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Alert Reduction and Telemonitoring Process Optimization for Improving Efficiency in Remote Patient Monitoring Programs:Framework Development Study
No full textBACKGROUND: Telemonitoring can enhance the efficiency of health care delivery by enabling risk stratification, thereby allowing health care professionals to focus on high-risk patients. Additionally, it reduces the need for physical care. In contrast, telemonitoring programs require a significant time investment for implementation and alert processing. A structured method for telemonitoring process optimization is lacking.OBJECTIVE: We propose a framework for optimizing efficient care delivery in telemonitoring programs based on alert data analysis and scenario analysis of a telemonitoring program for hypertension combined with a narrative literature review on methods to improve efficient telemonitoring care delivery.METHODS: We extracted 1-year alert processing data from the telemonitoring platform and electronic health records (June 2022-May 2023) from all users participating in the hypertension telemonitoring program in the outpatient clinic of the Department of Internal Medicine of the Maasstad Hospital. We analyzed the alert burden and alert processing data. Additionally, a scenario analysis with different threshold values was conducted for existing blood pressure alerts to assess the impact of threshold adjustments on the overall alert burden and processing. We searched for English language academic research papers and conference abstracts reporting clinical alert or workflow optimization in telemonitoring programs on May 24, 2024 in Embase, Medline, Cochrane, Web of Science, and Google Scholar.RESULTS: In total, 174 users were included and analyzed. On average, each user was active in the telemonitoring program for 207 days and a total of 30,184 measurements were performed. These triggered a total of 17,293 simple, complex, and inactive or overdue alerts: 13,647 were processed automatically by the telemonitoring platform, and 3646 were processed manually by e-nurses from the telemonitoring center, equivalent to 21 manually processed alerts per user. Additional analysis of the manually processed alerts revealed that 25 (15%) users triggered more than 50% of these specific alerts. Furthermore, scenario analysis of the alert thresholds revealed that a single increase of 5 and 10 mmHg for the diastolic and systolic blood pressure alerts would reduce the number of alerts by about 50%, resulting in a total reduced time investment for the e-nurse of 5973 minutes over 1 year. Literature search yielded 251 articles, of which 7 studies reported methods to improve efficiency in telemonitoring programs, including the introduction of complex alerts and clinical algorithms to triage alerts, scenario analysis with alert threshold adjustments, and a qualitative analysis to create an alert triage algorithm.CONCLUSIONS: Based on the data analysis and literature review, a 4-step framework was developed to optimize the efficiency of telemonitoring programs. The 4 steps include ensuring accurate measurements, telemonitoring algorithm and alert optimization, focusing on individual users' and user groups' needs, and improving telemonitoring process efficiency. This framework can be an important first step to improve the efficiency of 21st-century telemonitoring programs.</p
De rat-mens-chimeer als representatie van angst voor verval
No full textWaar mensen leven, leven ratten. De mens heeft de rat verschillende eigenschappen toegedicht: meestal zijn die negatief (het zouden ziekteverspreiders en kannibalen zijn), maar soms ook positief (slimme dieren die zich goed weten aan te passen om te overleven). Vanwege de aanwezigheid van de rat in het leefgebied van de mens en de sterke connotaties die mensen bij het dier hebben, komt het dier veelvuldig voor in allerlei cultuuruitingen. Naast verhalen waarin ratten als dier een rol spelen, zijn er ook andere vertellingen, met name fabels en verhalen voor kinderen, waarin meer antropomorfe ratten voorkomen. Tot slot zijn er verhalen te vinden waarin de rat hybride vormen aanneemt.In dit artikel onderzoeken we specifiek de rat-mens-chimeer in westerse cultuuruitingen. Vertrekkend vanuit de monster theory bestuderen we allereerst het personage Brown Jenkin(s), dat in 1933 in de weird fiction opduikt en later terugkeert in de postmoderne new weird-stroming. Daarnaast bestuderen we de geëvolueerde ratmensen (rensen) in De verwoesting van Hyperion van Hugo Raes (1978). De voornaamste angst die de door ons onderzochte rat-mens-chimeren vertegenwoordigen, is het verlies van de gevestigde menselijke orde, die als hoge cultuur wordt beschouwd
Design Principles for Developing Information Problem Solving Competence in Higher Education:A Systematic Review and Meta-Analysis
No full textNavigating disruption in the PID landscape:embracing opportunities and anticipating threats in the next ten years
No full textCan AI feedback stand alone for fostering students' presentation performance?:A comparison of AI-only versus teacher-supported AI feedback
No full textStudies have shown the potential of automated feedback for supportingstudents’ presentation skills in Immersive Virtual Reality (IVR)environments. However, the extent to which students canindependently uptake AI feedback messages, or whether they needteacher support in this task, remains unclear. This experimental studyaims to investigate the impact of AI feedback without (AI-only) andwith teacher support (teacher-supported AI feedback), received afterpresentation practice in IVR, on students’ presentation performance (i.e.presentation skills, perceptions of the presentation, and anxiety) andperceptions of the feedback utility. A total of sixty undergraduatestudents participated. Students’ presentation skills, perceptions, andanxiety were measured using rubrics and questionnaires. The resultsrevealed no significant differences between the two conditions,implying that AI-only feedback can be as effective as teacher-supportedAI feedback for supporting students’ presentation performance in IVR.Additionally, no significant difference was established betweenconditions for students’ perceptions of the feedback utility. Thesefindings suggest that students highly valued AI-only feedback andperceived it as an independent source of feedback for presentationperformance within IVR. This study contributes to the existing AIfeedback literature and advances our understanding regarding the useof AI as an independent feedback source in supporting student learning
Fine-Tuning Large Language Models for Digital Forensics: Case Study and General Recommendations
No full textDe verplichting tot wantrouwen in een vertrouwensrelatie:Over de verhouding tussen verplichtingen van banken tot doorbreking van vertrouwelijkheid ter bestrijding van financieel-economische criminaliteit en de tegengestelde verplichting van banken tot bewaring van vertrouwelijkheid
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Sense the Classroom:Using AI to Detect and Respond to Learning-Centered Affective States in Online Education
No full textOnline learning has become an essential part of modern education, offering flexibility,inclusivity, and accessibility for learners regardless of geographic or physicalconstraints. However, despite these advantages, online environments lackthe nonverbal cues inherent in face-to-face interactions, making it difficult for educatorsto recognize students’ learning-centered affective states (LCAS). Thisdisconnect can hinder timely pedagogical interventions and reduce the effectivenessof learning. Affective computing offers a compelling solution to this challengeby leveraging artificial intelligence (AI) to recognize students’ LCAS, suchas boredom, confusion, frustration, engagement, and curiosity. Unlike conventionalemotion recognition systems that focus on basic emotions such as happinessor anger, LCAS provides more meaningful indicators of learning processes.However, most existing technologies fall short in capturing these nuanced states,and often overlook ethical concerns such as users’ privacy.This doctoral thesis responds to these challenges by developing privacy- preserving,AI-driven methods to detect and communicate LCAS in online higher education.Through an interdisciplinary, design-based research approach, the thesisexplores how these technologies can support teachers in making informed,timely decisions that keep learning optimal. Structured across four parts andsix chapters, each study contributes to the overarching goal of designing, implementing,and evaluating tools that recognise students’ affective experiencesvisibly, responsibly, and effectively, in online classrooms.Part I provides a systematic literature review of affective computing in onlinehigher education in the period 2019-2024. This review identifies several shortcomings:insufficient attention to LCAS, limited empirical validation of emotiondetection models in educational settings, and a notable lack of ethical concerns.It also reveals a disciplinary divide between educational and technological research,underscoring the need for interdisciplinary collaboration.Part II focuses on technological development. Chapter 2 introduces an AImodel capable of detecting facial expressions in the form of Action Units (AUs)from webcam inputs. To collect high-quality data for model training, the study develops“FaceGame”, a gamified web application where participants mimic emotionsand receive feedback, enhancing both engagement and data quality. Chapter3 expands on this by embedding the AU detection model into a real-time,privacy-preserving system called “StC-live.” The system processes facial datalocally in the browser, ensuring no images or videos are stored or transmitted.Teachers receive anonymized, aggregated LCAS data through a dashboard, al-lowing them to monitor classroom dynamics without violating student privacy.Part III addresses the pedagogical dimension. Chapter 4 engages educatorsin