Usage Data Research Articles

Process evaluation of a Structured E-parenting Support (STEPS) in the OPTIMA randomised controlled trial: a protocol.

Structured E-parenting Support (STEPS) is a digital application (app) designed to help parents manage behaviour of their children who are referred to mental health services and are waiting for an assessment or treatment. STEPS is currently being evaluated in the Online Parent Training for the Initial Management of Attention-Deficit/Hyperactivity Disorder randomised controlled trial. Alongside the examination of STEPS' clinical and cost-effectiveness, we are conducting a process evaluation to better understand the contextual factors that may influence study outcomes. The purpose of this protocol is to describe the aims, objectives and methodology of the process evaluation prior to it taking place to add to the fidelity and rigour of the trial process and outcomes. Our goal is to adapt STEPS to optimise its benefits in future applications. In line with the Medical Research Council guidelines for evaluating complex interventions, the process evaluation will adopt a mixed method design using qualitative data collected from clinicians and parent interviews and app usage data from participants assigned to the intervention arm. Qualitative data from semistructured interviews and free text box responses included in trial questionnaires will be analysed thematically using framework analysis to better understand how parents use STEPS, how it works and key factors that could aid or hinder its effective implementation in routine clinical practice. The application for ethical approval for the study was submitted to the North West-Liverpool Central Research Ethics Committee and received a favourable opinion on further information on 26 November 2021, reference number 21/NW/0319. The process evaluation aims to explore how a digital app might support parents in managing their child's behaviour. Implications for policy and research will be explored and the clinical implications of offering the app to a wider audience to address the lack of support to parents as highlighted in this paper. We plan to publish findings in international, peer-reviewed journals as well as present at conferences. The trial has been prospectively registered on 18 November 2021; ISRCTN816523503. https://www.isrctn.com/ISRCTN16523503.

Semantic-enabled architecture for auditable privacy-preserving data analysis

Small and medium-sized organisations face challenges in acquiring, storing and analysing personal data, particularly sensitive data (e.g., data of medical nature), due to data protection regulations, such as the GDPR in the EU, which stipulates high standards in data protection. Consequently, these organisations often refrain from collecting data centrally, which means losing the potential of data analytics and learning from aggregated user data. To enable organisations to leverage the full-potential of the collected personal data, two main technical challenges need to be addressed: (i) organisations must preserve the privacy of individual users and honour their consent, while (ii) being able to provide data and algorithmic governance, e.g., in the form of audit trails, to increase trust in the result and support reproducibility of the data analysis tasks performed on the collected data. Such an auditable, privacy-preserving data analysis is currently challenging to achieve, as existing methods and tools only offer partial solutions to this problem, e.g., data representation of audit trails and user consent, automatic checking of usage policies or data anonymisation. To the best of our knowledge, there exists no approach providing an integrated architecture for auditable, privacy-preserving data analysis. To address these gaps, as the main contribution of this paper, we propose the WellFort approach, a semantic-enabled architecture for auditable, privacy-preserving data analysis which provides secure storage for users’ sensitive data with explicit consent, and delivers a trusted, auditable analysis environment for executing data analytic processes in a privacy-preserving manner. Additional contributions include the adaptation of Semantic Web technologies as an integral part of the WellFort architecture, and the demonstration of the approach through a feasibility study with a prototype supporting use cases from the medical domain. Our evaluation shows that WellFort enables privacy preserving analysis of data, and collects sufficient information in an automated way to support its auditability at the same time.

Estimated prophylactic dose required to achieve 3% trough as a function of age and concentrate class in multi-country severe WAPPS-Hemo haemophilia patients.

Web-Accessible Population-Pharmacokinetic Service-Haemophilia (WAPPS-Hemo) data are available to study factor-concentrate usage, defined as the required weekly dose to achieve a 3% trough (WD3T), across standard and extended half-life (SHL/EHL) products. To provide baseline usage data including (i) differences across plasma-derived (pdSHL) versus recombinant (rSHL) products, (ii) SHL versus EHL, and (iii) effect of age and positive inhibitor history. PK profiles (n=14,416 patients, 0.3-85.2years) and linear mixed effects models were used to estimate usage versus age, controlling for significant factors, using 95% confidence intervals to perform comparisons across all ages and posthoc tests to assess the differences. Average usage was significantly higher for pdSHL versus rSHL in patients with a positive inhibitor history (PIH; 1.9-2.5 times higher), for SHL versus EHL (4-10 times), and was significantly associated with age. Baseline usage patterns from 2017 to early 2023 provide a benchmark for assessing the impact of emerging technologies in haemophilia.

