The effects of ambient music on simulated anaesthesia monitoring

We examined the effect of no music, classical music or rock music on simulated patient monitoring. Twenty-four non-anaesthetist participants with high or low levels of musical training were trained to monitor visual and auditory displays of patients' vital signs. In nine anaesthesia test scenarios, participants were asked every 50-70 s whether one of five vital signs was abnormal and the trend of its direction. Abnormality judgements were unaffected by music or musical training. Trend judgements were more accurate when music was playing (p = 0.0004). Musical participants reported trends more accurately (p = 0.004), and non-musical participants tended to benefit more from music than did the musical participants (p = 0.063). Music may provide a pitch and rhythm standard from which participants can judge changes in vital signs from auditory displays. Nonetheless, both groups reported that it was easier to monitor the patient with no music (p = 0.0001), and easier to rely upon the auditory displays with no music (p = 0.014).

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD