Journal of Medical Systems最新文献

{"title":"Development and Implementation of Automated Referral Triaging System for Spinal Cord Stimulation Procedure in Pain Medicine.","authors":"Lan Jiang, Yu-Li Huang, Jungwei Fan, Christy L Hunt, Jason S Eldrige","doi":"10.1007/s10916-025-02148-5","DOIUrl":"https://doi.org/10.1007/s10916-025-02148-5","url":null,"abstract":"<p><p>Effective referral triaging enhances patient service outcomes, experience and access to care especially for specialized procedures. This study presents the development and implementation of an automated triaging system to predict patients who would benefit from Spinal Cord Stimulation (SCS) procedure for their pain management. The proposed triage system aims to improve the triage process by reducing unnecessary appointments before SCS assessment, ensuring appropriate pain management care. It compares various machine learning techniques for the prediction while addressing the class imbalance and overlap challenges inherent in the data. Both data-level and algorithm-level approaches were explored. Two years of patient data was collected including patient characteristics, diagnosis history, pain symptoms, appointment history, medication history, and concepts from clinical notes extracted using Natural Language Processing. EasyEnsemble with Ada Boosting method, an algorithm-level approach, showed the most promising results. The tenfold validation indicated the average area under curve of 0.82, true positive rate (TPR) of 77.3%, and true negative rate (TNR) of 73.0%. The probability threshold was adjusted to 0.575 to meet practice expectation of 15% or less on false positive rate (FPR). The implementation pipeline for the selected model was designed to be applicable to real clinical settings. The one-year implementation results showed TPR of 64.7% and TNR of 87.2%, which reduced FPR by 12.8% while reduced TPR by 12.6%. The trade-off was acceptable to practice. The proposed triage system demonstrated promising accuracy, leading to the enhancement of scheduling systems, patient care, and the reduction of unnecessary appointments in a pain medicine setting.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"14"},"PeriodicalIF":3.5,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143006990","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":"Effectiveness of Mobile Health Intervention in Medication Adherence: a Systematic Review and Meta-Analysis.","authors":"Sun Kyung Kim, Su Yeon Park, Hye Ri Hwang, Su Hee Moon, Jin Woo Park","doi":"10.1007/s10916-024-02135-2","DOIUrl":"https://doi.org/10.1007/s10916-024-02135-2","url":null,"abstract":"<p><p>Low medication adherence poses a great risk of poor treatment outcomes among patients with chronic diseases. Recently, mobile applications (apps) have been recognized as effective interventions, enabling patients to adhere to their prescriptions. This study aimed to establish the effectiveness of mobile app interventions for medication adherence, affecting features, and dropout rates by focusing on previous randomized controlled trials (RCTs). This study conducted a systematic review and meta-analysis of mobile app interventions targeting medication adherence in patients with chronic diseases. Electronic searches of eight databases were conducted on April 21, 2023, for studies published between 2013 and 2023. Comprehensive meta-analysis software was used to estimate the standardized mean difference (SMD) of pooled outcomes, odds ratios (ORs), and confidence intervals (CIs). Subgroup analysis was applied to investigate and compare the effectiveness of the interventional strategies and their features. The risk of bias of the included RCTs was evaluated by applying the risk of bias tool. Publication bias was examined using the fail-safe N method. Twenty-six studies with 5,174 participants were included (experimental group 2603, control group 2571). The meta-analysis findings showed a positive impact of mobile apps on improving medication adherence (OR = 2.371, SMD = 0.279). The subgroup analysis results revealed greater effectiveness of interventions using interactive strategies (OR = 2.652, SMD = 0.283), advanced reminders (OR = 1.849, SMD = 0.455), data-sharing (OR = 2.404, SMD = 0.346), and pill dispensers (OR = 2.453). The current study found that mobile interventions had significant effects on improving medication adherence. Subgroup analysis showed that the roles of stakeholders in health providers' interactions with patients and developers' understanding of patients and disease characteristics are critical. Future studies should incorporate advanced technology reflecting acceptability and the needs of the target population.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"13"},"PeriodicalIF":3.5,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143006948","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":"Influence of Practitioner Dashboard Feedback on Anesthetic Greenhouse Gas Emissions: A Prospective Performance Improvement Investigation.","authors":"Ronald A Kahn, Natalia Egorova, Yuxia Ouyang, Garrett W Burnett, Ira Hofer, David B Wax, Muoi Trinh","doi":"10.1007/s10916-025-02142-x","DOIUrl":"https://doi.org/10.1007/s10916-025-02142-x","url":null,"abstract":"<p><p>Anesthetic gases contribute to global warming. We described a two-year performance improvement project to examine the association of individualized provider dashboard feedback of anesthetic gas carbon dioxide equivalent (CDE₂₀) production and median perioperative fresh gas flows (FGF) during general anesthetics during perioperative management. Using a custom structured query language (SQL) query, hourly CDE₂₀ for each anesthetic gas and median FGF were determined. During the first year, practitioners were not given any feedback on their use