Journal of Medical Systems最新文献

{"title":"Application of Modular Architectures in the Medical Domain - a Scoping Review.","authors":"Franziska Bathelt, Stephan Lorenz, Jens Weidner, Martin Sedlmayr, Ines Reinecke","doi":"10.1007/s10916-025-02158-3","DOIUrl":"10.1007/s10916-025-02158-3","url":null,"abstract":"<p><p>The healthcare sector is notable for its reliance on discrete, self-contained information systems, which are often characterised by the presence of disparate data silos. The growing demands for documentation, quality assurance, and secondary use of medical data for research purposes has underscored the necessity for solutions that are more flexible, straightforward to maintain and interoperable. In this context, modular systems have the potential to act as a catalyst for change, offering the capacity to encapsulate and combine functionalities in an adaptable manner. The objective of this scoping review is to determine the extent to which modular systems are employed in the medical field. The review will provide a detailed overview of the effectiveness of service-oriented or microservice architectures, the challenges that should be addressed during implementation, and the lessons that can be learned from countries with productive use of such modular architectures. The review shows a rise in the use of microservices, indicating a shift towards encapsulated autonomous functions. The implementation should use HL7 FHIR as communication standard, deploy RESTful interfaces and standard protocols for technical data exchange, and apply HIPAA security rule for security purposes. User involvement is essential, as is integrating services into existing workflows. Modular architectures can facilitate flexibility and scalability. However, there are well-documented performance issues associated with microservice architectures, namely a high communication demand. One potential solution to this problem may be to integrate modular architectures into a cloud computing environment, which would require further investigation.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"27"},"PeriodicalIF":3.5,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11835905/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143441124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time Cost Awareness in Surgical Disposables: A Multispecialty Pre-Post Trial.","authors":"Thomas G Poder, Philippe Harris, Maxime Têtu, Pascal Mondoloni, Cathy Vernelus, Alexandre Mignault, Moishe Liberman","doi":"10.1007/s10916-025-02160-9","DOIUrl":"https://doi.org/10.1007/s10916-025-02160-9","url":null,"abstract":"<p><p>This study was conducted to demonstrate the impact of a software-based cost awareness intervention on the reduction of cost of surgical disposables used in thoracic, gynecological, colorectal and plastic surgery. We used a prospective, non-randomized, pre-post trial in video assisted thoracic surgery (VATS) lobectomy, total laparoscopic hysterectomy, laparoscopic low anterior resection, and deep inferior epigastric perforator (DIEP) flap breast reconstruction. Overall, 775 cases performed between February 2021 and August 2023 were included in the study, 521 for VATS lobectomy (control n = 164), 127 for laparoscopic total hysterectomy (control n = 19), 48 for laparoscopic colorectal surgery (control n = 8), and 79 for DIEP flap breast reconstruction (control n = 53). Average age was 63 years with 67% female and average BMI of 27.57 kg/m². Most patients had a cancer diagnosis (83%) and average ASA score was 2.46. The control period consisted of collecting baseline product usage and cost data without visibility to the clinical teams. During the intervention period, real-time post-operative reports detailing the disposable products used and their cost per intervention were sent out to surgeons. Overall adjusted cost savings were estimated at $631.34 or -24.5% [95%CI: -737.89; -524.78]. The main driver for cost savings was VATS lobectomy (-$793.61), followed by laparoscopic colorectal resections (-$520.93), DIEP flap breast reconstruction (-$198), and laparoscopic total hysterectomies (-$87.11) for the adjusted sample sizes. A computer-vision capture software provides real-time cost awareness on disposable products to clinical teams and is an effective tool to reduce the cost of disposable supplies in various surgical settings.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"25"},"PeriodicalIF":3.5,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143441127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Commentary on \"Emergency Medical Access Control System Based on Public Blockchain \".","authors":"Zekai Yu","doi":"10.1007/s10916-025-02162-7","DOIUrl":"https://doi.org/10.1007/s10916-025-02162-7","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"24"},"PeriodicalIF":3.5,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143414579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of the Performance of Three Large Language Models in Clinical Decision Support: A Comparative Study Based on Actual Cases.","authors":"Xueqi Wang, Haiyan Ye, Sumian Zhang, Mei Yang, Xuebin Wang","doi":"10.1007/s10916-025-02152-9","DOIUrl":"https://doi.org/10.1007/s10916-025-02152-9","url":null,"abstract":"<p><strong>Background: </strong>Generative large language models (LLMs) are increasingly integrated into the medical field. However, their actual efficacy in clinical decision-making remains partially unexplored. This study aimed to assess the performance of the three LLMs, ChatGPT-4, Gemini, and Med-Go, in the domain of professional medicine when confronted with actual clinical cases.</p><p><strong>Methods: </strong>This study involved 134 clinical cases spanning nine medical disciplines. Each LLM was required to provide suggestions for diagnosis, diagnostic criteria, differential diagnosis, examination and treatment for every case. Responses were scored by two experts using a predefined rubric.