Adaptive business intelligence in healthcare - A platform for optimising surgeries

Adaptive Business Intelligence (ABI) combines predictive with prospective analytics in order to give support to the decision making process. Surgery scheduling in hospital operating rooms is a high complex task due to huge volume of surgeries and the variety of combinations and constraints. This type of activity is critical and is often associated to constant delays and significant rescheduling. The main task of this work is to provide an ABI based platform capable of estimating the time of the surgeries and then optimising the scheduling (minimizing the waste of resources). Combining operational data with analytical tools this platform is able to present complex and competitive information to streamline surgery scheduling. A case study was explored using data from a portuguese hospital. The best achieved relative absolute error attained was 6.22%. The paper also shows that the approach can be used in more general applications.This work has been supported by FCT –Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/201

Decision models on therapies for intensive medicine

Decision support models are crucial in intensive care units as they allow intensivists to make faster and better decisions. The application of optimization models in these areas becomes challenging given its complexity and multidisciplinary nature. The main objective of this study is to use the stochastic Hill Climbing optimization model in order to identify the best medication to treat the Covid Pneumonia problem, considering the top 3 medications administered as well as the cost of treatment. It should be noted that the problem to be analyzed in the optimization model was selected considering that the extracted data is from the time when Covid-19 was ravaging the intensive care units, so it will be the most interesting. The results obtained in this study denote that the n_iterations parameter was crucial in obtaining the optimal solution since all scenarios with this parameter set to a value of 1000 were able to return the optimal solution, unlike the other ones.The work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: DSAIPA/DS/0084/2018

Utilizing artificial intelligence in perioperative patient flow:systematic literature review

Abstract. The purpose of this thesis was to map the existing landscape of artificial intelligence (AI) applications used in secondary healthcare, with a focus on perioperative care. The goal was to find out what systems have been developed, and how capable they are at controlling perioperative patient flow. The review was guided by the following research question: How is AI currently utilized in patient flow management in the context of perioperative care? This systematic literature review examined the current evidence regarding the use of AI in perioperative patient flow. A comprehensive search was conducted in four databases, resulting in 33 articles meeting the inclusion criteria. Findings demonstrated that AI technologies, such as machine learning (ML) algorithms and predictive analytics tools, have shown somewhat promising outcomes in optimizing perioperative patient flow. Specifically, AI systems have proven effective in predicting surgical case durations, assessing risks, planning treatments, supporting diagnosis, improving bed utilization, reducing cancellations and delays, and enhancing communication and collaboration among healthcare providers. However, several challenges were identified, including the need for accurate and reliable data sources, ethical considerations, and the potential for biased algorithms. Further research is needed to validate and optimize the application of AI in perioperative patient flow. The contribution of this thesis is summarizing the current state of the characteristics of AI application in perioperative patient flow. This systematic literature review provides information about the features of perioperative patient flow and the clinical tasks of AI applications previously identified