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Applied Smart Health Care Informatics Explores how intelligent systems offer new opportunities for optimizing the acquisition, storage, retrieval, and use of information in healthcare Applied Smart Health Care Informatics explores how health information technology and intelligent systems can be integrated and deployed to enhance healthcare management. Edited and authored by leading experts in the field, this timely volume introduces modern approaches for managing existing data in the healthcare sector by utilizing artificial intelligence (AI), meta-heuristic algorithms, deep learning, the Internet of Things (IoT), and other smart technologies. Detailed chapters review advances in areas including machine learning, computer vision, and soft computing techniques, and discuss various applications of healthcare management systems such as medical imaging, electronic medical records (EMR), and drug development assistance. Throughout the text, the authors propose new research directions and highlight the smart technologies that are central to establishing proactive health management, supporting enhanced coordination of care, and improving the overall quality of healthcare services. * Provides an overview of different deep learning applications for intelligent healthcare informatics management * Describes novel methodologies and emerging trends in artificial intelligence and computational intelligence and their relevance to health information engineering and management * Proposes IoT solutions that disseminate essential medical information for intelligent healthcare management * Discusses mobile-based healthcare management, content-based image retrieval, and computer-aided diagnosis using machine and deep learning techniques * Examines the use of exploratory data analysis in intelligent healthcare informatics systems Applied Smart Health Care Informatics: A Computational Intelligence Perspective is an invaluable text for graduate students, postdoctoral researchers, academic lecturers, and industry professionals working in the area of healthcare and intelligent soft computing.

Complex, chronic or hard‐to‐heal wounds are a prevalent health problem worldwide, with significant physical, psychological and social consequences. This study aims to identify factors associated with the healing process of these wounds and develop a mobile application for wound care that incorporates these factors. A prospective multicentre cohort study was conducted in nine health units in Portugal, involving data collection through a mobile application by nurses from April to October 2022. The study followed 46 patients with 57 wounds for up to 5 weeks, conducting six evaluations. Healing time was the main outcome measure, analysed using the Mann–Whitney test and three Cox regression models to calculate risk ratios. The study sample comprised various wound types, with pressure ulcers being the most common (61.4%), followed by venous leg ulcers (17.5%) and diabetic foot ulcers (8.8%). Factors that were found to impair the wound healing process included chronic kidney disease (U = 13.50; p = 0.046), obesity (U = 18.0; p = 0.021), non‐adherence to treatment (U = 1.0; p = 0.029) and interference of the wound with daily routines (U = 11.0; p = 0.028). Risk factors for delayed healing over time were identified as bone involvement (RR 3.91; p < 0.001), presence of odour (RR 3.36; p = 0.007), presence of neuropathy (RR 2.49; p = 0.002), use of anti‐inflammatory drugs (RR 2.45; p = 0.011), stalled wound (RR 2.26; p = 0.022), greater width (RR 2.03; p = 0.002), greater depth (RR 1.72; p = 0.036) and a high score on the healing scale (RR 1.21; p = 0.001). Integrating the identified risk factors for delayed healing into the assessment of patients and incorporating them into a mobile application can enhance decision‐making in wound care.

Background and Aims Due to the COVID‐19 pandemic, a precise and reliable diagnosis of this disease is critical. The use of clinical decision support systems (CDSS) can help facilitate the diagnosis of COVID‐19. This scoping review aimed to investigate the role of CDSS in diagnosing COVID‐19. Methods We searched four databases (Web of Science, PubMed, Scopus, and Embase) using three groups of keywords related to CDSS, COVID‐19, and diagnosis. To collect data from studies, we utilized a data extraction form that consisted of eight fields. Three researchers selected relevant articles and extracted data using a data collection form. To resolve any disagreements, we consulted with a fourth researcher. Results A search of the databases retrieved 2199 articles, of which 68 were included in this review after removing duplicates and irrelevant articles. The studies used nonknowledge‐based CDSS (n = 52) and knowledge‐based CDSS (n = 16). Convolutional Neural Networks (CNN) (n = 33) and Support Vector Machine (SVM) (n = 8) were employed to design the CDSS in most of the studies. Accuracy (n = 43) and sensitivity (n = 35) were the most common metrics for evaluating CDSS. Conclusion CDSS for COVID‐19 diagnosis have been developed mainly through machine learning (ML) methods. The greater use of these techniques can be due to their availability of public data sets about chest imaging. Although these studies indicate high accuracy for CDSS based on ML, their novelty and data set biases raise questions about replacing these systems as clinician assistants in decision‐making. Further studies are needed to improve and compare the robustness and reliability of nonknowledge‐based and knowledge‐based CDSS in COVID‐19 diagnosis.

