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BACKGROUND: Research on healthcare disparities in pediatric radiology is limited, leading to the persistence of missed care opportunities (MCO). We hypothesize that the COVID-19 pandemic exacerbated existing health disparities in access to pediatric radiology services. OBJECTIVE: Evaluate the social determinants of health and sociodemographic factors related to pediatric radiology MCO before, during, and after the COVID-19 pandemic. MATERIALS AND METHODS: The study examined all outpatient pediatric radiology exams at a pediatric medical center and its affiliate centers from 03/08/19 to 06/07/21 to identify missed care opportunities. Logistic regression with the least absolute shrinkage and selection operator (LASSO) method and classification and regression tree (CART) analysis were used to explore factors and visualize relationships between social determinants and missed care opportunities. RESULTS: A total of 62,009 orders were analyzed: 30,567 pre-pandemic, 3,205 pandemic, and 28,237 initial recovery phase. Median age was 11.34 years (IQR 5.24-15.02), with 50.8% females (31,513/62,009). MCO increased during the pandemic (1,075/3,205; 33.5%) compared to pre-pandemic (5,235/30,567; 17.1%) and initial recovery phase (4,664/28,237; 16.5%). The CART analysis identified changing predictors of missed care opportunities across different periods. Pre-pandemic, these were driven by exam-specific factors and patient age. During the pandemic, social determinants like income, distance, and ethnicity became key. In the initial recovery phase, the focus returned to exam-specific factors and age, but ethnicity continued to influence missed care, particularly in neurological exams for Hispanic patients. Logistic regression revealed similar results: during the pandemic, increased distance from the examination site (OR 1.1), residing outside the state (OR 1.57), Hispanic (OR 1.45), lower household income ($25,000-50,000 (OR 3.660) and $50,000-75,000 (OR 1.866)), orders for infants (OR 1.43), and fluoroscopy (OR 2.3) had higher odds. In the initial recovery phase, factors such as living outside the state (OR 1.19), orders for children (OR 0.79), and being Hispanic (OR 1.15) correlate with higher odds of MCO. CONCLUSION: The application of basic data science techniques is a valuable tool in uncovering complex relationships between sociodemographic factors and disparities in pediatric radiology, offering crucial insights into addressing inequalities in care.

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This study introduces a novel artificial intelligence (AI) force field, namely a graph-based pre-trained transformer force field (GPTFF), which can simulate arbitrary inorganic systems with good precision and generalizability. Harnessing a large trove of the data and the attention mechanism of transformer algorithms, the model can accurately predict energy, atomic force, and stress with mean absolute error (MAE) values of 32 meV/atom, 71 meV/Å, and 0.365 GPa, respectively. The dataset used to train the model includes 37.8 million single-point energies, 11.7 billion force pairs, and 340.2 million stresses. We also demonstrated that the GPTFF can be universally used to simulate various physical systems, such as crystal structure optimization, phase transition simulations, and mass transport. The model is publicly released with this paper, enabling anyone to use it immediately without needing to train it.

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Objective: The objective of the study was to establish an AI-driven decision support system by identifying the most important features in the severity of disease for Intensive Care Unit (ICU) with Mechanical Ventilation (MV) requirement, ICU, and InterMediate Care Unit (IMCU) admission for hospitalized patients with COVID-19 in South Florida. The features implicated in the risk factors identified by the model interpretability can be used to forecast treatment plans faster before critical conditions exacerbate. Methods: We analyzed eHR data from 5371 patients diagnosed with COVID-19 from South Florida Memorial Healthcare Systems admitted between March 2020 and January 2021 to predict the need for ICU with MV, ICU, and IMCU admission. A Random Forest classifier was trained on patients' data augmented by SMOTE, collected at hospital admission. We then compared the importance of features utilizing different model interpretability analyses, such as SHAP, MDI, and Permutation Importance. Results: The models for ICU with MV, ICU, and IMCU admission identified the following factors overlapping as the most important predictors among the three outcomes: age, race, sex, BMI, diarrhea, diabetes, hypertension, early stages of kidney disease, and pneumonia. It was observed that individuals over 65 years ('older adults'), males, current smokers, and BMI classified as 'overweight' and 'obese' were at greater risk of severity of illness. The severity was intensified by the co-occurrence of two interacting features (e.g., diarrhea and diabetes). Conclusions: The top features identified by the models' interpretability were from the 'sociodemographic characteristics', 'pre-hospital comorbidities', and 'medications' categories. However, 'pre-hospital comorbidities' played a vital role in different critical conditions. In addition to individual feature importance, the feature interactions also provide crucial information for predicting the most likely outcome of patients' conditions when urgent treatment plans are needed during the surge of patients during the pandemic.

