Objectives: Pilot 5 utilizes AI and robotics to develop a robotic nurse assisting hospital staff in response to workforce shortages and rising care demands due to an aging population. This project aims to optimize resources, reduce errors, and improve patient satisfaction through personalized care. Methods: The Living Lab approach was implemented to split the study into sprints. The first split involves working with project partners and stakeholders to define the problem, brainstorm functionalities, and identify limitations (24 participants). The second split focuses on further requirement gathering, exploring real-world use cases, and considering ethical and privacy concerns (51 participants). Results: The project used iterative development cycles (5-8 months) to continuously improve the solution. Surveys revealed high satisfaction rates, with average scores of 4.0 and 3.6 for Sprints 1 and 2, respectively. Similarly, a team morale survey indicated a positive trend, with average scores of 7.6 and 8.18 for Sprints 1 and 2, respectively. Conclusion: Pilot 5 offers a promising solution to the evolving needs of modern hospitals. This study explores the integration of a social robotic system into nursing care to enhance quality and emphasizes stakeholder engagement, participatory design, and user-centered approaches in AI healthcare solutions.

Objective: The current study aims to understand how Apple Watch helped users maintain wellness routines during the COVID-19 lockdown period, where access to public gyms and spaces was curtailed. We explore the effectiveness of biofeedback engagement aspects of Apple Watch: goals, alerts and notifications, and sociability aspects of the device or social interaction with other users. Methods: We report the results of a 2-week digital diary study based in the United States with 10 adults with 6months or longer exposure to Apple Watch, followed by online survey responses gathered from 330 additional users. Results: The study findings show how Apple Watch transforms notifications from distractions into positive wellness tools. Data suggests that personal context (custom goals and supported intent) combined with motivational nudges from alerts and notifications as well as contextually triggered nudges contribute to Apple Watch user adoption and satisfaction. Conclusion: This study highlights how Apple Watch transforms notifications from distractions into positive wellness tools; emphasizing the importance of balancing nudging with customization with user control. Sociability and privacy remain crucial, especially with biofeedback-enabled fitness trackers. We conclude that Apple Watch enhances user engagement by triggering context-relevant interactions, nudging users to achieve their goals through small, motivated behaviors.

Objectives: This study characterized toothache-related Portuguese Facebook posts, identifying factors driving misinformation production and user engagement. Methods: Investigators qualitatively analyzed 500 posts published between August 2018 and August 2022, screening on language and theme. Posts were selected using CrowdTangle and assessed for motivation, author profile, content, sentiment, facticity, and format. The interaction metrics (total interactions/overperforming scores) were compared between groups of dichotomized characteristics, including time of publication. Data were evaluated by descriptive analysis, the Mann-Whitney U test, and the path analysis by generalized structural equation modeling. Results: 39.6% of posts (n = 198) contained misinformation, significantly linked to noncommercial posts with positive sentiment, links, and videos from regular users motivated by financial motivation. Additionally, user engagement was only positively associated with business/health authors' profiles and the time of publication. Conclusion: Toothache-related posts often contain misinformation, shared by regular users in links and video formats, tied to positive sentiments, and generally with financial motivation.

Objectives: As COVID-19 continues to wreak havoc around the world, effective screening methods for safeguarding public health and bringing the pandemic under control are urgently required. Accordingly, this study proposes a robust pipeline for training and evaluating deep learning predictive models for the detection of COVID-19 from chest X-rays. Methods: The pipeline incorporates multiple techniques to combat overfitting and optimize the prediction results, including data augmentation, Bayesian optimization, the selection of appropriate performance metrics, and the use of early stopping during model training. The proposed pipeline is applied to three common deep learning models: ResNet50, NASNet-A-Mobile, and Xception. Results: The experimental results obtained using the COVID-XRay-5K v3 dataset with 2084 training and 3100 test examples show that the three models achieve AU-PRC (area under precision-recall curve) scores of 0.977, 0.963 and 0.900, respectively; and AU-ROC (area under receiver operating characteristic) scores of 0.994, 0.996 and 0.981. Moreover, at a 98.00% recall (sensitivity), the models achieve high specificities of 97.53%, 97.60% and 86.00%, respectively. Conclusion: Overall, the results suggest that all three models provide a promising approach for the rapid and reliable detection of COVID-19.

Background: Despite the ongoing efforts to digitalize the healthcare sector in developing countries, the full adoption of big data analytics in healthcare settings is yet to be attained Exploring opportunities and challenges encountered is essential for designing and implementing effective interventional strategies. Objective: Exploring opportunities and challenges towards integrating big data analytics technologies in the healthcare industry in developing countries. Methodology: This was a narrative review study design. A literature search on different databases was conducted including PubMed, ScienceDirect, MEDLINE, Scopus, and Google Scholar. Articles with predetermined keywords and written in English were included. Results: Big data analytics finds its application in population health management and clinical decision-support systems even in developing countries. The major challenges towards the integration of big data analytics in the healthcare sector in developing countries include fragmentation of healthcare data and lack of interoperability, data security, privacy and confidentiality concerns, limited resources and inadequate regulatory and policy frameworks for governing big data analytics technologies and limited reliable power and internet infrastructures. Conclusion: Digitalization of healthcare delivery in developing countries faces several significant challenges. However, the integration of big data analytics can potentially open new avenues for enhancing healthcare delivery with cost-effective benefits.

