Journal of Medical Systems最新文献

Most Weekday Discharge Times at Acute Care Hospitals in the State of Florida Occurred After 3 PM in 2022, Unchanged from Before the COVID-19 Pandemic.

IF 3.5 3区医学

Journal of Medical Systems Pub Date : 2025-02-25 DOI: 10.1007/s10916-025-02164-5

Richard H Epstein, Franklin Dexter, Brenda G Fahy

{"title":"Most Weekday Discharge Times at Acute Care Hospitals in the State of Florida Occurred After 3 PM in 2022, Unchanged from Before the COVID-19 Pandemic.","authors":"Richard H Epstein, Franklin Dexter, Brenda G Fahy","doi":"10.1007/s10916-025-02164-5","DOIUrl":"https://doi.org/10.1007/s10916-025-02164-5","url":null,"abstract":"When the hospital census is near-capacity, either from insufficient physical beds or nurse staffing, discharge delays can result in postanesthesia care unit (PACU) congestion that backs up the operating rooms. Hospital administrators often promote increasing morning discharges as mitigation. Before the COVID-19 pandemic, most hospitalized Florida patients were discharged after 3 PM, without change from 2010 through 2018. The current study extended the observation period through 2022 to determine if discharge pressure during the COVID-19 pandemic from persistent high census resulted in overall earlier hospital discharges. Results showed the percentages of patients discharged by 12 noon or 3 PM remained unchanged. Among 1,034,515 discharges at 197 hospitals during the last 2 quarters of 2022, most discharges (P < 0.0001 versus 50%) occurred after 3 PM. The pooled incidence of discharges by noon was 13.2%, while the estimate of the incidence inverse weighted by the hospitals' counts of discharges was 13.3% (97.5% 12.6% to 14.1%). The corresponding pooled incidences of discharges by 3 PM was 42.5%, and 43.7% (97.5% confidence interval 42.3% to 45.2%). All 136,924 combinations of hospital and Medicare severity diagnosis-related groups were evaluated to examine why discharges did not occur earlier. Among the 1377 such combinations (1% of the total) with a significant change in median length of stay, 95% (1313) were decreases in lengths of stay. The implication is that the pandemic had no salutatory effect on earlier discharges. Therefore, post-anesthesia care unit managers should continue to plan for most hospital beds to be unavailable until late afternoon.","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"31"},"PeriodicalIF":3.5,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143492369","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}

引用次数: 0

Reclaiming Patient-Centered Care: How Intelligent Time is Redefining Healthcare Priorities.

IF 3.5 3区医学

Journal of Medical Systems Pub Date : 2025-02-21 DOI: 10.1007/s10916-025-02163-6

Elena Giovanna Bignami, Michele Russo, Valentina Bellini

引用次数: 0

Artificial Intelligence Methods for Diagnostic and Decision-Making Assistance in Chronic Wounds: A Systematic Review.

IF 3.5 3区医学

Journal of Medical Systems Pub Date : 2025-02-19 DOI: 10.1007/s10916-025-02153-8

David Reifs Jiménez, Lorena Casanova-Lozano, Sergi Grau-Carrión, Ramon Reig-Bolaño