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Single nucleotide variants (SNVs) can exert substantial and extremely variable impacts on various cellular functions, making accurate predictions of their consequences challenging, albeit crucial especially in clinical settings such as in oncology. Laboratory-based experimental methods for assessing these effects are time-consuming and often impractical, highlighting the importance of in-silico tools for variant impact prediction. However, the performance metrics of currently available tools on breast cancer missense variants from benchmarking databases have not been thoroughly investigated, creating a knowledge gap in the accurate prediction of pathogenicity. In this study, the benchmarking datasets ClinVar and HGMD were used to evaluate 21 Artificial Intelligence (AI)-derived in-silico tools. Missense variants in breast cancer genes were extracted from ClinVar and HGMD professional v2023.1. The HGMD dataset focused on pathogenic variants only, to ensure balance, benign variants for the same genes were included from the ClinVar database. Interestingly, our analysis of both datasets revealed variants across genes with varying penetrance levels like low and moderate in addition to high, reinforcing the value of disease-specific tools. The top-performing tools on ClinVar dataset identified were MutPred (Accuracy = 0.73), Meta-RNN (Accuracy = 0.72), ClinPred (Accuracy = 0.71), Meta-SVM, REVEL, and Fathmm-XF (Accuracy = 0.70). While on HGMD dataset they were ClinPred (Accuracy = 0.72), MetaRNN (Accuracy = 0.71), CADD (Accuracy = 0.69), Fathmm-MKL (Accuracy = 0.68), and Fathmm-XF (Accuracy = 0.67). These findings offer clinicians and researchers valuable insights for selecting, improving, and developing effective in-silico tools for breast cancer pathogenicity prediction. Bridging this knowledge gap contributes to advancing precision medicine and enhancing diagnostic and therapeutic approaches for breast cancer patients with potential implications for other conditions.

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Introduction: The curriculum for a da Vinci surgeon in gynecology requires special training before a surgeon performs their first independent case, but standardized, objective assessments of a trainee's workflow or skills learned during clinical cases are lacking. This pilot study presents a methodology to evaluate intraoperative surgeon behavior in hysterectomy cases through standardized surgical step segmentation paired with objective performance indicators (OPIs) calculated directly from robotic data streams. This method can provide individual case analysis in a truly objective capacity. Materials and methods: Surgical data from six robot-assisted total laparoscopic hysterectomies (rTLH) performed by two experienced surgeons was collected prospectively using an Intuitive Data Recorder. Each rTLH video was annotated and segmented into specific, functional surgical steps based on the recorded video. Once annotated, OPIs were compared through workflow analysis and across surgeons during two critical surgical steps: colpotomy and vaginal cuff closure. Results: Through visualization of the individual steps over time, we observe workflow consistencies and variabilities across individual surgeons of a similar experience level at the same hospital, creating unique surgeon behavior signatures across each surgical case. OPI differences across surgeons were observed for both the colpotomy and vaginal cuff closure steps, specifically reflecting camera movement, energy usage and clutching behaviors. Comparing colpotomy and vaginal cuff closure time needed for the step and the events of energy use were significantly different (p < 0.001). For the comparison between the two surgeons only the event count for camera movement during colpotomy showed significant differences (p = 0.03). Conclusion: This pilot study presents a novel methodology to analyze and compare individual rTLH procedures with truly objective measurements. Through collection of robotic data streams and standardized segmentation, OPI measurements for specific rTLH surgery steps can be reliably calculated and compared to those of other surgeons. This provides opportunity for critical standardization to the gynecology field, which can be integrated into individualized training plans in the future. However, more studies are needed to establish context surrounding these metrics in gynecology.