</p><p><strong>Results: </strong>In overall performance among the models, Med-Go achieved the highest median score (37.5, IQR 31.9-41.5), while Gemini recorded the lowest (33.0, IQR 25.5-36.6), showing significant statistical difference among the three LLMs (p < 0.001). Analysis revealed that responses related to differential diagnosis were the weakest, while those pertaining to treatment recommendations were the strongest. Med-Go displayed notable performance advantages in gastroenterology, nephrology, and neurology.</p><p><strong>Conclusions: </strong>The findings show that all three LLMs achieved over 60% of the maximum possible score, indicating their potential applicability in clinical practice. However, inaccuracies that could lead to adverse decisions underscore the need for caution in their application. Med-Go's superior performance highlights the benefits of incorporating specialized medical knowledge into LLMs training. It is anticipated that further development and refinement of medical LLMs will enhance their precision and safety in clinical use.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"23"},"PeriodicalIF":3.5,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143414580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Radiomics for Survival Prediction in Non-Small-Cell Lung Cancer Patients from CT Images.","authors":"Viet Huan Le, Tran Nguyen Tuan Minh, Quang Hien Kha, Nguyen Quoc Khanh Le","doi":"10.1007/s10916-025-02156-5","DOIUrl":"https://doi.org/10.1007/s10916-025-02156-5","url":null,"abstract":"<p><p>This study aims to apply a multi-modal approach of the deep learning method for survival prediction in patients with non-small-cell lung cancer (NSCLC) using CT-based radiomics. We utilized two public data sets from the Cancer Imaging Archive (TCIA) comprising NSCLC patients, 420 patients and 516 patients for Lung 1 training and Lung 2 testing, respectively. A 3D convolutional neural network (CNN) survival was applied to extract 256 deep-radiomics features for each patient from a CT scan. Feature selection steps are used to choose the radiomics signatures highly associated with overall survival. Deep-radiomics and traditional-radiomics signatures, and clinical parameters were fed into the DeepSurv neural network. The C-index was used to evaluate the model's effectiveness. In the Lung 1 training set, the model combining traditional-radiomics and deep-radiomics performs better than the single parameter models, and models that combine all three markers (traditional-radiomics, deep-radiomics, and clinical) are most effective with C-index 0.641 for Cox proportional hazards (Cox-PH) and 0.733 for DeepSurv approach. In the Lung 2 testing set, the model combining traditional-radiomics, deep-radiomics, and clinical obtained a C-index of 0.746 for Cox-PH and 0.751 for DeepSurv approach. The DeepSurv method improves the model's prediction compared to the Cox-PH, and models that combine all three parameters with the DeepSurv have the highest efficiency in training and testing data sets (C-index: 0.733 and 0.751, respectively). DeepSurv CT-based deep-radiomics method outperformed Cox-PH in survival prediction of patients with NSCLC patients. Models' efficiency is increased when combining multi parameters.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"22"},"PeriodicalIF":3.5,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143391075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using the Nudge Theory to Enhance Attending Anesthesiologist Breaks.","authors":"Andrew R Locke, Noah Ben-Isvy, Chi Wang, Mohammed Minhaj, Steven B Greenberg, Mark A Deshur","doi":"10.1007/s10916-025-02155-6","DOIUrl":"https://doi.org/10.1007/s10916-025-02155-6","url":null,"abstract":"<p><p>It is well understood that human behavior can be influenced, often by the most subtle of factors. Otherwise known as \"nudging\", these attempts to affect human decision making in a predictable manner are possible due to individual cognitive boundaries, biases, routines, and habits. This quality improvement study analyzes the impact of a norms nudge on compliance of attending anesthesiologists providing breaks to the providers they are working with under the care team model. The nudge displays the top performing attendings as well as the top performing hospital to all attendings. Each individual can also view where they fall in relation to the department average as well as their deidentified partners. Implementation of the norms nudge was associated with a significant increase in the percentage of breaks given in the accepted time frame. Morning break compliance increased from 75.0 to 78.1% (p = 0.0002), lunch breaks increased from 83.0% to 86.3 (p < 0.0001), and afternoon breaks increased from 61.8 to 65.3% (p < 0.0001). The present study suggests that norm nudges may be able modify intraoperative practices with regards to breaks without significant cost, education, or other investment.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"21"},"PeriodicalIF":3.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143364986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Through ChatGPT's Eyes: The Large Language Model's Stereotypes and what They Reveal About Healthcare.","authors":"Annika Meyer, Wolfgang A Wetsch, Andrea U Steinbicker, Thomas Streichert","doi":"10.1007/s10916-025-02159-2","DOIUrl":"https://doi.org/10.1007/s10916-025-02159-2","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"20"},"PeriodicalIF":3.5,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143189418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of the Risk of Adverse Clinical Outcomes with Machine Learning Techniques in Patients with Noncommunicable Diseases.","authors":"Alejandro Hernández-Arango, María Isabel Arias, Viviana Pérez, Luis Daniel Chavarría, Fabian Jaimes","doi":"10.1007/s10916-025-02140-z","DOIUrl":"10.1007/s10916-025-02140-z","url":null,"abstract":"<p><p>Decision-making in chronic diseases guided by clinical decision support systems that use models including multiple variables based on artificial intelligence requires scientific validation in different populations to optimize the use of limited