Dataset Information

Diagnostic code agreement for electronic health records and claims data for tuberculosis.

ABSTRACT: OBJECTIVE: To measure the frequency of diseases related to latent tuberculosis infection (LTBI) and tuberculosis (TB), we assessed the agreement between diagnosis codes for TB or LTBI in electronic health records (EHRs) and insurance claims for the same person.METHODS: In a US population-based, retrospective cohort study, we matched TB-related Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) EHR codes and International Statistical Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) claims codes. Furthermore, LTBI was identified using a published ICD-based algorithm and all LTBI- and TB-related SNOMED CT codes.RESULTS: Of people with the 10 most frequent TB-related claim codes, 50% did not have an exact-matched EHR code. Positive tuberculin skin test was the most frequent unmatched EHR code and people with the 10 most frequent TB EHR codes, 40% did not have an exact-matched claim code. The most frequent unmatched claim code was TB screening encounter. EHR codes for LTBI matched to claims codes for TB testing; pulmonary TB; and nonspecific, positive or adverse tuberculin reaction.CONCLUSION: TB-related EHR codes and claims diagnostic codes often disagree, and people with claims codes for LTBI have unexpected EHR codes, indicating the need to reconcile these coding systems.

SUBMITTER: Iqbal SA

PROVIDER: S-EPMC7586722 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Diagnostic code agreement for electronic health records and claims data for tuberculosis.

Iqbal S A SA Isenhour C J CJ Mazurek G G Truman B I BI

The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease 20200701 7

OBJECTIVE: To measure the frequency of diseases related to latent tuberculosis infection (LTBI) and tuberculosis (TB), we assessed the agreement between diagnosis codes for TB or LTBI in electronic health records (EHRs) and insurance claims for the same person.METHODS: In a US population-based, retrospective cohort study, we matched TB-related Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) EHR codes and International Statistical Classification of Diseases, 10<s ...[more]

PMID: 32718404

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Project description:BackgroundThe Norwegian Trauma Registry (NTR) is designed to monitor and improve the quality and outcome of trauma care delivered by Norwegian trauma hospitals. Patient care is evaluated through specific quality indicators, which are constructed of variables reported to the registry by certified registrars. Having high-quality data recorded in the registry is essential for the validity, trust and use of data. This study aims to perform a data quality check of a subset of core data elements in the registry by assessing agreement between data in the NTR and corresponding data in electronic patient records (EPRs).MethodsWe validated 49 of the 118 variables registered in the NTR by comparing those with the corresponding ones in electronic patient records for 180 patients with a trauma diagnosis admitted in 2019 at eight public hospitals. Agreement was quantified by calculating observed agreement, Cohen's Kappa and Gwet's first agreement coefficient (AC1) with 95% confidence intervals (CIs) for 27 nominal variables, quadratic weighted Cohen's Kappa and Gwet's second agreement coefficient (AC2) for five ordinal variables. For nine continuous, one date and seven time variables, we calculated intraclass correlation coefficient (ICC).ResultsAlmost perfect agreement (AC1 /AC2/ ICC > 0.80) was observed for all examined variables. Nominal and ordinal variables showed Gwet's agreement coefficients ranging from 0.85 (95% CI: 0.79-0.91) to 1.00 (95% CI: 1.00-1.00). For continuous and time variables there were detected high values of intraclass correlation coefficients (ICC) between 0.88 (95% CI: 0.83-0.91) and 1.00 (CI 95%: 1.00-1.00). While missing values in both the NTR and EPRs were in general negligeable, we found a substantial amount of missing registrations for a continuous "Base excess" in the NTR. For some of the time variables missing values both in the NTR and EPRs were high.ConclusionAll tested variables in the Norwegian Trauma Registry displayed excellent agreement with the corresponding variables in electronic patient records. Variables in the registry that showed missing data need further examination.

Dataset Information

Diagnostic code agreement for electronic health records and claims data for tuberculosis.

Publications

Diagnostic code agreement for electronic health records and claims data for tuberculosis.

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