Evidence map›Paper›PMID 38237323›Full record

ArticleInternational journal of nursing studies2024

Improving escalation of deteriorating patients through cognitive task analysis: Understanding differences between work-as-prescribed and work-as-done.

J Ede, R Hutton, P Watkinson, B Kent, R Endacott

Open access · greenAbstract read
In one paragraph

Article in International journal of nursing studies, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.1field-weighted citation impact, top 13% 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

5 citing papers in PubMed, 6 citations in OpenAlex.

  1. Diurnal Variation in Mechanical Circulatory Support Initiation and Associated Outcomes.Journal of the Society for Cardiovascular Angiography & Interventions · 2026
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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

5 authors at 3 institutions in 2 countries.

J EdeOxford University Hospital NHS Foundation Trust, Oxford, United Kingdom; School of Nursing and Midwifery, University of Plymouth, Plymouth, United Kingdom. Electronic address: jody.ede@ouh.nhs.uk.
R HuttonUWE Psychology, University of West England, United Kingdom.
P WatkinsonOxford University Hospital NHS Foundation Trust, Oxford, United Kingdom; University of Oxford, Nuffield Department of Clinical Neurosciences, Oxford, United Kingdom.
B KentSchool of Nursing and Midwifery, University of Plymouth, Plymouth, United Kingdom.
R EndacottSchool of Nursing and Midwifery, University of Plymouth, Plymouth, United Kingdom; National Institute for Health and Care Research, London, United Kingdom; Medicine, Nursing and Health Sciences, Monash University, Melbourne, United Kingdom.
Oxford Health NHS Foundation Trust · GBUniversity of Plymouth · GBUniversity of the West of England · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAppropriate care escalation requires the detection and communication of in-hospital patient deterioration. Although deterioration in the ward environment is common, there continue to be patient deaths where problems escalating care have occurred. Learning from the everyday work of health care professionals (work-as-done) and identifying performance variability may provide a greater understanding of the escalation challenges and how they overcome these. The aims of this study were to i) develop a representative model detailing escalation of care ii) identify performance variability that may negatively or positively affect this process and iii) examine linkages between steps in the escalation process.

methodsThirty Applied Cognitive Task Analysis interviews were conducted with clinical experts (>4 years' experience) including Ward Nurses (n = 7), Outreach or Sepsis Nurses (n = 8), Nurse Manager or Consultant (n = 6), Physiotherapists (n = 4), Advanced Practitioners (n = 4), and Doctor (n = 1) from two National Health Service hospitals and analysed using Framework Analysis. Task-related elements of care escalation were identified and represented in a Functional Resonance Analysis Model.

findingsThe NEWS2's clinical escalation response constitutes eight unique tasks and illustrates work-as-prescribed, but our interview data uncovered an additional 24 tasks (n = 32) pertaining to clinical judgement, decisions or processes reflecting work-as-done. Over a quarter of these tasks (9/32, 28 %) were identified by experts as cognitively challenging with a high likelihood of performance variability. Three out of the nine variable tasks were closely coupled and interdependent within the Functional Resonance Analysis Model ('synthesising data points', 'making critical decision to escalate' and 'identifying interim actions') so representing points of potential escalation failure. Data assimilation from different clinical information systems with poor usability was identified as a key cognitive challenge.

conclusionOur data support the emphasis on the need to retain clinical judgement and suggest that future escalation protocols and audit guidance require in-built flexibility, supporting staff to incorporate their expertise of the patient condition and the clinical environment. Improved information systems to synthesise the required data surrounding an unwell patient to reduce staff cognitive load, facilitate decision-making, support the referral process and identify actions are required. Fundamentally, reducing the cognitive load when assimilating core escalation data allows staff to provide better and more creative care. Study registration (ISRCTN 38850) and ethical approval (REC Ref 20/HRA/3828; CAG-20CAG0106).

Indexed as

InpatientsState MedicineCognitionCommunicationHospitalsHumansClinical deteriorationCommunicationCuesDelivery of healthcareEarly warning score

Identifiers

PMID38237323
PMCPMC10882274
OpenAlexW4389513705

What OpenQuestion holds

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LicenceCC BY-NC
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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.