Analyzing the Energy Usage of a Community and the Benefits of Energy Storage

Understanding the energy usage of a community is crucial for policymaking, energy planning, and achieving sustainable development. The advent of the smart grid has made it feasible to gather fine-grain energy usage data at large-scales, providing us with new opportunities to understand demand patterns at different spatial and temporal scales. In this paper, we conduct a large-scale empirical study of energy usage of 14,849 residential and commercial energy consumers from a small city in the United States. We conduct a wide ranging analysis of energy usage at multiple granularities—citywide, transformer-level, and individual home levels. In doing so, we demonstrate how city-wide smart meter datasets can answer a variety of questions on energy consumption, such as the impact of weather on energy usage. For example, we show that extreme weather events significantly increase energy usage, e.g., by 36% and 11.5% on hot summer and cold winter days, respectively. As another example, we show 19.2% of transformers in the grid get overloaded during peak load periods. Finally, we evaluate the impact of incorporating varying amounts of energy storage within the distribution grid and the impact such deployments will have on the peak demand patterns seen by the grid as well as the ability to reduce overloads seen by distribution transformers during peak periods.

A review of strategic decision-making in marketing through big data and analytics

This review paper delves into the transformative impact of big data and analytics on strategic marketing decision-making. Examining the integration of vast datasets and analytical tools in marketing strategies highlights how these technological advancements enable a deeper understanding of customer behavior, enhance product development, and provide a competitive edge. The review underscores the importance of data-driven insights in formulating personalized marketing strategies and the critical role of analytics in predictive and prescriptive decision-making. It addresses the challenges and ethical considerations associated with big data usage, emphasizing the need for robust data governance and ethical practices. The paper suggests future research directions, focusing on emerging technologies and methodologies that could further influence strategic marketing decisions. Keywords: Big Data, Analytics, Strategic Marketing, Data-Driven Decision-Making, Ethical Considerations, Emerging Technologies.

Efficient Task-driven Video Data Privacy Protection for Smart Camera Surveillance System

As one of the most commonly used AIoT sensors, smart cameras and their supporting services, namely cloud video surveillance (CVS) systems, have brought great convenience to people’s lives. Recent CVS providers use different machine learning techniques to improve their services (regarded as tasks) based on the uploaded video. However, uploading data to the CVS providers may cause severe privacy issues. Existing works that remove privacy information could not achieve a high tradeoff between data usability and privacy, because the importance of information varies with the task. In addition, it is challenging to design a real-time privacy protection mechanism, especially in resource-constrained smart cameras. In this work, we design a task-driven and efficient video privacy protection mechanism for a better tradeoff between privacy and data usability. We use Class Activation Mapping to protect privacy while preserving data usability. To improve the efficiency, we utilize the motion vector and residual matrix produced during video codec. Our work outperforms the region of interest–based methods in data protection while preserving data usability. The attack accuracy drops 70%, while the task accuracy is comparable to those without protection (within ± 4%). The average protection frame rate of the High Definition video can exceed 16 fps+ even on a CPU.

Comprehensive energy demand and usage data for building automation

Buildings are essential in satisfying our daily need for comfort (privacy, protection from weather, etc.) and are responsible for almost half of the world’s total energy consumption. Research at the interface of room comfort and energy efficiency is of critical societal importance. At the same time, there is a lack of publicly available data to optimize important building functions automatically. It is only through data-driven approaches that building automation becomes financially affordable and achieves widespread adoption. In this publication, measurement data from three buildings of the NEST platform are made publicly available. The dataset includes detailed information on energy consumption (electricity, heating, cooling, domestic hot water), building operation (set points, valve openings, windows), and occupant practice (e.g., presence, operation of blinds and kitchen, showering patterns). All data have been measured over four years and with a temporal resolution of 1 minute. This combination of information allows learning the function of different building types (office and residential) and thus addresses important research gaps.

Pagadian Diocesan Schools Information Monitoring System through Data Visualization

The Pagadian Diocesan Schools Information Monitoring System through Data Visualization is a web-based application meticulously developed to address the challenges of managing educational data across multiple schools within the Pagadian Diocese. Utilizing HTML, MySQL, PHP, CSS, and Bootstrap, the system streamlines the login process for administrators, enhances data monitoring, and provides a centralized view for superintendents. Through advanced data visualization tools, the platform presents essential metrics such as student population, income, and expenses, contributing significantly to the Pagadian government’s education initiatives while adhering to ethical data usage practices. Continuous user feedback ensures the refinement of the system, guaranteeing efficient and effective data management within the Pagadian Diocese. The study also encompasses the design, development, and testing phases of the system, following the Waterfall Model. Through rigorous testing, the system demonstrated commendable performance, stability, and security measures, with high user-friendliness and compatibility. The ISO/Logical Summary Report identified strengths in Functional Stability, Portability, Reliability, Security, Usability, and Maintainability, leading to a Total ISO/Logical Flow score of 90.94%. Overall, the system promises a smooth, effective, and dependable user experience, with room for improvement in UI design and error handling to enhance its performance further.

Nudging intensive care unit personnel towards sustainable behaviour.

The health care sector is among the most carbon-intensive sectors, contributing to societal problems like climate change. Previous research demonstrated that especially the use of personal protective equipment (e.g., aprons) in critical care contributes to this problem. To reduce personal protective equipment waste, new sustainable policies are needed. Policies are only effective if people comply. Our aim is to examine whether compliance with sustainable policies in critical care can be increased through behavioural influencing. Specifically, we examined the effectiveness of two sets of nudges (i.e., a Prime + Visual prompt nudge and a Social norm nudge) on decreasing apron usage in an intensive care unit (ICU). We conducted a field experiment with a pre- and post-intervention measurement. Upon the introduction of the new sustainable policy, apron usage data were collected for 9 days before (132 observations) and 9 days after (114 observations) the nudge interventions were implemented. Neither the Prime + Visual prompt nudge, nor the Social norm nudge decreased apron usage. While previous studies have found that primes, visual nudges and social norm nudges can increase sustainable behaviour, we did not find evidence for this in our ICU field experiment. Future research is needed to determine whether this null finding reflects reality, or whether it was due to methodological decisions and limitations of the presented experiment. The presented study highlights the importance of studying behavioural interventions that were previously proven successful in the lab and in other field contexts, in the complex setting of critical care. Results previously found in other contexts may not generalize directly to a critical care context. The unique characteristics of the critical care context also pose methodological challenges that may have affected the outcomes of this experiment.

The predictive value of supervised machine learning models for insomnia symptoms through smartphone usage behavior

IntroductionDigital phenotyping can be an innovative and unobtrusive way to improve the detection of insomnia. This study explores the correlations between smartphone usage features (SUF) and insomnia symptoms and their predictive value for detecting insomnia symptoms. MethodsIn an observational study of a German convenience sample, the Insomnia Severity Index (ISI) and smartphone usage data (e.g., time the screen was active, longest time the screen was inactive in the night) for the previous 7 days were obtained. SUF (e.g., min, mean) were calculated from the smartphone usage data. Correlation analyses between the ISI and SUF were conducted. For the specification of the machine learning models (ML), 80 % of the data was allocated to training, 20 % to testing, and five-fold cross-validation was used. Six algorithms (support vector machine, XGBoost, Random Forest, k-Nearest-Neighbor, Naive Bayes, and Logistic Regressions) were specified to predict ISI scores ≥15. Results752 participants (51.1 % female, mean ISI = 10.23, mean age = 41.92) were included in the analyses. Small correlations between some of the SUF and insomnia symptoms were found. In the ML models, sensitivity was low, ranging from 0.05 to 0.27 in the testing subsample. Random Forest and Naive Bayes were the best-performing algorithms. Yet, their AUCs (0.57, 0.58 respectively) in the testing subsample indicated a low discrimination capacity. ConclusionsGiven the small magnitude of the correlations and low discrimination capacity of the ML models, SUFs, as measured in this study, do not appear to be sufficient for detecting insomnia symptoms. Further research is necessary to explore whether examining intra-individual variations and subpopulations or employing alternative smartphone sensors yields more promising outcomes.

Real-Time Electronic Patient Portal Use Among Emergency Department Patients

Patients with inequitable access to patient portals frequently present to emergency departments (EDs) for care. Little is known about portal use patterns among ED patients. To describe real-time patient portal usage trends among ED patients and compare demographic and clinical characteristics between portal users and nonusers. In this cross-sectional study of 12 teaching and 24 academic-affiliated EDs from 8 health systems in California, Connecticut, Massachusetts, Ohio, Tennessee, Texas, and Washington, patient portal access and usage data were evaluated for all ED patients 18 years or older between April 5, 2021, and April 4, 2022. Use of the patient portal during ED visit. The primary outcomes were the weekly proportions of ED patients who logged into the portal, viewed test results, and viewed clinical notes in real time. Pooled random-effects models were used to evaluate temporal trends and demographic and clinical characteristics associated with real-time portal use. The study included 1 280 924 unique patient encounters (53.5% female; 0.6% American Indian or Alaska Native, 3.7% Asian, 18.0% Black, 10.7% Hispanic, 0.4% Native Hawaiian or Pacific Islander, 66.5% White, 10.0% other race, and 4.0% with missing race or ethnicity; 91.2% English-speaking patients; mean [SD] age, 51.9 [19.2] years). During the study, 17.4% of patients logged into the portal while in the ED, whereas 14.1% viewed test results and 2.5% viewed clinical notes. The odds of accessing the portal (odds ratio [OR], 1.36; 95% CI, 1.19-1.56), viewing test results (OR, 1.63; 95% CI, 1.30-2.04), and viewing clinical notes (OR, 1.60; 95% CI, 1.19-2.15) were higher at the end of the study vs the beginning. Patients with active portal accounts at ED arrival had a higher odds of logging into the portal (OR, 17.73; 95% CI, 9.37-33.56), viewing test results (OR, 18.50; 95% CI, 9.62-35.57), and viewing clinical notes (OR, 18.40; 95% CI, 10.31-32.86). Patients who were male, Black, or without commercial insurance had lower odds of logging into the portal, viewing results, and viewing clinical notes. These findings suggest that real-time patient portal use during ED encounters has increased over time, but disparities exist in portal access that mirror trends in portal usage more generally. Given emergency medicine's role in caring for medically underserved patients, there are opportunities for EDs to enroll and train patients in using patient portals to promote engagement during and after their visits.

Az egészségügyi adatok másodlagos használata az EU-ban: Az európai egészségügyi adattér rendelet kapcsán felmerülő jogi kihívások a TEHDAS közös fellépés eredményeinek tükrében

Arguably, big data, data processing with advanced ICT solutions and data sharing were accelerated during the end of the 2010s, also fuelled by the COVID-19 pandemic. This latter can be reasonably considered a booster in health care for data-driven digital solutions and regulations, which led to issues concerning both primary and secondary use of digital (electronic) health data. There is an ongoing and comprehensive regulatory effort within the European Union to address the evoked challenges. Regarding the healthcare sector, the European Health Data Space Regulation can be considered a flagship legislation. The main ambition of the European Commission is, on the one hand, to create a common space where natural persons can easily control their electronic health data and, on the other hand, to make it possible for researchers, innovators and policymakers to use these health data sets for common societal purposes in a trusted and secure way based on a specific cross-border digital infrastructure. A European Union project, the Joint Action Towards the European Health Data Space (TEHDAS) has been initiated to support the establishment of the EHDS as regards the secondary use of data. Our analysis identified two important topics: legislative barriers to the secondary use of health data and challenges for citizens’ empowerment, including data altruism. The legal complexity of the ongoing strategic legislative initiatives within the EU poses several challenges for future application. Firstly, there can be a terminological issue based on the lack of a common definition of secondary use of data. Secondly, the regulation of the Member States adjusted to their national health and data utilisation systems can be diverse. Both of these may hinder the desired EU-level harmonisation regarding data-sharing. In this context, the division of regulatory competencies related to health care and the chosen legal basis can be decisive. Overall, the EHDS might be a regulation that can challenge the current legal paradigm of competence distribution among the EU and the Member States regarding digital transition, including data usage in health care. As for the citizens’ empowerment, the goal of an EU-wide ecosystem of health data utilisation requires individuals who are aware of the economic value of their health data and, via practising altruism, want to use these data also for the common good. Specific provisions of the EHDS, such as the right to data portability or the option for opt-out, are definitely for the empowerment of citizens. But, these can also reduce the potential health care-related benefits of data utilisation at both, individual and population level. Altogether, a concert of requirements should be fulfilled for meaningful citizens’ participation, engagement, and practice of data altruism, supported by the complex interplay of the relevant regulations.

