Development and Psychometric Validation of a Multidimensional Clinical Coding Quality Assessment Instrument in Casemix Systems

Abstract

Introduction In casemix-based payment systems, clinical coding quality plays a critical role in determining data validity, reimbursement accuracy, and overall performance. Financial inefficiencies, skewed morbidity data, and claim denials might result from incorrect coding. However, the majority of studies pay little attention to thorough and context-specific evaluation, instead concentrating mostly on coding accuracy. Objective The purpose of this study was to use the Indonesian Case-Based Groups (INA-CBG) as the empirical backdrop for the development and psychometric validation of a multidimensional instrument for evaluating clinical coding quality in casemix systems. Methods An instrument development strategy based on ICD-10, ICD-9-CM, WHO coding guidelines, national casemix requirements, and results from coding audits was used to conduct a methodological investigation. The Content Validity Ratio (CVR) and Content Validity Index (I-CVI, S-CVI) were used to evaluate the content validity. Cohen’s Kappa was used to assess inter-rater reliability, while Cronbach’s alpha with 95% confidence intervals was used to quantify internal consistency. JASP was used for data analysis. Results The Clinical Coding Quality Assessment Instrument for Casemix (CCQAI–CM) comprises seven conceptual domains operationalized through 11 assessment items: diagnosis documentation, procedure documentation, documentation support, coding compliance, coding accuracy, diagnosis reselection, and procedure reselection. The instrument demonstrated high content validity (S-CVI = 0.972), varying item-level inter-rater agreement, and very high overall internal consistency (Cronbach’s α = 0.982). Conclusion The findings provide initial evidence of the content validity and reliability of the CCQAI–CM as a multidimensional framework for assessing clinical coding quality in casemix systems. Further validation across healthcare settings and against relevant casemix outcomes is warranted.

The authors' abstract, as published at the source. F1000Research, 2026 · DOI ↗