Evidence map›Paper›PMID 33337928›Full record

ArticleThe European journal of general practice2020

Identifying multimorbid patients with high care needs - A study based on electronic medical record data.

Marianne Heins, Joke Korevaar, Francois Schellevis, Mieke Rijken

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Article in The European journal of general practice, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

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0cells of the map it votes in
11citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Marianne HeinsNivel (Netherlands Institute for Health Services Research), Department of Primary Care, Utrecht, The Netherlands.
Joke KorevaarNivel (Netherlands Institute for Health Services Research), Department of Primary Care, Utrecht, The Netherlands.
Francois SchellevisNivel (Netherlands Institute for Health Services Research), Department of Primary Care, Utrecht, The Netherlands.
Mieke RijkenNivel (Netherlands Institute for Health Services Research), Department of Primary Care, Utrecht, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatients with multimorbidity who frequently contact the general practice, use emergency care or have unplanned hospitalisations, may benefit from a proactive integrated care intervention. General practitioners are not always aware of who these 'high need' patients are. Electronic medical records are a potential source to identify them.

objectivesTo find predictors of high care needs in general practice electronic medical records of patients with multimorbidity and assess their predictive value.

methodsGeneral practice electronic medical records of 245,065 patients with ≥2 chronic diseases were linked to hospital claims data. Probit regression analysis was conducted to predict i) having at least 12 general practice contacts per year, ii) emergency department visit(s), and iii) unplanned hospitalisation(s). Predictors were patients' age, sex, morbidity, health services and medication use in the previous year.

results11% of multimorbid patients had ≥12 general practice contacts, which could be reliably predicted by the number of contacts in the previous year (PPV 42%). The model containing all predictors had only slightly better predictive value (PPV 44%). Emergency department visits and unplanned hospitalisations (12% and 7% of multimorbid patients, respectively) could be predicted less accurately (PPV 27% and 20%). Those with frequent contact with the general practice hardly overlapped with ED visitors (29%) or persons with unplanned hospitalisations (17%).

conclusionAmong multimorbid populations various 'high need' groups exist. Patients with high needs for general practice care can be identified by their previous use of general practice care. To identify frequent ED visitors and persons with unplanned hospitalisations, additional information is needed.

Indexed as

Electronic Health RecordsGeneral PracticeHealth Care CostsMultimorbidityAdolescentAdultAgedAged, 80 and overDatabases, FactualFemaleHumansInsurance Claim ReviewMaleMiddle AgedYoung Adultcomorbidityelectronic health recordsgeneral practiceMultimorbiditypatient selection

Identifiers

PMID33337928
PMCPMC7751396

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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.