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.

Nowadays, organizations in the most distinct sectors of activities, are generating enormous amounts of data, at high velocity and high variety. This phenomenon dictated a growing technological development, called big data, which is already recognized as one of the most important areas of the future of information. Due to this fact, organizations have been looking for new solutions to improve their services and take advantage of these new technologies. The reality in the healthcare industry is similar to the phenomenon described above. It is a sector where large amounts of data have been stored digitally and with enormous benefits from these new technologies. Despite this, there are very few health-related organizations making investments in big data and taking advantage of it. This article will address a SWOT analysis, more specifically the strengths, weaknesses, opportunities and threats of big data in healthcare in order to help organizations to evaluate its potential.

Nurses follow well-defined guidelines in order to avoid the occurrence of pressure ulcers (pU) in patients under their care, not being always successful. This work intends to produce prediction models using Data Mining (DM) techniques in order to anticipate uP treatment. The work was conducted in the Oporto Hospital Center (CHP). For the construction of this DM study, the phases of the CRISP DM methodology were taken into account. In particular, the DM focus is to show that the time factor and frequency of interventions may influence the prediction of pU classification models. To prove this, we used a data set (containing 1339 records) where different classification techniques were applied using WEKA tool. Through the classification technique (decision tree), it was possible to create a guideline that contains all the scenarios and instructions that the professional can use in order to avoid patients to develop pU. For its construction we used the model that presented a higher percentage of sensitivity (number of positive cases correctly classified as "NO" developed pU). The conclusions were: the factors studied are good predictors of PU and the guideline obtained, through automatic techniques, can help professionals apply care to the patient more quickly.

To a human it's very complicated to access all the information correctly without a technology to help. The healthcare information systems (HIS) preserves all the information related to a patient and the hospital. In order to be able to share between different HIS, there must be a platform to integrate and share all the information. In Hospital Center of Porto (CHP), the platform used is the AIDA-PCE. This article proposes to make a prototype of an online documentation manual for the platform used and evaluate its acceptance, through a questionnaire with 28 questions. This acceptance was guided by the constructs of the technology acceptance model and the Delphi method. Through bench-marking was chosen the most appropriate tool for creating this manual. The purpose is to enumerate potential improvements to the platform, to reduce and optimize the time of its use and to increase the acceptance of this manual by the professionals.

The integration of clinical workflows in electronic health records systems has been problematic due to the complex nature of clinical processes. For that reason, many health institutions have opted to maintain a few clinical workflows on paper, which has been compromising the quality and efficiency of several provided services. The purpose of this study is to investigate if the OpenEHR model can be applied in the configuration and management of clinical workflows using Business Process Modelling (BPM), with the focus on clinical forms based on OpenEHR archetypes and having has background the institution Centro Hospitalar do Porto (CHP). The need to review the workflows is pertinent due to the lack of integration of clinical workflows on their Electronic Health Records system. To analyse this possibility, a prototype was created containing: i) a BPM tool to configure and manage the clinical workflows; and ii) a web application to execute them and call the external clinical forms. The obtained results proved that the use of a BPM tool to configure clinical workflows allows the interoperability and flexibility of the prototype, which helps to improve the quality and efficiency of the clinical practice.

Summary Nowadays, companies want technologies that are able to help them to make the best decision. Data Mining is an excellent tool to estimate the sales. It allows the company to optimize its production and reduce costs, eg, in storage. When these models are combined with Lean Production, it becomes easier to remove waste and optimize industrial production. This case study followed the CRISP‐DM methodology in order to create a model able to reduce and, if possible, eliminate wastage. Several statistics measures were applied to the dataset. Regression algorithms were induced with the goal to find which one of the models are less likely to make mistakes, in other words, what model correctly predict the target result. After executing the tests, the model M1 from the scenario C1 with RandomTree algorithm, average data grouping and average method class creation is the less likely one to give errors regarding regression, having produced an RAE of 6.75%.

In this article, Data Mining classification techniques are employed, in order to classify as normal or not-normal the iron values from a patients’ blood analysis. The dataset used is relative to patients that were subjected to Continuous Ambulatory Peritoneal Dialysis (CAPD) treatment. Weka software was used for testing several classification algorithms into such data set. The main purpose is finding the best suitable classification algorithm, with a pleasing performance in classifying the instances of the data, whereas preserving low rate of false positives. The IBk algorithm achieved the best performance, being able to correctly classify 97.39% of the instances.

