A Data-Driven Approach for Analyzing Healthcare Services Extracted from Clinical Records

Abstract

Cancer remains one of the major public health challenges worldwide. After cardiovascular diseases, cancer is one of the first causes of death and morbidity in Europe, with more than 4 million new cases and 1.9 million deaths per year. The suboptimal management of cancer patients during treatment and subsequent follows up are major obstacles in achieving better outcomes of the patients and especially regarding cost and quality of life In this paper, we present an initial data-driven approach to analyze the resources and services that are used more frequently by lung-cancer patients with the aim of identifying where the care process can be improved by paying a special attention on services before diagnosis to being able to identify possible lung-cancer patients before they are diagnosed and by reducing the length of stay in the hospital. Our approach has been built by analyzing the clinical notes of those oncological patients to extract this information and their relationships with other variables of the patient. Although the approach shown in this manuscript is very preliminary, it shows that quite interesting outcomes can be derived from further analysis. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Description
Keywords
data-driven, lung cancer, patient management, EHR, hospitalization processes
Citation
Scurti, M., Menasalvas-Ruiz, E., Vidal, M.-E., Torrente, M., Vogiatzis, D., Paliouras, G., et al. (2020). A Data-Driven Approach for Analyzing Healthcare Services Extracted from Clinical Records (A. G. Seco de Herrera, A. Rodríguez González, K. C. Santosh, Z. Temesgen, & P. Soda, eds.). Piscataway, NJ : IEEE. https://doi.org//10.1109/CBMS49503.2020.00044
License
Es gilt deutsches Urheberrecht. Das Dokument darf zum eigenen Gebrauch kostenfrei genutzt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.