Abstract PO4-17-07: RELATE 2: A real-world observational study assessing feasibility, acceptability, perceived impacts of digital apps for patients receiving olaparib or trastuzumab deruxtecan (T-DXd) as part of routine clinical practice

Abstract Background: Safe and effective use of two new treatments, T-DXd or olaparib, in breast cancer (BC) requires awareness, education and co-ownership by patients and care teams. It is hypothesized that a patient-centered digital app that provides disease and treatment-specific guidance and symptom reporting functionality while collecting specific data could positively impact patients’ confidence, self-monitoring of symptoms and experience of care. This study will develop and assess two new treatment-specific versions of the accredited OWise app to evaluate how BC patients accept and use the app and how use in routine care appears to impact patients and their experience of care in relation to monitoring symptoms. As a novel element, this study will explore the feasibility of combining treatment-specific real-world data collection with digital health support to understand how treatment patterns and characteristics in the real-world setting. OWise is a specific BC app that has been endorsed by NHS digital and is widely available through NHS app library in UK and in Netherlands. It currently has over 18K registered users. Trial Design: The study involves two workstreams which include several sources for data collection. Workstream 1 involves collection of participants OWise data (including patient entered data as well as usage statistics) and a brief survey for participants to provide feedback on their experience using OWise every three months for up to 18 months. Survey questions are focused on their perceptions of the usefulness of the app for monitoring their health changes and communicating with their care team. Workstream 2, will launch approximately three months after the launch of Workstream 1 and will include qualitative interviews with two groups of people: 1) a small number of participants who are already enrolled in Workstream 1 and 2) clinicians (and multidisciplinary team/nurses) to further understand the perceived benefits of the app. The study’s duration will be 21 months Eligibility Criteria: The patients must be prescribed either T-DXd or Olaparib and be at least 18 years of age, be able to create an OWise account (e.g., mobile or web application) and have activated the unique study code for the treatment specific version of OWise and be able to provide online consent for study participation Aims: Primary objectives are to evaluate the feasibility, acceptability, and perceived impacts on patient experience, treatment use, and quality of care of two treatment specific OWise apps in routine BC care. Secondary objectives will focus on prescribing patterns, concomitant medicine use in UK practice, understanding strategies used in the real-world setting to manage AE and demographic and clinical characteristics of patients treated in the UK Statistical methods: Aggregated Participant-level quantitative data and usage data will be analyzed for response frequencies, means, standard deviations (SD), medians, ranges, and confidence intervals (CI). Descriptive statistics and advanced statistics will be used to identify core variables and trends, investigate data distribution links between data, test significant differences between datasets according to profile/status. Target Accrual: 300 patients T-DXd and 150 olaparib using the OWise app and being part of the real-world study. The prescription decision is independent of the study enrolment. Contact: Dr Sophie McGrath. sophie.mcgrath@rmh.nhs.uk Citation Format: Sophie McGrath, Peter Hall, Michael Acquadro, Soren Skovlund, Parveen Jayia, Stuart McIntosh, David Cameron. RELATE 2: A real-world observational study assessing feasibility, acceptability, perceived impacts of digital apps for patients receiving olaparib or trastuzumab deruxtecan (T-DXd) as part of routine clinical practice [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-17-07.

