Objective To propose and test an innovative model by integrating the Unified Theory of Acceptance and Use of Technology and Knowledge-Attitude-Practice model to explain the mechanisms influencing the adoption of digital health technologies by elderly patients with chronic diseases from the perspective of both internal and external factors, promoting the acceptance and utilisation of digital health technologies among elderly chronically ill patients.Study design A face-to-face questionnaire survey was conducted from July to September 2023.Study setting The study was conducted in 12 medical institutions in Shanghai, including 6 tertiary hospitals, 3 secondary hospitals and 3 community hospitals.Participants 1222 participants aged 60 years or more, diagnosed with one or more of the following chronic diseases: essential hypertension, type 2 diabetes, coronary atherosclerotic heart disease, stroke and chronic obstructive pulmonary disease, were involved in the study using convenience sampling. Critically ill emergency patients and those who were involved in medical disputes were excluded.Outcome measure The behavioural intention and usage behaviour of older patients with chronic diseases to use digital health technologies.Results The explanatory power of the proposed model for behavioural intention was 72.9%. There is a significant negative association between technology anxiety and the intention to use digital health technologies among older patients with chronic diseases (β=−0.224, p<0.001); effort expectancy (β=0.530, p<0.001) and performance expectancy (β=0.193, p<0.001) were also significantly associated with intention to use digital health technologies. Men (β=−0.104, p=0.016), relatively younger (β=−0.061, p=0.005), with experience in using digital health technologies (β=−0.452, p<0.001) were more likely to translate behavioural intention into use behaviour.Conclusions Acceptance of digital health technologies among older patients with chronic diseases was associated with a combination of internal and external factors, with the former playing a dominant role. These valuable findings provided insights and inspiration for improving digital health technologies acceptance and utilisation among older patients with chronic diseases.

This study aimed to develop an automated transthoracic echocardiography (TTE)-based system for detecting ventricular septal defects (VSD) by jointly analyzing systolic-phase temporal shunt dynamics and precisely localizing defect regions. This multicenter retrospective study analyzed transthoracic echocardiography videos from 324 pediatric patients (150 VSD-positive and 174 VSD-negative), supplemented by an external validation cohort of 60 cases. To address challenges inherent to dynamic cardiac hemodynamics and view-dependent variability, we developed a Temporal-Spatial Decoupled Network (TSDNet). The methodological contributions of TSDNet are fourfold: (1) a temporal-spatial decoupling strategy that separates transient systolic shunt dynamics from static anatomical localization, overcoming the limitations of conventional spatiotemporal coupling; (2) a dual-branch architecture that integrates a UniFormer-based temporal classifier with a YOLOv5-based spatial detector to jointly perform video-level diagnosis and frame-level shunt localization; (3) multi-view fusion across five standard echocardiographic views to capture complementary anatomical structures and hemodynamic signatures; and (4) a comprehensive evaluation framework encompassing accuracy, recall, specificity, F1-score, PPV, and NPV, supplemented by experiments across multiple temporal window lengths and comparative analyses against state-of-the-art object detection models. TSDNet demonstrated high performance in both video-level classification and frame-level VSD detection. Video-level accuracy on the internal test set reached 96.19%, with external performance remaining consistently strong across varying temporal windows (87.37%-90.37% accuracy). For frame-level detection, TSDNet achieved 86.86% accuracy on the internal test set and 73.75% on the external cohort, with longer temporal windows yielding progressively improved results. Across all evaluated metrics, TSDNet outperformed leading detection frameworks-including Sparse R-CNN, DINO, FCOS, RetinaNet, Faster R-CNN, and DCNv2. Multi-view analysis further confirmed stable detection performance, particularly in the A4C, PSAX, and subRVOT views, underscoring the effectiveness of combining diverse echocardiographic perspectives with temporal-spatial decoupling for robust VSD identification. TSDNet provides accurate, robust, and interpretable detection of VSD from pediatric echocardiography videos. Its strong generalizability and seamless alignment with clinical workflows underscore its potential to enhance diagnostic capability in primary healthcare settings and to support early identification of congenital heart disease.

Patent ductus arteriosus (PDA) is a common congenital heart defect that requires timely and accurate detection to guide clinical management. Although deep learning has shown considerable promise in medical imaging, its application to echocardiographic video analysis remains challenging due to complex temporal dynamics and heterogeneous imaging conditions. TimeSformer, a Transformer-based architecture for temporal video modeling, is well suited for capturing long-range dependencies in echocardiographic sequences. In this study, we propose a novel two-stage artificial intelligence framework for automated PDA detection using Doppler echocardiography videos. In the first stage, parasternal short-axis (PSA) views are automatically identified and extracted from raw ultrasound videos. In the second stage, temporal features are analyzed to perform video-level diagnostic classification. To ensure robustness and generalizability, the proposed framework was developed and validated using a diverse multi-center dataset comprising examinations from four medical centers and four different ultrasound devices. The proposed method achieves high accuracy in view classification and effectively discriminates between PDA-positive and PDA-negative cases, yielding an area under the receiver operating characteristic curve (AUC) of 0.95. These results demonstrate the effectiveness of TimeSformer for echocardiographic sequence interpretation. Furthermore, the multi-center and multi-device validation highlights the adaptability of the framework, supporting its potential role as an AI-assisted diagnostic tool to enhance clinical workflows and patient outcomes in congenital heart disease.

Knowledge graph construction (KGC) aims to extract useful information from text and organize it into structured knowledge graphs (KGs). Some recent methods have utilized the powerful generative abilities of large language models (LLMs) to overcome the limitations of generalization and labor costs in traditional methods. As a result, they have achieved success on small, domain-specific datasets, but still struggle with the challenges posed by the chaotic terms and false facts of open-domain text. The main reason is their lack of ability to refine further knowledge, leading to the accumulation of knowledge biases. To address this issue, we propose an adaptive correction method for open knowledge graph construction, namely KG-OBS, which aims to mitigate knowledge bias by both standardizing knowledge schemas and correcting factual errors. Specifically, we first design a knowledge perceiver to quickly extract knowledge from the text. Next, a knowledge canonicalizer standardizes the schemas of extracted knowledge. During this process, adaptive schema alignment and expansion are achieved using a cluster centroid approximation strategy. Finally, we explore a counterfactual-based knowledge corrector, enabling the model to purify knowledge and reduce factual errors. Additionally, the purified knowledge is encoded and stored in a knowledge retainer for unified management. Experimental results show that KG-OBS can extract high-quality knowledge without training across three KGC benchmarks. Compared with previous works, it not only adaptively expands schema information but also automatically corrects errors to improve KG quality.

Background:Large language models (LLMs) are increasingly used to generate health education materials, yet questions remain about whether LLM-generated content can balance professional accuracy with public accessibility and what ethical challenges may arise during deployment. Objective:This study aimed to evaluate Chinese pediatric asthma educational materials generated using LLMs, comparing AI-generated responses (AI-GRs) against published expert-authored responses (PEARs) on total scores, adoption intent among health care professionals, perceived usefulness among family members, and the effect of AI-simplified responses (AI-SRs), as well as to assess participants' ability to correctly identify the source of materials. Methods:In this randomized, double-blind evaluation study, participants (medical professionals and patient family members) were randomly assigned (1:1:1) to evaluate PEARs, AI-GRs, or AI-SRs. Each participant assessed 5 randomly selected items from their assigned set using a 19-item, 5-point Likert scale based on the information adoption model. Secondary outcomes included dimension-specific scores, source identification accuracy, and readability indices. Results:AI-GRs had numerically higher mean total scores on the 19-item questionnaire than PEARs among both medical professionals (76.17, SD 12.59 vs 72.97, SD 14.88; Holm-adjusted P=.53) and pediatric patient families (68.77, SD 16.47 vs 64.18, SD 15.76; Holm-adjusted P=.15). The corresponding mean scores for AI-simplified responses were 72.98 (SD 12.64) and 65.47 (SD 16.52), respectively, with no statistically significant differences from PEARs after Holm adjustment (both adjusted P>.99). No statistically significant differences were detected between either LLM group and PEARs in any of the 4 questionnaire dimensions after Holm adjustment. The material group-population interaction was also not statistically significant (F2,513=0.116; P=.89; partial η2<0.001). Regarding source identification, 80.6% (141/175) of participants assigned to PEARs identified the material as human-authored, whereas only 7.3% (25/344) of those assigned to the LLM groups identified the material as LLM-generated. Conclusions:This randomized, double-blind evaluation study provides preliminary participant-level evidence regarding the perceived quality and acceptance of LLM-assisted pediatric asthma education among medical professionals and pediatric patient families under controlled conditions. Additional language simplification did not improve acceptance in this predominantly highly educated sample, and the strong tendency to attribute materials to human authors highlights the importance of source transparency. Professional review and dedicated assessments of factual accuracy, clinical safety, comprehension, and behavioral outcomes remain necessary before practical implementation.

Background Previous studies have shown that electrocardiographic (ECG) alarms have high sensitivity and low specificity, have underreported adverse events, and may cause neonatal intensive care unit (NICU) staff fatigue or alarm ignoring. Moreover, prolonged noise stimuli in hospitalized neonates can disrupt neonatal development. Objective The aim of the study is to conduct a nationwide, multicenter, large-sample cross-sectional survey to identify current practices and investigate the decision-making requirements of health care providers regarding ECG alarms. Methods We conducted a nationwide, cross-sectional survey of NICU staff working in grade III level A hospitals in 27 Chinese provinces to investigate current clinical practices, perceptions, decision-making processes, and decision-support requirements for clinical ECG alarms. A comparative analysis was conducted on the results using the chi-square, Kruskal-Wallis, or Mann-Whitney U tests. Results In total, 1019 respondents participated in this study. NICU staff reported experiencing a significant number of nuisance alarms and negative perceptions as well as practices regarding ECG alarms. Compared to nurses, physicians had more negative perceptions. Individuals with higher education levels and job titles had more negative perceptions of alarm systems than those with lower education levels and job titles. The mean difficulty score for decision-making about ECG alarms was 2.96 (SD 0.27) of 5. A total of 62.32% (n=635) respondents reported difficulty in resetting or modifying alarm parameters. Intelligent module–assisted decision support systems were perceived as the most popular form of decision support. Conclusions This study highlights the negative perceptions and strong decision-making requirements of NICU staff related to ECG alarm handling. Health care policy makers must draw attention to the decision-making requirements and provide adequate decision support in different forms.

Background:Childhood asthma represents a significant challenge globally, especially in underdeveloped regions. Recent advancements in Large Language Models (LLMs), such as ChatGPT, offer promising improvements in medical service quality. Methods:This randomized controlled trial assessed the effectiveness of ChatGPT in enhancing physicians' childhood asthma management skills. A total of 192 doctors from varied healthcare environments in China were divided into a control group, receiving traditional medical literature training, and an intervention group, trained in utilizing ChatGPT. Assessments conducted before and after training, and a 2-week follow-up, measured the training's impact. Results:The intervention group showed significant improvement, with scores of test questions increasing by approximately 20 out of 100 (improving to 72 ± 8 from a baseline, vs. the control group's increase to 50 ± 9). Post-training, ChatGPT's regular usage among the intervention group jumped from 6.3% to 62%, markedly above the control group's 4.3%. Moreover, physicians in the intervention group reported higher levels of familiarity, effectiveness, satisfaction, and intention for future use of ChatGPT. Conclusion:ChatGPT training significantly improves childhood asthma management among physicians in underdeveloped regions. This underscores the utility of LLMs like ChatGPT as effective educational tools in medical training, highlighting the need for further research into their integration and patient outcome impacts.

Early detection and treatment of cardiovascular diseases (CVDs) can be significantly enhanced through the use of flexible wearable electrocardiogram (ECG) sensors, potentially reducing CVD-related mortality. This paper introduces an ECG-on-Chip (EoC) solution tailored for flexible ECG sensors, incorporating novel features to address common challenges in wearable ECG technology. The EoC integrates a chopper-stabilized capacitively-coupled instrumentation amplifier (CS-CCIA), ensuring a high common-mode rejection ratio (CMRR) and low noise performance. Performance is further boosted via a positive feedback loop (PFL) and a programmable gain amplifier (PGA) with shared on-chip calibration logic, which enhances input impedance and minimizes gain variability to ensure consistent algorithm performance. Additionally, a secondary chopping technique is employed to further reduce noise, achieving an input-referred noise level of 454 nVrms. The embedded algorithm within the EoC is designed to extract clinically meaningful features, facilitating robust real-time arrhythmia analysis. Fabricated using a 0.18 µm CMOS process, the EoC consumes 14.9 µW with a supply voltage of 1.2 V. The algorithm’s efficacy has been validated over 0.4 million heartbeats, demonstrating a sensitivity of over 99.7

BackgroundPediatric fever is a prevalent concern, often causing parental anxiety and frequent medical consultations. While large language models (LLMs) such as ChatGPT, Perplexity, and YouChat show promise in enhancing medical communication and education, their efficacy in addressing complex pediatric fever-related questions remains underexplored, particularly from the perspectives of medical professionals and patients’ relatives.ObjectiveThis study aimed to explore the differences and similarities among four common large language models (ChatGPT3.5, ChatGPT4.0, YouChat, and Perplexity) in answering thirty pediatric fever-related questions and to examine how doctors and pediatric patients’ relatives evaluate the LLM-generated answers based on predefined criteria.MethodsThe study selected thirty fever-related pediatric questions answered by the four models. Twenty doctors rated these responses across four dimensions. To conduct the survey among pediatric patients’ relatives, we eliminated certain responses that we deemed to pose safety risks or be misleading. Based on the doctors’ questionnaire, the thirty questions were divided into six groups, each evaluated by twenty pediatric relatives. The Tukey post-hoc test was used to check for significant differences. Some of pediatric relatives was revisited for deeper insights into the results.ResultsIn the doctors’ questionnaire, ChatGPT3.5 and ChatGPT4.0 outperformed YouChat and Perplexity in all dimensions, with no significant difference between ChatGPT3.5 and ChatGPT4.0 or between YouChat and Perplexity. All models scored significantly better in accuracy than other dimensions. In the pediatric relatives’ questionnaire, no significant differences were found among the models, with revisits revealing some reasons for these results.ConclusionsInternet searches (YouChat and Perplexity) did not improve the ability of large language models to answer medical questions as expected. Patients lacked the ability to understand and analyze model responses due to a lack of professional knowledge and a lack of central points in model answers. When developing large language models for patient use, it's important to highlight the central points of the answers and ensure they are easily understandable.

Delirium is an acute, fluctuating state of consciousness disturbance characterized by cognitive alterations and perceptual disturbances. Pediatric delirium has a notably higher incidence rate than adult delirium, and it is time-consuming and labor-intensive for clinicians to analyze, requiring effective recognition approaches. Deep learning has shown potential for the extraction of robust representations and improvement of patient outcomes. In this study, 129 video samples labeled by professional clinicians were collected from multiple hospitals, including 74 non-delirium and 55 delirium labeled samples. An 18-layer deep spatiotemporal convolutional neural network is employed, in which two-dimensional and one-dimensional convolutional filters are applied to individual video frames to extract frame-level and inter-frame-level features, respectively. The entire architecture is pretrained on a large-scale video analysis dataset, and a three-layer fully connected classification head is integrated for the delirium recognition task. The proposed model was fine-tuned with a training dataset and evaluated on a testing dataset, exploring various models and strategies. The proposed algorithm demonstrated robust classification performance, achieving an accuracy of 0.8718, precision of 0.8711, recall of 0.8730, and F1-score of 0.8715, with approximately 31.54 million model parameters. These metric results validate the clinical applicability and technical reliability of the model under various training and testing strategies. In addition, the developed delirium classification model is deployed a hospital system to enable intelligent video diagnosis. The independent test accuracy for 100 newly collected samples is 0.8800. Therefore, the proposed algorithm enables new methods for pediatric delirium recognition and cures.

Background:Digital health technologies (DHTs) promise enhanced health for older people, yet the digital divide hinders adoption and utilization. This study aimed to identify DHTs that can help older people in chronic disease management, specifically the facilitators, barriers, needs, and scenarios. Methods:We searched PubMed, Embase, Cochrane Library, Web of Science, Scopus, and IEEE for studies published in English between 2000 and 2024. Analysis of included articles included descriptive synthesis and thematic analysis. Results:Forty-seven studies were included, yielding 148 DHTs classified as Medical services and support (n = 94) or Self-management (n = 54). Experience of Use and Interpersonal Support were the main facilitators of the use of DHTs. Awareness of Competence, Technological Factors, Sense of Security, and Individual Factors are barriers. Needs can be categorized into Service Functions and Subjective Needs, and the main scenarios include Home, Community, and Hospital. Conclusions:Digital health technologies are widely used, especially in developed countries, but challenges remain in developing countries and among specific patient. Future studies should focus on addressing concerns about DHT availability, security, and reliability in older people, actively incorporating feedback, providing personalized service, and fully mobilizing positive social factors to promote DHTs.

The integration of circuits and systems within healthcare, supported by the IEEE Circuits and Systems Society (CASS), its Biomedical and Life Science Circuits and Systems (BioCAS) technical committee, and the Flexible and Wearable Circuits and Systems Standards Committee (FWSC), has contributed to improvements in smart healthcare technologies. The IEEE Standards Workshop on AI for Healthcare highlighted collaborative efforts across disciplines, shedding light on the practical applications of AI in healthcare, the challenges of smart hospitals, and the future of personalized medicine. This paper highlights the role of IEEE CASS and its initiatives in fostering the development of technologies that blend technology and biology in healthcare.

Left ventricular hypertrophy (LVH) is a common clinical manifestation associated with cardiovascular adverse events. Relying solely on the subjective judgment, it is challenging to promptly and accurately diagnose mild LVH. Quantitatively measuring the interventricular septum and left ventricular posterior wall to diagnose mild LVH is a time-consuming and labor-intensive process that is prone to errors. To propose a novel method for rapid and automatic end-to-end diagnosis of mild LVH. We propose a novel end-to-end automated method for detecting mild LVH. This method achieves rapid end-to-end detection of mild LVH in echocardiographic videos without the need for quantitative measurements. Initially, representative frames are extracted from echocardiographic videos, and these frames are then automatically diagnosed by the proposed network to detect LVH. The network architecture primarily consists of three key components: a feature extractor, bidirectional LSTM, and attention module. The Vit-b model achieved 88% video classification accuracy in the experiment where 32 frames are extracted, and the ViT-l model achieves 92% video classification accuracy in the experiment where 16 frames are extracted. It is shown experimentally that extracting fewer video frames can diagnose LVH more accurately. The experiments illustrate the superior performance and competitiveness of this method compared to other approaches, potentially applicable to the clinic.

The development of digital stethoscopes and automatic respiratory sound algorithms is crucial for accelerating diagnosis, reducing physician workload, and lowering mortality rates from respiratory diseases. However, transmitting data from digital stethoscopes to cloud or other storage devices requires substantial storage and transmission capacities, especially for wearable devices used for long-term monitoring. Besides, current automatic algorithms often predict labels for segmented sound events, lacking precision in detecting the onsets and offsets of respiratory sound events. To address these challenges, we organized a Grand Challenge inviting the community to develop data compression and event detection algorithms to reduce the storage and transmission burden and event segmentation workload. A new testing set was prepared to evaluate the performance of the submissions. The top teams presented their work at the 20th IEEE Biomedical Circuits and Systems Conference (BioCAS) 2024.

The unprecedented rapid growth of digital health has brought new opportunities to the health field. However, elderly patients with chronic diseases, as an important potential beneficiary group, are affected by the digital divide, leading to unsatisfactory usage of digital health technologies (DHTs). Our study focused on the factors influencing the adoption of DHTs among this vulnerable group. To extend the UTAUT theory, technology anxiety and several demographic predictors were included to address the age characteristics of the respondents. An on-site survey was conducted in general, district, and community hospitals in Shanghai (n = 309). Facilitating conditions negatively influenced technology anxiety. Technology anxiety hindered behavioural intention. Social influence had a significant but negative impact on behavioural intention. Education, whether older adults have had experience with DHTs and previous smartphone usage experiences were significantly associated with technology anxiety. The findings provide valuable information for multiple stakeholders, including family members of elderly users, product designers, and policymakers. Ameliorating facilitating conditions, improving devices’ usage experience, encouraging attempts and focusing on groups with lower educational levels can help to reduce technology anxiety and promote DHT acceptance and use in older age groups.

OBJECTIVE:This study aimed to investigate the composite effects of different kinds of phthalates on depression risk in the U.S population. METHODS:11731 participants were included from the National Health and Nutrition Examination Survey (NHANES), a national cross-sectional survey. Twelve urinary phthalate metabolites were used to evaluate the level of phthalates exposure. Phthalates levels were devided into four quartiles. High phthalate was defined as having values in the highest quartile. RESULTS:Urinary mono-isobutyl phthalate (MiBP) and mono-benzyl phthalate (MBzP) were estimated as the independent risk factors for depression by mutivariate logistic regression analyses. Compared with the lowest quartile group of MiBP or MBzP, an incrementally higher risk of depression and moderate/severe depression was observed in the highest quartile (all Ptrend <0.05). It was observed that incrementally higher risk of depression and moderate/severe depression were associated with more numbers of high phthalates parameter (Ptrend <0.001 and Ptrend = 0.003, respectively). A significant interaction between race (Non-Hispanic Black vs. Mexican American) and 2 parameters (having value in the highest quartile of both MiBP and MBzP) was detected for depression (Pinteraction = 0.023) and moderate/severe depression (Pinteraction = 0.029). CONCLUSION:Individuals with more numbers of high phthalates parameter were at higher risk of depression and moderate/severe depression. Non-Hispanic Black participants were more likely to be affected by high levels of MiBP and MBzP exposure than Mexican American participants.

非侵入性地评估肠道状态和功能，辅助诊断，指导治疗是发展自动化听诊肠鸣音（BS）的主要目的。得益于工程学的发展，自动化听诊BS的技术研究取得长足进步。自动化听诊主要分为BS信号采集与信号处理两个阶段。随着人工智能（AI）在信号处理方面的应用，自动化听诊BS的工程学技术进步迅速。自动化听诊BS的临床研究集中于肠梗阻识别、术后胃肠功能监测及肠易激综合征（IBS）诊断等方面，目前尚未成熟应用于临床，加速此过程的转化需要工程学与临床的良性互动和密切合作。

目的 深入分析国家医院智慧管理评估标准体系,为推动医院智慧管理建设以及科学评价提供参考依据.方法 选取上海市43家三甲医院,对60名医院管理者开展问卷调查.结果 运营管理、财务资产管理、基础安全具有较高的相关性和需求性,药品耗材管理相关性较高但需求性较低,医疗护理管理具有较低的相关性和需求性.质量效率、医患满意度、辅助决策能力以及成本控制是衡量智慧管理效果的重要维度.结论 公立医院智慧管理评估要以运营管理和资源配置为核心,基于实际需求和发展问题,动态化调整标准体系,实施差异化评估,以提升医患满意度,实现提质增效.

BACKGROUND:To promote the shared decision-making (SDM) between patients and doctors in pediatric outpatient departments, this study was designed to validate artificial intelligence (AI) -initiated medical tests for children with fever. METHODS:We designed an AI model, named Xiaoyi, to suggest necessary tests for a febrile child before visiting a pediatric outpatient clinic.We calculated the sensitivity, specificity, and F1 score to evaluate the efficacy of Xiaoyi's recommendations.The patients were divided into the rejection and acceptance groups.Then we analyzed the rejected examination items in order to obtain the corresponding reasons. RESULTS:We recruited a total of 11,867 children with fever who had used Xiaoyi in outpatient clinics.The recommended examinations given by Xiaoyi for 10,636 (89.6%) patients were qualified.The average F1 score reached 0.94.A total of 58.4% of the patients accepted Xiaoyi's suggestions (acceptance group), and 41.6% refused (rejection group).Imaging examinations were rejected by most patients (46.7%).The tests being time-consuming were rejected by 2,133 patients (43.2%), including rejecting pathogen studies in 1,347 patients (68.5%) and image studies in 732 patients (31.8%).The difficulty of sampling was the main reason for rejecting routine tests (41.9%).CONCLUSION: Our model has high accuracy and acceptability in recommending medical tests to febrile pediatric patients, and is worth promoting in facilitating SDM.

目的 评价2022年春季上海市中药协定方对新型冠状病毒感染者核酸转阴时间的影响.方法 回顾性收集2022年4月1日—5月31日上海交通大学医学院附属新华医院长兴收治点收治的成人新型冠状病毒无症状感染者、成人轻型患者145例和儿童轻型患者160例.收集患者的基本资料、症状和用药情况.采用多因素COX回归分析相关因素对核酸转阴率的影响,分别比较成人协定方组及儿童协定方组(服用中药协定方)和成人对照组及儿童对照组(未服用中药协定方)核酸转阴率的差异.利用COX回归分析排除相关影响因素后,分别比较成人患者及儿童患者的核酸转阴时间.结果 COX回归分析显示,成人和儿童的年龄、性别、症状评分、是否发热(仅儿童)、是否使用中成药对核酸转阴率的影响差异均无统计学意义(P＞0.05).成人协定方组平均核酸转阴时间为(6.80±2.57)天,明显短于成人对照组(8.52±5.27)天(P＜0.05).儿童协定方组平均核酸转阴时间为(4.88±1.92)天,明显短于儿童对照组(5.90±2.37)天(P＜0.05).结论 2022年春季上海市中药协定方可缩短成人无症状感染者、轻型患者和儿童轻型患者的核酸转阴时间,促进新冠病毒感染者康复.