Evidence map›Paper›PMID 41291697›Full record

ArticleBMC health services research2025

How does the mapped ICD data in an EHR system compare to the hospital DAD data in Alberta, Canada?

Namneet Sandhu, Debbie Onos, Bing Li, Danielle A Southern, Jeffrey A Bakal, Abdel Aziz Shaheen, Glenda Tower, Kathleen Addison, Sarah Whittle, Hude Quan

Abstract readComparative Study
In one paragraph

Article in BMC health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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

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

1 citing paper in PubMed.

  1. 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

10 authors.

Namneet SandhuCentre for Health Informatics, Cumming School of Medicine, University of Calgary, Cal Wenzal Precision Health Building, 3280 Hospital Drive NW, Calgary, Alberta, T2N 4Z6, Canada. namneet.sandhu@ucalgary.ca.
Debbie OnosHealth Information Management, Alberta Health Services, Alberta, Canada.
Bing LiCentre for Health Informatics, Cumming School of Medicine, University of Calgary, Cal Wenzal Precision Health Building, 3280 Hospital Drive NW, Calgary, Alberta, T2N 4Z6, Canada.
Danielle A SouthernCentre for Health Informatics, Cumming School of Medicine, University of Calgary, Cal Wenzal Precision Health Building, 3280 Hospital Drive NW, Calgary, Alberta, T2N 4Z6, Canada.
Jeffrey A BakalAlberta Health Services, Alberta, Canada.
Abdel Aziz ShaheenDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Glenda TowerHealth Information Management, Alberta Health Services, Alberta, Canada.
Kathleen AddisonHealth Information Management, Alberta Health Services, Alberta, Canada.
Sarah WhittleCentre for Health Informatics, Cumming School of Medicine, University of Calgary, Cal Wenzal Precision Health Building, 3280 Hospital Drive NW, Calgary, Alberta, T2N 4Z6, Canada.
Hude QuanCentre for Health Informatics, Cumming School of Medicine, University of Calgary, Cal Wenzal Precision Health Building, 3280 Hospital Drive NW, Calgary, Alberta, T2N 4Z6, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectiveCoding by human coders is burdensome to healthcare systems requiring advanced computerized techniques. Epic has collaborated with Intelligent Medical Objects (IMO) solution to integrate a solution where the clinical interface terminology is mapped to the International Classification of Diseases (ICD). This study assesses the agreement between the solution mapped ICD-10-CA codes in Alberta, Canada's EHR system (based on Epic) to the human coded ICD-10-CA codes in hospital Discharge Abstract Database (DAD). DESIGN AND

settingIn this retrospective analysis conducted in Alberta, Canada, we linked records in the acute care hospital DAD with the province wide EHR system for admissions between April 2021 and March 2024. MAIN OUTCOME(S) AND MEASURE(S): The primary outcome was the level of agreement between the solution mapped ICD-10-CA codes from the 'hospital problem list' in the EHR and hospital DAD data. The analysis was conducted at 3-digit and 4-digit ICD code level for main diagnosis and any diagnosis, and further stratified by physician specialty, hospital type and location, and length-of-stay.

resultsA total of 603,437 unique hospital records were linked between hospital DAD and EHR. The average level of agreement at 3-digit level of ICD-10-CA code for main diagnosis was 47.5% and any diagnosis was 37.0%. The average number of diagnoses coded by human coders in hospital DAD was higher than the solution mapped data in EHR. The agreement varied by specialty and length-of-stay with specialties with more complex patients and longer stays showing the lowest levels of agreement.

conclusionLevel of agreement between solution mapped EHR and hospital DAD for ICD-10-CA data was low, indicating significant differences between terminology mappings and the coding process.

Indexed as

Clinical CodingElectronic Health RecordsInternational Classification of DiseasesPatient DischargeAlbertaDatabases, FactualHumansRetrospective StudiesElectronic health recordsInternational Classification of Diseases (ICD)Mapped ICD codes

Identifiers

PMID41291697
PMCPMC12648991

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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.