Zambia is faced with a severe shortage of health workers and challenges in national health financing. This burdens the medical licentiate practitioner (MLP) program for training nonphysician clinical students in Zambia because of the shortage of qualified medical lecturers and learning resources at training sites. To address this shortage and strengthen the MLP program, a self-directed electronic health (eHealth) platform was introduced, comprising technology-supported learning (e-learning) for medical education and support for health care practice. MLP students were provided with tablets that were preloaded with content for offline access. This study aimed to explore MLP students' and medical lecturers' perceptions of the self-directed eHealth platform with an offline-based tablet as a training and health care practice support tool during the first year of full implementation. We conducted in-depth qualitative interviews with 8 MLP students and 5 lecturers and 2 focus group discussions with 16 students to gain insights on perceptions of the usefulness, ease of use, and adequacy of self-directed e-learning and health care practice support accessible through the offline-based tablet. Participants were purposively sampled. Verbatim transcripts were analyzed following hypothesis coding. The eHealth platform (e-platform), comprising e-learning for medical education and health care practice support, was positively received by students and medical lecturers and was seen as a step toward modernizing the MLP program. Tablets enabled equal access to offline learning contents, thus bridging the gap of slow or no internet connections. The study results indicated that the e-platform appears adequate to strengthen medical education within this low-resource setting. However, student self-reported usage was low, and medical lecturer usage was even lower. One stated reason was the lack of training in tablet usage and another was the quality of the tablets. The mediocre quality and quantity of most e-learning contents were perceived as a primary concern as materials were reported to be outdated, missing multimedia features, and addressing only part of the curriculum. Medical lecturers were noted to have little commitment to updating or creating new learning materials. Suggestions for improving the e-platform were given. To address identified major challenges, we plan to (1) introduce half-day training sessions at the beginning of each study year to better prepare users for tablet usage, (2) further update and expand e-learning content by fostering collaborations with MLP program stakeholders and nominating an e-platform coordinator, (3) set up an e-platform steering committee including medical lecturers, (4) incorporate e-learning and e-based health care practice support across the curriculum, as well as (5) implement processes to promote user-generated content. With these measures, we aim to sustainably strengthen the MLP program by implementing the tablet-based e-platform as a serious learning technology for medical education and health care practice support.

Little is known about how information available at discharge affects decision-making and its effect on readmission. We sought to define the association between information used for discharge and patients' subsequent risk of readmission. 2009–2014 patients from a tertiary academic medical center's surgical services were analyzed using a time-to-event model to identify criteria that statistically explained the timing of discharges. The data were subsequently used to develop a time-varying prediction model of unplanned hospital readmissions. These models were validated and statistically compared. The predictive discharge and readmission regression models were generated from a database of 20,970 patients totaling 115,976 patient-days with 1,565 readmissions (7.5%). 22 daily clinical measures were significant in both regression models. Both models demonstrated good discrimination (C statistic = 0.8 for all models). Comparison of discharge behaviors versus the predictive readmission model suggested important discordance with certain clinical measures (e.g., demographics, laboratory values) not being accounted for to optimize discharges. Decision-support tools for discharge may utilize variables that are not routinely considered by healthcare providers. How providers will then respond to these atypical findings may affect implementation. •Define the association between information used for discharge after surgery and patients’ subsequent risk of readmission.•Surgical patients were analyzed to identify criteria that statistically explained the timing of discharges.•The same data were also used to develop a time varying prediction model of unplanned hospital readmissions.•The models demonstrated discrepancy between what surgeons behaviourally used for discharge decision versus factors for preventing readmission.

Background The aim of this study was the development and evaluation of an algorithm-based diagnosis-tool, applicable on mobile phones, to support guardians in providing appropriate care to sick children. Methods The algorithm was developed on the basis of the Integrated Management of Childhood Illness (IMCI) guidelines and evaluated at a hospital in Ghana. Two hundred and thirty-seven guardians applied the tool to assess their child’s symptoms. Data recorded by the tool and health records completed by a physician were compared in terms of symptom detection, disease assessment and treatment recommendation. To compare both assessments, Kappa statistics and predictive values were calculated. Results The tool detected the symptoms of cough, fever, diarrhoea and vomiting with good agreement to the physicians’ findings (kappa = 0.64; 0.59; 0.57 and 0.42 respectively). The disease assessment barely coincided with the physicians’ findings. The tool’s treatment recommendation correlated with the physicians’ assessments in 93 out of 237 cases (39.2% agreement, kappa = 0.11), but underestimated a child’s condition in only seven cases (3.0%). Conclusions The algorithm-based tool achieved reliable symptom detection and treatment recommendations were administered conformably to the physicians’ assessment. Testing in domestic environment is envisaged.

In this view of the future of medicine, patient–provider interactions are informed and supported by massive amounts of data from interactions with similar patients. These data are collected and curated to provide the latest evidence-based assessment and recommendations.

Artificial intelligence (AI) is increasingly being developed for use in medicine, including for diagnosis and in treatment decision making. The use of AI in medical treatment raises many ethical issues that are yet to be explored in depth by bioethicists. In this paper, I focus specifically on the relationship between the ethical ideal of shared decision making and AI systems that generate treatment recommendations, using the example of IBM’s Watson for Oncology. I argue that use of this type of system creates both important risks and significant opportunities for promoting shared decision making. If value judgements are fixed and covert in AI systems, then we risk a shift back to more paternalistic medical care. However, if designed and used in an ethically informed way, AI could offer a potentially powerful way of supporting shared decision making. It could be used to incorporate explicit value reflection, promoting patient autonomy. In the context of medical treatment, we need value-flexible AI that can both respond to the values and treatment goals of individual patients and support clinicians to engage in shared decision making.

We need to consider the ethical challenges inherent in implementing machine learning in health care if its benefits are to be realized. Some of these challenges are straightforward, whereas others have less obvious risks but raise broader ethical concerns.