Evidence map›Paper›PMID 31048875›Full record

Trial reportPloS one2019

An integrated understanding of the complex drivers of emergency presentations and admissions in cancer patients: Qualitative modelling of secondary-care health professionals' experiences and views.

Hong Chen, Julie Walabyeki, Miriam Johnson, Elaine Boland, Julie Seymour, Una Macleod

Open access · goldAbstract readClinical Trial
In one paragraph

Trial report in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 3 pooled it
2.0field-weighted citation impact, top 14% 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

11 citing papers in PubMed, 3 syntheses or guidelines pooled it, 23 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Observational
  5. Article
  6. Article
  7. Article
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  9. Article
  10. Review
  11. Observational
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

6 authors at 2 institutions in 1 country.

Hong ChenAcademy of Primary Care, Institute of Clinical and Applied Heath Research, Hull York Medical School, University of Hull, Hull, United Kingdom.ORCID 0000-0002-5102-6302
Julie WalabyekiAcademy of Primary Care, Institute of Clinical and Applied Heath Research, Hull York Medical School, University of Hull, Hull, United Kingdom.ORCID 0000-0002-5596-6617
Miriam JohnsonWolfson Palliative Care Research Centre, Institute of Clinical and Applied Heath Research, Hull York Medical School, University of Hull, Hull, United Kingdom.
Elaine BolandQueen's Centre for Oncology and Haematology, Castle Hill Hospital, Hull and East Yorkshire Hospitals NHS Trust, Hull, United Kingdom.
Julie SeymourAcademy of Primary Care, Institute of Clinical and Applied Heath Research, Hull York Medical School, University of Hull, Hull, United Kingdom.
Una MacleodAcademy of Primary Care, Institute of Clinical and Applied Heath Research, Hull York Medical School, University of Hull, Hull, United Kingdom.
University of Hull · GBHull and East Yorkshire Hospitals NHS Trust · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The number of cancer-related emergency presentations and admissions has been steadily increasing in the UK. Drivers of this phenomenon are complex, multifactorial and interlinked. The main objective of this study was to understand the complexity of emergency hospital use in cancer patients. We conducted semi-structured interviews with 42 senior clinicians (20 doctors, 22 nurses) with diverse expertise and experience in caring for acutely ill cancer patients in the secondary care setting. Data analysis included thematic analysis and purposive text analysis to develop Causal Loop Diagrams. Our Causal Loop Diagrams represent an integrated understanding of the complex factors (13) influencing emergency hospital use in cancer patients. Eight factors formed five reinforcing feedback loops and therefore were high-leverage influences: Ability of patients and carers to self-care and cope; Effective and timely management of ambulatory care sensitive conditions by primary and community care; Sufficient and effective social care for patients and carers; Avoidable emergency hospital use; Bed capacity; Patients accessing timely appropriate specialist inpatient or ambulatory care; Prompt and effective management and prevention of acute episode; Timely and safe discharge with appropriate support. The loops show that reduction of avoidable hospital use helps relieve hospital bed pressure; improved bed capacity then has a decisive, positive influence on patient pathway and thus outcome and experience in the hospital; in turn, better in-hospital care and discharge help patients and carers self-care and cope better back home with better support from community-based health and social care services, which then reduces their future emergency hospital use. To optimise acute and emergency cancer care, it is also essential that patients, carers and other clinicians caring for cancer patients have prompt access to senior cancer specialists for advice, assessment, clinical decision and other support. The findings provide a useful framework and focus for service planners aiming to optimise care.

Indexed as

Emergency Medical ServicesEmergency Service, HospitalHealth PersonnelModels, TheoreticalSecondary Health CareAmbulatory CareFemaleHumansMaleNeoplasmsPatient Discharge

Identifiers

PMID31048875
PMCPMC6497383
OpenAlexW2943253401

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.