human, financial, and clinical resources in healthcare systems worldwide. This cohort study evaluated three machine learning algorithms-XGBoost, Elastic Net logistic regression, and an Artificial Neural Network-to develop a prediction model for three outcomes: mortality, hospitalization, and emergency department visits. The objective was to build a clinical decision support system for patients with noncommunicable diseases treated at the Alma Mater Hospital complex in Medellín, Colombia. We collected 4845 electronic medical record entries from 5000 patients included in the study. The median age was 71.83 years, with 63.8% women and 29.7% receiving home care. The most prevalent medical conditions were diabetes (52.9%), hypertension (67.2%), dyslipidemia (57.3%), and COPD (19.4%). For mortality prediction, the Elastic Net logistic regression model achieved an AUCROC of 0.883 (95% CI: 0.848-0.917), the XGBoost model reached an AUCROC of 0.896 (95% CI: 0.865-0.927), and the Neural Network achieved 0.886 (95% CI: 0.853-0.916). For hospitalization, the Elastic Net model had an AUCROC of 0.952 (95% CI: 0.937-0.965), the XGBoost model achieved 0.963 (95% CI: 0.952-0.974), and the Neural Network scored 0.932 (95% CI: 0.915-0.948). For emergency department visits, the AUCROC values were 0.980 (95% CI: 0.971-0.987) for Elastic Net, 0.977 (95% CI: 0.967-0.986) for XGBoost, and 0.976 (95% CI: 0.968-0.982) for the neural network. A dashboard was developed to interact with an ensemble risk categorization segmenting patient risk in the cohort to aid in clinical decision-making. A clinical decision support system based on artificial intelligence using electronic medical records possibly can help segmenting the risk in populations with Noncommunicable Diseases for effective decision-making.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"19"},"PeriodicalIF":3.5,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11790785/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143122975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mobile Applications for Longitudinal Data Collection: Web-based Survey Study of Former Intensive Care Patients.","authors":"Denise Molinnus, Anne Mainz, Angelique Kurth, Volker Lowitsch, Matthias Nüchter, Frank Bloos, Thomas Wendt, Philipp Potratz, Gernot Marx, Sven Meister, Johannes Bickenbach","doi":"10.1007/s10916-025-02151-w","DOIUrl":"10.1007/s10916-025-02151-w","url":null,"abstract":"<p><strong>Purpose: </strong>Mobile health plays an important role in providing individualized information about the health status of patients. Limited information exists on intensive care unit (ICU) patients with the risk of suffering from the post-intensive care syndrome (PICS), summarizing long-term physical, mental and cognitive impairment. This web-based survey study aims to identify specific needs of former ICU patients for utilizing a newly developed, so called Post-Intensive Care Outcome Surveillance (PICOS) app to collect relevant PICS-related parameters.</p><p><strong>Methods: </strong>A prototype app was developed following interaction principles for interactive systems of usability engineering. Patients from four different German hospitals were asked about demographics, interaction with technology and their perception of the prototype regarding hedonic motivation, perceived ease of use and performance expectancy.</p><p><strong>Results: </strong>123 patients participated in the survey; the majority owned and used smartphones. Nearly half of respondents would seek help from family members or caregivers using the app. There was a difference in affinity for technology for participants who own a smartphone and those who do not, t(116) = - 0.97, p = .335, and no significant difference in affinity for technology whether the participants would like support when using the app or not, t(97) = 1.81, p = .073. The average hedonic motivation for using the app was M = 4.44 (SD = 1.304).</p><p><strong>Conclusion: </strong>This app prototype was perceived as both beneficial and easy to use, indicating its success among former ICU patients. Due to aging and ongoing health impairments, every second patient would need assistance with the initial use of the app.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"18"},"PeriodicalIF":3.5,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11785681/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143065678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensuring Medical Device Safety: The Role of Standards Organizations and Regulatory Bodies.","authors":"Andres E Daryanani, Ugwuji N Maduekwe, Pat Baird, Jesse M Ehrenfeld","doi":"10.1007/s10916-025-02150-x","DOIUrl":"https://doi.org/10.1007/s10916-025-02150-x","url":null,"abstract":"<p><p>Medical devices significantly enhance healthcare by integrating advanced technology to improve patient outcomes. Ensuring their safety and reliability requires a delicate balance between innovation and rigorous oversight, managed through the collaborative efforts of standards development organizations, standards accrediting organizations, and regulatory agencies such as the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA). This article explores the historical evolution of medical device regulation, the role of standards organizations, and the impact of regulatory practices on device safety. Highlighting the critical need for stringent regulations, informed by instances where medical devices caused patient harm, we discuss the processes and collaborations between various international standards and regulatory frameworks that ensure device safety and effectiveness. This comprehensive review addresses the complexities of regulatory compliance and standardization, aiming to bridge the knowledge gap among healthcare providers and enhance the implementation of safety standards in medical technology.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"16"},"PeriodicalIF":3.5,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143039616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}