Sarah Milkov, Antonia Schmidt, Anja Burmann, Niklas Tschorn, Marcel Klötgen, Wolfgang Deiters, Christian Potthoff, Kirsten Neveling, Yvonne Weber, Maren Keuchel, Daniela Holle
Background: In aging populations, the demand for care, including care delivery in long-term care (LTC) facilities, is increasing. This situation highlights the need to optimize care processes through continuous scientific evaluation. The use of artificial intelligence (AI) has the potential for use in nursing research, but it experiences a lack of standardization and structuring of nursing data. Although solutions such as standardized nursing terminologies exist, their use in practice has thus far not been widespread and is often associated with high documentation costs.
Objective: This paper presents the conceptual and technical development of a nursing minimum dataset that focuses on a specific "fall prevention" use case. The aim of this work was to improve data standardization and usability for research and AI-based analysis in LTC settings.
Methods: A representation of the "fall prevention" use case was developed using literature analyses, co-design workshops, and a quantitative survey (n=158). Technical indexing was conducted by translating the results into the technical terminology of the Health Level Seven International Fast Healthcare Interoperability Resources standard.
Results: The "fall prevention" use case was developed as part of a German nursing minimum dataset for long-term residential care with 8 basic modules (patient or client demographics) and 11 extension modules (nursing care elements). The module of the "fall prevention" use case includes fall risk factors, interventions, and outcomes. The literature analysis included 4 international fall guidelines and 17 practice and transfer documents established in German LTC. In total, 12 experts from the fields of management, quality management, technical application support, nursing service management, department management, and members of the PFLIP (Pflege-Kerndatensatz und Intersektorales Pflegedaten-Repository [Nursing Minimum Data Set and Intersectoral Nursing Data Repository]) research project participated in the workshops. A total of 158 people participated in the quantitative survey, the majority of whom were female (117/158, 74%), with 63% (100/158) working directly in nursing care and an average of 24.9 years of professional experience, mainly in LTC (63/158, 40%), outpatient care (37/158, 23%), and hospitals (14/158, 9%). The relevant content, in the sense of a minimum set of items, was identified and prioritized in collaboration with nursing experts and translated into a Fast Healthcare Interoperability Resources-based implementation guide.
Conclusions: This approach addresses the lack of structured nursing data for AI and research and can serve as an example for interoperable, cross-sector solutions in global LTC.
{"title":"Advancing Nursing Data Integration Through a Nursing Minimum Dataset for the Conceptual and Technical Development of a \"Fall Prevention\" Data Module: Development Study.","authors":"Sarah Milkov, Antonia Schmidt, Anja Burmann, Niklas Tschorn, Marcel Klötgen, Wolfgang Deiters, Christian Potthoff, Kirsten Neveling, Yvonne Weber, Maren Keuchel, Daniela Holle","doi":"10.2196/82417","DOIUrl":"https://doi.org/10.2196/82417","url":null,"abstract":"<p><strong>Background: </strong>In aging populations, the demand for care, including care delivery in long-term care (LTC) facilities, is increasing. This situation highlights the need to optimize care processes through continuous scientific evaluation. The use of artificial intelligence (AI) has the potential for use in nursing research, but it experiences a lack of standardization and structuring of nursing data. Although solutions such as standardized nursing terminologies exist, their use in practice has thus far not been widespread and is often associated with high documentation costs.</p><p><strong>Objective: </strong>This paper presents the conceptual and technical development of a nursing minimum dataset that focuses on a specific \"fall prevention\" use case. The aim of this work was to improve data standardization and usability for research and AI-based analysis in LTC settings.</p><p><strong>Methods: </strong>A representation of the \"fall prevention\" use case was developed using literature analyses, co-design workshops, and a quantitative survey (n=158). Technical indexing was conducted by translating the results into the technical terminology of the Health Level Seven International Fast Healthcare Interoperability Resources standard.</p><p><strong>Results: </strong>The \"fall prevention\" use case was developed as part of a German nursing minimum dataset for long-term residential care with 8 basic modules (patient or client demographics) and 11 extension modules (nursing care elements). The module of the \"fall prevention\" use case includes fall risk factors, interventions, and outcomes. The literature analysis included 4 international fall guidelines and 17 practice and transfer documents established in German LTC. In total, 12 experts from the fields of management, quality management, technical application support, nursing service management, department management, and members of the PFLIP (Pflege-Kerndatensatz und Intersektorales Pflegedaten-Repository [Nursing Minimum Data Set and Intersectoral Nursing Data Repository]) research project participated in the workshops. A total of 158 people participated in the quantitative survey, the majority of whom were female (117/158, 74%), with 63% (100/158) working directly in nursing care and an average of 24.9 years of professional experience, mainly in LTC (63/158, 40%), outpatient care (37/158, 23%), and hospitals (14/158, 9%). The relevant content, in the sense of a minimum set of items, was identified and prioritized in collaboration with nursing experts and translated into a Fast Healthcare Interoperability Resources-based implementation guide.</p><p><strong>Conclusions: </strong>This approach addresses the lack of structured nursing data for AI and research and can serve as an example for interoperable, cross-sector solutions in global LTC.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e82417"},"PeriodicalIF":6.0,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147473969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Florian Reis, Louis Agha-Mir-Salim, Richard Hickstein, Moritz Reis, Sophie K Piper, Felix Balzer, Sebastian Daniel Boie
<p><strong>Background: </strong>"I'm not a doctor, but..." is a typical response when asking considerate laypeople for health advice. However, seeking medical advice has also shifted to digital settings, where the expertise of the other party is less transparent than in face-to-face interactions. Recently, large language models (LLMs) have emerged as easily accessible tools, offering a novel way to formulate medical questions and receive seemingly qualified advice. Given the sensitive nature of health-related queries and the lack of professional supervision, incorrect advice can pose serious health risks. Therefore, including explicit disclaimers and precise referrals in LLM responses to medical queries is crucial. However, little is known about how LLMs adapt their safety implementations in response to different urgency levels.</p><p><strong>Objective: </strong>This study evaluates disclaimer and referral patterns in responses from LLMs to authentic medical queries of different urgency levels using a systematic evaluation framework.</p><p><strong>Methods: </strong>This prospective, multimodel evaluation study generated and analyzed 908 responses from 4 popular LLMs (GPT-4o, Claude Sonnet-4, Grok-3, and DeepSeek-V3) to 227 authentic patient queries from a public dataset. Two human raters classified all 227 patient queries using a 3-level urgency scale. LLM responses were evaluated using a 5-point ordinal classification system for disclaimer and referral advice, ranging from "no disclaimer" to "urgent advice to consult a medical professional." GPT-4o served as the primary rater model for this task after conducting a subset validation against human expert annotations. Statistical analyses included Jonckheere-Terpstra tests to examine the relationship between case urgency and disclaimer ratings and Kruskal-Wallis tests for intermodel comparisons.</p><p><strong>Results: </strong>The 227 patient queries were distributed as 77 (34%) low-urgency, 110 (48%) intermediate-urgency, and 40 (18%) high-urgency cases. All 4 LLMs demonstrated statistically significant ordered trends (all P<.001), with higher-urgency queries receiving more explicit referral advice. Disclaimer and referral advice clustered toward higher categories across all models, with 97% (881/908) of responses indicating that a medical professional should be consulted. Sonnet-4, Grok-3, and GPT-4o demonstrated a conservative approach, with 89%, 89%, and 88%, respectively, of their responses being either explicit or urgent referrals. In contrast, DeepSeek-V3 showed a broader distribution, with 74% of responses falling into these categories. Interrater reliability between GPT-4o and human raters achieved moderate to substantial agreement, with weighted Cohen κ values between 0.415 and 0.707.</p><p><strong>Conclusions: </strong>Current LLMs exhibit urgency-responsive safety mechanisms when providing medical advice. All evaluated models adaptively incorporate more explicit disclaimers and urgent r
{"title":"Disclaimers and Referral Patterns for Medical Advice Across Urgency Levels: Large Language Model Evaluation Study.","authors":"Florian Reis, Louis Agha-Mir-Salim, Richard Hickstein, Moritz Reis, Sophie K Piper, Felix Balzer, Sebastian Daniel Boie","doi":"10.2196/84668","DOIUrl":"10.2196/84668","url":null,"abstract":"<p><strong>Background: </strong>\"I'm not a doctor, but...\" is a typical response when asking considerate laypeople for health advice. However, seeking medical advice has also shifted to digital settings, where the expertise of the other party is less transparent than in face-to-face interactions. Recently, large language models (LLMs) have emerged as easily accessible tools, offering a novel way to formulate medical questions and receive seemingly qualified advice. Given the sensitive nature of health-related queries and the lack of professional supervision, incorrect advice can pose serious health risks. Therefore, including explicit disclaimers and precise referrals in LLM responses to medical queries is crucial. However, little is known about how LLMs adapt their safety implementations in response to different urgency levels.</p><p><strong>Objective: </strong>This study evaluates disclaimer and referral patterns in responses from LLMs to authentic medical queries of different urgency levels using a systematic evaluation framework.</p><p><strong>Methods: </strong>This prospective, multimodel evaluation study generated and analyzed 908 responses from 4 popular LLMs (GPT-4o, Claude Sonnet-4, Grok-3, and DeepSeek-V3) to 227 authentic patient queries from a public dataset. Two human raters classified all 227 patient queries using a 3-level urgency scale. LLM responses were evaluated using a 5-point ordinal classification system for disclaimer and referral advice, ranging from \"no disclaimer\" to \"urgent advice to consult a medical professional.\" GPT-4o served as the primary rater model for this task after conducting a subset validation against human expert annotations. Statistical analyses included Jonckheere-Terpstra tests to examine the relationship between case urgency and disclaimer ratings and Kruskal-Wallis tests for intermodel comparisons.</p><p><strong>Results: </strong>The 227 patient queries were distributed as 77 (34%) low-urgency, 110 (48%) intermediate-urgency, and 40 (18%) high-urgency cases. All 4 LLMs demonstrated statistically significant ordered trends (all P<.001), with higher-urgency queries receiving more explicit referral advice. Disclaimer and referral advice clustered toward higher categories across all models, with 97% (881/908) of responses indicating that a medical professional should be consulted. Sonnet-4, Grok-3, and GPT-4o demonstrated a conservative approach, with 89%, 89%, and 88%, respectively, of their responses being either explicit or urgent referrals. In contrast, DeepSeek-V3 showed a broader distribution, with 74% of responses falling into these categories. Interrater reliability between GPT-4o and human raters achieved moderate to substantial agreement, with weighted Cohen κ values between 0.415 and 0.707.</p><p><strong>Conclusions: </strong>Current LLMs exhibit urgency-responsive safety mechanisms when providing medical advice. All evaluated models adaptively incorporate more explicit disclaimers and urgent r","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e84668"},"PeriodicalIF":6.0,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12991190/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147468240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Obada Almaabreh, Rukaya Al-Dafi, Aliya Tabassum, Ahmad Othman, Alaa Abd-Alrazaq
<p><strong>Background: </strong>Adult-type gliomas are among the most prevalent and lethal primary central nervous system tumors, where prompt and accurate diagnosis is essential for maximizing survival prospects. Molecular classification, particularly the detection of isocitrate dehydrogenase (IDH) mutations and 1p/19q codeletions, has become crucial for accurate diagnosis and prognosis. Artificial intelligence (AI) has emerged as a promising adjunct in enhancing diagnostic accuracy using histopathological images. Existing reviews mostly focused on radiology rather than histopathology, and no comprehensive systematic review has specifically evaluated AI performance exclusively from histopathological images for detecting these two molecular markers.</p><p><strong>Objective: </strong>This study aims to systematically evaluate the performance of AI models in detecting and classifying IDH mutation status and 1p/19q gene codeletion in adult-type gliomas using histopathological images.</p><p><strong>Methods: </strong>A systematic review was conducted in accordance with PRISMA-DTA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Extension for Diagnostic Test Accuracy) guidelines. Seven databases (MEDLINE, PsycINFO, Embase, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar) were searched for studies published between 2015 and 2025. Eligible studies used AI models on histopathological images for molecular classification of adult-type gliomas and reported performance metrics. Study selection, data extraction, and risk of bias assessment using a modified QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool were conducted independently by two reviewers. Extracted data were synthesized narratively.</p><p><strong>Results: </strong>A total of 2453 reports were identified, with 22 studies meeting the inclusion criteria. The pooled average accuracy, sensitivity, specificity, and area under the curve (AUC) across studies were 85.46%, 84.55%, 86.03%, and 86.53%, respectively. Hybrid models demonstrated the highest diagnostic performance (accuracy 92.80% and sensitivity 89.62%). In general, AI models that used multimodal data outperformed those that used unimodal data in terms of sensitivity (90.15% vs 84.31%) and AUC (88.93% vs 86.29%). Furthermore, models had a better overall performance in identifying IDH mutations than 1p/19q codeletions, with higher accuracy (86.13% vs 81.63%), specificity (86.61% vs 78.11%), and AUC (86.74% vs 85.15%). Unexpectedly, AI models designed for binary classification exhibited lower performance than those for multiclass classification in terms of both accuracy (91.98% vs 84.02%) and sensitivity (93.41% vs 80.18%). However, these differences should be interpreted as descriptive trends rather than statistically validated superiority, as formal between-group comparisons were not feasible.</p><p><strong>Conclusions: </strong>AI models show strong potential as complementary tools for the molec