of anesthetic gases. During the second year of the study protocol, a commercially available business intelligence platform was used to deliver individualized monthly dashboard of these parameters to each practitioner. Continuous values are expressed as median [first quartile, third quartile]. During the study period, 53,294 patients managed by 79 anesthesiologists were available for analysis. Bivariate analysis revealed an overall decrease in median FGF from 2.0 [1.9, 3.0] liters/minute (l/min) to 1.9 [1.7, 2.0] l/min (p < 0.001). There was a significant decrease in the overall total CDE₂₀ from 5.10 [0,12.3] to 3.59 [0,8.78] kg/hr (p < 0.001). Multivariate analysis demonstrated an initial decrease in monthly practitioner total CDE₂₀ production with the intervention (odds ratio (OR) 0.875 95% confidence interval (CI) 0.809-0.996, p < 0.001) and a faster decrease rate in monthly total CDE₂₀ (OR 0.986, 95% CI 0.976-0,996, p < 0.001). Dashboard distribution initially decreased isoflurane (intervention OR 0.97 95% CI 0.96-0.99, p = 0.001) and N₂O (OR 0.82 95% CI 0.73-0.94, p = 0.003) CDE₂₀ production and was associated with a steeper declining rate of isoflurane (OR 0.87, CI 0.79-0.94, p < 0.001) and desflurane (OR 0.9, 0.84-0.97, p = 0.005) CDE₂₀ production. The intervention did not have a significant effect on the monthly rate of decline of sevoflurane or N₂O CDE₂₀. The average practitioner FGF decreased by 0.3 l/m (95% confidence interval (CI): -0,011, -0.5, p = 0.002) with dashboard distributions. Dashboard distribution may be an effective tool to decrease FGF as well as components of anesthetic greenhouse gas emissions.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"12"},"PeriodicalIF":3.5,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143006955","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":"Assessing the Efficacy of ChatGPT Prompting Strategies in Enhancing Thyroid Cancer Patient Education: A Prospective Study.","authors":"Qi Xu, Jing Wang, Xiaohui Chen, Jiale Wang, Hanzhi Li, Zheng Wang, Weihan Li, Jinliang Gao, Chen Chen, Yuwan Gao","doi":"10.1007/s10916-024-02129-0","DOIUrl":"https://doi.org/10.1007/s10916-024-02129-0","url":null,"abstract":"<p><p>With the rise of AI platforms, patients increasingly use them for information, relying on advanced language models like ChatGPT for answers and advice. However, the effectiveness of ChatGPT in educating thyroid cancer patients remains unclear. We designed 50 questions covering key areas of thyroid cancer management and generated corresponding responses under four different prompt strategies. These answers were evaluated based on four dimensions: accuracy, comprehensiveness, human care, and satisfaction. Additionally, the readability of the responses was assessed using the Flesch-Kincaid grade level, Gunning Fog Index, Simple Measure of Gobbledygook, and Fry readability score. We also statistically analyzed the references in the responses generated by ChatGPT. The type of prompt significantly influences the quality of ChatGPT's responses. Notably, the \"statistics and references\" prompt yields the highest quality outcomes. Prompts tailored to a \"6th-grade level\" generated the most easily understandable text, whereas responses without specific prompts were the most complex. Additionally, the \"statistics and references\" prompt produced the longest responses while the \"6th-grade level\" prompt resulted in the shortest. Notably, 87.84% of citations referenced published medical literature, but 12.82% contained misinformation or errors. ChatGPT demonstrates considerable potential for enhancing the readability and quality of thyroid cancer patient education materials. By adjusting prompt strategies, ChatGPT can generate responses that cater to diverse patient needs, improving their understanding and management of the disease. However, AI-generated content must be carefully supervised to ensure that the information it provides is accurate.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"11"},"PeriodicalIF":3.5,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143006987","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":"Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency.","authors":"Soumitra S Bhuyan, Vidyoth Sateesh, Naya Mukul, Alay Galvankar, Asos Mahmood, Muhammad Nauman, Akash Rai, Kahuwa Bordoloi, Urmi Basu, Jim Samuel","doi":"10.1007/s10916-024-02136-1","DOIUrl":"https://doi.org/10.1007/s10916-024-02136-1","url":null,"abstract":"<p><p>Generative Artificial Intelligence (Gen AI) has transformative potential in healthcare to enhance patient care, personalize treatment options, train healthcare professionals, and advance medical research. This paper examines various clinical and non-clinical applications of Gen AI. In clinical settings, Gen AI supports the creation of customized treatment plans, generation of synthetic data, analysis of medical images, nursing workflow management, risk prediction, pandemic preparedness, and population health management. By automating administrative tasks such as medical documentations, Gen AI has the potential to reduce clinician burnout, freeing more time for direct patient care. Furthermore, application of Gen AI may enhance surgical outcomes by providing real-time feedback and automation of certain tasks in operating rooms. The generation of synthetic data opens new avenues for model training for diseases and simulation, enhancing research capabilities and improving predictive accuracy. In non-clinical contexts, Gen AI improves medical education, public relations, revenue cycle management, healthcare marketing etc. Its capacity for continuous learning and adaptation enables it to drive ongoing improvements in clinical and operational efficiencies, making healthcare delivery more proactive, predictive, and precise.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"10"},"PeriodicalIF":3.5,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11739231/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143006951","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":"Forecasting Mortality Associated Emergency Department Crowding with LightGBM and Time Series Data.","authors":"Jalmari Nevanlinna, Anna Eidstø, Jari Ylä-Mattila, Teemu Koivistoinen, Niku Oksala, Juho Kanniainen, Ari Palomäki, Antti Roine","doi":"10.1007/s10916-024-02137-0","DOIUrl":"10.1007/s10916-024-02137-0","url":null,"abstract":"<p><p>Emergency department (ED) crowding is a global public health issue that has been repeatedly associated with increased mortality. Predicting future service demand would enable preventative measures aiming to eliminate crowding along with its detrimental effects. Recent findings in our ED indicate that occupancy ratios exceeding 90% are associated with increased 10-day mortality. In this paper, we aim to predict these crisis periods using retrospective time series data such as weather, availability of hospital beds, calendar variables and occupancy statistics from a large Nordic ED with a LightGBM model. We predict mortality associated crowding for the whole ED and individually for its different operational sections. We demonstrate that afternoon crowding can be predicted at 11 a.m. with an AUC of 0.82 (95% CI 0.78-0.86) and at 8 a.m. with an AUC up to 0.79 (95% CI 0.75-0.83). Consequently we show that forecasting mortality-associated crowding using time series data is feasible.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"9"},"PeriodicalIF":3.5,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11732927/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142983824","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":"Development of Predictive Model of Surgical Case Durations Using Machine Learning Approach.","authors":"Jung-Bin Park, Gyun-Ho Roh, Kwangsoo Kim, Hee-Soo Kim","doi":"10.1007/s10916-025-02141-y","DOIUrl":"10.1007/s10916-025-02141-y","url":null,"abstract":"<p><p>Optimizing operating room (OR) utilization is critical for enhancing hospital management and operational efficiency. Accurate surgical case duration predictions are essential for achieving this optimization. Our study aimed to refine the accuracy of these predictions beyond traditional estimation methods by developing Random Forest models tailored to specific surgical departments. Utilizing a comprehensive dataset, we applied several machine learning algorithms, including RandomForest, XGBoost, Linear Regression, LightGBM, and CatBoost, and assessed their performance using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-Squared (R²) metrics. Our findings highlighted that Random Forest models excelled in department-specific applications, achieving an MAE of 16.32, an RMSE of 31.19, and an R² of 0.92, significantly outperforming general models and conventional estimates. This improvement emphasizes the advantage of customizing models to fit the distinct characteristics and data patterns of each department. Additionally, our SHAP-based feature importance analysis identified morning operation timing, ICU ward assignments, operation codes, and surgeon IDs as key factors influencing surgical duration. This suggests that a detailed and nuanced approach to model development can substantially increase prediction accuracy. By providing a more accurate, reliable tool for predicting surgical case durations, our department-specific Random Forest models promise to enhance surgical scheduling, leading to more effective OR management. This approach underscores the importance of leveraging tailored, data-driven models to improve healthcare outcomes and operational efficiency.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"8"},"PeriodicalIF":3.5,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11732958/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142978638","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":"Lilobot: A Cognitive Conversational Agent to Train Counsellors at Children's Helplines : Design and Initial Evaluation.","authors":"Sharon Grundmann, Mohammed Al Owayyed, Merijn Bruijnes, Ellen Vroonhof, Willem-Paul Brinkman","doi":"10.1007/s10916-024-02121-8","DOIUrl":"10.1007/s10916-024-02121-8","url":null,"abstract":"<p><p>To equip new counsellors at a Dutch child helpline with the needed counselling skills, the helpline uses role-playing, a form of learning through simulation in which one counsellor-in-training portrays a child seeking help and the other portrays a counsellor. However, this process is time-intensive and logistically challenging-issues that a conversational agent could help address. In this paper, we propose an initial design for a computer agent that acts as a child help-seeker to be used in a role-play setting. Our agent, Lilobot, is based on a Belief-Desire-Intention (BDI) model to simulate the reasoning process of a child who is being bullied at school. Through interaction with Lilobot, counsellors-in-training can practise the Five Phase Model, a conversation strategy that underpins the helpline's counselling principle of keeping conversations child-centred. We compared a training session with Lilobot to a text-based training, inviting experienced counsellors from the Dutch child helpline to participate in both sessions. We conducted pre- and post-measurement comparisons for both training sessions. Contrary to our expectations, the results show a decrease in counselling self-efficacy at post-measurement, particularly in Lilobot's condition. Still, the counsellors' qualitative feedback indicated that, with further development and refinements, they believed Lilobot could potentially serve as a useful supplementary tool for training new helpline counsellors. Our work also highlights three future research directions for training simulators in this domain: integrating emotions into the model, providing guided feedback to the counsellor, and incorporating Large Language Models (LLMs) into the conversations.