a co-design process to understand their needs and preferences for LCASfeedback systems. Teachers prioritize five LCAS and emphasize the importanceof minimizing cognitive overload while retaining control over the feedbackthey receive. Chapter 5 develops standardized educational videos designed toevoke specific LCAS, based on literature-derived principles and expert feedback.These stimuli are critical for studying LCAS reliably in experimental settings.Part IV integrates the technological and educational elements. Chapter 6 investigatesthe correlation between students’ self-reported LCAS, detected facialexpressions, and personal background (e.g., topic familiarity, interest). The findingsconfirm that students with higher affinity and prior knowledge report moreengagement and curiosity, while those with less experience are more likely tofeel bored or confused. Weak but notable correlations are observed betweencertain AUs and LCAS, highlighting both the potential and limitations of facialexpression models in real-world conditions.The thesis concludes with a discussion of contributions, limitations, and futuredirections. Key technological contributions include AU detection models,a gamified data collection platform, and the privacy-conscious StC-live system.Educationally, the thesis contributes a validated taxonomy of LCAS, standardizedelicitation materials, and insights into how background factors influence affectivestates in learning. However, several challenges remain: integrating thecomponents into a production-ready tool, collecting more diverse training data,improving model robustness under varied conditions, and assessing the educationalimpact of these tools in live classrooms. Ethically, the research emphasizestransparency, informed consent, and data privacy. All human-subjectstudies were approved by an ethics board, and technologies were designed tomitigate the risk of misuse. Nevertheless, evolving regulations, particularly theEU’s AI Act, pose uncertainties about deploying such systems in real educationalsettings. This doctoral work offers a foundational framework for advancing affectivecomputing in online higher education, combining technological innovationwith educational sensitivity and ethical responsibility. It lays the groundwork forfuture research and development aimed at making virtual learning more responsive,empathetic, and effective
A Multimodal Analysis of Online Information Foraging in Health-Related Topics Based on Stimulus-Engagement Alignment:Observational Feasibility Study
No full textBACKGROUND: The recent increase in online health information-seeking has prompted extensive user appraisal of encountered content. Information consumption depends crucially on the quality of encountered information and the user's ability to evaluate it; yet, within the context of web-based, organic search behavior, few studies take into account both these aspects simultaneously.OBJECTIVE: We aimed to explore a method to bridge these two aspects and grant even consideration to both the stimulus (web page content) and the user (ability to appraise encountered content). We examined novices and experts in information retrieval and appraisal to demonstrate a novel approach to studying information foraging theory: stimulus-engagement alignment (SEA).METHODS: We sampled from experts and novices in information retrieval and assessment, asking participants to conduct a 10-minute search task with a specific information goal. We used an observational and a retrospective think-aloud protocol to collect data within the framework of an interview. Data from 3 streams (think-aloud, human-computer interaction, and screen content) were manually coded in the Reproducible Open Coding Kit standard and subsequently aligned and represented in a tabularized format with the R package {rock}. SEA scores were derived from designated code co-occurrences in specific segments of data within the stimulus data stream versus the think-aloud and human-computer interaction data streams.RESULTS: SEA scores represented a meaningful comparison of what participants encountered and what they engaged with. Operationalizing codes as either "present" or "absent" in a particular data stream allowed us to inspect not only which credibility cues participants engaged with with the most frequency, but also whether participants noticed the absence of cues. Code co-occurrence frequencies could thus indicate case-, time-, and context-sensitive information appraisal that also takes into account the quality of information encountered.CONCLUSIONS: Using SEA allowed us to retain epistemic access to idiosyncratic manifestations of both stimuli and engagement. In addition, by using the same coding scheme and designated co-occurrences across participants, we were able to pinpoint trends within our sample and subsamples. We believe our approach offers a powerful analysis encompassing the breadth and depth of data, both on par with each other in the feat of understanding organic, web-based search behavior.</p