Evidence map›Paper›PMID 35672699›Full record

ArticleBMC medical informatics and decision making2022

Decision algorithm for when to use the ICD-11 3-part model for healthcare harms.

Cathy A Eastwood, Shahreen Khair, Danielle A Southern

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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  3. Review
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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

3 authors.

Cathy A EastwoodCentre for Health Informatics, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive NW, TRW 5E06, Calgary, AB, T2N 4Z6, Canada. caeastwo@ucalgary.ca.ORCID 0000-0002-4569-8014
Shahreen KhairDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Canada.
Danielle A SouthernCentre for Health Informatics, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive NW, TRW 5E06, Calgary, AB, T2N 4Z6, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate data collection of healthcare-related adverse events provides a foundation for quality and health system improvement. The International Classification of Diseases for Mortality and Morbidity Statistics, 11th revision (ICD-11 MMS) includes new codes to identify harm or injury and the events or actions leading to the adverse events. However, it is difficult to choose the correct codes without in-depth understanding of which event may be classified as an injury or harm. A 3-part model will be available in the ICD-11 MMS to cluster the codes for the harm or injury that occurred, the causal factors, and the mode (mechanism) involved. While field testing coding of adverse events, our team developed a decision tree (algorithm), which guides when to use the 3-part model. The decision tree now resides in the ICD-11 Reference Guide. This paper is part of a special ICD-11 paper series and outlines the steps used in the decision-tree (algorithm) and provides examples to help understand the process.While it may take coders some time to gain experience to use the 3-part model and decision-tree, the ICD-11 Reference Guide and this paper can be helpful resources to help clarify the process.

Indexed as

Health FacilitiesInternational Classification of DiseasesAlgorithmsDelivery of Health CareHumans3-part modelDecision-treeHarmsICDICD-11Patient safety

Identifiers

PMID35672699
PMCPMC9171926

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.