Abstract PO4-13-10: The anti-metastatic role of BMP4 through cholesterol biosynthesis inhibition and consequent interaction with statin benefit in breast cancer

Abstract We have previously identified a role for bone morphogenic protein 4 (BMP4) in reducing distant relapse risk after early breast cancer (BrCa) (1). We therefore set out to identify mechanisms underlying the protective effect of BMP4, looking to translate this finding for patient benefit. A highly metastatic variant of the triple negative MDA-MB-231 human BrCa line (231-HM-turboGFP) was transduced with BMP4, leading to sustained BMP4 secretion. These modified tumours were established in NSG mice, resected at a given size and turboGFP-positive cancer cells were recovered from primary tumours for RNA sequencing analysis. Gene set enrichment analysis (GSEA) was completed to identify signalling pathways that were significantly modulated by BMP4. The four most significantly downregulated gene sets were associated with cholesterol synthesis, with the overwhelming majority of genes in this pathway being downregulated. BMP4 downregulation of 3-hydroxy-3-methylglutaryl-CoA synthase 1 (HMGCS1, involved in cholesterol biosynthesis) was confirmed at the protein level by western blotting on whole tumour lysates. Forced expression of BMP4 also led to a significant reduction in the levels of free and total cholesterol. In a confirmatory analysis of the METABRIC human BrCa dataset, the expression of cholesterol biosynthesis-related genes inversely correlated with the expression of BMP4. Further, for the majority of these genes, expression was elevated in high-grade breast tumours, and predicted worse overall survival of patients. Finally, we tested the effect of a lipophilic statin, lovastatin, on the growth and metastasis of 231-HM-turboGFP tumours. Treatment did not affect the growth of primary tumours but significantly less metastases were observed in the lungs of treated mice, at least partially replicating the anti-metastatic effect of BMP4. Having identified inhibition of cholesterol biosynthesis as a potential mechanism of protective BMP4 action we then asked if BMP4 status interacted with any protective effect of co-administered statins. We therefore returned to the cohort initially employed to demonstrate BMP4 benefit (1) and looked at the interaction between BMP4 protein levels and statin usage with regards to recurrence. BMP4 protein and statin usage data were available on a cohort of 417 patients with early BrCa. Statin use compared to none led to a reduced risk of distant relapse (8 v 22%, 0.0029) at 15 years. BMP4 led to borderline reduction in distant relapse (16 v 24%, p=0.052). In BMP4 negative tumours, statin protected against any relapse (p=0.0025) whereas in BMP4 positive disease no significant risk reduction was not significant (p=0.074). Absence of BMP4 expression may be a biomarker of both higher relapse risk and statin benefit in early BrCa. 1. Eckhardt et al, Cancer Res. 2020;80(6):1304-1315 Citation Format: Lap Hing Chi, Andrew Redfern, Allan Burrows, Suraya Roslan, Leone Oh, Lisa Spalding, Robin Anderson. The anti-metastatic role of BMP4 through cholesterol biosynthesis inhibition and consequent interaction with statin benefit in breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-13-10.

Abstract PO1-10-10: Leveraging technology to optimize symptom management and patient empowerment in routine breast cancer care: implementation of a remote patient monitoring (RPM) pathway across 24 hospitals in Europe

