Gamification as an educational tool to address antimicrobial resistance: a systematic review.

Background: Antimicrobial resistance (AMR) poses a serious threat to global healthcare, and inadequate education has been identified as a major challenge by the WHO. The human , animal and agricultural sectors contribute to the emergence of AMR. Gamification has emerged as an innovative tool to improve knowledge and change behaviours. Our study provides an overview of the literature on existing games in prescribers' education across the One Health sectors, with a particular focus on the impact of gamification on learning. Methods: Using the PRISMA guidelines, we searched Cochrane, PubMed, Scopus and Google Scholar for articles related to gamification for future prescribers of antimicrobials from inception until 28 March 2023. Retrieval and screening of articles was done using a structured search protocol with strict inclusion/exclusion criteria. Results: A total of 120 articles were retrieved, of which 6 articles met the inclusion criteria for final analysis. High-income countries had the most studies, with one global study incorporating low- to middle-income countries. All games were evaluated in the human sector. Board and card games, featuring scoring and point systems, were the most prevalent game types. Most games focused on improving knowledge and prescribing behaviours of medical students, with bacteria or antibiotics as the only content. All studies highlighted the significant potential of gamification in mitigating AMR, promoting antimicrobial stewardship, and improving retention of information compared with conventional lectures. Conclusions: Our review found an absence of studies in the animal and environmental sectors, disproportionately focused on medical students with questionable sample size, inadequate assessment of game content and effectiveness, and opportunities for game developers.

Enrichment of a Data Lake to Support Population Health Outcomes Studies Using Social Determinants Linked EHR Data.

The integration of electronic health records (EHRs) with social determinants of health (SDoH) is crucial for population health outcome research, but it requires the collection of identifiable information and poses security risks. This study presents a framework for facilitating de-identified clinical data with privacy-preserved geocoded linked SDoH data in a Data Lake. A reidentification risk detection algorithm was also developed to evaluate the transmission risk of the data. The utility of this framework was demonstrated through one population health outcomes research analyzing the correlation between socioeconomic status and the risk of having chronic conditions. The results of this study inform the development of evidence-based interventions and support the use of this framework in understanding the complex relationships between SDoH and health outcomes. This framework reduces computational and administrative workload and security risks for researchers and preserves data privacy and enables rapid and reliable research on SDoH-connected clinical data for research institutes.

The Role of Remote Sensing and Geospatial Analysis for Understanding COVID-19 Population Severity: A Systematic Review.

Remote sensing (RS), satellite imaging (SI), and geospatial analysis have established themselves as extremely useful and very diverse domains for research associated with space, spatio-temporal components, and geography. We evaluated in this review the existing evidence on the application of those geospatial techniques, tools, and methods in the coronavirus pandemic. We reviewed and retrieved nine research studies that directly used geospatial techniques, remote sensing, or satellite imaging as part of their research analysis. Articles included studies from Europe, Somalia, the USA, Indonesia, Iran, Ecuador, China, and India. Two papers used only satellite imaging data, three papers used remote sensing, three papers used a combination of both satellite imaging and remote sensing. One paper mentioned the use of spatiotemporal data. Many studies used reports from healthcare facilities and geospatial agencies to collect the type of data. The aim of this review was to show the use of remote sensing, satellite imaging, and geospatial data in defining features and relationships that are related to the spread and mortality rate of COVID-19 around the world. This review should ensure that these innovations and technologies are instantly available to assist decision-making and robust scientific research that will improve the population health diseases outcomes around the globe.

A Privacy-Preserved Transfer Learning Concept to Predict Diabetic Kidney Disease at Out-of-Network Siloed Sites Using an In-Network Federated Model on Real-World Data.

Successful implementation of data-driven artificial intelligence (AI) applications requires access to large datasets. Healthcare institutions can establish coordinated data-sharing networks to address the complexity of large clinical data accessibility for scientific advancements. However, persisting challenges from controlled access, safe data transferring, license restrictions from regulatory and legal concerns discourage data sharing among the in-network hospitals. In contrast, out-of-network healthcare institutions are deprived of access to any big EHR database; hence, limiting their research scope. The main objective of this study is to design a privacy-preserved transfer learning architecture that can utilize the knowledge from a federated model developed from in-network hospital-site EHR data for predicting diabetic kidney cases at out-of-network siloed hospital sites. In all our experiments, transfer learning showed improved performance compared to models trained with out-of-network site datasets. Thus, we demonstrate the proof-of-concept of transferring knowledge from established networks to aid data-driven AI discoveries at siloed sites.

ECG and SpO2 Signal-Based Real-Time Sleep Apnea Detection Using Feed-Forward Artificial Neural Network.

Sleep apnea (SA) is a common sleep disorder characterized by respiratory disturbance during sleep. Polysomnography (PSG) is the gold standard for apnea diagnosis, but it is time-consuming, expensive, and requires manual scoring. As an alternative to PSG, we investigated a real-time SA detection system using oxygen saturation level (SpO2) and electrocardiogram (ECG) signals individually as well as a combination of both. A series of R-R intervals were derived from the raw ECG data and a feed-forward deep artificial neural network is employed for the detection of SA. Three different models were built using 1-minute-long sequences of SpO2 and R-R interval signals. The 10-fold cross-validation result showed that the SpO2-based model performed better than the ECG-based model with an accuracy of 90.78 ± 10.12% and 80.04 ± 7.7%, respectively. Once combined, these two signals complemented each other and resulted in a better model with an accuracy of 91.83 ± 1.51%.

Clinicians' Perspectives in Using Patient-Generated Health Data to Improve Ischemic Heart Disease Management.

Patients suffering from ischemic heart disease (IHD) should be monitored closely after being discharged. With recent advances in digital health tools, collecting, using, and sharing patient-generated health data (PGHD) has become more achievable. PGHD can complement the existing clinical data and provide a comprehensive picture of the patient's health status. Despite the potential value of PGHD in healthcare, its implementation currently remains limited due to the clinicians' concern with the reliability and accuracy of the gathered data to support decision-making and concerns regarding the added workload that PGHD might cause to clinical workflow. The main objective of the study was to investigate the clinicians' perspectives towards the use of PGHD for IHD management, focusing on data sharing, interpretation, and efficiency in decision-making. The study consists of semi-structured interviews with seven clinicians. Study results identified four main themes: data generation, data integration, data presentation, data interpretation and utilization in clinical decision-making.