{"title":"Artificial Intelligence Methods for Diagnostic and Decision-Making Assistance in Chronic Wounds: A Systematic Review.","authors":"David Reifs Jiménez, Lorena Casanova-Lozano, Sergi Grau-Carrión, Ramon Reig-Bolaño","doi":"10.1007/s10916-025-02153-8","DOIUrl":"10.1007/s10916-025-02153-8","url":null,"abstract":"Chronic wounds, which take over four weeks to heal, are a major global health issue linked to conditions such as diabetes, venous insufficiency, arterial diseases, and pressure ulcers. These wounds cause pain, reduce quality of life, and impose significant economic burdens. This systematic review explores the impact of technological advancements on the diagnosis of chronic wounds, focusing on how computational methods in wound image and data analysis improve diagnostic precision and patient outcomes. A literature search was conducted in databases including ACM, IEEE, PubMed, Scopus, and Web of Science, covering studies from 2013 to 2023. The focus was on articles applying complex computational techniques to analyze chronic wound images and clinical data. Exclusion criteria were non-image samples, review articles, and non-English or non-Spanish texts. From 2,791 articles identified, 93 full-text studies were selected for final analysis. The review identified significant advancements in tissue classification, wound measurement, segmentation, prediction of wound aetiology, risk indicators, and healing potential. The use of image-based and data-driven methods has proven to enhance diagnostic accuracy and treatment efficiency in chronic wound care. The integration of technology into chronic wound diagnosis has shown a transformative effect, improving diagnostic capabilities, patient care, and reducing healthcare costs. Continued research and innovation in computational techniques are essential to unlock their full potential in managing chronic wounds effectively.","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"29"},"PeriodicalIF":3.5,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11839728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143449322","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}

引用次数: 0

Artificial Intelligence (AI) - Powered Documentation Systems in Healthcare: A Systematic Review.

IF 3.5 3区医学

Journal of Medical Systems Pub Date : 2025-02-18 DOI: 10.1007/s10916-025-02157-4

Aisling Bracken, Clodagh Reilly, Aoife Feeley, Eoin Sheehan, Khalid Merghani, Iain Feeley

{"title":"Artificial Intelligence (AI) - Powered Documentation Systems in Healthcare: A Systematic Review.","authors":"Aisling Bracken, Clodagh Reilly, Aoife Feeley, Eoin Sheehan, Khalid Merghani, Iain Feeley","doi":"10.1007/s10916-025-02157-4","DOIUrl":"10.1007/s10916-025-02157-4","url":null,"abstract":"Artificial Intelligence (AI) driven documentation systems are positioned to enhance documentation efficiency and reduce documentation burden in the healthcare setting. The administrative burden associated with clinical documentation has been identified as a major contributor to health care professional (HCP) burnout. The current systematic review aims to evaluate the efficiency, quality, and stakeholder opinion regarding the use of AI-driven documentation systems. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines a comprehensive search was conducted across PubMed, Embase and Cochrane library. Two independent reviewers applied inclusion and exclusion criteria to identify eligible studies. Details of AI technology, document type, document quality and stakeholder experience were extracted. The review included 11 studies. All included studies utilised Chat generated pretrained transformer (Chat GPT, Open AI, CA, USA) or an ambient AI technology. Both forms of AI demonstrated significant potential to improve documentation efficiency. Despite efficiency gains, the quality of AI-generated documentation varied across studies. The heterogeneity of methods utilised to assess document quality influenced interpretation of results. HCP opinion was generally positive, users highlighted ease of use and reduced task load as primary benefits. However, HCPs also expressed concerns about the reliability and validity of AI-generated documentation. Chat GPT and ambient AI show promise in enhancing the efficiency and quality of clinical documentation. While the efficiency benefits are clear, the challenges associated with accuracy and consistency need to be addressed. HCP experiences indicate a cautious optimism towards AI integration, however reliability will depend on continued refinement and validation of the technology.","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"28"},"PeriodicalIF":3.5,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11835907/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143449316","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}

引用次数: 0

Electronic Prescribing in the Neonatal Intensive Care Unit: Analysis of Prescribing Errors and Risk Factors.