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The estimation of the global burden of animal diseases requires the integration of multidisciplinary models: economic, statistical, mathematical and conceptual. The output of one model often serves as input for another; therefore, consistency of the model components is critical. The Global Burden of Animal Diseases (GBADs) Informatics team aims to strengthen the scientific foundations of modelling by creating tools that address challenges related to reproducibility, as well as model, data and metadata interoperability. Aligning with these aims, several tools are under development: a) GBADs'Trusted Animal Information Portal (TAIL) is a data acquisition platform that enhances the discoverability of data and literature and improves the user experience of acquiring data. TAIL leverages advanced semantic enrichment techniques (natural language processing and ontologies) and graph databases to provide users with a comprehensive repository of livestock data and literature resources. b) The interoperability of GBADs'models is being improved through the development of an R-based modelling package and standardisation of parameter formats. This initiative aims to foster reproducibility, facilitate data sharing and enable seamless collaboration among stakeholders. c) The GBADs Knowledge Engine is being built to foster an inclusive and dynamic user community by offering data in multiple formats and providing user-friendly mechanisms to garner feedback from the community. These initiatives are critical in addressing complex challenges in animal health and underscore the importance of combining scientific rigour with user-friendly interfaces to empower global efforts in safeguarding animal populations and public health.

L'estimation de l'impact mondial des maladies animales nécessite l'utilisation intégrée de modèles issus de diverses disciplines : économiques, statistiques, mathématiques et conceptuels. Les données de sortie d'un modèle constituent souvent celles d'entrée d'un autre modèle ; la cohérence des composantes des différents modèles est donc primordiale. L'équipe informatique du programme " Impact mondial des maladies animales " (GBADs) s'efforce de consolider les bases scientifiques de l'utilisation des modèles en mettant au point des outils permettant de résoudre les problèmes de reproductibilité et d'améliorer l'interopérabilité entre les différents modèles, données et métadonnées. En phase avec ces objectifs, plusieurs outils sont en cours de développement : a) le Portail du GBADs " Trusted Animal Information Portal " (TAIL) est une plateforme d'acquisition de données qui facilite l'accès aux données et à la littérature, tout en améliorant l'expérience utilisateur lors de l'acquisition des données. Le portail TAIL s'appuie sur des techniques avancées d'enrichissement sémantique (traitement du langage naturel et ontologies) et sur des bases de données graphiques pour apporter aux utilisateurs un référentiel complet des données et des ressources documentaires relatives aux animaux d'élevage ; b) l'interopérabilité des modèles du GBADs est en voie d'amélioration grâce à la mise au point d'un progiciel de modélisation fondé sur R et à la normalisation des formats de paramètres. Cette initiative vise à favoriser la reproductibilité, à faciliter le partage de données et à permettre une collaboration transparente entre les parties prenantes ; c) le moteur de connaissances du GBADs, en cours de construction, vise à encourager une communauté d'utilisateurs inclusive et dynamique en proposant des données dans une multiplicité de formats ainsi que des mécanismes conviviaux pour recueillir les commentaires de la communauté. Ces initiatives se révéleront indispensables pour relever les défis complexes de la santé animale et soulignent l'importance d'associer une grande rigueur scientifique à la convivialité des interfaces, afin de donner encore plus d'élan aux efforts déployés dans le monde pour protéger les populations animales et la santé publique.

La estimación del impacto global de las enfermedades animales requiere la integración de modelos multidisciplinarios: económicos, estadísticos, matemáticos y conceptuales. El resultado de un modelo a menudo sirve de entrada para otro; por lo tanto, la coherencia entre los distintos componentes es fundamental. El equipo de informática del programa sobre el Impacto Global de las Enfermedades Animales (GBADs) tiene como objetivo fortalecer los fundamentos científicos de la modelización mediante la creación de herramientas que aborden los retos relacionados con la reproducibilidad, así como con la interoperabilidad de los modelos, datos y metadatos. En consonancia con estos objetivos, se están desarrollando varias herramientas: a) El Portal del GBADs "Trusted Animal Information Portal" (TAIL) es una plataforma de adquisición de datos que mejora tanto la descubribilidad de datos y bibliografía como la experiencia del usuario a la hora de obtener datos. El portal TAIL utiliza técnicas avanzadas de enriquecimiento semántico (procesamiento del lenguaje natural y ontologías), así como bases de datos de grafos, para ofrecer a los usuarios un repositorio completo de datos sobre ganadería y recursos bibliográficos. b) Se está mejorando la interoperabilidad de los modelos del GBADs mediante el desarrollo de un paquete de modelización en R y la normalización de los formatos de los parámetros. Esta iniciativa pretende fomentar la reproducibilidad, facilitar el intercambio de datos y permitir una colaboración fluida entre las partes interesadas. c) El Motor de Conocimiento del GBADs se está construyendo con el objetivo de fomentar una comunidad de usuarios inclusiva y dinámica, ofreciendo datos en diferentes formatos y proporcionando mecanismos fáciles de usar para recopilar comentarios de la comunidad. Estas iniciativas son fundamentales para hacer frente a los complejos retos en el ámbito de la sanidad animal y subrayan la importancia de combinar el rigor científico con interfaces fáciles de usar para potenciar los esfuerzos mundiales encaminados a proteger a las poblaciones animales y la salud pública.

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INTRODUCTION: To support research projects that require medical data from multiple sites is one of the goals of the German Medical Informatics Initiative (MII). The data integration centers (DIC) at university medical centers in Germany provide patient data via FHIR® in compliance with the MII core data set (CDS). Requirements for data protection and other legal bases for processing prefer decentralized processing of the relevant data in the DICs and the subsequent exchange of aggregated results for cross-site evaluation. METHODS: Requirements from clinical experts were obtained in the context of the MII use case INTERPOLAR. A software architecture was then developed, modeled using 3LGM2, finally implemented and published in a github repository. RESULTS: With the CDS tool chain, we have created software components for decentralized processing on the basis of the MII CDS. The CDS tool chain requires access to a local FHIR endpoint and then transfers the data to an SQL database. This is accessed by the DataProcessor component, which performs calculations with the help of rules (input repo) and writes the results back to the database. The CDS tool chain also has a frontend module (REDCap), which is used to display the output data and calculated results, and allows verification, evaluation, comments and other responses. This feedback is also persisted in the database and is available for further use, analysis or data sharing in the future. DISCUSSION: Other solutions are conceivable. Our solution utilizes the advantages of an SQL database. This enables flexible and direct processing of the stored data using established analysis methods. Due to the modularization, adjustments can be made so that it can be used in other projects. We are planning further developments to support pseudonymization and data sharing. Initial experience is being gathered. An evaluation is pending and planned.

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Grapevine downy mildew (GDM), caused by the oomycete Plasmopara viticola, can cause 100% yield loss and vine death under conducive conditions. High resolution multispectral satellite platforms offer the opportunity to track rapidly spreading diseases like GDM over large, heterogeneous fields. Here, we investigate the capacity of PlanetScope (3 m) and SkySat (50 cm) imagery for season-long GDM detection and surveillance. A team of trained scouts rated GDM severity and incidence at a research vineyard in Geneva, NY, USA from June to August of 2020, 2021, and 2022. Satellite imagery acquired within 72 hours of scouting was processed to extract single-band reflectance and vegetation indices (VIs). Random forest models trained on spectral bands and VIs from both image datasets could classify areas of high and low GDM incidence and severity with maximum accuracies of 0.85 (SkySat) and 0.92 (PlanetScope). However, we did not observe significant differences between VIs of high and low damage classes until late July-early August. We identified cloud cover, image co-registration, and low spectral resolution as key challenges to operationalizing satellite-based GDM surveillance. This work establishes the capacity of spaceborne multispectral sensors to detect late-stage GDM and outlines steps towards incorporating satellite remote sensing in grapevine disease surveillance systems.

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Given their central role in signal transduction, protein kinases (PKs) were first implicated in cancer development, caused by aberrant intracellular signaling events. Since then, PKs have become major targets in different therapeutic areas. The preferred approach to therapeutic intervention of PK-dependent diseases is the use of small molecules to inhibit their catalytic phosphate group transfer activity. PK inhibitors (PKIs) are among the most intensely pursued drug candidates, with currently 80 approved compounds and several hundred in clinical trials. Following the elucidation of the human kinome and development of robust PK expression systems and high-throughput assays, large volumes of PK/PKI data have been produced in industrial and academic environments, more so than for many other pharmaceutical targets. In addition, hundreds of X-ray structures of PKs and their complexes with PKIs have been reported. Substantial amounts of PK/PKI data have been made publicly available in part as a result of open science initiatives. PK drug discovery is further supported through the incorporation of data science approaches, including the development of various specialized databases and online resources. Compound and activity data wealth compared to other targets has also made PKs a focal point for the application of artificial intelligence (AI) in pharmaceutical research. Herein, we discuss the interplay of open and data science in PK drug discovery and review exemplary studies that have substantially contributed to its development, including kinome profiling or the analysis of PKI promiscuity versus selectivity. We also take a close look at how AI approaches are beginning to impact PK drug discovery in light of their increasing data orientation.

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Objectives: Insufficient physical activity (PA) has long been a global health issue, and a number of studies have explored correlates of PA to identify the mechanisms underlying inactive lifestyles. In the literature, dozens of correlates have been identified at different (eg, individual, environmental) levels, but there is little or no direct evidence for the mutual associations of these correlates. This study analysed 44 variables identified as theoretically and empirically relevant for PA to clarify the factors directly and indirectly associated with PA. Methods: A cross-sectional survey dataset of 19 005 Japanese-speaking adults (mean age=53.50 years, SD=17.40; 9706 women) was analysed. The data encompassed demographic and anthropometric variables; self-reported PA levels; perceived social support and environments (eg, awareness of urban facilities for PA); psychological traits and health-behaviour characteristics (eg, personality, motivation, self-efficacy, decisional balance, process of change strategies); and technology use (eg, mobile health apps). Results: Network analyses were performed to select meaningful associations (partial correlations) among variables, which identified nine variables directly positively associated with PA: job/employment status, self-efficacy, perceived social support, intrinsic motivation, stage of change, counter conditioning, self-reevaluation, environment and technology use. Indirect associations (two-step neighbourhood) were identified for 40 (out of 44) variables, implying that most of the known PA-correlates are associated with PA-at least indirectly. Conclusion: These identified associations echo the importance of the multilevel perspective in understanding how people maintain (in)active lifestyles. Interventions for PA could have mixed-level targets, including intraindividual characteristics, social support and physical and digital environments.

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BACKGROUND: Learning and teaching interdisciplinary health data science (HDS) is highly challenging, and despite the growing interest in HDS education, little is known about the learning experiences and preferences of HDS students. OBJECTIVE: We conducted a systematic review to identify learning preferences and strategies in the HDS discipline. METHODS: We searched 10 bibliographic databases (PubMed, ACM Digital Library, Web of Science, Cochrane Library, Wiley Online Library, ScienceDirect, SpringerLink, EBSCOhost, ERIC, and IEEE Xplore) from the date of inception until June 2023. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and included primary studies written in English that investigated the learning preferences or strategies of students in HDS-related disciplines, such as bioinformatics, at any academic level. Risk of bias was independently assessed by 2 screeners using the Mixed Methods Appraisal Tool, and we used narrative data synthesis to present the study results. RESULTS: After abstract screening and full-text reviewing of the 849 papers retrieved from the databases, 8 (0.9%) studies, published between 2009 and 2021, were selected for narrative synthesis. The majority of these papers (7/8, 88%) investigated learning preferences, while only 1 (12%) paper studied learning strategies in HDS courses. The systematic review revealed that most HDS learners prefer visual presentations as their primary learning input. In terms of learning process and organization, they mostly tend to follow logical, linear, and sequential steps. Moreover, they focus more on abstract information, rather than detailed and concrete information. Regarding collaboration, HDS students sometimes prefer teamwork, and sometimes they prefer to work alone. CONCLUSIONS: The studies' quality, assessed using the Mixed Methods Appraisal Tool, ranged between 73% and 100%, indicating excellent quality overall. However, the number of studies in this area is small, and the results of all studies are based on self-reported data. Therefore, more research needs to be conducted to provide insight into HDS education. We provide some suggestions, such as using learning analytics and educational data mining methods, for conducting future research to address gaps in the literature. We also discuss implications for HDS educators, and we make recommendations for HDS course design; for example, we recommend including visual materials, such as diagrams and videos, and offering step-by-step instructions for students.

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Collaborative quantitative scientists, including biostatisticians, epidemiologists, bio-informaticists, and data-related professionals, play vital roles in research, from study design to data analysis and dissemination. It is imperative that academic health care centers (AHCs) establish an environment that provides opportunities for the quantitative scientists who are hired as staff to develop and advance their careers. With the rapid growth of clinical and translational research, AHCs are charged with establishing organizational methods, training tools, best practices, and guidelines to accelerate and support hiring, training, and retaining this staff workforce. This paper describes three essential elements for building and maintaining a successful unit of collaborative staff quantitative scientists in academic health care centers: (1) organizational infrastructure and management, (2) recruitment, and (3) career development and retention. Specific strategies are provided as examples of how AHCs can excel in these areas.

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OBJECTIVES: This project aims to determine the feasibility of predicting future critical care bed availability using data-driven computational forecast modelling and routinely collected hospital bed management data. METHODS: In this proof-of-concept, single-centre data informatics feasibility study, regression-based and classification data science techniques were applied retrospectively to prospectively collect routine hospital-wide bed management data to forecast critical care bed capacity. The availability of at least one critical care bed was forecasted using a forecast horizon of 1, 7 and 14 days in advance. RESULTS: We demonstrated for the first time the feasibility of forecasting critical care bed capacity without requiring detailed patient-level data using only routinely collected hospital bed management data and interpretable models. Predictive performance for bed availability 1 day in the future was better than 14 days (mean absolute error 1.33 vs 1.61 and area under the curve 0.78 vs 0.73, respectively). By analysing feature importance, we demonstrated that the models relied mainly on critical care and temporal data rather than data from other wards in the hospital. DISCUSSION: Our data-driven forecasting tool only required hospital bed management data to forecast critical care bed availability. This novel approach means no patient-sensitive data are required in the modelling and warrants further work to refine this approach in future bed availability forecast in other hospital wards. CONCLUSIONS: Data-driven critical care bed availability prediction was possible. Further investigations into its utility in multicentre critical care settings or in other clinical settings are warranted.

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Methods for causal inference from observational data are common in human disease epidemiology and social sciences but are used relatively little in plant pathology. We draw upon an extensive data set of the incidence of hop plants with powdery mildew (Podosphaera macularis) collected from yards in Oregon during 2014 to 2017 and associated metadata on grower cultural practices, cultivar susceptibility to powdery mildew, and pesticide application records to understand variation in and causes of growers' fungicide use and associated costs. An instrumental causal forest model identified growers' spring pruning thoroughness, cultivar susceptibility to two of the dominant pathogenic races of P. macularis, network centrality of a yards during May-June and June-July time transitions, and the initial strain of the fungus were important variables determining the number of pesticide active constituents applied by growers and the associated costs they incurred in response to powdery mildew. Exposure-response function models fit after covariate weighting indicated both the number of pesticide active constituents applied and their associated costs scaled linearly with the seasonal mean incidence of plants with powdery mildew. While the causes of pesticide use intensity are multifaceted, biological and production factors collectively influence the incidence of powdery mildew, which has a direct exposure-response relationship on the number of pesticide active constituents that growers apply and their costs. Our analyses point to several potential strategies for reducing pesticide use and costs for management of powdery mildew on hop. We also highlight the utility of these methods for causal inference in observational studies.

