Development of Data Quality Indicators for Improving Hospital International Classification of Diseases–Coded Health Data Quality Globally

Bibliographic Data

ID9104808
AuthorsLucía Otero-Varela (Centre for Health Informatics, Calgary, AB, Canada), Lucia Otero Varela (0000-0002-4406-6314, Libin Cardiovascular Institute of Alberta, corresponding author), Namneet Sandhu (0000-0003-3870-0320, Centre for Health Informatics, Calgary, AB, Canada, corresponding author), Robin L Walker (0000-0002-8008-4413, University of Calgary), Danielle A Southern (0000-0002-0006-0033, Centre for Health Informatics, Calgary, AB, Canada, corresponding author), Hude Quan (0000-0002-7848-7256, Centre for Health Informatics, Calgary, AB, Canada, corresponding author), Cathy A Eastwood (0000-0002-4569-8014, Centre for Health Informatics, Calgary, AB, Canada, corresponding author)
Year2024
Volume62
Issue9
Pages575-582
Publication date2024-09-01
Open AccessYes
TypeARTICLE
VenueMedical Care (JOURNAL)
Journal identifiersISSN: 0025-7079 • E-ISSN: 1537-1948
PublisherOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0000000000002024
PMID38986115
OpenAlexW4400483642
LanguageEN
References cited29

BACKGROUND: Hospital inpatient data, coded using the International Classification of Diseases (ICD), is widely used to monitor diseases, allocate resources and funding, and evaluate patient outcomes. As such, hospital data quality should be measured before use; however, currently, there is no standard and international approach to assess ICD-coded data quality. OBJECTIVE: To develop a standardized method for assessing hospital ICD-coded data quality that could be applied across countries: Data quality indicators (DQIs). RESEARCH DESIGN: To identify a set of candidate DQIs, we performed an environmental scan, reviewing gray and academic literature on data quality frameworks and existing methods to assess data quality. Indicators from the literature were then appraised and selected through a 3-round Delphi process. The first round involved face-to-face group and individual meetings for idea generation, while the second and third rounds were conducted remotely to collect online ratings. Final DQIs were selected based on the panelists' quantitative and qualitative feedback. SUBJECTS: Participants included international experts with expertise in administrative health data, data quality, and ICD coding. RESULTS: The resulting 24 DQIs encompass 5 dimensions of data quality: relevance, accuracy and reliability; comparability and coherence; timeliness; and Accessibility and clarity. These will help stakeholders (eg, World Health Organization) to assess hospital data quality using the same standard across countries and highlight areas in need of improvement. CONCLUSIONS: This novel area of research will facilitate international comparisons of ICD-coded data quality and be valuable to future studies and initiatives aimed at improving hospital administrative data quality

Business · Data mining · Data quality · Medical emergency · MEDLINE · Quality (philosophy) · Chronic Disease Management Strategies · Computer Science · Electronic Health Records Systems · Medical Coding and Health Information · Medicine

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