Abstract
In Medical Imaging (MI), various technologies can be used to monitor the human body for diagnosing, monitoring or treating disease. Each type of technology provides different information about the body area that is being investigated or treated for a possible illness, injury or effectiveness of a medical treatment. Routine screening has identified malfunction detection in many otherwise asymptomatic patient images such as computed tomography or magnetic resonance. Studies have shown that, compared to patients whose disease was symptomatic (i.e., self-recognizing), screen-detected diseases may have more favorable clinicopathological features, leading to better prognosis and better outcome. This paper aims to assess the issue of health care wait screening. It deviates from a decision support system that evaluates the waiting times in diagnostic MI based on operational data from various information systems. Last but not least, one’s assumptions may have an important impact in determining the usefulness of routine laboratory testing at admission.
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This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.
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Neves, J. et al. (2018). Waiting Time Screening in Healthcare. In: Jung, J., Kim, P., Choi, K. (eds) Big Data Technologies and Applications. BDTA 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 248. Springer, Cham. https://doi.org/10.1007/978-3-319-98752-1_14
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DOI: https://doi.org/10.1007/978-3-319-98752-1_14
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