Miscoding alerts within hospital datasets: An unsupervised machine learning approach

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Souza, J (Author) Other; Santos, JV (Author) FMUP; Lopes F (Author) FMUP; Viana, J (Author) FMUP; Freitas A (Author) FMUP

Conference proceedings International

Title: Advances in Intelligent Systems and Computing

Pages: 1198-1207

Indexing

Scopus - 3 Citations

Other information

Authenticus ID: P-00N-VZ5

DOI: 10.1007/978-3-319-77712-2_115

Abstract (EN): 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. © Springer International Publishing AG, part of Springer Nature 2018.

Language: English

Type (Professor's evaluation): Scientific