Measuring Variability in Acute Myocardial Infarction Coding Using a Statistical Process Control and Probabilistic Temporal Data Quality Control Approaches

• Type: Article in International Conference Proceedings Book
• Date: 2021

Authors

Souza, J (Author) Other; Caballero, I (Author) Other; Santos, JV (Author) Other; Lobo, MF (Author) FMUP; Pinto, A (Author) Other; Viana, J (Author) FMUP; Saez, C (Author) Other; Freitas A (Author) FMUP

Conference proceedings International

Title: Advances in Intelligent Systems and Computing

Pages: 193-202

World Conference on Information Systems and Technologies, WorldCIST 2021

30 March 2021 through 2 April 2021

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-00T-PSQ

DOI: 10.1007/978-3-030-72651-5_19

Abstract (EN): 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. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific