Objectives: The purpose of this study was to investigate the incidence of deep vein thrombosis (DVT) and clarify the risk factors of DVT in patients with femoral neck fracture. Methods: A self-designed questionnaire was used to collect the clinical data of 1209 patients with femoral neck fracture in our hospital from January 2019 to December 2019. The content of the questionnaire mainly includes general information, past medical history, history of present illness, operation related information, occurrence of DVT. The collected data were entered into Excel to analyze the incidence and risk factors of DVT in patients with femoral neck fracture. Chi square test and binary logistic regression model was used to screen the risk factors of DVT. Results: 1209 cases of femoral neck fracture were included in this study. The incidence of DVT was 28.0% (339 patients). Among them, 71.7% (243 patients) were preoperative DVT and 28.3% (96 patients) were postoperative DVT. For the risk-factor analysis, gender, age, time from injury to hospitalization, operative method, anesthesia method and intraoperative blood loss were independent risk factors for DVT. Conclusion: The incidence of DVT in patients with femoral neck fracture is relatively high, and there are many related risk factors.

Background: Multidisciplinary rounds (MDRs) are scheduled, patient-focused communication mechanisms among multidisciplinary providers in the intensive care unit (ICU). Objective: i-Dashboard is a custom-developed visualization dashboard that supports (1) key information retrieval and reorganization, (2) time-series data, and (3) display on large touch screens during MDRs. This study aimed to evaluate the performance, including the efficiency of prerounding data gathering, communication accuracy, and information exchange, and clinical satisfaction of integrating i-Dashboard as a platform to facilitate MDRs. Methods: A cluster-randomized controlled trial was performed in 2 surgical ICUs at a university hospital. Study participants included all multidisciplinary care team members. The performance and clinical satisfaction of i-Dashboard during MDRs were compared with those of the established electronic medical record (EMR) through direct observation and questionnaire surveys. Results: Between April 26 and July 18, 2021, a total of 78 and 91 MDRs were performed with the established EMR and i-Dashboard, respectively. For prerounding data gathering, the median time was 10.4 (IQR 9.1-11.8) and 4.6 (IQR 3.5-5.8) minutes using the established EMR and i-Dashboard (P<.001), respectively. During MDRs, data misrepresentations were significantly less frequent with i-Dashboard (median 0, IQR 0-0) than with the established EMR (4, IQR 3-5; P<.001). Further, effective recommendations were significantly more frequent with i-Dashboard than with the established EMR (P<.001). The questionnaire results revealed that participants favored using i-Dashboard in association with the enhancement of care plan development and team participation during MDRs. Conclusions: i-Dashboard increases efficiency in data gathering. Displaying i-Dashboard on large touch screens in MDRs may enhance communication accuracy, information exchange, and clinical satisfaction. The design concepts of i-Dashboard may help develop visualization dashboards that are more applicable for ICU MDRs. Trial Registration: ClinicalTrials.gov NCT04845698; https://clinicaltrials.gov/ct2/show/NCT04845698

Purpose: Reduced physical activity is reported in the elderly, especially in institutional residents. Institutionalized older adults exhibit a high prevalence of frailty. In this work, we developed an artificial intelligence of things (AIoT)-based feedback assistive strengthening ergometer (AIFASE), for the physical strengthening of the elderly with intelligent assistance. Methods: We conducted a 12-week intervention in a long-term care facility. In total, 16 participants (84.38 ± 6.0 years; 4 males and 12 females) were recruited with 1:1 randomization of exercise to control groups. The muscle strength of the lower extremities, timed up and go test (TUG), and Short-form Physical Performance Battery (SPPB) of the participants were measured. The AIFASE system allows the clinical staff to record the personal physical performance of the elderly and generates personalized exercise prescriptions accordingly. AIFASE also displays the current usage status of all ergometers and the users’ physiological conditions. The algorithms were developed to generate warning alerts when the training workload was too large by personal physiological detection. AIFASE automatically customized the exercise prescription according to the user’s exercise performance. Results: After a 12-week AIFASE intervention, the intervention group exhibited significant improvements in the strength of the hip flexor, Semi-Tandem Stand, and Tandem Stand. Conclusion: In this study, we developed an AIoT ergometer that delivered customized physical training prescriptions to improve the physical performance of long-term care facility residents. We believe that the application of AIFASE will help improve the quality of institutional care.

The Physical Resilience Instrument for Older Adults (PRIFOR) is a questionnaire for assessing physical resilience in older adults suffering from acute heal