Nowadays, the presence of technologies in people’s daily life is constantly increasing and, consequently, accessing information is easier. Keeping this in mind and allying it to the need to captivate students attention to take advantage of activities and events, TechPaper game was created. The goal of this game is to stimulate the interest of the participants by being captivating, disruptive, innovative, and because it consisted of a set of fun challenges, very enriching (soft and hard skills) and practiced outdoors. The TechPaper was created entirely from scratch by students in articulation with a professor of the information systems department, with the purpose of including the game in the event (TSI.2.MARKET) and integrate all the participants of the event assigning them a role. This game consists of several challenges designed to test the knowledge of participants and compel them to work in teams with people of different ages, genres and profiles. A proof of concept of this game was deployed, and the results are motivating. Considering all the opinions, we can conclude that the implementation of this game was a success at all levels and that it has the capability to be implemented and played in other realities, events, scenarios or institutions.

[Excerpt] Expert Systems (ES) are a core element of human decision making. Initially, in the 70s and 80s, ES were focused on extracting explicit knowledge from human experts. With the availability of big data, after the 2000s, ES incorporated data-driven models, thus being associated with business intelligence, big data, data science and machine learning systems [Cortez and Santos, 2017]. The importance of data-driven models in the ES area is confirmed by the recent Wiley’s Expert Systems (EXSY) literature survey that analyzed all journal research articles published from 2000 to 2016 [Cortez et al., 2018]. The survey revealed data-driven as the most prevalent ES method type, corresponding to around 35% of all recently published EXSY papers. [...]

[Excerpt] Expert Systems were proposed in the mid 1970s (Arnott & Pervan, 2014) with the goal of building computerized systems that mimic human behavior to solve real-world tasks. Such systems were based on artificial intelligence (AI) techniques, typically by adopting explicit (human understandable) knowledge, extracted from domain experts (e.g. by using interviews) and that was stored in a knowledge base (Buchanan, 1986).

As a matter of fact, an Intensive Care Unit (ICU) stands for a hospital facility where patients require close observation and monitoring. Indeed, predicting Length-of-Stay (LoS) at ICUs is essential not only to provide them with improved Quality-of-Care, but also to help the hospital management to cope with hospital resources. Therefore, in this work one‘s aim is to present an Artificial Intelligence based Decision Support System to assist on the prediction of LoS at ICUs, which will be centered on a formal framework based on a Logic Programming acquaintance for knowledge representation and reasoning, complemented with a Case Based approach to computing, and able to handle unknown, incomplete, or even contradictory data, information or knowledge.

and leader of the Intelligent Data Systems group of Centro Algoritmi, with interests in the fields of business intelligence, data mining and learning classifier systems.He participated in several

Decision making is one of the most critical activities in Intensive Care Units (ICU). Moreover, it is extremely difficult for health professionals to interpret in real time all the available data. In order to improve the decision process, classification models have been developed to predict patient’s readmission in ICU. Knowing the probability of readmission in advance will allow for a more efficient planning of discharge. Consequently, the use of these models results in a lower rates of readmission and a cost reduction, usually associated with premature discharges and unplanned readmissions. In this work was followed a numerical index, called Stability and Workload Index for Transfer (SWIFT). The data used to induce the classification models are from ICU of Centro Hospitalar do Porto, Portugal. The results obtained so far, in terms of accuracy, were very satisfactory (98.91%). Those results were achieved through the use of Naïve Bayes technique. The models will allow health professionals to have a better perception on patient’s future condition in the moment of the hospital discharge. Therefore it will be possible to know the probability of a patient being readmitted into the ICU.

The emergency services are usually pressured to make quick decisions with incomplete information on most cases, and this situation has a significant impact on healthcare as well on increasing medical errors. On the other hand, there has been an increase of the Electronic Health at Maternity Care. The combination of these two factors allows the construction of a Decision Support System specific for Maternity Care Unit using Business Intelligence technology. This solution is supported by a Data Warehouse, that uses the dimensional structure snowflake and makes the modeling of the maternity care database. With this solution it is intended to turn possible a clinical evidence-based practice, allowing for real time medical decision making with pervasive and interoperable characteristics. This paper presents the architecture, KPIs and benefits of Business Intelligence solution for the real context. This platform has several modules of clinical importance. The Obstetric Gynecological Emergency and the Voluntary Interruption of Pregnancy modules are object of study. This solution has an innovative contribution to the medical and scientific community studying the problem in Maternity area.

Presents a transaction logic (I/sub R/) formal specification of a distributed architecture for learning classifier systems (LCS) based on a blackboard as a shared database and a set of agents, an architecture that brings parallelism to the LCS functionalities and caters for the distribution of classifiers and associate processes-the DICE system. /spl Ifr//sub R/ captures the behaviour of the system in terms of state changes due to elementary updates embedded in the agent's transactions. The blackboard is viewed as an active database system and the agents as a transaction bases; that is, sets of active rules and transactions.