Evidence map›Paper›PMID 37974148›Full record

ReviewBMC medical informatics and decision making2023

Interpreting and coding causal relationships for quality and safety using ICD-11.

Jean-Marie Januel, Danielle A Southern, William A Ghali

Abstract readReview
In one paragraph

Review in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Jean-Marie JanuelDepartment of Biomedical Informatics, Rouen University Hospital, 37 Boulevard Gambetta, Rouen, 76000, France. jean-marie.januel@hotmail.com.ORCID 0000-0001-5857-6553
Danielle A SouthernCentre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, Canada.
William A GhaliCentre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, Canada.

Funding

Agence Nationale de la Recherche ANR-19-P3IA-0003
6 · The paper itself

Abstract

Many circumstances necessitate judgments regarding causation in health information systems, but these can be tricky in medicine and epidemiology. In this article, we reflect on what the ICD-11 Reference Guide provides on coding for causation and judging when relationships between clinical concepts are causal. Based on the use of different types of codes and the development of a new mechanism for coding potential causal relationships, the ICD-11 provides an in-depth transformation of coding expectations as compared to ICD-10. An essential part of the causal relationship interpretation relies on the presence of "connecting terms," key elements in assessing the level of certainty regarding a potential relationship and how to proceed in coding a causal relationship using the new ICD-11 coding convention of postcoordination (i.e., clustering of codes). In addition, determining causation involves using documentation from healthcare providers, which is the foundation for coding health information. The coding guidelines and examples (taken from the quality and patient safety domain) presented in this article underline how new ICD-11 features and coding rules will enhance future health information systems and healthcare.

Indexed as

DocumentationInternational Classification of DiseasesCausalityClinical CodingDelivery of Health CareHumansPatient SafetyAdverse eventsCausationICD-11International Classification of DiseasesQuality and safety

Identifiers

PMID37974148
PMCPMC10655490

What OpenQuestion holds

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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.