Operating Room Usage Time Estimation with Machine Learning Models.

Effectively handling the limited number of surgery operating rooms equipped with expensive equipment is a challenging task for hospital management such as reducing the case-time duration and reducing idle time. Improving the efficiency of operating room usage via reducing the idle time with better scheduling would rely on accurate estimation of surgery duration. Our model can achieve a good prediction result on surgery duration with a dozen of features. We have found the result of our best performing department-specific XGBoost model with the values 31.6 min, 18.71 min, 0.71, 28% and 27% for the metrics of root-mean-square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), mean absolute percentage error (MAPE) and proportion of estimated result within 10% variation, respectively. We have presented each department-specific result with our estimated results between 5 and 10 min deviation would be more informative to the users in the real application. Our study shows comparable performance with previous studies, and the machine learning methods use fewer features that are better suited for universal usability.

A Novel IoT Based Positioning and Shadowing System for Dementia Training.

A rapid increase in the number of patients with dementia, particularly memory decline or impairment, has led to the loss of self-care ability in more individuals and increases in medical and social costs. Numerous studies, and clinical service experience, have revealed that the intervention of nonpharmacological management for people with dementia is effective in delaying the degeneration caused by dementia. Due to recent rapid developments in information and communications technology, many innovative research and development and cross-domain applications have been effectively used in the dementia care environment. This study proposed a new short-term memory support and cognitive training application technology, a "positioning and shadowing system," to delay short-term memory degeneration in dementia. Training courses that integrate physical and digital technologies for the indoor location of patients with dementia were constructed using technologies such as Bluetooth Low Energy, fingerprint location algorithm, and short-range wireless communication. The Internet of Things was effectively applied to a clinical training environment for short-term memory. A pilot test verified that the results demonstrated learning effects in cognitive training and that the system can assist medical personnel in training and nursing work. Participants responded with favorable feedback regarding course satisfaction and system usability. This study can be used as a reference for future digital smart cognitive training that allows observation of the performance of patients with dementia in activities of daily living.

Efficacy of a decision support intervention for reducing decisional conflict in patients with elevated serum prostate-specific antigen: A randomized controlled trial.

PURPOSE: The study purpose is to test the efficacy of a decision support intervention for reducing decisional conflict, increasing prostate biopsy knowledge, and enhancing decision self-efficacy in patients with elevated serum prostate-specific antigen. METHOD: The study is based on a randomized pre-post test design. A convenience sample of men with elevated prostate-specific antigen was recruited and 1:1 randomized to the intervention and control groups. The intervention group received the decision support intervention and the control group received health education. Data were collected at the baseline and post-test by using self-reported questionnaires, including the Prostate Biopsy Knowledge Scale, the Decision Self-Efficacy Scale, the Decisional Conflict Scale, and questions regarding the prostate biopsy decision (post-test only). Data on prostate-specific antigen levels were collected from the patients' medical records. RESULTS: A total of 110 patients participated in the study. At baseline, the intervention group had significantly higher knowledge scores than the control group. The analysis of the covariance model with the baseline score as a covariate was used to analyze the intervention effect. After controlling for the baseline scores, the mean differences (95% CI) between the two groups were 11.75 (11.17-12.32), 76.45 (72.52-80.37), and -23.53 (-26.31-20.20) for knowledge, decision self-efficacy, and decisional conflict, respectively. The between-group difference in willingness to accept prostate biopsy at the post-test was not statistically significant (χ2= 1.704). CONCLUSIONS: The decision support intervention significantly reduced patients' decisional conflict while improving their knowledge and self-efficacy. However, the intervention did not affect patients' biopsy decision.

On the medication distribution system for home health care through convenience stores, lockers, and home delivery.

Medication distribution service can be delivered based on a combination of home delivery and customer pickup. That is, medications are delivered either to customers' homes directly or to the pickup facilities (e.g. lockers) close to customers' homes. In Taiwan, there are more than 11,000 convenience stores that provide a 24-h service for customers to pick up the ordered items from e-commerce, which is unique to the world. In the medication distribution system, convenience stores can provide a unique opportunity for customers to more conveniently collect medications at stores, and also can reduce the operating cost for a logistics company providing the medication delivery service. Therefore, this work proposes a medication distribution system through convenience stores, lockers, and home delivery. Under this system, this work investigates how to simultaneously determine employment of convenience store chains, the convenience store locations to be visited, locations of lockers, vehicle routes for convenience stores and lockers, and vehicle routes for customers' homes, so that the total operating cost is minimized. This work further proposes a genetic algorithm to solve the medication distribution problem. Through simulation, the experimental results show that the proposed algorithm is able to solve the problem efficiently.

Using RFID Positioning Technology to Construct an Automatic Rehabilitation Scheduling Mechanism.

Accurately and efficiently identifying the location of patients during the course of rehabilitation is an important issue. Wireless transmission technology can reach this goal. Tracking technologies such as RFID (Radio frequency identification) can support process improvement and improve efficiencies of rehabilitation. There are few published models or methods to solve the problem of positioning and apply this technology in the rehabilitation center. We propose a mechanism to enhance the accuracy of positioning technology and provide information about turns and obstacles on the path; and user-centered services based on location-aware to enhanced quality care in rehabilitation environment. This paper outlines the requirements and the role of RFID in assisting rehabilitation environment. A prototype RFID hospital support tool is established. It is designed to provide assistance for monitoring rehabilitation patients. It can simultaneously calculate the rehabilitant's location and the duration of treatment, and automatically record the rehabilitation course of the rehabilitant, so as to improve the management efficiency of the rehabilitation program.

Intelligent postoperative morbidity prediction of heart disease using artificial intelligence techniques.

Endovascular aneurysm repair (EVAR) is an advanced minimally invasive surgical technology that is helpful for reducing patients' recovery time, postoperative morbidity and mortality. This study proposes an ensemble model to predict postoperative morbidity after EVAR. The ensemble model was developed using a training set of consecutive patients who underwent EVAR between 2000 and 2009. All data required for prediction modeling, including patient demographics, preoperative, co-morbidities, and complication as outcome variables, was collected prospectively and entered into a clinical database. A discretization approach was used to categorize numerical values into informative feature space. Then, the Bayesian network (BN), artificial neural network (ANN), and support vector machine (SVM) were adopted as base models, and stacking combined multiple models. The research outcomes consisted of an ensemble model to predict postoperative morbidity after EVAR, the occurrence of postoperative complications prospectively recorded, and the causal effect knowledge by BNs with Markov blanket concept.