Abstract Background: In clinical trials, RPM has been proven to improve the quality of life and toxicity management for patients receiving systemic treatment and is recommended by international guidelines for routine clinical practice (Di Maio et al., Ann Oncol 2022). Resilience is an oncology-specific RPM system (CE marked, class IIa medical device - mobile application [app] or web interface) which prompts patients to complete a weekly survey including core symptoms from NCI’s PRO-CTCAE questionnaire. Severe or worsening symptoms trigger an alert notification sent to the patient’s care team. In addition to the RPM system, the mobile app offers a large pool of breast cancer-specific educational, self-management, and patient empowerment resources that are personalized according to symptoms reported, disease stage, and treatments received. Here we present the implementation of the Resilience RPM pathway in routine breast cancer care across 24 highly diverse cancer centers. Methods: The implementation process followed a phasic process of Exploration, Preparation, Implementation, and Sustainability that involved close collaboration with technology providers, hospital managers, and care teams at participating cancer centers to coordinate, train, provide technical assistance, and respond to site-specific needs. In addition, monthly webinars offered participating centers an opportunity to share best practices and ways to overcome implementation barriers. A dedicated platform with educational RPM resources for healthcare providers was also available. A process evaluation according to the RE-AIM framework was performed assessing the reach, adoption, implementation, and maintenance of the RPM pathway in routine care. The effectiveness of the pathway was assessed as the ability of the care team to timely respond to alerts. Aggregated system level metric of RPM was collected covering patients with breast cancer under active systemic treatment followed by Resilience from 01-11-2021 to 29-06-2023. Usage data of mobile app resources was tracked from 01-01-2023 to 29-06-2023. Qualitative assessments with focus groups complemented this evaluation. Results: 985 patients, 1% male, 99% female, with a median age of 57y (range: 21-95y; P25-75 38.5-64.5y), were registered across 24 cancer centers. In the last 6 months, the predominant RPM interface chosen by patients was the mobile app (64.1 vs 35.9% for the web platform). Adherence to weekly ePRO reporting was 89.4% in the mobile app and 86.6% in the web platform. The median time from prompt receipt to survey completion was 5.6 hours. The median time for the nurse navigator (NN) to process an alert was 3h50min. Most alerts (91%) were handled directly by NN with 9% requiring the support of a physician. The mean number of individual content consumed per patient was 13 (6744 resources/523 pts, 100% of app users accessed resources). Preferred formats of content delivery were written articles (66.5%), followed by videos (23.1%) and podcasts (10.4%). Most assessed contents referred to: symptom management advice (16%), followed by information about breast cancer disease characteristics (12.5%), supportive care strategies (11.5%), self-guided exercises of mindfulness-based stress reduction (9.4%), and breast cancer treatment journey (8.3%). Qualitative assessments of the app interface, educational content, and self-management programs revealed high levels of satisfaction. The RPM pathway is sustained in the 24 cancer centers and implementation is expanding to additional centers. Conclusion: The Resilience RPM pathway was successfully implemented in routine care across a diverse group of cancer centers with elevated levels of patient engagement and responsiveness from NN. In addition, patients accessed evidence-based educational and empowerment resources, adapted to their medical profile according to their preferences and learning style. Citation Format: Maria Alice Franzoi, Arlindo R Ferreira, Juliette Fanton d'Andon, Thomas Grellety, Eliane Ithurbide-Dachary, Laura Polastro, Laurence Caravella, Joseph Rodriguez, Nathanaelle Leprevier, Elsa Blanchard, Stéphane Remy, Belen d' Ythurbide, Romain Rivoirard, Raoudha Boughzala, Joana M Ribeiro, Ines Vaz Luis. Leveraging technology to optimize symptom management and patient empowerment in routine breast cancer care: implementation of a remote patient monitoring (RPM) pathway across 24 hospitals in Europe [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-10-10.

Data-Centric Machine Learning in Nursing: A Concept Clarification.

The ubiquity of electronic health records and health information exchanges has generated abundant administrative and clinical healthcare data. The vastness of this rich dataset presents an opportunity for emerging technologies (eg, artificial intelligence and machine learning) to assist clinicians and healthcare administrators with decision-making, predictive analytics, and more. Multiple studies have cited various applications for artificial intelligence and machine learning in nursing. However, what is unknown in the nursing discipline is that while greater than 90% of machine-learning implementations use a model-centric strategy, a fundamental change is occurring. Because of the limitations of this approach, the industry is beginning to pivot toward data-centric artificial intelligence. Nurses should be aware of the differences, including how each approach affects their engagement in designing human-intelligent-like technologies and their data usage, especially regarding electronic health records. Using the Norris Concept Clarification method, this article elucidates the data-centric machine learning concept for nursing. This is accomplished by (1) exploring the concept's origins in the data and computer science disciplines; (2) differentiating data- versus model-centric machine learning approaches, including introducing the machine-learning operation life cycle and process; and (3) explaining the advantages of the data-centric phenomenon, especially concerning nurses' engagement in technological design and proper data usage.

An improved hybrid solar cell defect detection approach using Generative Adversarial Networks and weighted classification

Quality control has a vital role in manufacturing processes. Electroluminescence (EL) imaging is one of the main non-destructive inspection methods for quality assessment in the Photovoltaic (PV) module production industry. EL test reveals PV cell defects such as micro cracks, broken cells, finger interruptions and provides detailed information about production quality. In recent years, automated detection and classification systems using deep neural networks for PV module inspection have gained increasing attention. However, deep learning-based systems usually require large amount of labeled data and high computational power. In this work, we proposed a compact classification framework based on hybrid data augmentation and deep learning models for detection of the defective solar cells. In the proposed method, the limited and imbalanced EL datasets were augmented through various Generative Adversarial Networks (GAN), and defect detection was achieved by customized pre-trained Convolutional Neural Networks (CNN). A novel hybrid EL dataset for training was formed by combining public ELPV dataset, our custom real-world dataset and synthetic images created with different GAN architectures such as GAN, cGAN and WGAN-GP. The datasets were classified through proposed customized VGG-16 and other state-of-art CNN models. The best results obtained by the proposed CNN model with WGAN-GP augmented dataset are 94.11% mean accuracy in the ELPV test dataset and 93.08% mean accuracy in the custom real-world test dataset. Therefore, the proposed detection system achieved superior performance with lesser resource usage and limited data.