Background: As healthcare depends on health information technology, there is a growing need for Health Informatics competencies in daily practice. This review aimed to explore how the teaching of education in HI has been arranged. 28 publications, published in English between 2016 and 2020 and obtained from selected bibliographic databases, were reviewed. The data was analyzed using deductive content analysis with the following pre-formulated topics: target audience, course content and learning arrangements. The results highlight three key competencies: documentation and communication, management, and understanding of health information technology. It underlines a blended teaching method to improve the competencies of healthcare professionals, graduates, undergraduates, and suggests adding active interactions, multi-professional interactions, and hands-on skills. This study highlights the importance of adapting to changes in healthcare, improving HI competencies in healthcare, and fostering positive digital experiences. It underlined the need for practical training, in theory and hands-on sessions, including key competencies in documentation and communication, management and health information systems.

Population Health Management - often abbreviated to PHM - is a relatively new approach for healthcare planning, requiring the application of analytical techniques to linked patient level data. Despite expectations for greater uptake of PHM, there is a deficit of available solutions to help health services embed it into routine use. This paper concerns the development, application and use of an interactive tool which can be linked to a healthcare system's data warehouse and employed to readily perform key PHM tasks such as population segmentation, risk stratification, and deriving various performance metrics and descriptive summaries. Developed through open-source code in a large healthcare system in South West England, and used by others around the country, this paper demonstrates the importance of a scalable, purpose-built solution for improving the uptake of PHM in health services.

Objective: This study sought to assess the impact of a novel electronic audit and feedback (e-A&F) system on patient outcomes. Methods: The e-A&F intervention was implemented in a tertiary hospital and involved near real-time feedback via web-based dashboards. We used a segmented regression analysis of interrupted time series. We modelled the pre-post change in outcomes for the (1) announcement of this priority list, and (2) implementation of the e-A&F intervention to have affected patient outcomes. Results: Across the study period there were 222,792 episodes of inpatient care, of which 13,904 episodes were found to contain one or more HACs, a risk of 6.24%. From the point of the first intervention until the end of the study the overall risk of a HAC reduced from 8.57% to 4.12% - a 51.93% reduction. Of this reduction the proportion attributed to each of these interventions was found to be 29.99% for the announcement of the priority list and 21.93% for the implementation of the e-A&F intervention. Discussion: Our findings lend evidence to a mechanism that the announcement of a measurement framework, at a national level, can lead to local strategies, such as e-A&F, that lead to significant continued improvements over time.

This study develops machine learning-based algorithms that facilitate accurate prediction of cerebral oxygen saturation using waveform data in the near-infrared range from a multi-modal oxygen saturation sensor. Data were obtained from 150,000 observations of a popular cerebral oximeter, Masimo O3™ regional oximetry (Co., United States) and a multi-modal cerebral oximeter, Votem (Inc., Korea). Among these observations, 112,500 (75%) and 37,500 (25%) were used for training and test sets, respectively. The dependent variable was the cerebral oxygen saturation value from the Masimo O3™ (0-100%). The independent variables were the time of measurement (0-300,000 ms) and the 16-bit decimal amplitudes values (infrared and red) from Votem (0-65,535). For the right part of the forehead, the root mean square error of the random forest (0.06) was much smaller than those of linear regression (1.22) and the artificial neural network with one, two or three hidden layers (2.58). The result was similar for the left part of forehead, that is, random forest (0.05) vs logistic regression (1.22) and the artificial neural network with one, two or three hidden layers (2.97). Machine learning aids in accurately predicting of cerebral oxygen saturation, employing the data from a multi-modal cerebral oximeter.

Clinical decision support (CDS) alerts are designed to work according to a set of clearly defined criteria and have the potential to improve clinical care. To efficiently and proactively review abnormally functioning CDS alerts, we postulate that the introduction of a dashboard with statistical process control (SPC) charting will lead to effective detection of erratic alert behavior. We identified custom CDS alerts from an academic medical center that were recorded and monitored in a longitudinal fashion and the data warehouses where this information was stored. We created a dashboard of alert frequency using SPC charts, applied SPC rules for classification of variation, and validated dashboard data. From June-August 2022, the dashboard effectively pulled in data to visually depict alert behavior. SPC-defined parameters for standard deviation from the mean were applied to visualizations and allowed for rapid review of alerts with greatest variation. These alerts were subsequently investigated, and it was determined that they were functioning correctly. The most profound abnormalities detected during implementation reflected changes in practice and not system errors, though further investigation into thresholds for statistical significance will benefit this field. We conclude that SPC visualizations are a time-efficient and effective method of identifying CDS malfunctions.