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"5"},"PeriodicalIF":3.5,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729055/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142978656","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":"Predictive Optimization of Patient No-Show Management in Primary Healthcare Using Machine Learning.","authors":"Andrés Leiva-Araos, Cristián Contreras, Hemani Kaushal, Zornitza Prodanoff","doi":"10.1007/s10916-025-02143-w","DOIUrl":"https://doi.org/10.1007/s10916-025-02143-w","url":null,"abstract":"<p><p>The \"no-show\" problem in healthcare refers to the prevalent phenomenon where patients schedule appointments with healthcare providers but fail to attend them without prior cancellation or rescheduling. In addressing this issue, our study delves into a multivariate analysis over a five-year period involving 21,969 patients. Our study introduces a predictive model framework that offers a holistic approach to managing the no-show problem in healthcare, incorporating elements into the objective function that address not only the accurate prediction of no-shows but also the management of service capacity, overbooking, and idle resource allocation resulting from mispredictions. Our approach simplifies preprocessing and eliminates the need for expert judgment in variable selection, thereby enhancing the model's usability in routine healthcare operations. Our research revealed that key predictors of no-shows are consistent across various studies. We employed semi-automatic feature selection techniques, achieving results comparable to state-of-the-art approaches but with significantly reduced complexity in their selection. This method not only streamlines the feature selection process but also enhances the overall efficiency and scalability of our predictive models, making them more adaptable to diverse healthcare settings. This comprehensive strategy enables healthcare providers to optimize resource allocation and improve service delivery, making our findings relevant for healthcare systems globally facing similar challenges. Future work aims to expand the analysis by incorporating additional third-party data sources, such as weather and commuting activities, to explore the broader impacts of external factors on patient no-show behavior. To the best of our knowledge, this innovative approach is expected to provide deeper insights and further enhance the predictability and effectiveness of no-show mitigation strategies in healthcare systems.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"7"},"PeriodicalIF":3.5,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142978661","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":"Could Empathy Be Taught? The Role of Advanced Technologies to Foster Empathy in Medical Students and Healthcare Professionals: A Systematic Review.","authors":"Giorgio Li Pira, Chiara Ruini, Francesca Vescovelli, Rosa Baños, Sara Ventura","doi":"10.1007/s10916-025-02144-9","DOIUrl":"10.1007/s10916-025-02144-9","url":null,"abstract":"<p><p>The physician-patient relationship relies mostly on doctors' empathetic abilities to understand and manage patients' emotions, enhancing patient satisfaction and treatment adherence. With the advent of digital technologies in education, innovative empathy training methods such as virtual reality, simulation training systems, mobile apps, and wearable devices, have emerged for teaching empathy. However, there is a gap in the literature regarding the efficacy of these technologies in teaching empathy, the most effective types, and the primary beneficiaries -students or advanced healthcare professionals-. This study aims to address this gap through a literature review following PRISMA guidelines. A comprehensive literature search was conducted in the PsychINFO, Scopus, PubMed, and Web of Science databases using specific keywords. Inclusion criteria for articles were established, and two researchers independently rated the selected articles, resolving any disagreements by consensus. Out of 1137 articles screened, a total of 14 articles were included in this review with a total of 1285 participants, who received empathic training integrated with the use of digital technologies. Only 9 articles defined the construct of empathy, focusing on cognitive, affective, clinical, or cultural aspects. Empathy was assessed with various methods and promoted through various digital technologies, including wearables (e.g. HMDs, SymPulse™ armband) and non-wearable devices (computer monitors, Mobile Apps, Kinect System). Participants were primarily medical students (68.1%), with few healthcare workers (31.9%) and nurses (2.9%). All digital technologies effectively promoted empathy among the target population except for 3 studies that involved advanced career healthcare workers. This review highlights the potential efficacy of digital technologies in fostering empathy among medical students, though not as effectively among advanced healthcare professionals. These insights have implications for designing targeted educational programs that address the distinct needs of healthcare professionals at varying career stages. Limitations and future research directions are also discussed.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"6"},"PeriodicalIF":3.5,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729101/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142978633","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}