Airborne Influenza Virus in Daycare Centers.

In this study, we investigated the concentration of airborne influenza virus in daycare centers and influencing factors, such as common cold prevalence, air pollutants, and meteorological factors. A total of 209 air samples were collected from daycare centers in Kaohsiung and the influenza virus was analyzed using real-time quantitative polymerase chain reaction. Air pollutants and metrological factors were measured using real-time monitoring equipment. Winter had the highest positive rates of airborne influenza virus and the highest prevalence of the common cold, followed by summer and autumn. The concentration of CO was significantly positively correlated with airborne influenza virus. Daycare center A, with natural ventilation and air condition systems, had a higher concentration of airborne influenza A virus, airborne fungi, and airborne bacteria, as well as a higher prevalence of the common cold, than daycare center B, with a mechanical ventilation system and air purifiers, while the concentrations of CO2, CO, and UFPs in daycare center A were lower than those in daycare center B. We successfully detected airborne influenza virus in daycare centers, demonstrating that aerosol sampling for influenza can provide novel epidemiological insights and inform the management of influenza in daycare centers.

An App Developed for Detecting Nurse Burnouts Using the Convolutional Neural Networks in Microsoft Excel: Population-Based Questionnaire Study.

BACKGROUND: Burnout (BO), a critical syndrome particularly for nurses in health care settings, substantially affects their physical and psychological status, the institute's well-being, and indirectly, patient outcomes. However, objectively classifying BO levels has not been defined and noticed in the literature. OBJECTIVE: The aim of this study is to build a model using the convolutional neural network (CNN) to develop an app for automatic detection and classification of nurse BO using the Maslach Burnout Inventory-Human Services Survey (MBI-HSS) to help assess nurse BO at an earlier stage. METHODS: We recruited 1002 nurses working in a medical center in Taiwan to complete the Chinese version of the 20-item MBI-HSS in August 2016. The k-mean and CNN were used as unsupervised and supervised learnings for dividing nurses into two classes (n=531 and n=471 of suspicious BO+ and BO-, respectively) and building a BO predictive model to estimate 38 parameters. Data were separated into training and testing sets in a proportion 70%:30%, and the former was used to predict the latter. We calculated the sensitivity, specificity, and receiver operating characteristic curve (area under the curve) across studies for comparison. An app predicting respondent BO was developed involving the model's 38 estimated parameters for a website assessment. RESULTS: We observed that (1) the 20-item model yields a higher accuracy rate (0.95) with an area under the curve of 0.97 (95% CI 0.94-0.95) based on the 1002 cases, (2) the scheme named matching personal response to adapt for the correct classification in model drives the prior model's predictive accuracy at 100%, (3) the 700-case training set with 0.96 accuracy predicts the 302-case testing set reaching an accuracy of 0.91, and (4) an available MBI-HSS app for nurses predicting BO was successfully developed and demonstrated in this study. CONCLUSIONS: The 20-item model with the 38 parameters estimated by using CNN for improving the accuracy of nurse BO has been particularly demonstrated in Excel (Microsoft Corp). An app developed for helping nurses to self-assess job BO at an early stage is required for application in the future.

Constructing Inpatient Pressure Injury Prediction Models Using Machine Learning Techniques.

The incidence rate of pressure injury is a critical nursing quality indicator in clinic care; consequently, factors causing pressure injury are diverse and complex. The early prevention of pressure injury and monitoring of these complex high-risk factors are critical to reduce the patients' pain, prevent further surgical treatment, avoid prolonged hospital stay, decrease the risk of wound infection, and lower associated medical costs and expenses. Although a number of risk assessment scales of pressure injury have been adopted in various countries, their criteria are set for specific populations, which may not be suitable for the medical care systems of other countries. This study constructs three prediction models of inpatient pressure injury using machine learning techniques, including decision tree, logistic regression, and random forest. A total of 11 838 inpatient records were collected, and 30 sets of training samples were adopted for data analysis in the experiment. The experimental results and evaluations of the models suggest that the prediction model built using random forest had most favorable classification performance of 0.845. The critical risk factors for pressure injury identified in this study were skin integrity, systolic blood pressure, expression ability, capillary refill time, and level of consciousness.

Detecting hospital behaviors of up-coding on DRGs using Rasch model of continuous variables and online cloud computing in Taiwan.

BACKGROUND: This work aims to apply data-detection algorithms to predict the possible deductions of reimbursement from Taiwan's Bureau of National Health Insurance (BNHI), and to design an online dashboard to send alerts and reminders to physicians after completing their patient discharge summaries. METHODS: Reimbursement data for discharged patients were extracted from a Taiwan medical center in 2016. Using the Rasch model of continuous variables, we applied standardized residual analyses to 20 sets of norm-referenced diagnosis-related group (DRGs), each with 300 cases, and compared these to 194 cases with deducted records from the BNHI. We then examine whether the results of prediction using the Rasch model have a high probability associated with the deducted cases. Furthermore, an online dashboard was designed for use in the online monitoring of possible deductions on fee items in medical settings. RESULTS: The results show that 1) the effects deducted by the NHRI can be predicted with an accuracy rate of 0.82 using the standardized residual approach of the Rasch model; 2) the accuracies for drug, medical material and examination fees are not associated among different years, and all of those areas under the ROC curve (AUC) are significantly greater than the randomized probability of 0.50; and 3) the online dashboard showing the possible deductions on fee items can be used by hospitals in the future. CONCLUSION: The DRG-based comparisons in the possible deductions on medical fees, along with the algorithm based on Rasch modeling, can be a complementary tool in upgrading the efficiency and accuracy in processing medical fee applications in the discernable future.

Application of a multidimensional computerized adaptive test for a Clinical Dementia Rating Scale through computer-aided techniques.

BACKGROUND: With the increasingly rapid growth of the elderly population, individuals aged 65 years and above now compose 14% of Taiwanese citizens, thereby making Taiwanese society an aged society. A leading factor that affects the elderly population is dementia. A method of precisely and efficiently examining patients with dementia through multidimensional computer adaptive testing (MCAT) to accurately determine the patients' stage of dementia needs to be developed. This study aimed to develop online MCAT that family members can use on their own computers, tablets, or smartphones to predict the extent of dementia for patients responding to the Clinical Dementia Rating (CDR) instrument. METHODS: The CDR was applied to 366 outpatients in a hospital in Taiwan. MCAT was employed with parameters for items across eight dimensions, and responses were simulated to compare the efficiency and precision between MCAT and non-adaptive testing (NAT). The number of items saved and the estimated person measures was compared between the results of MCAT and NAT, respectively. RESULTS: MCAT yielded substantially more precise measurements and was considerably more efficient than NAT. MCAT achieved 20.19% (= [53 - 42.3]/53) saving in item length when the measurement differences were less than 5%. Pearson correlation coefficients were highly consistent among the eight domains. The cut-off points for the overall measures were - 1.4, - 0.4, 0.4, and 1.4 logits, which was equivalent to 20% for each portion in percentile scores. Substantially fewer items were answered through MCAT than through NAT without compromising the precision of MCAT. CONCLUSIONS: Developing a website that family members can use on their own computers, tablets, and smartphones to help them perform online screening and prediction of dementia in older adults is useful and manageable.