The complexity and challenges of the International Classification of Diseases, Ninth Revision, Clinical Modification to International Classification of Diseases, 10th Revision, Clinical Modification transition in EDs

Krive, Jacob; Patel, Mahatkumar; Gehm, Lisa; Mackey, Mark; Kulstad, Erik; Li, Jianrong John; Lussier, Yves A.; Boyd, Andrew D.

doi:10.1016/j.ajem.2015.03.001

Cited by 34 publications

(25 citation statements)

References 8 publications

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“…Some researchers are concerned about the quality of data during the initial transition (Krive et al. ), as well as potential ongoing coding problems (Utter et al. ).…”

Section: Resultsmentioning

confidence: 99%

Statewide Hospital Discharge Data: Collection, Use, Limitations, and Improvements

Andrews

2015

Health Services Research

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“…Some researchers are concerned about the quality of data during the initial transition (Krive et al. ), as well as potential ongoing coding problems (Utter et al. ).…”

Section: Resultsmentioning

confidence: 99%

Statewide Hospital Discharge Data: Collection, Use, Limitations, and Improvements

Andrews

2015

Health Services Research

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“…The mappings primarily included tools or equivalency tables that were created, funded, or published by the organizations involved in the creation and management of a coding system. Mappings to and from SNOMED CT and ICD (ICD-9, ICD-10, and ICD-O) were among the most common [38,39,40,41].…”

Section: Coding System Characteristicsmentioning

confidence: 99%

A Systematic Review of Coding Systems Used in Pharmacoepidemiology and Database Research

Chen¹,

Zivkovic²,

Wang³

et al. 2018

Methods Inf Med

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Summary Background: Clinical coding systems have been developed to translate real-world healthcare information such as prescriptions, diagnoses and procedures into standardized codes appropriate for use in large healthcare datasets. Due to the lack of information on coding system characteristics and insufficient uniformity in coding practices, there is a growing need for better understanding of coding systems and their use in pharmacoepidemiology and observational real world data research. Objectives: To determine: 1) the number of available coding systems and their characteristics, 2) which pharmacoepidemiology databases are they adopted in, 3) what outcomes and exposures can be identified from each coding system, and 4) how robust they are with respect to consistency and validity in pharmacoepidemiology and observational database studies. Methods: Electronic literature database and unpublished literature searches, as well as hand searching of relevant journals were conducted to identify eligible articles discussing characteristics and applications of coding systems in use and published in the English language between 1986 and 2016. Characteristics considered included type of information captured by codes, clinical setting(s) of use, adoption by a pharmacoepidemiology database, region, and available mappings. Applications articles describing the use and validity of specific codes, code lists, or algorithms were also included. Data extraction was performed independently by two reviewers and a narrative synthesis was performed. Results: A total of 897 unique articles and 57 coding systems were identified, 17% of which included country-specific modifications or multiple versions. Procedures (55%), diagnoses (36%), drugs (38%), and site of disease (39%) were most commonly and directly captured by these coding systems. The systems were used to capture information from the following clinical settings: inpatient (63%), ambulatory (55%), emergency department (ED, 34%), and pharmacy (13%). More than half of all coding systems were used in Europe (59%) and North America (57%). 34% of the reviewed coding systems were utilized in at least 1 of the 16 pharmacoepidemiology databases of interest evaluated. 21% of coding systems had studies evaluating the validity and consistency of their use in research within pharmacoepidemiology databases of interest. The most prevalent validation method was comparison with a review of patient charts, case notes or medical records (64% of reviewed validation studies). The reported performance measures in the reviewed studies varied across a large range of values (PPV 0-100%, NPV 6-100%, sensitivity 0-100%, specificity 23-100% and accuracy 16-100%) and were dependent on many factors including coding system(s), therapeutic area, pharmacoepidemiology database, and outcome. Conclusions: Coding systems vary by type of information captured, clinical setting, and pharmacoepidemiology database and region of use. Of the 57 reviewed coding systems, few are routinely and widely applied in pharmacoepidemiology database research. Indication and outcome dependent heterogeneity in coding system performance suggest that accurate definitions and algorithms for capturing specific exposures and outcomes within large healthcare datasets should be developed on a case-by-case basis and in consultation with clinical experts.

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“…For example, ICD-9 code 250.81 (Diabetes with other specified manifestations, type I [juvenile type], not stated as uncontrolled) maps to ten different ICD-10 codes. There are a handful of conversion tools available now to map ICD-9 to ICD-10[36]. However, it is a significant undertaking to convert existing ICD-9 codes in the EHR systems to ICD-10 codes and make them research ready, especially when there are no research acceptable ICD-10 diagnosis codes as of yet.…”

Section: Phenotype Data In Phewasmentioning

confidence: 99%

Current Scope and Challenges in Phenome-Wide Association Studies

Verma

Ritchie

2017

Curr Epidemiol Rep

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Purpose of Review Over many decades, researchers have been designing studies to investigate the relationship between genotypes and phenotypes to gain an understanding about the effect of genetics on disease. Recently, a high-throughput approach called phenome-wide associations studies (PheWAS) have been extensively used to identify associations between genetic variants and many diseases and traits simultaneously. In this review, we describe the value of PheWAS along with methodological issues and challenges in interpretation for current applications of PheWAS. Recent findings PheWAS have uncovered a paradigm to identify new associations for genetic loci across many diseases. The application of PheWAS have been effective with phenotype data from electronic health records, epidemiological studies, and clinical trials data. Summary The key strength of a PheWAS is to identify the association of one or more genetic variants with multiple phenotypes, which can showcase interconnections among the phenotypes due to shared genetic associations. While the PheWAS approach appears promising, there are a number of challenges that need to be addressed to provide additional robustness to PheWAS findings.

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The complexity and challenges of the International Classification of Diseases, Ninth Revision, Clinical Modification to International Classification of Diseases, 10th Revision, Clinical Modification transition in EDs

Cited by 34 publications

References 8 publications

Statewide Hospital Discharge Data: Collection, Use, Limitations, and Improvements

Statewide Hospital Discharge Data: Collection, Use, Limitations, and Improvements

A Systematic Review of Coding Systems Used in Pharmacoepidemiology and Database Research

Current Scope and Challenges in Phenome-Wide Association Studies

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