Studies in health technology and informatics最新文献

During an emergency, fast and reliable transmission of patient vitals improves hospital preparedness and medical responsiveness. The International Standard Accident Number (ISAN) links data from different stages of the rescue chain. We apply the ISAN system for direct communication between the responding system and the curing system. A framework for smart bands - cheap, flexible, and sanitary devices for measuring vital signs - prepares the integration of medical devices in emergency vehicles. We simulate the transmission of a patient's electrocardiogram from the driving rescue vehicle to the hospital's emergency unit, demonstrating reliability and feasibility.

Background: One in 100 children is diagnosed with autism spectrum disorder (ASD). Social robots have proven to be a promising technology for children with ASD. The emergence of the social robot LOVOT adds new dimensions to the interaction between robots and children with ASD.

Aim: To explore how staff experience using the social robot as a pedagogical tool for children with ASD.

Method: This study was conducted at an institution in Denmark that specializes in special education programs for children with ASD. The interactions between children with ASD and the social robot were tested in individual sessions twice per week for a total of four weeks. Four children with ASD between 9-14 years were included (n=4). A triangulation of data collection techniques was used: Participant observation (n = 15 hours), children's questionnaire (n = 4), and semi-structured interviews with staff (n = 3).

Findings: Findings can be summarized as follows: Acceptance of the social robot, positive changes in mood and behavior of children with ASD, a secure relationship, technical and practical issues to overcome, and ethical considerations.

Conclusion: The professional staff saw a potential for using the robot with AI functionalities as a pedagogical tool for children with ASD.

Heart Failure (HF), a life-threatening condition, poses a significant global health challenge. Monitoring patients' symptoms and weight is essential, as key HF symptoms tend to cause weight gain and indicate decompensation. The aim of this study is to evaluate the effect of a traffic light algorithm on home monitoring of weight in HF patients. In the project 'Future Patient - Telerehabilitation of Patients with HF II', HF patients monitored their weight, blood pressure, pulse, steps, and sleep at home. Data has been transmitted to the web portal HeartPortal. Each measurement in the weight overview was color-coded using a traffic light algorithm that would indicate whether the patient's weight changes were within the acceptable range. The patients' monitoring data, along with their questionnaire responses, were then analyzed and interpreted. The analysis of the data suggests that the traffic light was effective in alerting patients to weight changes according to clinical guidelines. Most of the patients noticed the traffic light and understood the significance of the colors. The study demonstrates that the traffic light is an effective tool for alerting HF patients to weight fluctuations. The traffic light provides early warnings, enabling timely interventions, and encouraging patient engagement in understanding the causes behind weight changes.

Wearable devices are increasingly used to create patient generated health data (PGHD), yet existing models lack the specificity to fully capture the nuances of this data. This paper presents an initial work on WearPGHDProv, an extension of the PGHDProvO ontology, designed to address this gap. We extended the PGHDProvO ontology using a top-down approach, incorporating concepts from the W3C Provenance Ontology (PROV-O) and domain-specific terms related to wearable devices and data generation. WearPGHDProv introduces new classes and properties to model device-specific information, additional related information unique to wearable data, and the context of data collection. This extension enhances the ability to track the source, pertinent information, and quality of wearable-generated PGHD, facilitating its reliable use in electronic health records (EHRs) and research.

Mobile health (mHealth) applications have become important in modern healthcare, yet tailored design recommendations for third-party anamnesis (anamnesis by proxy) - particularly for lay caregivers of infants and other care-needing individuals - remain limited. This systematic literature review addresses this gap by investigating existing design recommendations for mHealth apps in this context. A comprehensive search was conducted across several databases. The extracted recommendations include e.g. remote support by medical professionals, real-time notifications, information on self-management and conditions, peer support, and non-functional aspects, including ease of use and offline accessibility. The consolidated recommendations aim to assist developers and researchers in creating mHealth applications for third-party anamnesis by lay caregivers.

Health information systems (HIS) have been extensively adopted to reduce medication errors. Unfortunately, complex socio-technical issues in HIS can create new error risks, resulting in unintended patient harm. Rigorous evaluation can therefore provide insights to inform risk factors and mitigation measures. This paper presents the validation methods of a proposed human, organisation, process, and technology-fit (HOPT-fit) framework in multiple qualitative case study evaluations using observation, interview, and document analysis in Japanese and Malaysian clinical settings. Findings validated the HOPT-fit applicability in evaluating HIS effectiveness and safety's complex and dynamic nature.

This study assesses feature distribution differences across five intensive care databases using Kullback-Leibler Divergence (KLD). Analyzing bidirectional KLD patterns between individual databases and the composite of others, stratifying by transfusion status. Results reveal heterogeneity: HiRID shows highest divergence in both directions, particularly among transfusion cases; UKA exhibits moderate overall divergence but pronounced differences in transfusion scenarios; MIMIC-IV shows minimal divergence, indicating closest alignment with group distributions. Notably, transfusion cases consistently display higher divergence than non-transfusion cases across all databases, highlighting institution-specific practices. These findings stress the importance of assessing data heterogeneity before implementing federated learning models to understand generalization capabilities.

The implementation of evidence-based practice continues to pose significant challenges across healthcare settings. This study investigated how factors influencing the use of an intranet folder system (IFS) providing evidence-based guidelines change during the transition to a Clinical Decision Support System (CDSS). A two-wave survey was conducted among nursing staff at a Swiss hospital, assessing system utilisation, Evidence-Based Nursing (EBN) related factors, selected variables from the Unified Theory of Acceptance and Use of Technology (UTAUT), and demographic information. Structural equation modelling was applied to identify key determinants of utilisation. Facilitating Conditions and Social Influence significantly influenced utilisation in both systems. EBN factors, specifically Trust and Knowledge, impacted Effort Expectancy only in the CDSS model. These results suggest that while organisational and social factors consistently promote system use, the successful adoption of a CDSS additionally requires the development of new trust in the technology and expanded knowledge of its functions to reduce perceived effort and enhance utilisation. The findings highlight the need for structural support, social endorsement, and targeted training to facilitate the effective implementation of CDSS in clinical practice.

This study investigates the impact of implementing smart, digitally supported workflows on administrative efficiency in a large German hospital network through the ScanProCare! project. The primary aim was to assess whether the introduction of standardized digital processes leads to measurable time savings across different clinical departments. A longitudinal evaluation design was employed, comparing pre- and post-implementation data. The study focused on quantifying changes in process frequency and duration in operating rooms (ORs), intensive care units (ICUs), and normal wards. Results demonstrated time reductions in several areas: OR documentation decreased by 9.09% (582 hours/year), with implant and material documentation reduced by 65% and 50%. Material request times halved, saving 2,720 hours annually, while normal wards saw the largest savings-9,235 hours for documentation and 5,804 for requests. Some tasks became more time-consuming or were unchanged. incomplete master data, and ongoing training needs limited full benefits, highlighting the importance of continuous system refinement and user support. The study concludes that while digital workflows can enhance operational efficiency, continuous system refinement and user support are crucial to maximizing benefits.

Background: Traditional electronic medical records (EMRs) are often document-centric and poorly structured for real-time clinical decision support and workflow automation. To address this, we developed a Clinical Events Catalog by decomposing patient pathways into discrete, meaningful clinical events.

Objective: To define atomic clinical events that can support dynamic workflows, structured documentation, and decision support systems.

Methods: A multidisciplinary team analyzed clinical pathways across specialties and identified 168 atomic clinical events. Each event was defined with a textual definition, associated data payload, and performance metrics (KPIs), and categorized into thematic domains. The catalog was developed through iterative validation and expert consensus.

Results: The resulting Clinical Events Catalog covers nine clinical domains and provides standardized, actionable representations of clinical moments. Examples include "Vitals Examined," "Medication Administered," and "Risk Identified," each linked to measurable indicators. These events can serve as modular triggers for workflow engines and clinical decision support.

Discussion & conclusion: The catalog reflects a transition from static documentation to process-aware EMRs. While real-world deployment is planned, the catalog already offers a framework for improving data structure, auditability, and workflow transparency. This study lays the foundation for more responsive and intelligent digital health systems that support interoperability, clinical safety, and decision support integration.