Explore the vast range of titles available.

ObjectiveTo 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 designA face-to-face questionnaire survey was conducted from July to September 2023.Study settingThe study was conducted in 12 medical institutions in Shanghai, including 6 tertiary hospitals, 3 secondary hospitals and 3 community hospitals.Participants1222 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 measureThe behavioural intention and usage behaviour of older patients with chronic diseases to use digital health technologies.ResultsThe 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.ConclusionsAcceptance 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.

Background Many studies suggest that patient satisfaction is significantly negatively correlated with the waiting time. A well-designed healthcare system should not keep patients waiting too long for an appointment and consultation. However, in China, patients spend notable time waiting, and the actual time spent on diagnosis and treatment in the consulting room is comparatively less. Methods We developed an artificial intelligence (AI)-assisted module and name it XIAO YI. It could help outpatients automatically order imaging examinations or laboratory tests based on their chief complaints. Thus, outpatients could get examined or tested before they went to see the doctor. People who saw the doctor in the traditional way were allocated to the conventional group, and those who used XIAO YI were assigned to the AI-assisted group. We conducted a retrospective cohort study from August 1, 2019 to January 31, 2020. Propensity score matching was used to balance the confounding factor between the two groups. And waiting time was defined as the time from registration to preparation for laboratory tests or imaging examinations. The total cost included the registration fee, test fee, examination fee, and drug fee. We used Wilcoxon rank-sum test to compare the differences in time and cost. The statistical significance level was set at 0.05 for two sides. Results Twelve thousand and three hundred forty-two visits were recruited, consisting of 6171 visits in the conventional group and 6171 visits in the AI-assisted group. The median waiting time was 0.38 (interquartile range: 0.20, 1.33) hours for the AI-assisted group compared with 1.97 (0.76, 3.48) hours for the conventional group ( p  < 0.05). The total cost was 335.97 (interquartile range: 244.80, 437.60) CNY (Chinese Yuan) for the AI-assisted group and 364.58 (249.70, 497.76) CNY for the conventional group ( p  < 0.05). Conclusions Using XIAO YI can significantly reduce the waiting time of patients, and thus, improve the outpatient service process of hospitals.

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. 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. 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. 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. 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 Bone marrow (BM) is the most common site of metastatic disease at diagnosis and a frequent site of relapse in neuroblastoma. Digital cytology images of BM smears offer a rich data source for artificial intelligence models, which may potentially facilitate more cost-effective risk stratification within the diagnostic workflow. This study aims to develop an interpretable cytology model for detecting BM metastasis in pediatric neuroblastoma. Methods This retrospective diagnostic study used Wright-Giemsa–stained BM cytology images from 359 neuroblastoma patients who underwent BM screening between January 2019 and June 2024 across multiple centers in China. After the quality evaluation, we generated 1384,007 patches from BM digital cytology to develop and validate the cytology model. In the model construction, we integrated a multiple-instance learning framework with convolutional neural networks to extract cytology features, referred to as cMIL. The cytology model was trained for BM metastasis detection and risk stratification with interpretability. Results For metastasis detection, the cytology model achieved an AUC of 0.924 (95% CI, 0.775–1.000) in the training cohort. Performance remained strong in external validation, with AUCs of 0.826 (95% CI, 0.741–0.911) in Cohort A and 0.795 (95% CI, 0.684–0.906) in Cohort B, indicating consistent performance across independent multicenter cohorts. The cMIL score also successfully stratified patients in terms of survival outcomes (log-rank p  < 0.05). Interpretability analyses further demonstrated that the model’s predictions were associated with clinically relevant cytological features. Conclusions In this retrospective diagnostic study, the developed cytology model demonstrated high discriminative performance in detecting BM metastasis and captured the underlying complexity and heterogeneity of BM. These findings suggest that the cytology model could serve as a promising tool for improving metastasis detection and risk stratification in patients with neuroblastoma, potentially contributing to personalized treatment strategies and enhanced disease monitoring. Trial registration This retrospective study was registered with ClinicalTrials.gov (NCT06703944) on November 21, 2024. Study title: bone marrow cytology-based artificial intelligence model for detection and prognosis of neuroblastoma. ( https://register.clinicaltrials.gov ).

