Evidence map›Paper›PMID 40074787›Full record

ArticleScientific reports2025

Understanding hospital activity and outcomes for people with multimorbidity using electronic health records.

Konstantin Georgiev, Joanne McPeake, Susan D Shenkin, Jacques Fleuriot, Nazir Lone, Bruce Guthrie, Julie A Jacko, Atul Anand

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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
–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

5 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

8 authors.

Konstantin GeorgievBHF Centre for Cardiovascular Science, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SU, UK.
Joanne McPeakeDepartment of Public Health and Primary Care, The Healthcare Improvement Studies Institute, University of Cambridge, Cambridge, UK.
Susan D ShenkinAdvanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, UK.
Jacques FleuriotArtificial Intelligence and its Applications Institute, School of Informatics, University of Edinburgh, Edinburgh, UK.
Nazir LoneCentre for Medical Informatics, The Usher Institute, University of Edinburgh, Edinburgh, UK.
Bruce GuthrieAdvanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, UK.
Julie A JackoCentre for Medical Informatics, The Usher Institute, University of Edinburgh, Edinburgh, UK.
Atul AnandBHF Centre for Cardiovascular Science, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SU, UK. atul.anand@ed.ac.uk.

Funding

National Institute for Health and Care Research NIHR157712
6 · The paper itself

Abstract

As the prevalence of multimorbidity grows, provision of effective healthcare is more challenging. Both multimorbidity and complexity in healthcare delivery may be associated with worse outcomes. We studied consecutive, unique emergency non-surgical hospitalisations for patients over 50 years old to three hospitals in Scotland, UK between 2016 and 2024 using linked primary care and hospital records to define multimorbidity (2 + long-term conditions), and timestamped hospital electronic health record (EHR) contacts with nursing and rehabilitation providers to describe intensity of inpatient care. The primary outcome was emergency hospital readmission within 30 days of discharge, analysed using multivariable logistic regression. Across 98,242 consecutive admissions, 84% of the study population had multimorbidity, 50% had 4 + long-term conditions, and 37% had both physical and mental health conditions. Both higher condition count and contacts (nursing and rehabilitation) were independently associated with the primary outcome in fully adjusted models (example adjusted odds ratio [aOR] 1.62, 95% CI 1.52 to 1.73 for 4 + conditions compared to no multimorbidity, p < 0.001; aOR 1.35, 95% CI 1.28 to 1.42 for > 8 nursing contacts compared to 1-3, p < 0.001). While multimorbidity was associated with longer hospital stays with more nursing and rehabilitation contacts, the distribution of contacts and activity did not differ by multimorbidity or subsequent emergency readmission status. Higher count multimorbidity was associated with an increased risk of readmission, but we observed uniformity in care despite differential outcomes across multimorbidity groups. This may suggest that EHR data-driven approaches could inform person-centred care and improve hospital resource allocation.

Indexed as

Electronic Health RecordsHospitalizationMultimorbidityAgedAged, 80 and overFemaleHumansMaleMiddle AgedPatient ReadmissionScotlandElectronic health recordsMultimorbidityReadmissionRehabilitation

Identifiers

PMID40074787
PMCPMC11903850

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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 OpenQuestion graph.