Abstract
Acute Myocardial Infarction (AMI) is frequently reported when coding hospital encounters, being commonly monitored through acute care outcomes. Variability in clinical coding within hospital administrative databases, however, may indicate data quality issues and thereby negatively affect quality assessment of care delivered to AMI patients, apart from impacting health care management, decision making and research as a whole. In this study, we applied statistical process control and probabilistic temporal data quality control approaches to assess inter-hospital and temporal variability in coding AMI episodes within a nationwide Portuguese hospitalization database. The application of the present methods identified affected data distributions that can be potentially linked to data quality issues. A total of 12 out of 36 institutions substantially differed in coding AMI when compared to their peers, mostly presenting lower than expected hospitalizations of AMI. Results also indicate the existence of abnormal temporal patterns demanding additional investigation, as well as dissimilarities of temporal data batches in the periods comprising the recent transition to the International Classification of Diseases, 10th revision, Clinical Modification (ICD-10-CM) and changes in the Diagnosis-Related Group (DRG) software. Hence, the main contribution of this paper is the use of reproducible, feasible and easy-to-interpret methods that can be employed to monitor the variability in clinical coding and that could be integrated into data quality assessment frameworks.
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Acknowledgements
The authors would like to thank the Central Authority for Health Services, I.P. (ACSS) for providing access to the data. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was financed by FEDER-Fundo Europeu de Desenvolvimento Regional funds through the COMPETE 2020-Operacional Programme for Competitiveness and Internationalisation (POCI) and by Portuguese funds through FCT-Fundação para a Ciência e a Tecnologia in the framework of the project POCI-01–0145-FEDER-030766 (“1st.IndiQare-Quality indicators in primary health care: validation and implementation of quality indicators as an assessment and comparison tool”). In addition, we would like to thank to project GEMA(SBPLY/17/180501/000293)- Generation and Evaluation of Models for Data Quality, funded by the Department of Education, Culture and Sports of the JCCM and FEDER; and to ECLIPSE project (RTI2018–094283-B-C31) funded by the Spanish Ministry of Science, Innovation and Universities, and FEDER.
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Souza, J. et al. (2021). Measuring Variability in Acute Myocardial Infarction Coding Using a Statistical Process Control and Probabilistic Temporal Data Quality Control Approaches. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies . WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1366. Springer, Cham. https://doi.org/10.1007/978-3-030-72651-5_19
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DOI: https://doi.org/10.1007/978-3-030-72651-5_19
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