Journal of Medical Systems最新文献

Causal networks provide a mechanistic understanding of clinical phenomena, allowing for personalized and explainable decision-making. Causal discovery, namely the task of constructing such models, is challenging, particularly for rare diseases, where observational data are sparse, medical knowledge is incomplete, and diseases develop over time. This work proposes a new and original expert-in-the-loop causal discovery workflow that iteratively refines a set of causal networks associated with different disease mechanisms. When applied to soft tissue sarcoma, a heterogeneous group of rare cancers, the workflow allows for the first comprehensive causal description of the disease's natural history. Indeed, three causal networks associated with different disease mechanisms shed light on the complex interplay between patients' covariates and disease behavior. These results have the potential to enhance clinical decision-making by allowing the development of personalized treatment strategies. The proposed workflow paves the way to agile, modular, and flexible causal discovery for clinical domains characterized by data sparsity, longitudinal dynamics, and heterogeneous expert knowledge.

Software as a Medical Device (SaMD) has become indispensable in diagnostics, treatment planning, and patient monitoring. While high-income countries have introduced clear regulatory frameworks, Bangladesh and many low- and middle-income countries (LMICs) still lack tailored pathways for medical software approval (IMDRF. Software as a Medical Device (SaMD): Key Definitions (IMDRF/SaMD WG/N10FINAL:(2013)); IMDRF. Software as a Medical Device (SaMD): Clinical Evaluation (IMDRF/SaMD WG/N41FINAL:(2017)); U.S. Food and Drug Administration (FDA). Software as a Medical Device (SAMD): Clinical Evaluation Guidance for Industry and FDA Staff (2017)). The current reliance on manual processes designed for physical devices leads to inefficiencies, inconsistent decisions, and potential risks to patient safety. This Comment proposes a semi-automated, risk-based intake roadmap for Bangladesh's Directorate General of Drug Administration (DGDA). Drawing on IMDRF, EU MDCG, and U.S. FDA frameworks, it presents a tangible workflow showing which submissions can be automatically triaged, which require human review, and where human override is maintained (European Commission (MDCG). Guidance on Qualification and Classification of Software in Regulation (EU) 2017/745 (MDCG 2019-11) and World Health Organization (WHO) Global Model Regulatory Framework for medical devices including IVDs (draft; WHO) (n.d.)). Key intake fields, escalation rules, and measurable performance indicators are defined. Anchored to Bangladesh's current DGDA and national digital health context, the proposal identifies specific legal and infrastructural gaps and outlines steps for phased modernization that may guide other LMICs.

Deep vein thrombosis (DVT) in fracture patients is often clinically silent, with a high incidence of thrombosis and associated mortality. Static machine learning methods struggle to address the challenge of early DVT diagnosis due to their inability to adapt to heterogeneous data across patients. In contrast, Dynamic Ensemble Selection (DES) improves clinical decision-making and therapeutic interventions by dynamically adapting to variations in data characteristics. Here, we developed and validated a risk prediction model for DVT using electronic medical record data from fracture patients upon admission. By employing the DES method to optimize the prediction process, the model generates patient-specific probabilities of DVT occurrence, enabling personalized clinical risk assessment. Validation results showed that the DES model achieved strong performance in predicting DVT, with an accuracy of 0.875 and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.906. Notably, it demonstrated a high recall of 0.918 for DVT. Furthermore, in the prospective test set, DES exhibited excellent generalization capability, maintaining robust performance with an accuracy of 0.813 and an AUC of 0.876. We further developed an interactive clinical tool based on the DES algorithm to facilitate model interpretation and implementation. By integrating this user-friendly solution into clinical workflows, DES not only improves early DVT detection but also optimizes the allocation of healthcare resources.

Artificial intelligence (AI) and large language models (LLMs) such as ChatGPT-5 are increasingly applied in medical education. However, their potential role in clinical simulation remains largely unexplored. This descriptive proof-of-concept study aimed to examine ChatGPT-5's ability to synthesize and generate educational content related to clinical simulation, focusing on the coherence, factual accuracy, and understandability of its outputs. Seven exploratory questions covering conceptual, historical, and technological aspects of clinical simulation were submitted to ChatGPT-5. Each query was regenerated three times to assess consistency. Responses were independently evaluated by multiple reviewers using a five-point Likert scale for content quality and accuracy, and the Patient Education Materials Assessment Tool (PEMAT) for understandability. Authenticity of AI-generated references was verified through PubMed and Google Scholar. ChatGPT-5 produced coherent and organized responses reflecting major milestones and trends in clinical simulation. Approximately 80% of cited references were verifiable, while some inconsistencies indicated residual fabrication. The average agreement score for accuracy and coherence was 4 ("agree"), suggesting generally acceptable quality. PEMAT analysis showed that content was structured and clear but occasionally used complex terminology, limiting accessibility. Within the exploratory scope of this proof-of-concept study, ChatGPT-5 demonstrated potential as a supportive tool for synthesizing information about clinical simulation. Nonetheless, interpretive depth, citation reliability, and pedagogical adaptation require further refinement. Future research should assess the integration of LLMs into immersive simulation environments under robust ethical and educational frameworks.

Background: The global trend of population aging is escalating, presenting profound challenges to healthcare systems worldwide. Digital technologies have emerged as pivotal solutions to address these pressing issues. However, the application of digital technologies in healthcare for older adults remains an area that warrants further exploration. This study aims to systematically evaluate the current state of how older adults (55 years and older) utilize digital technology for healthcare, comprehensively analyze its various types, target populations, and impacts, thereby providing a scientific basis for future research endeavors and practical applications.

Methods: This study adheres to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A comprehensive search was conducted across six databases (Web of Science, Scopus, PubMed, IEEE Xplore, ScienceDirect, and APA PsycInfo). A total of 17 articles were ultimately included in the study.

Results: The research findings identified six types of digital technologies applied in older adults' healthcare. Among them, applications for chronic disease management were the most prevalent, followed by those for rehabilitation treatment and health monitoring. These technologies were applied across seven healthcare domains, with chronic disease management, rehabilitation, and health monitoring emerging as the core areas. Regarding the target populations, the studies primarily focused on chronic disease patients, individuals with cognitive impairments, and other vulnerable groups.

Conclusion: This review highlights the potential of digital technologies in meeting the unique needs of older adults. Digital technologies enhance older adults' access to health information, facilitating improved health management. Notable progress has been achieved in areas such as chronic disease management and remote rehabilitation. Future research should prioritize interdisciplinary collaborations to develop aging-friendly digital technologies that can effectively support older adults' healthcare.

National Cancer Registries (NCRs) are essential for monitoring cancer incidence, prevalence, and outcomes at the population level, supporting evidence-based policies and resource allocation. In Chile, fragmented health information systems and infrastructure gaps have historically hindered the establishment of a nationwide registry. In response, a technological NCR was developed under the National Cancer Plan and Cancer Act. This article presents a validation study assessing the NCR's usability from the perspective of healthcare professionals involved in cancer registration. A quasi-experimental, within-subjects design was applied, where 26 healthcare professionals from 22 institutions across Chile completed five core registry tasks using both their current systems and the NCR platform. Results show statistically significant reductions (≈ 40-50%) in perceived task difficulty across all tasks, with large effect sizes (r > 0.7), indicating improved usability and lower workload when using the NCR platform. These findings highlight the platform's potential to overcome institutional barriers to adoption and contribute to the comprehensive and sustainable implementation of a national cancer surveillance system in Chile.

The increasing adoption of digital education, including mobile learning (mLearning), is transforming the training of future health professionals, offering advantages such as improved accessibility, timeliness, and affordability. While mLearning enhances clinical training by providing flexible access to information and supporting practical skills development, challenges such as inadequate resources and resistance from staff and patients need to be addressed for successful integration. This review aims to explore the factors for successful adoption of mLearning in clinical placements, providing valuable insights to inform best practices in its implementation. A scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for scoping reviews (PRISMA-ScR). Arksey and O'Malley and Joanna Briggs Institute (JBI) methodology was applied. Searching 6 electronic databases plus manual searching returned 5178 articles, with 76 articles included in analysis. All studies were published between 2008 and 2024, with a significant proportion originating from Canada (n = 12), the United States (n = 12), the United Kingdom (n = 11), and Australia (n = 11). The majority of the studies involved nursing (n = 47) and medical (n = 24) students. The analysis revealed 6 facilitators and 6 barriers. It identified positive attitudes toward mobile applications, highlighting their role in enhancing education in clinical environment, organizing training, and improving patient care. However, barriers such as the absence of regulations, ethical concerns, and technical issues were also noted, along with the need to address information literacy and skills. Mapping research regarding facilitators and barriers for introducing mobile learning technologies into undergraduate education in clinical environment has helped in creating a set of solutions which are capable to ensure the success and sustainability of mLearning. These solutions should be considered at the innovation's design, implementation, and post-implementation stages to guarantee its effectiveness in education in clinical environments. This may help to enhance the learning experience, improve knowledge retention, and develop clinical skills, while providing a cost-effective solution for clinical training programs for healthcare professions. This, in turn, has positive implications for quality of care provided.

The purpose of this scoping review is to explore the current state of digital scribe technology in primary care, focusing on how automatic speech recognition (ASR) and natural language processing (NLP), which are foundational technologies behind artificial intelligence (AI) systems used in digital scribes contribute to their effectiveness, integration, and adoption. The Joanna Briggs Institute (JBI) guidelines for scoping reviews was utilized alongside reporting according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. Searches through PubMed, Web of Science, Scopus, and Cumulative Index to Nursing and Allied Health Literature yielded 29 relevant studies from 14,866 studies, spanning six countries and from 2018 to 2024. Digital scribes demonstrated effectiveness in reducing documentation time, which directly enhances workflow efficiency and allows clinicians to spend more time interacting with patients. Digital scribes, while promising in improving clinical documentation, face significant integration challenges and adoption barriers, particularly in adapting to diverse healthcare workflows. The findings of this scoping review reveal several implications for the existing literature on digital scribes, particularly regarding the need for comprehensive studies on effectiveness in real-world primary care settings. This study highlights the promising role of digital scribes in primary care, where ASR and NLP technologies have demonstrated the potential to enhance documentation accuracy, streamline workflows, and reduce clinician burden.

Assessing usability and satisfaction is vital to ensure the efficiency and optimal use of mobile health (mHealth) applications. Nevertheless, existing questionnaires revolve around computerized systems and lack validation for evaluating mHealth applications. We aimed to develop and validate a tri-language questionnaire to assess usability and satisfaction of mobile health applications (USHA). This study consisted of three phases: item development, translation, and validation. During the item development phase, a preliminary English version of the USHA questionnaire that comprised Likert-scale and demographic items was designed. Subsequently, forward-backward translation was performed to produce Malay and Chinese versions. Content validation was conducted with eight experts, followed by face validation with five diabetes mellitus patients. Reliability testing was conducted through test-retest analysis among diabetes mellitus patients. The initial tri-language USHA questionnaire consisted of 18 Likert-scale items and 8 demographic items. Following expert validation, five Likert-scale items and one demographic item were eliminated for lack of relevance, importance, or clarity, while four Likert-scale items were rephrased. During face validation, additional one demographic item was removed. The finalized questionnaire demonstrated high reliability, with a Cronbach's alpha of 0.956 and an intraclass correlation coefficient of 0.845. Consequently, the tri-language USHA questionnaire consisted of 13 Likert-scale items and six demographic items, is a valid and reliable instrument that enhances accessibility and enables assessment of the usability and satisfaction of interactive mHealth applications, especially for diabetes mellitus care across a broad range of users.