Background With the initially defined thresholds, the most widely used serum biomarkers for staging liver fibrosis (ie, APRI and FIB‐4 scores) proved to be ineffective among patients with chronic hepatitis B virus infection (CHB). Whether optimizing the FIB‐4 and APRI thresholds could improve their diagnostic accuracy requires further research. Methods Using data of treat‐naïve CHB patients from three tertiary hospitals, we explored the optimal FIB‐4 and APRI thresholds to rule in liver fibrosis accurately. Subsequently, we validated the applicability of the newly defined thresholds to the CHB patients from another two tertiary hospitals. Results The fibrosis stages between discovery cohort (n = 433) and the external validation cohort (n = 568) were statistically different (P < .001). When ruling in significant fibrosis and advanced fibrosis by the newly defined FIB‐4 thresholds (2.25 and 3.00, respectively), 24.0% and 14.3% of patients, respectively, could be classified with excellent accuracy (PPVs of 91.3% and 80.6%, respectively; misdiagnosis rates of 6.0% and 5.4%, respectively), supported by the internal and external validation tests. Regrettably, the more accurate and robust thresholds of APRI score for ruling in significant fibrosis and advanced fibrosis could not be found. Besides, the FIB‐4 and APRI scores should not be recommended for ruling in cirrhosis because of poor clinical diagnostic performance. Conclusion The newly defined FIB‐4 thresholds for ruling in significant fibrosis and advanced fibrosis showed superior and reproducible clinical diagnostic accuracy. The well‐validated threshold (≥2.25) of FIB‐4 score could aid in antiviral treatment decisions for treat‐naïve adult CHB patients by accurately ruling in significant fibrosis in tertiary care settings. The novel strategy for re‐defining the optimal FIB‐4 thresholds. For accurately (ie with high PPVs and low misdiagnosis rates) ruling in as more patients with hepatic fibrosis as possible, the optimal FIB‐4 thresholds were re‐defined based on the violin plot and scatterplot. Take the detection of significant fibrosis (ie F2_4) as an example: compared to the pre‐defined FIB‐4 threshold of 1.90, the newly‐defined FIB‐4 threshold of 2.25 showed preferable and reproducible misdiagnosis rate and positive predictive value.

Pediatric 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. This 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. The 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 test was used to check for significant differences. Some of pediatric relatives was revisited for deeper insights into the results. In 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. Internet 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.

Background Adherence to asthma treatment among children is usually poor. We sought to explore whether electronic adherence monitoring combined with weekly feedback regarding adherence along with a reminder to use inhaled corticosteroids (ICS) would lead to improved compliance with ICS in infants and younger children with asthma. Methods 96 recruited children (aged 6 months to 3 years) with mild or moderate persistent asthma who were on regular inhaled corticosteroids were randomly allocated to receive electronic monitoring combined with instant messaging software (IMS)-based weekly feedback regarding adherence along with a reminder to keep taking the ICS (intervention group) and to receive electronic monitoring only (control group). Results The mean device-monitored adherence was significantly higher in the intervention group (80%) than in the control group (45.9%), with a difference of 34.0% (95% confidence interval [CI], 26.8–41.3%; P  < 0.001). No difference in the mean caregiver-reported adherence between the interventional group (89.7%) and the control group (92.7%) was observed ( P  = 0.452). Conclusions Electronic monitoring combined with IMS-based weekly feedback regarding adherence along with a reminder to keep taking the ICS significantly improved the treatment compliance of infants and younger children with asthma. Caregiver-reported adherence is an unreliable monitoring indicator. Trial registration ClinicalTrials.gov, NCT03277664. Registered 11 September 2017—Retrospectively registered, https://clinicaltrials.gov/ct2/results?cond=&term=NCT03277664

