Data Quality Evaluation is a critical problem, specially in Healthcare, where people may take decisions based on confident, acceptable and secure information. In this paper we show how data quality can be evaluated from electronic health records, in particular in a hospital setting. We construct a dynamic virtual world of complex entities or agents, driven by one criterion alone, intelligence, for the provision of healthcare. This virtual world will witness the emergence and will be based on a versatile and powerful paradigm, where the candidate solutions (here understood as agents) are seen as evolutionary logic programs or theories, being the test if a solution is optimal based on a measure of the quality-of-information that stems from them.

Background: Health care is facing many challenges. The recent pandemic has caused a global reflection on how clinical and organizational processes should be organized, which requires the optimization of decision-making among managers and health care professionals to deliver care that is increasingly patient-centered. The efficiency of surgical scheduling is particularly critical, as it affects waiting list management and is susceptible to suboptimal decisions due to its complexity and constraints. Objective: In this study, in collaboration with one of the leading hospitals in Portugal, Centro Hospitalar e Universitário de Santo António (CHUdSA), a heuristic approach is proposed to optimize the management of the surgical center. Methods: CHUdSA’s surgical scheduling process was analyzed over a specific period. By testing an optimization approach, the research team was able to prove the potential of artificial intelligence (AI)–based heuristic models in minimizing scheduling penalties—the financial costs incurred by procedures that were not scheduled on time. Results: The application of this approach demonstrated potential for significant improvements in scheduling efficiency. Notably, the implementation of the hill climbing (HC) and simulated annealing (SA) algorithms stood out in this implementation and minimized the scheduling penalty, scheduling 96.7% (415/429) and 84.4% (362/429) of surgeries, respectively. For the HC algorithm, the penalty score was 0 in the urology, obesity, and pediatric plastic surgery medical specialties. For the SA algorithm, the penalty score was 5100 in urology, 1240 in obesity, and 30 in pediatric plastic surgery. Together, this highlighted the ability of AI-heuristics to optimize the efficiency of this process and allowed for the scheduling of surgeries at closer dates compared to the manual method used by hospital professionals. Conclusions: Integrating these solutions into the surgical scheduling process increases efficiency and reduces costs. The practical implications are significant. By implementing these AI-driven strategies, hospitals can minimize patient wait times, maximize resource use, and enhance surgical outcomes through improved planning. This development highlights how AI algorithms can effectively adapt to changing health care environments, having a transformative impact.

The Electronic Health Records (EHR) is a longitudinal electronic record of patient heath information generated by one or more encounters in any care delivery setting. The information included in the type of records can be progress notes, problems, medication, vital signs, medical history, laboratory data, radiology reports and much more. The usability of an EHR is crucial to achieve success, as well as guaranteeing a high level of safe and effective level of patient care system. Therefore, it is important for care providers to take steps to ensure that their EHR will be usable. Aiming to assess the usability of the EHR, a usability evaluation was performed. This paper describes how two usability evaluation methods (heuristic walkthrough and surveys) were used to evaluate its usability. With those evaluations, it was able to recognize the level of usability present in the EHR, along with the usability issues that can be disposed.

A multichannel interaction service is a practice whereby organizations communicate and interact with their existing customers and potential new customers through different channels. This article presents a brief case study of multichannel interaction in healthcare services, which studies the viability of continuous multichannel interaction for personalized healthcare services to enable health professionals to follow up and monitor patients in home-based care. Furthermore, this study aims to explore the possibility of the continuity and complementarity of the interactions across different communication channels with the patients. The data used for this study was gathered during the first wave of the COVID-19 pandemic. This study showed that despite this type of interaction being relatively new in healthcare services, it has considerable potential for improving the relationship between patients, health professionals, and care providers. Upon completion of the data analysis, several conclusions were drawn. One such conclusion was the ability to maintain continuity of interaction across multiple channels, as well as the synergy between the different channels of interaction available to patients and the impact this has on the way patients and health professionals interact. Additionally, it was determined that the complementarity of different interaction channels is crucial when implementing multichannel interaction services. Furthermore, the implementation of this solution resulted in improved communication between patients and health professionals. Also, it has decreased health professional’s workload and reduced care providers costs regarding remote patient follow-up.

The semantic and syntactic interoperability introduces the capability of two machines to communicate and understand each other improving then the quality of Electronic Health Records. In this work, is presented an independent application of the medical record, using a web service (with protocol TCP/IP) capable of provide human interaction with interfaces in different devices (web, android app., browser). SNOMED CT is a comprehensive and scientifically validated health care terminology resulting in an organized computer processable collection of medical terms. This can be mapped into other systems of codes like ICD also used in our application. A data base (SNOMED codes and relations) was created capable of answer to all sort of queries from the users using the browser or the mobile application. The first hospital unit to enjoy this system was the pathological anatomy unit of the CHAA hospital. Here after receiving a “piece” and a task, the responsible performs all kind of procedures with the purpose of performing a report (diagnosis for example). With the implementation of SNOMED CT, to produce reports a physician could search for the name of the “piece” or the code and immediately upload in that patient HER the diagnosis. The usage of this codes leads to report uniformed that could be read and understood around the world. Another important feature of the application is the incorporation within the AIDA, AIDA-PCE and AIDA-BI. Experiments with real user show a successful software implementation judging by the utilization rate and medical personal acceptance. The mobile application should suffer an upgrade allowing the patient usage for example.