Evidence map›Paper›PMID 38642920›Full record

ArticleBMJ health & care informatics2024

Development of a scoring system to quantify errors from semantic characteristics in incident reports.

Haruhiro Uematsu, Masakazu Uemura, Masaru Kurihara, Hiroo Yamamoto, Tomomi Umemura, Fumimasa Kitano, Mariko Hiramatsu, Yoshimasa Nagao

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In one paragraph

Article in BMJ health & care informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Haruhiro UematsuDepartment of Quality and Patient Safety, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan hiro_uematsu@hotmail.com.ORCID http://orcid.org/0000-0003-2800-6802
Masakazu UemuraDepartment of Patient Safety, Nagoya University Hospital, Nagoya, Aichi, Japan.
Masaru KuriharaDepartment of Patient Safety, Nagoya University Hospital, Nagoya, Aichi, Japan.ORCID http://orcid.org/0000-0001-9195-4202
Hiroo YamamotoDepartment of Patient Safety, Nagoya University Hospital, Nagoya, Aichi, Japan.
Tomomi UmemuraDepartment of Patient Safety, Nagoya University Hospital, Nagoya, Aichi, Japan.
Fumimasa KitanoDepartment of Patient Safety, Nagoya University Hospital, Nagoya, Aichi, Japan.
Mariko HiramatsuDepartment of Patient Safety, Nagoya University Hospital, Nagoya, Aichi, Japan.
Yoshimasa NagaoDepartment of Quality and Patient Safety, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesIncident reporting systems are widely used to identify risks and enable organisational learning. Free-text descriptions contain important information about factors associated with incidents. This study aimed to develop error scores by extracting information about the presence of error factors in incidents using an original decision-making model that partly relies on natural language processing techniques.

methodsWe retrospectively analysed free-text data from reports of incidents between January 2012 and December 2022 from Nagoya University Hospital, Japan. The sample data were randomly allocated to equal-sized training and validation datasets. We conducted morphological analysis on free text to segment terms from sentences in the training dataset. We calculated error scores for terms, individual reports and reports from staff groups according to report volume size and compared these with conventional classifications by patient safety experts. We also calculated accuracy, recall, precision and F-score values from the proposed 'report error score'.

resultsOverall, 114 013 reports were included. We calculated 36 131 'term error scores' from the 57 006 reports in the training dataset. There was a significant difference in error scores between reports of incidents categorised by experts as arising from errors (p<0.001,

conclusionOur error scoring system could provide insights to improve patient safety using aggregated incident report data.

Indexed as

Risk ManagementSemanticsHospitals, UniversityHumansPatient SafetyRetrospective StudiesArtificial intelligenceDecision Making, Computer-AssistedSafety Management

Identifiers

PMID38642920
PMCPMC11033660

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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.