Miscoding alerts within hospital datasets: An unsupervised machine learning approach

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2018

Autores

Souza, J (Autor) Outra; Santos, JV (Autor) FMUP; Lopes F (Autor) FMUP; Viana, J (Autor) FMUP; Freitas A (Autor) FMUP

Ata de Conferência Internacional

Título: Advances in Intelligent Systems and Computing

Páginas: 1198-1207

Indexação

Scopus - 3 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica