Studies in health technology and informatics最新文献

AI chatbots have introduced a new dimension to how we provide healthcare and support healthcare clinicians. However, despite the benefits of chatbots, their adoption raises ethical concerns related to the effects on users. This study discusses the ethical implications of psychological dependency on autonomy and decision-making in the context of healthcare delivery. Ten studies were analysed, revealing a cascading hierarchy involving the interconnected risks of threats to autonomy, disruption of critical thinking due to over-reliance, and psychological dependency, as well as issues of bias and misinformation in chatbot outputs, limitations in trust and reliability, and a mixed impact on clinicians' well-being. These findings underscore the importance of adopting a balanced approach to integrating AI chatbots into clinical practice, with a strong emphasis on preserving clinical autonomy to maintain the overall well-being of healthcare practitioners.

Artificial Intelligence (AI) is a rapidly growing technology within health informatics, but it is not subject to the rigor of scientific and safety validation required for all other new health techniques. Moreover, some functions of health AI cannot only introduce biases but can then reinforce and spread them by building on them. Thus, while health AI may bring benefit, it can also pose risks for safety and efficiency, as end users cannot rely on rigorous pre-implementation evidence or in-use validation. This review aims to revisit the principles and techniques already developed in health informatics, to build scientific principles for AI evaluation and the production of evidence. The Precautionary Principle provides further justification for such processes, and continuous quality improvement methods can add assurance. Developers should be expected to provide a robust evidence and evaluation trail, and clinicians and patient groups should expect this to be required by policy makers. This needs to be balanced with a need for developing pragmatic and agile evaluation methods in this fast-evolving area, to deepen knowledge and to guard against the risk of hidden perpetuation of errors.

Biometric authentication based on physiological signals offers promising potential for enhancing security in mobile patient monitoring. 'Intelligent medical devices', which check the identity of a patient before usage to address safety risks from device-patient mix-ups, do not yet exist. In this project, an AI-based identification system that uses vital signs for biometric authentication will be realized in order to enable the identification on the basis of biometric patterns. By integrating this component into a patient monitoring platform, a seamless and reliable method for verifying patient identity before device use is established, supporting safer and more efficient clinical workflows.

Hospital at home (HaH) models involve treating patients at home for conditions that typically require hospitalisation. This paper reports on an expert survey to validate patient selection criteria from the literature. Feedback from 20 experts led to consensus on four criteria: medical condition, clinical suitability, living conditions and social support. No consensus was reached on the criteria demographics, technological readiness and literacy. Five other characteristics were identified. These criteria emphasise the importance of selecting patients on the basis of clinical need, safety, and ability to receive care at home, while taking into account potential inequalities. Future efforts should focus on improving digital readiness, integrating multidisciplinary perspectives, and ensuring equitable access to HaH services.

Extracorporeal Membrane Oxygenation (ECMO) is a life-saving cardiopulmonary support for patients with acute heart failure. However, the process of weaning from veno-arterial (V-A) ECMO remains complex and risky. We developed a machine learning-based predictive model to assist clinicians in identifying patients with a high probability of successful weaning. This retrospective monocentric study included 122 patients admitted to Rennes University Hospital between January 2020 and January 2023. Data from the eHOP clinical data warehouse were used to train and evaluate various machine learning algorithms, including Random Forest, XGBoost, KNN, SVM, and regularized logistic regressions. The best-performing models showed an AUC of 0.84-0.86, with XGBoost offering the highest results (0.86 [0.72-0.96]). Key predictors included ECMO flow rate, oxygenation fraction (FmO2), and duration of ECMO. While these results are promising, further validation is required before such tools can be translated into clinical decision-making processes.

The rise in infectious waste during the COVID-19 pandemic exposed critical challenges in Thailand's waste management systems, particularly within sub-district public health facilities. This study aimed to develop and implement an infectious waste management database system for 14 Sub-District Health Promoting Hospitals (HPHs) in Kantharawichai District, Maha Sarakham Province. Using a Research and Development (R&D) model and the knowledge-attitudes-practices (KAP) model to understand behaviors. The development phase engaged 145 community caregivers, of whom 95.17% were female and 74.48% aged between 30-59 years. Results showed that 56.55% of participants had a high knowledge of infectious waste management, while 42.76% expressed a high level of positive attitudes. In terms of behavior, 37.93% demonstrated high compliance with appropriate waste handling practices. Data derived from KAP, and interviews were used as the main inputs to develop the database system. The system included real-time dashboards, GPS-tagged data inputs, automated alerts, and data visualization tools using Microsoft Excel and Power BI. This research offers a scalable digital solution for enhancing infectious waste management, particularly in resource-limited community health settings.

STOPP/START v3 is a set of criteria for optimizing therapy for elderly patients with polypharmacy. Implementing these criteria in prescribing software requires to formalize them, which is a difficult task. This project aimed to automate the formalization of these criteria using large language models (LLMs), specifically leveraging Retrieval-Augmented Generation (RAG) for enhanced accuracy. We employed DeepSeek and GPT-4o-mini for entity extraction, code mapping to ICD-10, LOINC, and ATC, and the generation of executable Python code. A preliminary evaluation conducted on a subset of rules yielded a notably high F1-score (0.90, 0.92, 1 for drug, disease and observation entity mapping respectively and perfect results for medical entity extraction and code logic consistency). These results confirm the model's effectiveness in accurately transforming complex clinical rules into executable code. In conclusion, we successfully automated the creation of executable code from medical guidelines, proving that LLMs, supported by RAG, can be effective for automating clinical decision support tasks and formalizing medical rules.

Unplanned interruptions in radiation therapy (RT) increase clinical risks, yet proactive, personalized psychosocial support remains limited. This study presents a proof-of-concept framework that simulates and evaluates Empathic AI-patient interactions using large language models (LLMs) and synthetic oncology patient personas. Leveraging a de-identified dataset of patient demographics, clinical features, and social determinants of health (SDoH), we created realistic personas that interact with an empathic AI assistant in simulated dialogues. The system uses dual LLMs, one for persona generation and another for empathic response, which engage in multi-turn dialogue pairs per persona. We evaluated the outputs using statistical similarity tests, quantitative metrics (BERTScore, SDoH relevance, empathy, persona distinctness), and qualitative human assessment. The results demonstrate the feasibility of scalable, secure, and context-aware dialogue for early-stage AI development. This HIPAA/GDPR compliant framework supports ethical testing of empathic clinical support tools and lays the groundwork for AI-driven interventions to improve RT adherence.

Large Language Models (LLMs) are promoted as solutions to many problems in medicine and wider health care. However, the empirical evidence of these claims is currently limited, as clinical trials usually take several years until publication. Clinical trial registries, such as ClinicalTrials.gov, allow for a glimpse into the topics on which publications can be expected in the future. The aim of the present study is to identify studies on ClinicalTrials.gov that use LLMs and to summarize their characteristics and topics. We identified 94 studies involving LLMs after keyword-based screening and subsequent manual inspection. All studies had start dates in 2023 or later. Compared to other studies, LLM-studies relatively often had the primary purpose "health services research", while "treatment" was relatively rare. The most common topics of LLM-studies were diagnostics, clinical recommendations, and other supportive functions. These findings underscore that LLMs are currently not being evaluated for treatment, prevention, or drug discovery, but rather for their linguistic and reasoning capabilities as assistive tools.

The National HTA Programme (PNHTA) - Medical Devices is designed to promote collaboration among the entities responsible for decision-making processes, with the purpose of developing and implementing tools based on Health Technology Assessment (HTA), ensuring more effective governance of medical devices. This study focuses on the implementation of a new strategy for managing the procurement requests of innovative medical devices, in line with the PNHTA. Specifically, it aims to develop a support method for healthcare organizations planning to introduce new technologies into clinical practice, providing a useful tool to guide decisions regarding the adoption or exclusion of each device. The innovation lies in identifying a method aimed at improving the robustness of healthcare decisions. The proposed model uses the Analytic Hierarchy Process (AHP) method to conduct a multicriteria analysis of the innovative devices, in order to strengthen the decision-making process. This method allows for the comparison and evaluation of different alternatives based on specific criteria and sub-criteria, with the objective of identifying the most advantageous solution.