Development of International Indicators for Assessing the Quality of ICD-coded Administrative Health Data
Date
2020-12-22
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Abstract
Introduction: Health data are generated at each patient encounter with the healthcare system worldwide, then collected and stored as administrative health data. As an example, inpatient data are coded in the hospital morbidity database using the International Classification of Diseases (ICD), which is a reference standard for reporting diseases and health conditions globally. The quality of ICD-coded data is affected by multiple factors, such as worldwide variations in ICD use and its meta-features across countries, which can hinder meaningful comparisons of morbidity data. Assessing data quality is therefore essential for the ultimate goal of improving it. Given the current lack of an international approach for, we aimed to develop a standardized method for assessing hospital morbidity data quality. Methods: First, we conducted an international online questionnaire to better understand the differences in coding practices and hospital data collection systems across countries. Second, through the combination of a comprehensive environmental scan and a Delphi consensus process, we developed a set of global data quality indicators (DQIs) for the hospital morbidity database. Results: The international questionnaire revealed variances in all aspects of ICD data collection features, including: the maximum number of coding fields allowed for diagnosis and interventions, the definition of main condition, as well as the data fields that are mandatory to capture in the hospital morbidity database. The Delphi exercise resulted in 24 DQIs, encompassing five dimensions of data quality (e.g., Relevance, Accuracy and reliability, Comparability and coherence, Timeliness, and Accessibility and clarity), and can be used to assess data quality using the same standard across countries and to highlight areas in need of improvement. Conclusion: Emphasis should be placed on standardizing ICD data collection systems and enhancing the quality of ICD-coded data. These findings could facilitate international comparisons of health data and data quality, and could serve as a guidance for policy- and decision-makers worldwide.
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data quality, administrative health data, international classification of diseases, international comparability, quality assessment
Citation
Otero Varela, L. (2020). Development of International Indicators for Assessing the Quality of ICD-coded Administrative Health Data (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.