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OBJECTIVE: Collaborate, Analyse, Research and Audit (CARA) project set out to provide an infrastructure to enable Irish general practitioners (GPs) to use their routinely collected patient management software (PMS) data to better understand their patient population, disease management and prescribing through data dashboards. This paper explains the design and development of the CARA infrastructure. METHODS: The first exemplar dashboard was developed with GPs and focused on antibiotic prescribing to develop and showcase the proposed infrastructure. The data integration process involved extracting, loading and transforming de-identified patient data into data models which connect to the interactive dashboards for GPs to visualise, compare and audit their data. RESULTS: The architecture of the CARA infrastructure includes two main sections: extract, load and transform process (ELT, de-identified patient data into data models) and a Representational State Transfer Application Programming Interface (REST API) (which provides the security barrier between the data models and their visualisation on the CARA dashboard). CARAconnect was created to facilitate the extraction and de-identification of patient data from the practice database. DISCUSSION: The CARA infrastructure allows seamless connectivity with and compatibility with the main PMS in Irish general practice and provides a reproducible template to access and visualise patient data. CARA includes two dashboards, a practice overview and a topic-specific dashboard (example focused on antibiotic prescribing), which includes an audit tool, filters (within practice) and between-practice comparisons. CONCLUSION: CARA supports evidence-based decision-making by providing GPs with valuable insights through interactive data dashboards to optimise patient care, identify potential areas for improvement and benchmark their performance against other practices.Supplementary file 1. Graphical abstract.

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BACKGROUND: Metadata describe and provide context for other data, playing a pivotal role in enabling findability, accessibility, interoperability, and reusability (FAIR) data principles. By providing comprehensive and machine-readable descriptions of digital resources, metadata empower both machines and human users to seamlessly discover, access, integrate, and reuse data or content across diverse platforms and applications. However, the limited accessibility and machine-interpretability of existing metadata for population health data hinder effective data discovery and reuse. OBJECTIVE: To address these challenges, we propose a comprehensive framework using standardized formats, vocabularies, and protocols to render population health data machine-readable, significantly enhancing their FAIRness and enabling seamless discovery, access, and integration across diverse platforms and research applications. METHODS: The framework implements a 3-stage approach. The first stage is Data Documentation Initiative (DDI) integration, which involves leveraging the DDI Codebook metadata and documentation of detailed information for data and associated assets, while ensuring transparency and comprehensiveness. The second stage is Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardization. In this stage, the data are harmonized and standardized into the OMOP CDM, facilitating unified analysis across heterogeneous data sets. The third stage involves the integration of Schema.org and JavaScript Object Notation for Linked Data (JSON-LD), in which machine-readable metadata are generated using Schema.org entities and embedded within the data using JSON-LD, boosting discoverability and comprehension for both machines and human users. We demonstrated the implementation of these 3 stages using the Integrated Disease Surveillance and Response (IDSR) data from Malawi and Kenya. RESULTS: The implementation of our framework significantly enhanced the FAIRness of population health data, resulting in improved discoverability through seamless integration with platforms such as Google Dataset Search. The adoption of standardized formats and protocols streamlined data accessibility and integration across various research environments, fostering collaboration and knowledge sharing. Additionally, the use of machine-interpretable metadata empowered researchers to efficiently reuse data for targeted analyses and insights, thereby maximizing the overall value of population health resources. The JSON-LD codes are accessible via a GitHub repository and the HTML code integrated with JSON-LD is available on the Implementation Network for Sharing Population Information from Research Entities website. CONCLUSIONS: The adoption of machine-readable metadata standards is essential for ensuring the FAIRness of population health data. By embracing these standards, organizations can enhance diverse resource visibility, accessibility, and utility, leading to a broader impact, particularly in low- and middle-income countries. Machine-readable metadata can accelerate research, improve health care decision-making, and ultimately promote better health outcomes for populations worldwide.