IF 3.5 3区医学

Journal of Medical Systems Pub Date : 2025-02-18 DOI: 10.1007/s10916-025-02161-8

M D Canales-Siguero, C García-Muñoz, J M Caro-Teller, S Piris-Borregas, S Martín-Aragón, J M Ferrari-Piquero, M T Moral-Pumarega, C R Pallás-Alonso

{"title":"Electronic Prescribing in the Neonatal Intensive Care Unit: Analysis of Prescribing Errors and Risk Factors.","authors":"M D Canales-Siguero, C García-Muñoz, J M Caro-Teller, S Piris-Borregas, S Martín-Aragón, J M Ferrari-Piquero, M T Moral-Pumarega, C R Pallás-Alonso","doi":"10.1007/s10916-025-02161-8","DOIUrl":"https://doi.org/10.1007/s10916-025-02161-8","url":null,"abstract":"Patients admitted to neonatal intensive care units are up to eight times more likely to experience medication errors than patients admitted to adult intensive care units. Prescribing errors account for up to 74% of medication errors. Electronic prescribing has been postulated as a tool to reduce errors. The objective was to analyse prescribing errors with the e-prescribing system and risk factors. All patients who were admitted for at least 24 h and who received active pharmacological treatment during the study period were included. Prescriptions were made using electronic assisted prescription software integrated into the medical record system. Treatment was reviewed daily by a pharmacist, and errors were graded according to taxonomic criteria. A total of 240 patients were included, 13,876 prescriptions were reviewed and 455 errors were found (3.3% of prescriptions were wrong). Prescribing errors were concentrated in 40 drugs/nutritional products. The most frequent error was a discrepancy between the prescription and the associated text-free field (n = 196). The drugs with the most errors were Lactobacillus acidophilus, caffeine citrate, acetaminophen, gentamycin and cholecalciferol. Patients with a birth weight from 1000 to 1500 g were 82% more likely to experience an error than those with an extremely low birth weight (< 1000 g) (OR = 1.81, 95% CI = 1.42-2.89, p < 0.05). Patients at the highest risk were those with gestational ages from 28 to 32 weeks, with a 29.80% greater risk of prescribing errors than those with gestational ages less than 28 weeks (OR = 1.29, 95% CI = 1.02-1.65, p < 0.05). Prescribing errors occur due to complex dosing rules based on patient characteristics and free-text use, highlighting process issues rather than specific medication risks.","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"26"},"PeriodicalIF":3.5,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143441064","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}

引用次数: 0

Application of Modular Architectures in the Medical Domain - a Scoping Review.

IF 3.5 3区医学

Journal of Medical Systems Pub Date : 2025-02-18 DOI: 10.1007/s10916-025-02158-3

Franziska Bathelt, Stephan Lorenz, Jens Weidner, Martin Sedlmayr, Ines Reinecke

{"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":"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.","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}

引用次数: 0

Real-Time Cost Awareness in Surgical Disposables: A Multispecialty Pre-Post Trial.

IF 3.5 3区医学

Journal of Medical Systems Pub Date : 2025-02-17 DOI: 10.1007/s10916-025-02160-9

Thomas G Poder, Philippe Harris, Maxime Têtu, Pascal Mondoloni, Cathy Vernelus, Alexandre Mignault, Moishe Liberman

{"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":"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.","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}

引用次数: 0

Commentary on "Emergency Medical Access Control System Based on Public Blockchain ".

IF 3.5 3区医学

Journal of Medical Systems Pub Date : 2025-02-14 DOI: 10.1007/s10916-025-02162-7

Zekai Yu

引用次数: 0

Evaluation of the Performance of Three Large Language Models in Clinical Decision Support: A Comparative Study Based on Actual Cases.

IF 3.5 3区医学

Journal of Medical Systems Pub Date : 2025-02-14 DOI: 10.1007/s10916-025-02152-9

Xueqi Wang, Haiyan Ye, Sumian Zhang, Mei Yang, Xuebin Wang

{"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":"Background: 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.Methods: 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.Results: 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.Conclusions: 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.","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}

引用次数: 0

Deep Learning Radiomics for Survival Prediction in Non-Small-Cell Lung Cancer Patients from CT Images.

IF 3.5 3区医学

Journal of Medical Systems Pub Date : 2025-02-11 DOI: 10.1007/s10916-025-02156-5

Viet Huan Le, Tran Nguyen Tuan Minh, Quang Hien Kha, Nguyen Quoc Khanh Le

{"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":"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.","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}

引用次数: 0