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ICD-10 Coding Will Challenge Researchers: Caution and Collaboration may Reduce Measurement Error and Improve Comparability Over Time.

MEDICAL CARE(2019)

引用 28|浏览6
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摘要
Background: The October 1, 2015 US health care diagnosis and procedure codes update, from the 9th to 10th version of the International Classification of Diseases (ICD), abruptly changed the structure, number, and diversity of codes in health care administrative data. Translation from ICD-9 to ICD-10 risks introducing artificial changes in claims-based measures of health and health services. Objective: Using published ICD-9 and ICD-10 definitions and translation software, we explored discontinuity in common diagnoses to quantify measurement changes introduced by the upgrade. Design: Using 100% Medicare inpatient data, 2012-2015, we calculated the quarterly frequency of condition-specific diagnoses on hospital discharge records. Years 2012-2014 provided baseline frequencies and historic, annual fourth-quarter changes. We compared these to fourth quarter of 2015, the first months after ICD-10 adoption, using Centers for Medicare and Medicaid Services Chronic Conditions Data Warehouse (CCW) ICD-9 and ICD-10 definitions and other commonly used definitions sets. Results: Discontinuities of recorded CCW-defined conditions in fourth quarter of 2015 varied widely. For example, compared with diagnosis appearance in 2014 fourth quarter, in 2015 we saw a sudden 3.2% increase in chronic lung disease and a 1.8% decrease in depression; frequency of acute myocardial infarction was stable. Using published software to translate Charlson-Deyo and Elixhauser conditions yielded discontinuities ranging from -8.9% to +10.9%. Conclusions: ICD-9 to ICD-10 translations do not always align, producing discontinuity over time. This may compromise ICD-based measurements and risk-adjustment. To address the challenge, we propose a public resource for researchers to share discovered discontinuities introduced by ICD-10 adoption and the solutions they develop.
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关键词
claims,administrative data,methodology,diagnosis codes
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