinally assess the implementation barriers of new CDS. The consistency of results across participant roles suggests similarities in the work each role took on during implementation. The decline in engagement over time suggests the need for more frequent contact to maintain momentum. Using NPT to evaluate this implementation provides insight into domains which can be addressed with participants to improve success of new electronic health record technologies. TRIAL REGISTRATION NUMBER: NCT02534987.

Indexed as

WorkflowDecision Support Systems, ClinicalElectronic Health RecordsHumansModels, TheoreticalPrimary Health CareProfessional RoleSurveys and QuestionnairesUtahWisconsinclinical decision supportelectronic health recordsimplementationnormalization process theoryquantitative survey

Identifiers

PMID31630113
PMCPMC7062348

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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Registered trials

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.