Performance gap analysis for Korean building energy efficiency certification

Performance gap between predicted and measured building energy use can be caused by simplifications and assumptions introduced in the building energy modeling process, uncertainties in building operation such as heating and cooling setpoint temperature, operational hours, occupant behavior, heat generated from lights and equipment, etc. In South Korea, the building energy efficiency certification system (BEEC) evaluates annual energy use intensity (EUI, kWh/m2/yr) and assigns ten certificate levels using ECO2, a EUI calculation tool developed by the Korean government that is based on ISO 52016 and DIN V 18599. Firstly, we collected information on 158 non-residential BEEC-certified buildings and their actual energy usage data. Subsequently, we analyzed the performance gap between predicted and measured EUI for 158 non-residential buildings in terms of building types, heating and cooling degree days, chiller types, gross floor area, window-wall ratio, and envelope U-value. The study reveals that the performance gap is significant, showing mean absolute error ranging from 21.3 to 417.4 kWh/m2/yr, mean biased error from −3.2 to 73.4 %, and the coefficient of variance of the root mean squared error from 57.0 to 304.9 %, respectively. It is also noteworthy that even individual building’s annual consumption during eight years (2014–2021) is annually fluctuating, indicating that even the performance gap is a moving target. This study reassures that building energy certification in the design stage should be understood as a rating, not for a prediction because of many unpredictable and stochastic behavior of building energy systems and occupants.

Impacto de la telemonitorización como sistema para una adecuada titulación y adaptación de la CPAP domiciliaria en la apnea obstructiva del sueño (proyecto T-CPAP)

IntroductionContinuous Positive Airway Pressure (CPAP) constitutes the most effective treatment for Obstructive Sleep Apnea (OSA). Automatic titration systems (ATS) are predominantly used to achieve adaptation to the equipment. Home CPAP devices allow telemonitoring (TM) of the same parameters as those provided by ATS but with access to continuous usage data. Under this premise, we conducted a study on the potential validity of TM for home CPAP devices as a titration system, its direct impact on proper adaptation (AD) to the equipment, and secondarily on the healthcare resources employed to achieve it. MethodologyAn observational study involving 318 patients with OSA who were titrated using TM to achieve AD to CPAP. Patients with OSA were consecutively recruited and evaluated at 1, 3, and 6 months after initiating treatment. Results were compared with a historical group of 307 patients with OSA who achieved AD to CPAP using ATS. Additionally, we assessed the impact on required healthcare resources. ResultsPatients with OSA who initiated CPAP treatment with TM over the first six months showed a similar AD rate compared to the historical group titrated using ATS, with lower resource usage in the TM group. ConclusionData provided by TM of home CPAP devices allow for titration and achieving similar AD as with ATS in non-complex patients.

A Blockchain-Based Proxy Re-Encryption Method for Safe Data Sharing in the Internet of Things

Data sharing has emerged as one of the Internet of Things' most beneficial uses in cloud computing. Even though this technology has been visually appealing, one of its challenges is data security because improper usage of data can result in various harms. We suggest a safe data exchange method for cloud environments in this article: proxy re-encryption. Data owners can utilize identity-based encryption to send their encrypted data to the cloud, where it will be accessible to authorized users through proxy re- encryption construction. An edge device serves as a proxy server to manage demanding calculations since Internet of Things devices are resource- constrained. Additionally, by efficiently delivering cached content in the proxy and utilizing the capabilities of information-centric networking, we enhance service quality and optimize network bandwidth. Additionally, the foundation of our system concept is blockchain, a revolutionary technology that permits decentralization in data sharing. Fine-grained access control to data is achieved, and bottlenecks in centralized systems are lessened.