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Current research in nephrology is increasingly focused on elucidating the complexity inherent in tightly interwoven molecular systems and their correlation with pathology and related therapeutics, including dialysis and renal transplantation. Rapid advances in the omics sciences, medical device sensorization, and networked digital medical devices have made such research increasingly data centered. Data-centric science requires the support of computationally powerful and sophisticated tools able to handle the overflow of novel biomarkers and therapeutic targets. This is a context in which artificial intelligence (AI) and, more specifically, machine learning (ML) can provide a clear analytical advantage, given the rapid advances in their ability to harness multimodal data, from genomic information to signal, image and even heterogeneous electronic health records (EHR). However, paradoxically, only a small fraction of ML-based medical decision support systems undergo validation and demonstrate clinical usefulness. To effectively translate all this new knowledge into clinical practice, the development of clinically compliant support systems based on interpretable and explainable ML-based methods and clear analytical strategies for personalized medicine are imperative. Intelligent nephrology, that is, the design and development of AI-based strategies for a data-centric approach to nephrology, is just taking its first steps and is by no means yet close to its coming of age. These first steps are not even homogeneously taken, as a digital divide in access to technology has become evident between developed and developing countries, also affecting underrepresented minorities. With all this in mind, this editorial aim to provide a selective overview of the current use of AI technologies in nephrology and heralds the "Artificial Intelligence in Nephrology" special issue launched by BMC Nephrology.

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BACKGROUND: The success of big data initiatives depends on public support. Public involvement and engagement could be a way of establishing public support for big data research. OBJECTIVE: This review aims to synthesize the evidence on public involvement and engagement in big data research. METHODS: This scoping review mapped the current evidence on public involvement and engagement activities in big data research. We searched 5 electronic databases, followed by additional manual searches of Google Scholar and gray literature. In total, 2 public contributors were involved at all stages of the review. RESULTS: A total of 53 papers were included in the scoping review. The review showed the ways in which the public could be involved and engaged in big data research. The papers discussed a broad range of involvement activities, who could be involved or engaged, and the importance of the context in which public involvement and engagement occur. The findings show how public involvement, engagement, and consultation could be delivered in big data research. Furthermore, the review provides examples of potential outcomes that were produced by involving and engaging the public in big data research. CONCLUSIONS: This review provides an overview of the current evidence on public involvement and engagement in big data research. While the evidence is mostly derived from discussion papers, it is still valuable in illustrating how public involvement and engagement in big data research can be implemented and what outcomes they may yield. Further research and evaluation of public involvement and engagement in big data research are needed to better understand how to effectively involve and engage the public in big data research. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-https://doi.org/10.1136/bmjopen-2021-050167.

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Climate change, local epidemics, future pandemics, and forced displacements pose significant public health threats worldwide. To cope successfully, people and communities are faced with the challenging task of developing resilience to these stressors. Our viewpoint is that the powerful capabilities of modern informatics technologies including artificial intelligence, biomedical and environmental sensors, augmented or virtual reality, data science, and other digital hardware or software, have great potential to promote, sustain, and support resilience in people and communities. However, there is no "one size fits all" solution for resilience. Solutions must match the specific effects of the stressor, cultural dimensions, social determinants of health, technology infrastructure, and many other factors.

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BACKGROUND: Routine surgical video recording has multiple benefits. Video acts as an objective record of the operative record, allows video-based coaching and is integral to the development of digital technologies. Despite these benefits, adoption is not widespread. To date, only questionnaire studies have explored this failure in adoption. This study aims to determine the barriers and provide recommendations for the implementation of routine surgical video recording. MATERIALS AND METHODS: A pre- and post-pilot questionnaire surrounding a real-world implementation of a C-SATS©, an educational recording and surgical analytics platform, was conducted in a university teaching hospital trust. Usage metrics from the pilot study and descriptive analyses of questionnaire responses were used with the non-adoption, abandonment, scale-up, spread, sustainability (NASSS) framework to create topic guides for semi-structured interviews. Transcripts of interviews were evaluated in an inductive thematic analysis. RESULTS: Engagement with the C-SATS© platform failed to reach consistent levels with only 57 videos uploaded. Three attending surgeons, four surgical residents, one scrub nurse, three patients, one lawyer, and one industry representative were interviewed, all of which perceived value in recording. Barriers of 'change,' 'resource,' and 'governance,' were identified as the main themes. Resistance was centred on patient misinterpretation of videos. Participants believed availability of infrastructure would facilitate adoption but integration into surgical workflow is required. Regulatory uncertainty was centred around anonymity and data ownership. CONCLUSION: Barriers to the adoption of routine surgical video recording exist beyond technological barriers alone. Priorities for implementation include integration recording into the patient record, engaging all stakeholders to ensure buy-in, and formalising consent processes to establish patient trust.