This study aimed to assess the associations of caesarean delivery (CD) with risk of wheezing diseases and changes of immune cells in children. The cross-sectional study was conducted between May, 2020 and April, 2021. The study was conducted in Shanghai Children's Medical Center, Shanghai, China. A total of 2079 children with a mean age of 36.97 ± 40.27 months and their guardians were included in the present study face-to-face inquiry and physical examination by clinicians. Logistic regression was applied to estimate odds ratio (ORs) and 95% confidence intervals (CIs) for the association between CD and first episode of wheezing (FEW) or asthma. Models were adjusted for premature or full-term delivery, exclusive breastfeeding (at least 4 months) or not. Among the 2079 children, 987 children (47.47%) were born by CD and 1092 (52.53%) by vaginal delivery (VD). Children delivered by caesarean had significantly lower gestational age (P<0.01) compared with those who delivered vaginally. Our results also showed that CD was related to increased risk of FEW by the age of 3(adjusted OR 1.50, 95%CI 1.06, 2.12) and increased tendency to develop asthma by the age of 4 (adjusted OR 3.16, 95%CI 1.25, 9.01). The subgroup analysis revealed that the negative effects of CD on asthma were more obvious in children without exclusive breastfeeding (adjusted OR 4.93, 95%CI 1.53, 21.96) or without postnatal smoking exposure (adjusted OR 3.58, 95%CI 1.20, 13.13). Furthermore, compared with children born through VD, a significant change of the T cells (increased proportion of CD4+ T cells and decreased number and proportion of CD8+ T cells) were observed before the age of one in the CD group. However, the changes were insignificant in children over 1 year old. This study showed age-dependent associations of CD with asthma and FEW in offspring. Moreover, CD appeared to have an effect on the cellular immunity in infants, the disorder of which may contribute to the development of asthma in children.

Secundum atrial septal defect (ASD) is one of the most common congenital heart diseases (CHDs). This study aims to evaluate the feasibility and accuracy of automatic detection of ASD in children based on color Doppler echocardiographic images using convolutional neural networks. In this study, we propose a fully automatic detection system for ASD, which includes three stages. The first stage is used to identify four target echocardiographic views (that is, the subcostal view focusing on the atrium septum, the apical four-chamber view, the low parasternal four-chamber view, and the parasternal short-axis view). These four echocardiographic views are most useful for the diagnosis of ASD clinically. The second stage aims to segment the target cardiac structure and detect candidates for ASD. The third stage is to infer the final detection by utilizing the segmentation and detection results of the second stage. The proposed ASD detection system was developed and validated using a training set of 4,031 cases containing 370,057 echocardiographic images and an independent test set of 229 cases containing 203,619 images, of which 105 cases with ASD and 124 cases with intact atrial septum. Experimental results showed that the proposed ASD detection system achieved accuracy, recall, precision, specificity, and F1 score of 0.8833, 0.8545, 0.8577, 0.9136, and 0.8546, respectively on the image-level averages of the four most clinically useful echocardiographic views. The proposed system can automatically and accurately identify ASD, laying a good foundation for the subsequent artificial intelligence diagnosis of CHDs.

ObjectiveThe limited existing asthma control questionnaires that are available for children 5 years of age or younger in China mostly assess only the impairment domain of asthma control. Here, the English version of the Test for Respiratory and Asthma Control in Kids (TRACK) was translated into Chinese and validated for its application in asthma control in preschool children.DesignProspective validation study.Setting and participantsA total of 321 Chinese preschool children suffering from asthma completed the study from December 2017 to February 2018.MethodThe TRACK translation into Chinese employed the translation and back translation technique. The caregivers of the preschool children with asthma symptoms completed TRACK during two clinical visits over 4–6 weeks. Moreover, the physicians completed a Global Initiative for Asthma (GINA)-based asthma control survey at both visits. The utility of TRACK for assessing the change in asthma control status and its reliability and discriminant validity were evaluated.ResultsThe Chinese version of TRACK showed internal consistency reliability values of 0.63 and 0.71 at each visit, respectively (Cronbach’s α). The test–retest reliability was 0.62 for individuals whose GINA-based assessment results were the same at both visits (n=206). The TRACK scores for the children in the various asthma control categories were significantly different (p<0.001). Children recommended for increased treatment by the physicians had lower TRACK scores than those recommended for no change in treatment or decreased treatment (p<0.001).ConclusionThe study verifies the validity and reliability of the Chinese version of TRACK. Changes in the TRACK scores effectively reflected the level of asthma control in preschool children and guided further treatment strategies.Trial registration numberNCT02649803