Dados do Resumo

Título

Data Science in oncology: barriers and facilities

Introdução

The discovery of writing revolutionized human evolution by enabling the preservation and transmission of knowledge. Today, with the vast accumulation of information, data sciences have become fundamental in oncology, allowing the analysis of large volumes of clinical, genomic, and epidemiological data. Techniques such as machine learning and predictive analytics help personalize treatment and improve clinical decision-making. However, the effective integration of these technologies faces significant barriers, including technical, ethical, and organizational challenges that impact their clinical adoption.

Objetivo

Investigate the barriers and facilities in implementing data sciences in oncology, assessing how these factors influence clinical practice and patient outcomes.

Métodos

A systematic review was conducted in the PubMed, Scopus, and Web of Science Databases, covering studies published between 2010 and2023. The inclusion criteria were: (1) articles that address the application of data sciences in oncology; (2) identification of barriers and facilities; (3) studies involving health professionals and patients. The searches use terms such as "data science", "oncology", "barriers", and "facilitators". Data were extracted and organized into thematic categories, and the quality of the studies was assessed using the PRISMA tool.

Resultados

The analysis included 35 studies that identified several barriers to the implementation of data sciences, such as the lack of adequate infrastructure, resistance to change by professionals, and ethical concerns related to data privacy. The facilities highlighted were the improvement in the accuracy of diagnosis, the ability to personalize treatments and the optimization of health resources. Most studies have reported that, despite barriers, the acceptance of data sciences is growing, especially with the formation of interdisciplinary partnerships and increased familiarity with technology.

Conclusões

Data sciences present great potential to revolutionize oncology , but their implementation faces significant challenges. Identifying and addressing these barriers is crucial to maximizing the benefits. Collaboration between healthcare professionals and data scientists is key to overcoming obstacles and promoting effective integration into patient care routines. The promotion of training and the creation of appropriate public policies can facilitate the adoption of these innovations.

Palavras Chave

Oncology; Data Administration; Technology