Abstract
The appropriate funding of hospital services may depend upon grouping hospital episodes into Diagnosis Related Groups (DRGs). DRGs rely on the quality of clinical data held in administrative healthcare databases, mainly proper diagnoses and procedure codes. This work proposes a methodology based on unsupervised machine learning and statistical methods to generate alerts of suspect cases of up- and under-coding in healthcare administrative databases. The administrative database, with a DRG assigned to each hospital episode, was split into homogeneous patient subgroups by applying decision tree-based algorithms. The proportions of specific diagnosis and procedure codes were compared within targeted subgroups to identify hospitals with abnormal distributions. Preliminary results indicate that the proposed methodology has the potential to automatically identify upcoding and under-coding suspect cases, as well as other relevant types of discrepancies regarding coding practices. Nevertheless, additional evaluation under the medical perspective need to be incorporated in the methodology.
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Acknowledgments
Project NORTE-01-0145-FEDER-000016 (NanoSTIMA) is financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF). The authors would also like to thank the Central Authority for Health Services, I.P. (ACSS) for providing access to the data.
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Souza, J., Santos, J.V., Lopes, F., Viana, J., Freitas, A. (2018). Miscoding Alerts Within Hospital Datasets: An Unsupervised Machine Learning Approach. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 746. Springer, Cham. https://doi.org/10.1007/978-3-319-77712-2_115
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