Evidence map›Paper›PMID 39223512›Full record

ArticleBMC medical informatics and decision making2024

Clinician perspectives and recommendations regarding design of clinical prediction models for deteriorating patients in acute care.

Robin Blythe, Sundresan Naicker, Nicole White, Raelene Donovan, Ian A Scott, Andrew McKelliget, Steven M McPhail

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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

7 authors.

Robin BlytheAustralian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, Brisbane, QLD, 4059, Australia. robin.blythe@qut.edu.au.ORCID http://orcid.org/0000-0002-3643-4332
Sundresan NaickerAustralian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, Brisbane, QLD, 4059, Australia.ORCID http://orcid.org/0000-0002-2392-4981
Nicole WhiteAustralian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, Brisbane, QLD, 4059, Australia.ORCID http://orcid.org/0000-0002-9292-0773
Raelene DonovanPrincess Alexandra Hospital, Metro South Health, Woolloongabba, QLD, Australia.ORCID http://orcid.org/0000-0003-0737-7719
Ian A ScottQueensland Digital Health Centre, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia.ORCID http://orcid.org/0000-0002-7596-0837
Andrew McKelligetPrincess Alexandra Hospital, Metro South Health, Woolloongabba, QLD, Australia.
Steven M McPhailAustralian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, Brisbane, QLD, 4059, Australia.ORCID http://orcid.org/0000-0002-1463-662X

Funding

National Health and Medical Research Council 1181138
6 · The paper itself

Abstract

backgroundSuccessful deployment of clinical prediction models for clinical deterioration relates not only to predictive performance but to integration into the decision making process. Models may demonstrate good discrimination and calibration, but fail to match the needs of practising acute care clinicians who receive, interpret, and act upon model outputs or alerts. We sought to understand how prediction models for clinical deterioration, also known as early warning scores (EWS), influence the decision-making of clinicians who regularly use them and elicit their perspectives on model design to guide future deterioration model development and implementation.

methodsNurses and doctors who regularly receive or respond to EWS alerts in two digital metropolitan hospitals were interviewed for up to one hour between February 2022 and March 2023 using semi-structured formats. We grouped interview data into sub-themes and then into general themes using reflexive thematic analysis. Themes were then mapped to a model of clinical decision making using deductive framework mapping to develop a set of practical recommendations for future deterioration model development and deployment.

resultsFifteen nurses (n = 8) and doctors (n = 7) were interviewed for a mean duration of 42 min. Participants emphasised the importance of using predictive tools for supporting rather than supplanting critical thinking, avoiding over-protocolising care, incorporating important contextual information and focusing on how clinicians generate, test, and select diagnostic hypotheses when managing deteriorating patients. These themes were incorporated into a conceptual model which informed recommendations that clinical deterioration prediction models demonstrate transparency and interactivity, generate outputs tailored to the tasks and responsibilities of end-users, avoid priming clinicians with potential diagnoses before patients were physically assessed, and support the process of deciding upon subsequent management.

conclusionsPrediction models for deteriorating inpatients may be more impactful if they are designed in accordance with the decision-making processes of acute care clinicians. Models should produce actionable outputs that assist with, rather than supplant, critical thinking.

Indexed as

Clinical Decision-MakingClinical DeteriorationEarly Warning ScoreAdultAttitude of Health PersonnelCritical CareFemaleHumansMalePhysiciansClinical decision-makingClinical decision support systemsClinical deteriorationClinical prediction modelsEarly warning score

Identifiers

PMID39223512
PMCPMC11367817

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.