Evidence map›Paper›PMID 28797300›Full record

ArticleBMC research notes2017

Can routine clinical data identify older patients at risk of poor healthcare outcomes on admission to hospital?

Kinda Ibrahim, Charlotte Owen, Harnish P Patel, Carl May, Mark Baxter, Avan A Sayer, Helen C Roberts

Open access · goldAbstract read
In one paragraph

Article in BMC research notes, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.9field-weighted citation impact, top 27% 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

7 citing papers in PubMed, 11 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Observational
  5. Article
  6. Article
  7. Computer screening for palliative care needs in primary care: a mixed-methods study.The British journal of general practice : the journal of the Royal College of General Practitioners · 2018
    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 at 4 institutions in 1 country.

Kinda IbrahimAcademic Geriatric Medicine, University of Southampton, Southampton General Hospital, Tremona Road, Mailpoint 807, Southampton, SO16 6YD, UK. k.ibrahim@soton.ac.uk.
Charlotte OwenAcademic Geriatric Medicine, University of Southampton, Southampton General Hospital, Tremona Road, Mailpoint 807, Southampton, SO16 6YD, UK.
Harnish P PatelAcademic Geriatric Medicine, University of Southampton, Southampton General Hospital, Tremona Road, Mailpoint 807, Southampton, SO16 6YD, UK.
Carl MayNIHR CLAHRC Wessex, Faculty of Health Science, University of Southampton, Highfield, Southampton, SO17 1BJ, UK.
Mark BaxterUniversity Hospital Southampton NHS Foundation Trust, Tremona Road, Southampton, SO16 6YD, UK.
Avan A SayerAcademic Geriatric Medicine, University of Southampton, Southampton General Hospital, Tremona Road, Mailpoint 807, Southampton, SO16 6YD, UK.
Helen C RobertsAcademic Geriatric Medicine, University of Southampton, Southampton General Hospital, Tremona Road, Mailpoint 807, Southampton, SO16 6YD, UK.
University of Southampton · GBUniversity Hospital Southampton NHS Foundation Trust · GBNIHR Newcastle Biomedical Research Centre · GBSouthampton General Hospital · GB

Funding

Medical Research Council MC_UP_A620_1015Medical Research Council MC_UU_12011/2
6 · The paper itself

Abstract

objectiveOlder patients who are at risk of poor healthcare outcomes should be recognised early during hospital admission to allow appropriate interventions. It is unclear whether routinely collected data can identify high-risk patients. The aim of this study was to define current practice with regard to the identification of older patients at high risk of poor healthcare outcomes on admission to hospital.

resultsInterviews/focus groups were conducted to establish the views of 22 healthcare staff across five acute medicine for older people wards in one hospital including seven nurses, four dieticians, seven doctors, and four therapists. In addition, a random sample of 60 patients' clinical records were reviewed to characterise the older patients, identify risk assessments performed routinely on admission, and describe usual care. We found that staff relied on their clinical judgment to identify high risk patients which was influenced by a number of factors such as reasons for admission, staff familiarity with patients, patients' general condition, visible frailty, and patients' ability to manage at home. "Therapy assessment" and patients' engagement with therapy were also reported to be important in recognising high-risk patients. However, staff recognised that making clinical judgments was often difficult and that it might occur several days after admission potentially delaying specific interventions. Routine risk assessments carried out on admission to identify single healthcare needs included risk of malnutrition (completed for 85% patients), falls risk (95%), moving and handling assessments (85%), and pressure ulcer risk assessments (88%). These were not used collectively to highlight patients at risk of poor healthcare outcomes. Thus, patients at risk of poor healthcare outcomes were not explicitly identified on admission using routinely collected data. There is a need for an early identification of these patients using a valid measure alongside staff clinical judgment to allow timely interventions to improve healthcare outcomes.

Indexed as

Health Services for the AgedPatient AdmissionPersonnel, HospitalAgedAged, 80 and overClinical Decision-MakingFemaleGeriatricsHumansMaleQualitative ResearchRisk AssessmentHealthcare outcomesHospitalMedical notesOlder patientsQualitative dataRisk assessment

Identifiers

PMID28797300
PMCPMC5553791
OpenAlexW2742419329

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.