Comparison of Validity of Mapping between Drug Indications and ICD-10

Youngik, Choi; Jung, Chan-Kwon; Chae, Younbyoung; Kang, Min-Jeoung; Kim, Jooyoun; Joung, K.; Lim, Jennifer N. W.; Cho, S.; Sung, Sumi; Lee, E.; Kim, S.

doi:10.3414/me13-01-0053

Cited by 7 publications

(9 citation statements)

References 11 publications

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“…The application of the terms, to the statements, was done via a computer application designed by an information technology specialist. It is an alternative way to keep the data as consistent as data from the legacy approach, but more efficient [11]. There were three more measures to keep the process valid.…”

Section: Reliability and Validity Of Data Managementmentioning

confidence: 99%

Appropriateness of ICNP in Korean home care nursing

Kang

Kim

Lee

et al. 2015

International Journal of Medical Informatics

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Section: Reliability and Validity Of Data Managementmentioning

confidence: 99%

Appropriateness of ICNP in Korean home care nursing

Kang

Kim

Lee

et al. 2015

International Journal of Medical Informatics

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“…Two-thirds of the coding systems identified in the review are used in North America and/or Europe. Despite the concentration of coding system use in the Western countries with sophisticated healthcare infrastructures, there is still great variability in coding system use between countries and geographic regions, including country-specific modifications or multiple versions [14,23,24,30,82,83,[180][181][182][183][184]. Most of the country-specific modifications exist as a result of linguistic differences between countries using the coding systems.…”

Section: Discussionmentioning

confidence: 99%

“…16% (n=9) of the identified coding systems presented country-specific modifications or multiple versions (due to updates and revisions of code definitions, consolidation between systems, etc. ): Diagnosis-Related Groups (DRGs), ICD-10, ICD-9, ICD-O, International Classification of Primary Care (ICPC), Assistive Products for Persons with Disability -Classification and Terminology (ISO 9999), Nordic Medico-Statistical Committee (NOMESCO) classification of surgical procedures (NCSP), and Read codes (▶ Table 1) [11,14,[16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. 13% (n=7) of the identified coding systems were designed for specific conditions or therapeutic areas, including the American Joint Committee on Table 1 List of all coding systems identified in the review, abbreviated name and number of articles captured in the review (characteristics and applications).…”

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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“…[7][8][9] In previous studies it has been shown to be feasible for prescription data to be used to derive patients' diagnoses (mapping from ATC to ICD codes). [10][11][12] However, many of these studies mainly focused on avoiding billing mistakes or documentation errors. 10,11 To demonstrate that this approach may also help to improve medication safety, we performed a proof of principle analysis.…”

Section: Introductionmentioning

confidence: 99%

Use of medication data alone to identify diagnoses and related contraindications: Application of algorithms to close a common documentation gap

Andrikyan

Then

Gaßmann

et al. 2022

Brit J Clinical Pharma

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Aims Automated checks for medication‐related problems have become a cornerstone of medication safety. In many clinical settings medication checks remain confined to drug–drug interactions because only medication data are available in an adequately coded form, leaving possible contraindicated drug–disease combinations unaccounted for. Therefore, we devised algorithms that identify frequently contraindicated diagnoses based on medication patterns related to these diagnoses. Methods We identified drugs that are associated with diseases constituting common contraindications based on their exclusive use for these conditions (such as allopurinol for gout or salbutamol for bronchial obstruction). Expert‐based and machine learning algorithms were developed to identify diagnoses based on highly specific medication patterns. The applicability, sensitivity and specificity of the approach were assessed by using an anonymized real‐life sample of medication and diagnosis data excerpts from 3506 discharge records of geriatric patients. Results Depending on the algorithm, the desired focus (i.e., sensitivity vs. specificity) and the disease, we were able to identify the diagnoses gout, epilepsy, coronary artery disease, congestive heart failure and bronchial obstruction with a specificity of 44.0–99.8% (95% CI 41.7–100.0%) and a sensitivity of 3.8–83.1% (95% CI 1.0–86.1%). Using only medication data, we were able to identify 123 (51.3%) of 240 contraindications identified by experts with access to medication data and diagnoses. Conclusion This study provides a proof of principle that some key diagnosis‐related contraindications can be identified based on a patient's medication data alone, while others cannot be identified. This approach offers new opportunities to analyse drug–disease contraindications in community pharmacy or clinical routine data.

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Comparison of Validity of Mapping between Drug Indications and ICD-10

Abstract: Considering no differences in the F1 scores, both approaches may be used in practice for mapping drug indications to ICD-10. However, in terms of consistency, time and manpower, better results are expected from the indirect terminology based approach.

Cited by 7 publications

References 11 publications

Appropriateness of ICNP in Korean home care nursing

Appropriateness of ICNP in Korean home care nursing

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

Use of medication data alone to identify diagnoses and related contraindications: Application of algorithms to close a common documentation gap

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