Investigation of the parameters affecting patients waiting time in the radiotherapy treatment by using algorithms to evaluate electronic health records

• Autor(a) principal: EMILIOZZI, CAROLINE Z.S.
• Data de Publicação: 2020
• Outros Autores: MENEZES, MARIO O. de, MOREIRA, EDSON G., INTERNATIONAL NUCLEAR ATLANTIC CONFERENCE
• Tipo de documento: Artigo de conferência
• Título da fonte: Repositório Institucional do IPEN
• Texto Completo: http://repositorio.ipen.br/handle/123456789/30905
• topic: algorithms; delayed radiation effects; neoplasms; patients; radiotherapy; records management; schedules; time delay

Cancer is the second leading cause of death worldwide and Radiotherapy (RT) is an important modality in the treatment of these patients, which consists in deploying ionising radiation to destroy or damage cancer cells. With this growing global burden, demand for RT has been increasing continuously and supply-demand imbalances have become a major concern. The reason is that delays in radiotherapy can affect the outcome by permitting local proliferation of clonogenic cells and spread of the cancer beyond the treatment volume. Studies show a common cause of anxiety for radiotherapy patients is the fact that they do not know how long they will have to wait for treatment to start. In this study, we analyze the data of electronic health records to attempt to provide a better understanding of the problem and provide an initial estimate of radiotherapy patient???s waiting time. The data for this project comes from a subset of MOSAIQ, a relational database system developed by Elekta and used as an electronic health record system by the Radiation Oncology Department at the Hospital das Cl??nicas de S??o Paulo (HCFMUSP). The dataset consists of real historical data collected between January 2016 and December 2018. Visual Basic for application (VBA) and RSTUDIO Software were used to extract and analyze the data. Our work goal is to investigate a set of factors and verify their influence on patient waiting time. Factors as diagnosis, patient???s age, priority of the diagnosis, and the season in which treatment planning has initiated may reveal crucial information about overall efficiency and guide us to improve clinical procedures and practices.