背景：成人型胶质瘤是最常见和最致命的原发性中枢神经系统肿瘤之一，及时准确的诊断对于最大限度地提高生存前景至关重要。分子分类，特别是检测异柠檬酸脱氢酶（IDH）突变和1p/19q共缺失，已经成为准确诊断和预后的关键。人工智能（AI）已经成为利用组织病理学图像提高诊断准确性的有前途的辅助手段。现有的综述主要集中在放射学而不是组织病理学上，并且没有全面的系统综述专门从组织病理学图像来评估人工智能的性能，以检测这两种分子标记。目的：本研究旨在系统评价人工智能模型在成人型胶质瘤中IDH突变状态和1p/19q基因编码检测与分类中的表现。方法：根据PRISMA-DTA（系统评价和元分析首选报告项目-诊断测试准确性扩展）指南进行系统评价。7个数据库（MEDLINE, PsycINFO, Embase, IEEE Xplore， ACM数字图书馆，Scopus和谷歌Scholar）检索了2015年至2025年间发表的研究。符合条件的研究在组织病理学图像上使用人工智能模型进行成人型胶质瘤的分子分类和报告的性能指标。研究选择、数据提取和使用改进的QUADAS-2（诊断准确性研究质量评估2）工具进行偏倚风险评估由两位审稿人独立进行。提取的数据以叙述的方式合成。结果：共纳入2453份报告，其中22项研究符合纳入标准。各研究的合并平均准确率、灵敏度、特异性和曲线下面积（AUC）分别为85.46%、84.55%、86.03%和86.53%。混合模型的诊断准确率为92.80%，灵敏度为89.62%。总体而言，使用多模态数据的AI模型在灵敏度（90.15% vs 84.31%）和AUC （88.93% vs 86.29%）方面优于使用单模态数据的AI模型。此外，模型在识别IDH突变方面的总体表现优于1p/19q共缺失，准确率（86.13% vs 81.63%）、特异性（86.61% vs 78.11%）和AUC （86.74% vs 85.15%）更高。出乎意料的是，用于二元分类的AI模型在准确率（91.98% vs 84.02%）和灵敏度（93.41% vs 80.18%）方面都低于用于多类分类的AI模型。然而，这些差异应该被解释为描述性趋势，而不是统计上证实的优势，因为正式的组间比较是不可行的。结论：人工智能模型显示出强大的潜力，可以作为利用组织病理学图像对成人型胶质瘤进行分子分类的补充工具，特别是用于IDH突变检测。然而，这些发现受到研究数量的限制，主要集中在成人型胶质瘤，缺乏荟萃分析，以及英语出版物的限制。虽然人工智能提供了宝贵的诊断支持，但它必须与专家的临床判断相结合。未来的研究应优先考虑更大、更多样化的数据集和多模态人工智能框架，并扩展到其他脑肿瘤类型，以获得更广泛的适用性。
{"title":"The Performance of Artificial Intelligence in Classifying Molecular Markers in Adult-Type Gliomas Using Histopathological Images: Systematic Review.","authors":"Obada Almaabreh, Rukaya Al-Dafi, Aliya Tabassum, Ahmad Othman, Alaa Abd-Alrazaq","doi":"10.2196/78377","DOIUrl":"https://doi.org/10.2196/78377","url":null,"abstract":"<p><strong>Background: </strong>Adult-type gliomas are among the most prevalent and lethal primary central nervous system tumors, where prompt and accurate diagnosis is essential for maximizing survival prospects. Molecular classification, particularly the detection of isocitrate dehydrogenase (IDH) mutations and 1p/19q codeletions, has become crucial for accurate diagnosis and prognosis. Artificial intelligence (AI) has emerged as a promising adjunct in enhancing diagnostic accuracy using histopathological images. Existing reviews mostly focused on radiology rather than histopathology, and no comprehensive systematic review has specifically evaluated AI performance exclusively from histopathological images for detecting these two molecular markers.</p><p><strong>Objective: </strong>This study aims to systematically evaluate the performance of AI models in detecting and classifying IDH mutation status and 1p/19q gene codeletion in adult-type gliomas using histopathological images.</p><p><strong>Methods: </strong>A systematic review was conducted in accordance with PRISMA-DTA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Extension for Diagnostic Test Accuracy) guidelines. Seven databases (MEDLINE, PsycINFO, Embase, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar) were searched for studies published between 2015 and 2025. Eligible studies used AI models on histopathological images for molecular classification of adult-type gliomas and reported performance metrics. Study selection, data extraction, and risk of bias assessment using a modified QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool were conducted independently by two reviewers. Extracted data were synthesized narratively.</p><p><strong>Results: </strong>A total of 2453 reports were identified, with 22 studies meeting the inclusion criteria. The pooled average accuracy, sensitivity, specificity, and area under the curve (AUC) across studies were 85.46%, 84.55%, 86.03%, and 86.53%, respectively. Hybrid models demonstrated the highest diagnostic performance (accuracy 92.80% and sensitivity 89.62%). In general, AI models that used multimodal data outperformed those that used unimodal data in terms of sensitivity (90.15% vs 84.31%) and AUC (88.93% vs 86.29%). Furthermore, models had a better overall performance in identifying IDH mutations than 1p/19q codeletions, with higher accuracy (86.13% vs 81.63%), specificity (86.61% vs 78.11%), and AUC (86.74% vs 85.15%). Unexpectedly, AI models designed for binary classification exhibited lower performance than those for multiclass classification in terms of both accuracy (91.98% vs 84.02%) and sensitivity (93.41% vs 80.18%). However, these differences should be interpreted as descriptive trends rather than statistically validated superiority, as formal between-group comparisons were not feasible.</p><p><strong>Conclusions: </strong>AI models show strong potential as complementary tools for the molec","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e78377"},"PeriodicalIF":6.0,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12986776/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147458012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jie Xiao, Qing Wang, Shenglan Tan, Lei Chen, Daxiong Xiang, Haiyan Yuan, Xia Li, Shuting Huang, Bingjie Tang, Yan Guo, Haiying Huang, Danhui Zhao, Yue Li, Li Wang, Qun Li, Juan Liu, Ping Xu
Background: Evidence-based physician-pharmacist collaborative clinics have demonstrated significant short-term benefits for patients with type 2 diabetes (T2D), but their long-term effectiveness remains unclear, especially in primary health care settings.
Objective: This study aimed to explore the long-term effectiveness and cost-effectiveness of a novel, digital-driven, multifaceted physician-pharmacist collaborative model for managing patients with T2D in underresourced settings.
Methods: We conducted a 12-month cluster randomized controlled trial from May 2021 to December 2022 across 6 primary health care settings in China. Guided by the theory of planned behavior, the intervention involved routine therapy from physicians along with pharmaceutical interventions from pharmacists. These were delivered through a combination of face-to-face visits and mobile health care. The intervention group received 4 face-to-face visits and biweekly remote education sessions over the 12 months. We conducted intention-to-treat analyses to estimate differences in clinical and behavior indicators between the intervention and control groups. Primary outcomes included glycosylated hemoglobin and 10-year atherosclerotic cardiovascular risk. Data were analyzed using adjusted generalized estimation equations.
Results: This study included 574 patients (291 in the intervention group and 283 in the control group). Over 12 months, patients in the intervention group had significant reductions in hemoglobin A1c (-2.57 vs -1.96, respectively; P<.001; 95% CI -1.027 to -0.238) and 10-year atherosclerotic cardiovascular risk (-1.35 vs 0.01, respectively; P<.001; 95% CI -1.690 to -0.630) compared with the control group. Substantial improvements were also observed in several secondary outcomes, including fasting blood glucose, 2-hour postprandial blood glucose, waist circumference, waist-to-hip ratio, blood pressure, triglyceride, and total cholesterol. Total diabetes-related costs decreased, and patient satisfaction improved significantly in the intervention group. There were no significant differences in BMI, high-density lipoprotein, or low-density lipoprotein.
Conclusions: These findings suggest that the physician-pharmacist collaborative model could improve the long-term quality and efficiency of T2D management and reduce medical costs in underresourced areas globally. Patients with T2D, especially those with central obesity or high cardiovascular risk, may benefit more from collaborative clinics.
Trial registration: Chinese Clinical Trial Registry ChiCTR2000031839; https://www.chictr.org.cn/showproj.html?proj=51910.
背景：基于证据的医师-药剂师合作诊所已经证明对2型糖尿病（T2D）患者有显着的短期益处，但其长期有效性尚不清楚，特别是在初级卫生保健机构。目的：本研究旨在探讨一种新型的、数字化驱动的、多方面的医生-药剂师合作模式在资源不足的情况下管理T2D患者的长期有效性和成本效益。方法：从2021年5月至2022年12月，我们在中国的6个初级卫生保健机构进行了一项为期12个月的随机对照试验。在计划行为理论的指导下，干预包括医生的常规治疗和药剂师的药物干预。这些服务是通过面对面访问和流动保健相结合的方式提供的。干预组在12个月内接受了4次面对面访问和每两周的远程教育。我们进行了意向治疗分析，以估计干预组和对照组在临床和行为指标上的差异。主要结局包括糖化血红蛋白和10年动脉粥样硬化心血管风险。采用调整后的广义估计方程对数据进行分析。结果：本研究纳入574例患者，其中干预组291例，对照组283例。干预组患者的糖化血红蛋白在12个月内显著降低（分别为-2.57 vs -1.96）。结论：在全球资源不足地区，医药师协作模式可以提高T2D管理的长期质量和效率，降低医疗成本。t2dm患者，尤其是中枢性肥胖或心血管疾病高危患者，可能从合作诊所获益更多。试验注册：中国临床试验注册中心ChiCTR2000031839；https://www.chictr.org.cn/showproj.html?proj=51910。
{"title":"Effect of a Digital-Driven Physician-Pharmacist Collaborative Model for Diabetes in Primary Health Care: Cluster Randomized Trial.","authors":"Jie Xiao, Qing Wang, Shenglan Tan, Lei Chen, Daxiong Xiang, Haiyan Yuan, Xia Li, Shuting Huang, Bingjie Tang, Yan Guo, Haiying Huang, Danhui Zhao, Yue Li, Li Wang, Qun Li, Juan Liu, Ping Xu","doi":"10.2196/77470","DOIUrl":"https://doi.org/10.2196/77470","url":null,"abstract":"<p><strong>Background: </strong>Evidence-based physician-pharmacist collaborative clinics have demonstrated significant short-term benefits for patients with type 2 diabetes (T2D), but their long-term effectiveness remains unclear, especially in primary health care settings.</p><p><strong>Objective: </strong>This study aimed to explore the long-term effectiveness and cost-effectiveness of a novel, digital-driven, multifaceted physician-pharmacist collaborative model for managing patients with T2D in underresourced settings.</p><p><strong>Methods: </strong>We conducted a 12-month cluster randomized controlled trial from May 2021 to December 2022 across 6 primary health care settings in China. Guided by the theory of planned behavior, the intervention involved routine therapy from physicians along with pharmaceutical interventions from pharmacists. These were delivered through a combination of face-to-face visits and mobile health care. The intervention group received 4 face-to-face visits and biweekly remote education sessions over the 12 months. We conducted intention-to-treat analyses to estimate differences in clinical and behavior indicators between the intervention and control groups. Primary outcomes included glycosylated hemoglobin and 10-year atherosclerotic cardiovascular risk. Data were analyzed using adjusted generalized estimation equations.</p><p><strong>Results: </strong>This study included 574 patients (291 in the intervention group and 283 in the control group). Over 12 months, patients in the intervention group had significant reductions in hemoglobin A<sub>1c</sub> (-2.57 vs -1.96, respectively; P<.001; 95% CI -1.027 to -0.238) and 10-year atherosclerotic cardiovascular risk (-1.35 vs 0.01, respectively; P<.001; 95% CI -1.690 to -0.630) compared with the control group. Substantial improvements were also observed in several secondary outcomes, including fasting blood glucose, 2-hour postprandial blood glucose, waist circumference, waist-to-hip ratio, blood pressure, triglyceride, and total cholesterol. Total diabetes-related costs decreased, and patient satisfaction improved significantly in the intervention group. There were no significant differences in BMI, high-density lipoprotein, or low-density lipoprotein.</p><p><strong>Conclusions: </strong>These findings suggest that the physician-pharmacist collaborative model could improve the long-term quality and efficiency of T2D management and reduce medical costs in underresourced areas globally. Patients with T2D, especially those with central obesity or high cardiovascular risk, may benefit more from collaborative clinics.</p><p><strong>Trial registration: </strong>Chinese Clinical Trial Registry ChiCTR2000031839; https://www.chictr.org.cn/showproj.html?proj=51910.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e77470"},"PeriodicalIF":6.0,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147458014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Viktoria Zander, Maja Holm, Monir Mazaheri, Christine Gustafsson, Sara Landerdahl Stridsberg, Ragnhild Hedman
Background: As more individuals live longer with complex conditions, the need for effective palliative care (PC) grows. It has been stated that access to PC should be integrated early and delivered in a timely manner to patients with life-threatening illnesses. Health and welfare technologies (HWTs) offer tools to enhance care delivery, particularly in home and rural settings. Although there is a profound lack of evidence regarding the impact when used in PC, it is necessary to critically assess the current state of knowledge regarding impacts and consequences of technologies, ensuring that their integration considers broader implications for patients, caregivers, and health care systems in PC.
Objective: This review explores health and welfare technology used in PC, aiming to inform practice and improve care quality.
Methods: This state-of-the-art review included empirical studies describing the use of HWT in PC for adult patients. We used a thematic synthesis approach to compare studies and provide a synthesis of the key points.
Results: Based on the inclusion criteria, 94 studies were included. PC is both a clinical specialty and an overall approach to care that focuses on improving quality of life and relieving suffering for patients and families facing serious illness, based on needs and not prognosis. HWT shows potential to increase access and continuity of care, for symptom management to support patients to remain at home and prevent frequent emergency visits. It can have the potential to build and remain relationships between patients, their families, and the health care team, as well as for interprofessional collaboration and support. However, there are challenges to overcome that might affect the quality of care when using technology.
Conclusions: HWT shows potential as a complement to usual PC. Our findings point toward the importance of caution in choosing when to use HWT in PC, and for which patients.
{"title":"Use of Health and Welfare Technology in Palliative Care: State-of-the-Art Review.","authors":"Viktoria Zander, Maja Holm, Monir Mazaheri, Christine Gustafsson, Sara Landerdahl Stridsberg, Ragnhild Hedman","doi":"10.2196/79637","DOIUrl":"https://doi.org/10.2196/79637","url":null,"abstract":"<p><strong>Background: </strong>As more individuals live longer with complex conditions, the need for effective palliative care (PC) grows. It has been stated that access to PC should be integrated early and delivered in a timely manner to patients with life-threatening illnesses. Health and welfare technologies (HWTs) offer tools to enhance care delivery, particularly in home and rural settings. Although there is a profound lack of evidence regarding the impact when used in PC, it is necessary to critically assess the current state of knowledge regarding impacts and consequences of technologies, ensuring that their integration considers broader implications for patients, caregivers, and health care systems in PC.</p><p><strong>Objective: </strong>This review explores health and welfare technology used in PC, aiming to inform practice and improve care quality.</p><p><strong>Methods: </strong>This state-of-the-art review included empirical studies describing the use of HWT in PC for adult patients. We used a thematic synthesis approach to compare studies and provide a synthesis of the key points.</p><p><strong>Results: </strong>Based on the inclusion criteria, 94 studies were included. PC is both a clinical specialty and an overall approach to care that focuses on improving quality of life and relieving suffering for patients and families facing serious illness, based on needs and not prognosis. HWT shows potential to increase access and continuity of care, for symptom management to support patients to remain at home and prevent frequent emergency visits. It can have the potential to build and remain relationships between patients, their families, and the health care team, as well as for interprofessional collaboration and support. However, there are challenges to overcome that might affect the quality of care when using technology.</p><p><strong>Conclusions: </strong>HWT shows potential as a complement to usual PC. Our findings point toward the importance of caution in choosing when to use HWT in PC, and for which patients.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e79637"},"PeriodicalIF":6.0,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147443794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p><strong>Background: </strong>Short-video platforms, characterized by algorithmic curation and passive consumption, have emerged as dominant components of digital life. However, the associations between short-video platform use and health across different groups and usage behaviors remain understudied.</p><p><strong>Objective: </strong>This study investigates associations between short-video platform use and health, examining whether these relationships vary across health status, usage behaviors, and socioeconomic status.</p><p><strong>Methods: </strong>A cross-sectional study was conducted using multistage stratified sampling across eastern, central, and western China from July to September 2024. The inclusion criteria were age 18 years or older, ability to communicate effectively, and no cognitive disorders or mental disturbance. Of 7725 participants enrolled, 46.96% (n=3628) were male, and the average age was 65.49 (SD 8.39) years. The data were collected via face-to-face interviews using a structured questionnaire. Self-rated health and relative health deprivation (Kakwani index) were used to measure health. Quantile regression explored associations between whether using short-video platform and health varies across the health distribution, while linear regression examined associations of years, frequency, daily duration, and purpose diversity of short-video platform use with health. Moderating effect analysis explored the role of socioeconomic status in the relationship between the daily duration of use and health.</p><p><strong>Results: </strong>Coefficients were tested using 2-tailed t tests, and statistical significance was defined as a 2-sided P value less than .05. Quantile regression revealed heterogeneous associations. Compared to nonusers, short-video platform users had better self-rated health at the 70th to 90th quantiles and lower relative health deprivation at the 10th to 30th quantiles. However, the users at the 10th quantile of self-rated health had worse self-rated health (β=-2.224, 95% CI -3.835 to -0.613). Longer engagement (≥3 y) correlated with lower relative health deprivation (β=1.970, 95% CI 0.308-3.632), while daily use of 1-4 hours was associated with poorer self-rated health (β=-3.385, 95% CI -4.872 to -1.898; β=-3.038, 95% CI -5.054 to -1.022) and higher relative health deprivation (β=0.035, 95% CI 0.021-0.050; P<.001; β=0.034, 95% CI 0.014-0.054). Compared to no purposeful use, using with 2 purposes was associated with better self-rated health (β=6.082, 95% CI 0.250-11.914) and lower relative health deprivation (β=-0.063, 95% CI -0.120 to -0.005). The association was stronger for use with 3 or more purposes. Socioeconomic status moderated the relationship between daily duration of use and health.</p><p><strong>Conclusions: </strong>This study provides a more specific investigation of how these associations vary across health strata and usage patterns. The findings reveal patterns of benefit and risk across popula
背景：以算法管理和被动消费为特征的短视频平台已经成为数字生活的主导组成部分。然而，短视频平台的使用与不同群体和使用行为之间的关系仍未得到充分研究。目的：本研究探讨短视频平台使用与健康之间的关系，考察这些关系是否因健康状况、使用行为和社会经济状况而异。方法：采用多阶段分层抽样方法，于2024年7 - 9月在中国东部、中部和西部地区进行横断面研究。纳入标准为年龄≥18岁，有有效沟通能力，无认知障碍或精神障碍。在纳入的7725名参与者中，46.96% （n=3628）为男性，平均年龄为65.49岁（SD 8.39）。数据是通过面对面访谈收集的，使用结构化问卷。使用自评健康和相对健康剥夺（Kakwani指数）来衡量健康。分位数回归探讨了短视频平台使用与健康之间的关系，而线性回归研究了短视频平台使用的年限、频率、每日持续时间和目的多样性与健康之间的关系。调节效应分析探讨了社会经济地位在每日使用时间与健康之间的关系中的作用。结果：系数采用双尾t检验，以双侧P值小于0.05为统计学显著性。分位数回归显示异质性关联。与非用户相比，短视频平台用户在第70至90分位数的自评健康状况更好，在第10至30分位数的相对健康剥夺程度更低。然而，在自评健康的第10分位数的用户有较差的自评健康（β=-2.224, 95% CI -3.835至-0.613）。较长的工作时间（≥3小时）与较低的相对健康剥夺相关（β=1.970, 95% CI 0.308-3.632），而每天使用1-4小时与较差的自我评价健康相关（β=-3.385, 95% CI -4.872至-1.898;β=-3.038, 95% CI -5.054至-1.022）和较高的相对健康剥夺相关（β=0.035, 95% CI 0.021-0.050）。结论：本研究提供了更具体的调查这些关联如何在健康阶层和使用模式之间变化。研究结果揭示了不同人群的利益和风险模式，强调了个人如何以及为什么使用平台比仅仅访问或频率更重要。这些见解需要有针对性的数字福祉政策，以保护弱势群体，特别是健康状况不佳或社会经济地位较低的群体。此外，政策应积极鼓励有意的、基于功能的使用，以减少卫生不平等和促进公平的数字包容。
{"title":"Associations Between Short-Video Platform Use and Health Across Health Distribution and Usage Behaviors in China: Cross-Sectional Questionnaire Study.","authors":"Yangyang Pan, Kangkang Zhang, Yilin Wei, Yangzhen Huang, Chengxu Long, Chenxin Yang, Shangfeng Tang","doi":"10.2196/86526","DOIUrl":"10.2196/86526","url":null,"abstract":"<p><strong>Background: </strong>Short-video platforms, characterized by algorithmic curation and passive consumption, have emerged as dominant components of digital life. However, the associations between short-video platform use and health across different groups and usage behaviors remain understudied.</p><p><strong>Objective: </strong>This study investigates associations between short-video platform use and health, examining whether these relationships vary across health status, usage behaviors, and socioeconomic status.</p><p><strong>Methods: </strong>A cross-sectional study was conducted using multistage stratified sampling across eastern, central, and western China from July to September 2024. The inclusion criteria were age 18 years or older, ability to communicate effectively, and no cognitive disorders or mental disturbance. Of 7725 participants enrolled, 46.96% (n=3628) were male, and the average age was 65.49 (SD 8.39) years. The data were collected via face-to-face interviews using a structured questionnaire. Self-rated health and relative health deprivation (Kakwani index) were used to measure health. Quantile regression explored associations between whether using short-video platform and health varies across the health distribution, while linear regression examined associations of years, frequency, daily duration, and purpose diversity of short-video platform use with health. Moderating effect analysis explored the role of socioeconomic status in the relationship between the daily duration of use and health.</p><p><strong>Results: </strong>Coefficients were tested using 2-tailed t tests, and statistical significance was defined as a 2-sided P value less than .05. Quantile regression revealed heterogeneous associations. Compared to nonusers, short-video platform users had better self-rated health at the 70th to 90th quantiles and lower relative health deprivation at the 10th to 30th quantiles. However, the users at the 10th quantile of self-rated health had worse self-rated health (β=-2.224, 95% CI -3.835 to -0.613). Longer engagement (≥3 y) correlated with lower relative health deprivation (β=1.970, 95% CI 0.308-3.632), while daily use of 1-4 hours was associated with poorer self-rated health (β=-3.385, 95% CI -4.872 to -1.898; β=-3.038, 95% CI -5.054 to -1.022) and higher relative health deprivation (β=0.035, 95% CI 0.021-0.050; P<.001; β=0.034, 95% CI 0.014-0.054). Compared to no purposeful use, using with 2 purposes was associated with better self-rated health (β=6.082, 95% CI 0.250-11.914) and lower relative health deprivation (β=-0.063, 95% CI -0.120 to -0.005). The association was stronger for use with 3 or more purposes. Socioeconomic status moderated the relationship between daily duration of use and health.</p><p><strong>Conclusions: </strong>This study provides a more specific investigation of how these associations vary across health strata and usage patterns. The findings reveal patterns of benefit and risk across popula","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e86526"},"PeriodicalIF":6.0,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12982707/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147443756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hannah Grace Jones, Grace Lavelle, Elly Aylwin-Foster, Ciara Regan, Alan Simpson, Ewan Carr, Matthew Hotopf, Vanessa Lawrence
<p><strong>Background: </strong>Online peer support can help people living with long-term physical health conditions to manage their mental well-being. Although the potential negative events that can occur and risks associated with web-based peer communities are well recognized, our understanding of how best to moderate these spaces is relatively limited, particularly with regard to new communities. Previous work has focused on the experiences of either moderators or community members.</p><p><strong>Objective: </strong>This study aims to explore the perspectives of both members and moderators of a new online peer support community to evaluate the moderation procedures and inform recommendations for best practice.</p><p><strong>Methods: </strong>Community members (n=39) who participated in a research trial of a new online peer community, CommonGround, were interviewed. The moderation team (n=5) was invited to a focus group. Community member interviews explored their opinions of moderation policies and the behavior of the moderation team. The moderator focus group explored their experiences of moderating the community, including perceived benefits, common challenges, and areas for improvement. All interviews and the focus group were conducted online, audio-recorded, and transcribed verbatim. An inductive thematic analysis was conducted to sort the data into overarching themes through an iterative process.</p><p><strong>Results: </strong>Effective moderation was considered critical in creating a safe space that members wanted to engage with and for mitigating any risks, particularly around the spread of medical misinformation. Both moderators and community members felt that the moderation policies and practices were appropriate and applicable to the community. Moderators found navigating the moderation threshold, where they balanced safety against free speech, challenging when determining whether to intervene or not. Being part of a team with mixed clinical expertise helped moderators build confidence in navigating this threshold and also presented other benefits of easy access to support and improving the consistency of their moderation practices. It was suggested that in order for a community to flourish, community members would self-moderate. However, moderators and members felt that the strong community culture and high levels of member engagement that are needed to support self-moderation had not yet evolved. Proposed improvements to moderation included new features to support the efficiency of identifying new content for review and reviewing the rule of anonymity.</p><p><strong>Conclusions: </strong>Moderation is critical in making online peer communities feel safe and engaging. Moderation practices should be co-produced with the target audience to ensure that they are aligned with the community's unique moderation wants and needs, including clear escalation pathways, transparent communication patterns, and plans to review and update policies
{"title":"Insights and Recommendations From Moderators and Community Members for Keeping Online Peer Support Safe: Thematic Analysis.","authors":"Hannah Grace Jones, Grace Lavelle, Elly Aylwin-Foster, Ciara Regan, Alan Simpson, Ewan Carr, Matthew Hotopf, Vanessa Lawrence","doi":"10.2196/81943","DOIUrl":"https://doi.org/10.2196/81943","url":null,"abstract":"<p><strong>Background: </strong>Online peer support can help people living with long-term physical health conditions to manage their mental well-being. Although the potential negative events that can occur and risks associated with web-based peer communities are well recognized, our understanding of how best to moderate these spaces is relatively limited, particularly with regard to new communities. Previous work has focused on the experiences of either moderators or community members.</p><p><strong>Objective: </strong>This study aims to explore the perspectives of both members and moderators of a new online peer support community to evaluate the moderation procedures and inform recommendations for best practice.</p><p><strong>Methods: </strong>Community members (n=39) who participated in a research trial of a new online peer community, CommonGround, were interviewed. The moderation team (n=5) was invited to a focus group. Community member interviews explored their opinions of moderation policies and the behavior of the moderation team. The moderator focus group explored their experiences of moderating the community, including perceived benefits, common challenges, and areas for improvement. All interviews and the focus group were conducted online, audio-recorded, and transcribed verbatim. An inductive thematic analysis was conducted to sort the data into overarching themes through an iterative process.</p><p><strong>Results: </strong>Effective moderation was considered critical in creating a safe space that members wanted to engage with and for mitigating any risks, particularly around the spread of medical misinformation. Both moderators and community members felt that the moderation policies and practices were appropriate and applicable to the community. Moderators found navigating the moderation threshold, where they balanced safety against free speech, challenging when determining whether to intervene or not. Being part of a team with mixed clinical expertise helped moderators build confidence in navigating this threshold and also presented other benefits of easy access to support and improving the consistency of their moderation practices. It was suggested that in order for a community to flourish, community members would self-moderate. However, moderators and members felt that the strong community culture and high levels of member engagement that are needed to support self-moderation had not yet evolved. Proposed improvements to moderation included new features to support the efficiency of identifying new content for review and reviewing the rule of anonymity.</p><p><strong>Conclusions: </strong>Moderation is critical in making online peer communities feel safe and engaging. Moderation practices should be co-produced with the target audience to ensure that they are aligned with the community's unique moderation wants and needs, including clear escalation pathways, transparent communication patterns, and plans to review and update policies ","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e81943"},"PeriodicalIF":6.0,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147443782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p><strong>Background: </strong>Interorganizational knowledge sharing is vital for scaling digital transformation efforts that span multiple organizations and system-wide change. However, existing frameworks provide limited insights into the cross-level dynamics that shape how learning ecosystems emerge, evolve, and operate across multiple organizations. This gap leaves practitioners without clear guidance on how multilevel contextual conditions and mechanisms interact to influence the development and sustainability of formal and informal knowledge-sharing relationships.</p><p><strong>Objective: </strong>This study aimed to examine how knowledge is orchestrated across organizations in the digital transformation of health care, identifying key factors that foster an evolving interorganizational learning ecosystem. We developed an integrative model that explains how these influences give rise to diverse modes of collaboration and partnership.</p><p><strong>Methods: </strong>We adopted a qualitative approach using a multilevel perspective to examine visions and experiences across individual, organizational, interorganizational, and sectoral levels. Drawing on a formative evaluation (2018-2023) of England's Global Digital Exemplar (GDE) program, we used multiple case studies and conducted interviews with experts both within and beyond organizational settings for data collection and adopted a grounded theory approach to analyze the data.</p><p><strong>Results: </strong>The study identified a set of interconnected factors operating at the macroenvironmental, interorganizational, organizational, and individual levels that influence how interorganizational relationships and partnerships are initiated, structured, and sustained. Macro-level influences included policy developments, program mandates, technology supplier strategies, and intermediary actions. Interorganizational mechanisms involved relational recognition, collective identity, governance structures, proximity, and coordination practices. Organizational factors included external search strategies, absorptive capacity, past collaboration experience, and internal knowledge routines. Individual-level mechanisms encompassed intrinsic and extrinsic motivations as well as personal inhibitors. Synthesizing these findings, we have proposed an integrative model that positions relationship type along a 2D spectrum (formal-informal, internal-external origins) and illustrates how different factors trigger, mandate, control, and enable the evolution of an interorganizational learning ecosystem.</p><p><strong>Conclusions: </strong>This study advances the theoretical understanding of learning ecosystems by explaining how multilevel contextual conditions activate mechanisms that give rise to diverse and evolving forms of interorganizational collaboration. Practically, we offer diagnostic and reflective tools that support policymakers and practitioners in assessing contextual conditions, selecting appropriate k
{"title":"Analysis of Multilevel Factors Mobilizing the Spectrum of Interorganizational Knowledge Sharing for Facilitating Digital Transformation at Scale: Qualitative Study.","authors":"Hajar Mozaffar, Robin Williams, Kathrin Cresswell","doi":"10.2196/83345","DOIUrl":"10.2196/83345","url":null,"abstract":"<p><strong>Background: </strong>Interorganizational knowledge sharing is vital for scaling digital transformation efforts that span multiple organizations and system-wide change. However, existing frameworks provide limited insights into the cross-level dynamics that shape how learning ecosystems emerge, evolve, and operate across multiple organizations. This gap leaves practitioners without clear guidance on how multilevel contextual conditions and mechanisms interact to influence the development and sustainability of formal and informal knowledge-sharing relationships.</p><p><strong>Objective: </strong>This study aimed to examine how knowledge is orchestrated across organizations in the digital transformation of health care, identifying key factors that foster an evolving interorganizational learning ecosystem. We developed an integrative model that explains how these influences give rise to diverse modes of collaboration and partnership.</p><p><strong>Methods: </strong>We adopted a qualitative approach using a multilevel perspective to examine visions and experiences across individual, organizational, interorganizational, and sectoral levels. Drawing on a formative evaluation (2018-2023) of England's Global Digital Exemplar (GDE) program, we used multiple case studies and conducted interviews with experts both within and beyond organizational settings for data collection and adopted a grounded theory approach to analyze the data.</p><p><strong>Results: </strong>The study identified a set of interconnected factors operating at the macroenvironmental, interorganizational, organizational, and individual levels that influence how interorganizational relationships and partnerships are initiated, structured, and sustained. Macro-level influences included policy developments, program mandates, technology supplier strategies, and intermediary actions. Interorganizational mechanisms involved relational recognition, collective identity, governance structures, proximity, and coordination practices. Organizational factors included external search strategies, absorptive capacity, past collaboration experience, and internal knowledge routines. Individual-level mechanisms encompassed intrinsic and extrinsic motivations as well as personal inhibitors. Synthesizing these findings, we have proposed an integrative model that positions relationship type along a 2D spectrum (formal-informal, internal-external origins) and illustrates how different factors trigger, mandate, control, and enable the evolution of an interorganizational learning ecosystem.</p><p><strong>Conclusions: </strong>This study advances the theoretical understanding of learning ecosystems by explaining how multilevel contextual conditions activate mechanisms that give rise to diverse and evolving forms of interorganizational collaboration. Practically, we offer diagnostic and reflective tools that support policymakers and practitioners in assessing contextual conditions, selecting appropriate k","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e83345"},"PeriodicalIF":6.0,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12981373/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147443768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p><strong>Background: </strong>The global nursing shortage, exacerbated by heavy workloads and high turnover rates associated with the COVID-19 pandemic, continues to undermine care quality and nurse well-being. Although digital health technologies have enhanced coordination, improved communication, and reduced clinical errors in nursing practice, they have also increased nurses' documentation burden. Advances in large language models (LLMs) and other generative artificial intelligence (GenAI) tools facilitate the generation of accurate reports from electronic medical records (EMRs), thereby streamlining documentation workflows, saving time, and reducing nurses' workloads. Accordingly, integrating LLMs into electronic nursing documentation systems warrants further exploration.</p><p><strong>Objective: </strong>This study examines the integration of an LLM into an in-house nursing information system (NIS) implemented across 3 hospitals in Taiwan to reduce the time and effort required for nursing handover documentation and to preliminarily assess the operational and economic implications of GenAI-assisted workflows.</p><p><strong>Methods: </strong>A multidisciplinary team of nursing specialists and information technology experts at Taipei Medical University (TMU) restructured the organization's existing nursing handover documentation process to facilitate interaction with the LLM. The team also developed prompt-based interfaces to automatically generate section-specific content for the nursing handover document. The LLM-integrated NIS was subsequently deployed across 3 hospitals in Taiwan: Taipei Medical University Hospital (TMUH), Wan Fang Hospital (WFH), and Shuang Ho Hospital (SHH). We then extracted and analyzed NIS log data to compare documentation times before and after LLM implementation, thereby quantifying time savings.</p><p><strong>Results: </strong>Integration of the LLM into nursing handover documentation was associated with shorter per-patient documentation time in routine clinical use across TMUH, WFH, and SHH. Based on preintegration NIS logs (September 2024), the average handover document completion time per patient ranged from 3.45 (SD 3.82) to 4.32 (SD 4.48) minutes across hospitals and shifts, providing a preliminary baseline for subsequent comparisons. In postintegration NIS logs (October-December 2024), the overall handover document completion time per patient (mean) was substantially lower, ranging from 1.17 (SD 1.86) to 2.54 (SD 2.82) minutes across hospitals and shifts. Using monthly patient volume to estimate time savings, 113-273, 160-314, and 198-391 hours were saved per month at TMUH, WFH, and SHH, respectively, corresponding to aggregate savings of 474-981 hours per month across hospitals during the study period.</p><p><strong>Conclusions: </strong>We integrated an LLM into an NIS to generate nursing handover documents without altering existing workflows. Across 3 hospitals within TMU's health system, GenAI assistance
{"title":"Integrating a Large Language Model to Streamline Nursing Handover Documentation Across Multiple Hospitals in Taiwan: Development and Implementation Study.","authors":"Ray-Jade Chen, Mai-Szu Wu, Lung-Wen Tsai, Shy-Shin Chang, Shu-Tai Shen Hsiao, Yu-Sheng Lo","doi":"10.2196/81604","DOIUrl":"https://doi.org/10.2196/81604","url":null,"abstract":"<p><strong>Background: </strong>The global nursing shortage, exacerbated by heavy workloads and high turnover rates associated with the COVID-19 pandemic, continues to undermine care quality and nurse well-being. Although digital health technologies have enhanced coordination, improved communication, and reduced clinical errors in nursing practice, they have also increased nurses' documentation burden. Advances in large language models (LLMs) and other generative artificial intelligence (GenAI) tools facilitate the generation of accurate reports from electronic medical records (EMRs), thereby streamlining documentation workflows, saving time, and reducing nurses' workloads. Accordingly, integrating LLMs into electronic nursing documentation systems warrants further exploration.</p><p><strong>Objective: </strong>This study examines the integration of an LLM into an in-house nursing information system (NIS) implemented across 3 hospitals in Taiwan to reduce the time and effort required for nursing handover documentation and to preliminarily assess the operational and economic implications of GenAI-assisted workflows.</p><p><strong>Methods: </strong>A multidisciplinary team of nursing specialists and information technology experts at Taipei Medical University (TMU) restructured the organization's existing nursing handover documentation process to facilitate interaction with the LLM. The team also developed prompt-based interfaces to automatically generate section-specific content for the nursing handover document. The LLM-integrated NIS was subsequently deployed across 3 hospitals in Taiwan: Taipei Medical University Hospital (TMUH), Wan Fang Hospital (WFH), and Shuang Ho Hospital (SHH). We then extracted and analyzed NIS log data to compare documentation times before and after LLM implementation, thereby quantifying time savings.</p><p><strong>Results: </strong>Integration of the LLM into nursing handover documentation was associated with shorter per-patient documentation time in routine clinical use across TMUH, WFH, and SHH. Based on preintegration NIS logs (September 2024), the average handover document completion time per patient ranged from 3.45 (SD 3.82) to 4.32 (SD 4.48) minutes across hospitals and shifts, providing a preliminary baseline for subsequent comparisons. In postintegration NIS logs (October-December 2024), the overall handover document completion time per patient (mean) was substantially lower, ranging from 1.17 (SD 1.86) to 2.54 (SD 2.82) minutes across hospitals and shifts. Using monthly patient volume to estimate time savings, 113-273, 160-314, and 198-391 hours were saved per month at TMUH, WFH, and SHH, respectively, corresponding to aggregate savings of 474-981 hours per month across hospitals during the study period.</p><p><strong>Conclusions: </strong>We integrated an LLM into an NIS to generate nursing handover documents without altering existing workflows. Across 3 hospitals within TMU's health system, GenAI assistance","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e81604"},"PeriodicalIF":6.0,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147443808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p><strong>Background: </strong>Hip fractures in older adults increasingly challenge public health, making traditional rehabilitation very challenging. Digital health interventions (DHIs) have emerged as a promising solution for postoperative rehabilitation. However, evidence on DHIs' effects on functional and psychological outcomes remains insufficient.</p><p><strong>Objective: </strong>This systematic review aimed to comprehensively examine the effects of DHIs on functional and psychological outcomes in older adults with hip fractures.</p><p><strong>Methods: </strong>Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we searched 9 databases (PubMed, Embase, CENTRAL, APA PsycINFO, Web of Science, PEDro, CNKI, WANFANG, and SinoMed) from inception to November 13, 2025. Included studies enrolled adults aged 60 years and older with hip fractures, delivered DHIs, assessed functional and psychological outcomes, set usual care or no intervention as the control, and had a randomized controlled trial design. Studies were excluded if they enrolled nonhospitalized patients in the emergency department, patients discharged to nonhome settings, or had inaccessible full text or insufficient data. Study quality was evaluated using the Cochrane Risk of Bias tool 2.0 (Cochrane Collaboration), and evidence certainty was assessed using GRADE (Grading of Recommendations, Assessment, Development and Evaluation). The literature screening, data extraction, and quality assessment were independently conducted by 2 researchers, and any disputes were resolved by the third researcher. We performed analysis using R version 4.0.3 (R Foundation for Statistical Computing) with a random-effects model.</p><p><strong>Results: </strong>Of 17,723 studies screened, 13 met the inclusion criteria. DHIs, compared to the control, significantly improved hip function (standardized mean difference [SMD] 0.80, 95% CI 0.33-1.26; 95% prediction interval [PI] -0.24 to 1.83; P=.007) and functional independence (SMD 1.23, 95% CI 0.34-2.11; 95% PI -0.98 to 3.34; P=.02). Despite favorable pooled effects, a wide 95% PI spanning positive or negative values signals substantial heterogeneity. No significant difference was observed in balance function, risk of falling, and quality of life. Only a single available study reported a 70% adherence rate in the DHIs group. Subgroup analyses stratified by intervention duration revealed no significant intersubgroup differences for hip function (χ<sub>1</sub><sup>2</sup>=0.1; P=.75) or functional independence (χ<sub>1</sub><sup>2</sup>=2.93; P=.09). For hip function, the point estimate favored the 3 months subgroup (SMD 0.89, 95% CI 0.36-1.41; I<sup>2</sup>=7%; P=.41) over the <3 months subgroup. Conversely, for functional independence, the point estimate favored shorter intervention duration (SMD 0.67, 95% CI 0.12-1.23; I²=0%; P=.72).</p><p><strong>Conclusions: </strong>This review incorporates the latest randomize
背景：老年人髋部骨折日益挑战公共卫生，使传统的康复非常具有挑战性。数字健康干预（DHIs）已成为一种有希望的术后康复解决方案。然而，关于DHIs对功能和心理结果的影响的证据仍然不足。目的：本系统综述旨在全面研究DHIs对老年髋部骨折患者功能和心理结局的影响。方法：根据PRISMA （Preferred Reporting Items for Systematic Reviews and meta - analysis）指南，我们检索了9个数据库（PubMed、Embase、CENTRAL、APA PsycINFO、Web of Science、PEDro、CNKI、万方、中国医学信息网），检索时间自成立至2025年11月13日。纳入的研究纳入了60岁及以上髋部骨折的成年人，进行DHIs，评估功能和心理结果，将常规护理或不干预作为对照，并采用随机对照试验设计。如果研究纳入了急诊科的非住院患者，出院到非家庭环境的患者，或无法获得全文或数据不足，则排除研究。使用Cochrane风险偏倚工具2.0 （Cochrane Collaboration）评估研究质量，使用GRADE（推荐、评估、发展和评价分级）评估证据确定性。文献筛选、数据提取、质量评价均由2名研究者独立完成，争议由第三名研究者解决。我们使用R 4.0.3版本（R Foundation for Statistical Computing），采用随机效应模型进行分析。结果：在筛选的17,723项研究中，有13项符合纳入标准。与对照组相比，DHIs显著改善了髋关节功能（标准化平均差[SMD] 0.80, 95% CI 0.33-1.26； 95%预测区间[PI] -0.24 - 1.83； P=.007）和功能独立性（SMD 1.23, 95% CI 0.34-2.11; 95% PI -0.98 - 3.34； P=.02）。尽管有良好的综合效应，但95%的PI跨越正值或负值表明存在实质性的异质性。在平衡功能、跌倒风险和生活质量方面没有观察到显著差异。只有一项可用的研究报告了DHIs组70%的依从率。按干预时间分层的亚组分析显示，髋关节功能（χ12=0.1； P= 0.75）或功能独立性（χ12=2.93； P= 0.09）在亚组间无显著差异。对于髋关节功能，点估计更倾向于3个月亚组（SMD 0.89, 95% CI 0.36-1.41; I2=7%； P= 0.41），而不是结论：本综述纳入了最新的随机对照试验，全面评估了老年髋部骨折患者DHIs的功能和心理结果，不同于以往的研究只关注功能结果。虽然95% CI支持DHIs改善髋关节功能和功能独立性的潜力，但95% PI宽表明现实世界的反应变化很大，这需要谨慎解释，为针对性的DHIs康复方案的设计提供了信息，需要进一步研究临床实践中的最佳技术和剂量。试验注册：PROSPERO CRD42024626186；https://www.crd.york.ac.uk/PROSPERO/view/CRD42024626186。
{"title":"Effects of Digital Health Interventions on Functional and Psychological Outcomes in Older Patients With Hip Fractures: Systematic Review and Meta-Analysis of Randomized Controlled Trials.","authors":"Wei Fan, Qi Zhang, Qunfeng Lu","doi":"10.2196/79563","DOIUrl":"https://doi.org/10.2196/79563","url":null,"abstract":"<p><strong>Background: </strong>Hip fractures in older adults increasingly challenge public health, making traditional rehabilitation very challenging. Digital health interventions (DHIs) have emerged as a promising solution for postoperative rehabilitation. However, evidence on DHIs' effects on functional and psychological outcomes remains insufficient.</p><p><strong>Objective: </strong>This systematic review aimed to comprehensively examine the effects of DHIs on functional and psychological outcomes in older adults with hip fractures.</p><p><strong>Methods: </strong>Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we searched 9 databases (PubMed, Embase, CENTRAL, APA PsycINFO, Web of Science, PEDro, CNKI, WANFANG, and SinoMed) from inception to November 13, 2025. Included studies enrolled adults aged 60 years and older with hip fractures, delivered DHIs, assessed functional and psychological outcomes, set usual care or no intervention as the control, and had a randomized controlled trial design. Studies were excluded if they enrolled nonhospitalized patients in the emergency department, patients discharged to nonhome settings, or had inaccessible full text or insufficient data. Study quality was evaluated using the Cochrane Risk of Bias tool 2.0 (Cochrane Collaboration), and evidence certainty was assessed using GRADE (Grading of Recommendations, Assessment, Development and Evaluation). The literature screening, data extraction, and quality assessment were independently conducted by 2 researchers, and any disputes were resolved by the third researcher. We performed analysis using R version 4.0.3 (R Foundation for Statistical Computing) with a random-effects model.</p><p><strong>Results: </strong>Of 17,723 studies screened, 13 met the inclusion criteria. DHIs, compared to the control, significantly improved hip function (standardized mean difference [SMD] 0.80, 95% CI 0.33-1.26; 95% prediction interval [PI] -0.24 to 1.83; P=.007) and functional independence (SMD 1.23, 95% CI 0.34-2.11; 95% PI -0.98 to 3.34; P=.02). Despite favorable pooled effects, a wide 95% PI spanning positive or negative values signals substantial heterogeneity. No significant difference was observed in balance function, risk of falling, and quality of life. Only a single available study reported a 70% adherence rate in the DHIs group. Subgroup analyses stratified by intervention duration revealed no significant intersubgroup differences for hip function (χ<sub>1</sub><sup>2</sup>=0.1; P=.75) or functional independence (χ<sub>1</sub><sup>2</sup>=2.93; P=.09). For hip function, the point estimate favored the 3 months subgroup (SMD 0.89, 95% CI 0.36-1.41; I<sup>2</sup>=7%; P=.41) over the <3 months subgroup. Conversely, for functional independence, the point estimate favored shorter intervention duration (SMD 0.67, 95% CI 0.12-1.23; I²=0%; P=.72).</p><p><strong>Conclusions: </strong>This review incorporates the latest randomize","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e79563"},"PeriodicalIF":6.0,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147443832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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