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

Optimization of surgery scheduling problems based on prescriptive analytics

Surgery scheduling plays a crucial role in modern healthcare systems, ensuring efficient use of resources, minimising patient waiting times and improving organisations’ operational performance. Additionally, healthcare faces enormous challenges, with a general modernisation of all clinical and administrative processes expected, requiring organisations to keep up with the latest advances in Information Technology. The scheduling of surgeries is a crucial sector for the good functioning of hospitals, and the management of waiting lists is directly related to this process, which has seen the COVID-19 pandemic cause a significant increase in waiting times in some specialities. Surgery scheduling is considered a highly complex problem, influenced by numerous factors such as resource availability, operating shifts, patient priorities and scheduling restrictions, putting significant challenges to healthcare providers. In this research, in collaboration with one of the leading hospitals in P ortugal, the Centro Hospitalar Universitário de Santo António (CHUdSA), we propose an approach based on Prescriptive Analytics, using optimisation algorithms to evaluate their performance in the management of the operating room. The results allow identifying the feasibility of this approach, taking into account the number of surgeries to be scheduled and surgical spaces in a time perspective, prevailing the priority of each surgery in the waiting list

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

Knowledge-based clinical pathway for medical quality improvement

Clinical pathways have been adopted for various diseases in clinical departments for quality improvement as a result of standardization of medical activities in treatment process. Using knowledge-based decision support on the basis of clinical pathways is a promising strategy to improve medical quality effectively. However, the clinical pathway knowledge has not been fully integrated into treatment process and thus cannot provide comprehensive support to the actual work practice. Therefore this paper proposes a knowledgebased clinical pathway management method which contributes to make use of clinical knowledge to support and optimize medical practice. We have developed a knowledgebased clinical pathway management system to demonstrate how the clinical pathway knowledge comprehensively supports the treatment process. The experiences from the use of this system show that the treatment quality can be effectively improved by the extracted and classified clinical pathway knowledge, seamless integration of patient-specific clinical pathway recommendations with medical tasks and the evaluating pathway deviations for optimization

Business Intelligence systems development in hospitals using an Agile Project Management approach

"Measure to manage" is a widely used expression to demonstrate that good governance must necessarily go through obtaining good data and information. These will allow managers to know the past and the momentum of the business and also to predict, estimate and take the best-informed decisions. The greater the complexity of the business, the greater this need. Healthcare units, specifically hospitals, are organizations that, due to their function and diversity of areas, are considered one of the most complex. In this context, projects for the development of business intelligence solutions, with huge impact and scope, undergo the need for continuous improvement and incremental evolution. Agile methods, by their nature and principles, are suitable to fulfil this need. The purpose of this dissertation is to support future research towards better models with agile tools to develop business intelligence system in hospitals and, manly, to understand how can Agile methodology improve a Business Intelligence System Implementation. This will be done mainly through bibliographical research on the covered topics, namely, Hospitals, Business Intelligence, Agile and Project Management. The expect results will be some clear practical guidelines, that any IT Project Manager could use for an efficient Business Intelligence System implementation using an Agile methodology. This will be done with the presentation of two use cases, from implementations in two hospitals in Portugal, where the Agile proposed model could be used to improve the outcomes of the projects. For that a deep analysis of the various phases of Business Intelligence development was carried out on the basis of information obtained in the literature and on the basis of information obtained in the practical development of Business Intelligence implementation projects. In the end it can be seen that the application of Agile can bring enormous benefits to the development of this kind of project, as, in addition to the advantages listed and widely known about Agile, it can help intensively to bring together and involve all the stakeholders of a project in a common goal of success and effectiveness

DICOM metadata: a useful resource for big data analytics

Postprint (published version

Towards More Nuanced Patient Management: Decomposing Readmission Risk with Survival Models

Unplanned hospital readmissions are costly and associated with poorer patient outcomes. Overall readmission rates have also come to be used as performance metrics in reimbursement in healthcare policy, further motivating hospitals to identify and manage high-risk patients. Many models predicting readmission risk have been developed to facilitate the equitable measurement of readmission rates and to support hospital decision-makers in prioritising patients for interventions. However, these models consider the overall risk of readmission and are often restricted to a single time point. This work aims to develop the use of survival models to better support hospital decision-makers in managing readmission risk. First, semi-parametric statistical and nonparametric machine learning models are applied to adult patients admitted via the emergency department at Gold Coast University Hospital (n = 46,659) and Robina Hospital (n = 23,976) in Queensland, Australia. Overall model performance is assessed based on discrimination and calibration, as measured by time-dependent concordance and D-calibration. Second, a framework based on iterative hypothesis development and model fitting is proposed for decomposing readmission risk into persistent, patient-specific baselines and transient, care-related components using a sum of exponential hazards structure. Third, criteria for patient prioritisation based on the duration and magnitude of care-related risk components are developed. The extensibility of the framework and subsequent prioritisation criteria are considered for alternative populations, such as outpatient admissions and specific diagnosis groups, and different modelling techniques. Time-to-event models have rarely been applied for readmission modelling but can provide a rich description of the evolution of readmission risk post-discharge and support more nuanced patient management decisions than simple classification models