Evidence map›Paper›PMID 33260204›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2021

"A catalyst for action": Factors for implementing clinical risk prediction models of infection in home care settings.

Dawn Dowding, David Russell, Margaret V McDonald, Marygrace Trifilio, Jiyoun Song, Carlin Brickner, Jingjing Shang

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing 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.

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

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

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. 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

7 authors.

Dawn DowdingDivision of Nursing, Midwifery and Social Work, School of Health Sciences, University of Manchester, Manchester, United Kingdom.
David RussellDepartment of Sociology, Appalachian State University, Boone, North Carolina, USA.
Margaret V McDonaldCenter for Home Care Policy and Research, Visiting Nurse Service of New York, New York, New York, USA.
Marygrace TrifilioCenter for Home Care Policy and Research, Visiting Nurse Service of New York, New York, New York, USA.
Jiyoun SongColumbia University School of Nursing, New York, New York, USA.
Carlin BricknerCenter for Home Care Policy and Research, Visiting Nurse Service of New York, New York, New York, USA.
Jingjing ShangColumbia University School of Nursing, New York, New York, USA.

Funding

Infection Control in Home Care and Predictive Risk ModelingR01HS024723 · AHRQ · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI SHANG, JINGJING · 2016 to 2018
$1.4M
AHRQ HHS R01 HS024723
6 · The paper itself

Abstract

objectiveThe study sought to outline how a clinical risk prediction model for identifying patients at risk of infection is perceived by home care nurses, and to inform how the output of the model could be integrated into a clinical workflow. MATERIALS AND

methodsThis was a qualitative study using semi-structured interviews with 50 home care nurses. Interviews explored nurses' perceptions of clinical risk prediction models, their experiences using them in practice, and what elements are important for the implementation of a clinical risk prediction model focusing on infection. Interviews were audio-taped and transcribed, with data evaluated using thematic analysis.

resultsTwo themes were derived from the data: (1) informing nursing practice, which outlined how a clinical risk prediction model could inform nurse clinical judgment and be used to modify their care plan interventions, and (2) operationalizing the score, which summarized how the clinical risk prediction model could be incorporated in home care settings. DISCUSSION: The findings indicate that home care nurses would find a clinical risk prediction model for infection useful, as long as it provided both context around the reasons why a patient was deemed to be at high risk and provided some guidance for action.

conclusionsIt is important to evaluate the potential feasibility and acceptability of a clinical risk prediction model, to inform the intervention design and implementation strategy. The results of this study can provide guidance for the development of the clinical risk prediction tool as an intervention for integration in home care settings.

Indexed as

Decision Support TechniquesHome Care ServicesInfectionsNurses, Community HealthHumansRisk AssessmentRisk Factorsclinical risk predictiondecision support modelhome care servicesinfection controlnursing

Identifiers

PMID33260204
PMCPMC7883974

What OpenQuestion holds

Textmetadata
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.