Evidence map›Paper›PMID 32873340›Full record

ArticleJournal of biomedical semantics2020

Assisting nurses in care documentation: from automated sentence classification to coherent document structures with subject headings.

Hans Moen, Kai Hakala, Laura-Maria Peltonen, Hanna-Maria Matinolli, Henry Suhonen, Kirsi Terho, Riitta Danielsson-Ojala, Maija Valta, Filip Ginter, Tapio Salakoski and 1 more

Abstract read
In one paragraph

Article in Journal of biomedical semantics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

11 authors.

Hans MoenDepartment of Future Technologies, University of Turku, Vesilinnantie 5, Turku, 20500, Finland. hnsmoen@gmail.com.ORCID 0000-0003-1418-7892
Kai HakalaDepartment of Future Technologies, University of Turku, Vesilinnantie 5, Turku, 20500, Finland.
Laura-Maria PeltonenDepartment of Nursing Science, University of Turku, Joukahaisenkatu 3-5, Turku, 20520, Finland.
Hanna-Maria MatinolliDepartment of Nursing Science, University of Turku, Joukahaisenkatu 3-5, Turku, 20520, Finland.
Henry SuhonenDepartment of Nursing Science, University of Turku, Joukahaisenkatu 3-5, Turku, 20520, Finland.
Kirsi TerhoDepartment of Nursing Science, University of Turku, Joukahaisenkatu 3-5, Turku, 20520, Finland.
Riitta Danielsson-OjalaDepartment of Nursing Science, University of Turku, Joukahaisenkatu 3-5, Turku, 20520, Finland.
Maija ValtaTurku University Hospital, Kiinamyllynkatu 4-8, Turku, 20521, Finland.
Filip GinterDepartment of Future Technologies, University of Turku, Vesilinnantie 5, Turku, 20500, Finland.
Tapio SalakoskiDepartment of Future Technologies, University of Turku, Vesilinnantie 5, Turku, 20500, Finland.
Sanna SalanteräDepartment of Nursing Science, University of Turku, Joukahaisenkatu 3-5, Turku, 20520, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUp to 35% of nurses' working time is spent on care documentation. We describe the evaluation of a system aimed at assisting nurses in documenting patient care and potentially reducing the documentation workload. Our goal is to enable nurses to write or dictate nursing notes in a narrative manner without having to manually structure their text under subject headings. In the current care classification standard used in the targeted hospital, there are more than 500 subject headings to choose from, making it challenging and time consuming for nurses to use.

methodsThe task of the presented system is to automatically group sentences into paragraphs and assign subject headings. For classification the system relies on a neural network-based text classification model. The nursing notes are initially classified on sentence level. Subsequently coherent paragraphs are constructed from related sentences.

resultsBased on a manual evaluation conducted by a group of three domain experts, we find that in about 69% of the paragraphs formed by the system the topics of the sentences are coherent and the assigned paragraph headings correctly describe the topics. We also show that the use of a paragraph merging step reduces the number of paragraphs produced by 23% without affecting the performance of the system.

conclusionsThe study shows that the presented system produces a coherent and logical structure for freely written nursing narratives and has the potential to reduce the time and effort nurses are currently spending on documenting care in hospitals.

Indexed as

DocumentationNursesAutomationHospitalsHumansLanguageSubject HeadingsElectronic health recordsModel interpretationNatural language processingNeural networksNursing documentationPatient care documentationText classification

Identifiers

PMID32873340
PMCPMC7465411

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.