Evidence map›Paper›PMID 37689648›Full record

ArticleBMC health services research2023

Clusters of medical specialties around patients with multimorbidity - employing fuzzy c-means clustering to explore multidisciplinary collaboration.

Marlies Verhoeff, Liann I Weil, Hung Chu, Yolande Vermeeren, Janke de Groot, Jako S Burgers, Patrick P T Jeurissen, Leslie R Zwerwer, Barbara C van Munster

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.7field-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

3 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
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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

9 authors at 5 institutions in 1 country.

Marlies Verhoeff *Department of Geriatric Medicine, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
Liann I Weil *Department of Geriatric Medicine, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands. l.i.weil@umcg.nl.
Hung ChuDonald Smits Center for Information and Technology, University of Groningen, Groningen, the Netherlands.
Yolande VermeerenDepartment of Internal Medicine, Gelre Hospitals, Apeldoorn/ Zutphen, the Netherlands.
Janke de GrootKnowledge Institute of the Federation of Medical Specialists, Utrecht, the Netherlands.
Jako S BurgersMaastricht University, Care and Public Health Research Institute (CAPHRI), Maastricht, the Netherlands.
Patrick P T JeurissenScientific Center for Quality of Healthcare (IQ healthcare), Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands.
Leslie R ZwerwerDonald Smits Center for Information and Technology, University of Groningen, Groningen, the Netherlands.
Barbara C van MunsterDepartment of Geriatric Medicine, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
University of Groningen · NLNetherlands Federation of University Medical Centres · NLGelre Hospitals · NLMaastricht University · NLRadboud University Medical Center · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHospital care organization, structured around medical specialties and focused on the separate treatment of individual organ systems, is challenged by the increasing prevalence of multimorbidity. To support the hospitals' realization of multidisciplinary care, we hypothesized that using machine learning on clinical data helps to identify groups of medical specialties who are simultaneously involved in hospital care for patients with multimorbidity.

methodsWe conducted a cross-sectional study of patients in a Dutch general hospital and used a fuzzy c-means clustering algorithm for the analysis. We explored the patients' membership degrees in each cluster to identify subgroups of medical specialties that provide care to the same patients with multimorbidity. We used retrospectively collected electronic health record data from 2017. We extracted data from 22,133 patients aged ≥18 years who had received outpatient clinical care for two or more chronic and/ or oncological diagnoses.

resultsWe found six clusters of medical specialties and identified 22 subgroups. The clusters were labeled based on the specialties that most characterized them: 1. dermatology/ plastic surgery, 2. six specialties (gynecology/ rheumatology/ orthopedic surgery/ urology/ gastroenterology/ otorhinolaryngology), 3. pulmonology, 4. internal medicine/ cardiology/ geriatrics, 5. neurology/ physiatry (rehabilitation)/ anesthesiology, and 6. internal medicine. Most patients had a full or dominant membership to one of these clusters of medical specialties (11 subgroups), whereas fewer patients had a membership to two clusters. The prevalence of specific diagnosis groups, patient characteristics, and healthcare utilization differed between subgroups.

conclusionOur study shows that clusters and subgroups of medical specialties simultaneously involved in hospital care for patients with multimorbidity can be identified with fuzzy c-means cluster analysis using clinical data. Clusters and subgroups differed regarding the involved medical specialties, diagnoses, patient characteristics, and healthcare utilization. With this strategy, hospitals and medical specialists can further analyze which subgroups are target populations that might benefit from improved multidisciplinary collaboration.

Indexed as

AnesthesiologyMultimorbidityAdolescentAdultCluster AnalysisCross-Sectional StudiesHumansRetrospective StudiesCare coordinationCluster analysisElectronic health recordsMachine learningMultidisciplinary collaborationMultimorbidity

Identifiers

PMID37689648
PMCPMC10492354
OpenAlexW4386579826

What OpenQuestion holds

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LicenceCC BY
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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.