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Purpose Quantitative analytic methods are being increasingly used in postmarketing surveillance. However, currently existing methods are limited to spontaneous reporting data and are inapplicable to hospital electronic medical record (EMR) data. The principal objectives of this study were to propose a novel algorithm for detecting the signals of adverse drug reactions using EMR data focused on laboratory abnormalities after treatment with medication, and to evaluate the potential use of this method as a signal detection tool. Methods We developed an algorithm referred to as the Comparison on Extreme Laboratory Test results, which takes an extreme representative value pair according to the types of laboratory abnormalities on the basis of each patient's medication point. We used 10 years' EMR data from a tertiary teaching hospital, containing 32 033 710 prescriptions and 115 241 147 laboratory tests for 530 829 individual patients. Ten drugs were selected randomly for analysis, and 51 laboratory values were matched. The sensitivity, specificity, positive predictive value, and negative predictive value of the algorithm were calculated. Results The mean number of detected laboratory abnormality signals for each drug was 27 (±7.5). The sensitivity, specificity, positive predictive value, and negative predictive value of the algorithm were 64 -100%, 22 -76%, 22 -75%, and 54 -100%, respectively. Conclusions The results of this study demonstrated that the Comparison on Extreme Laboratory Test results algorithm described herein was extremely effective in detecting the signals characteristic of adverse drug reactions. This algorithm can be regarded as a useful signal detection tool, which can be routinely applied to EMR data.

ObjectivesTo develop and evaluate time series models to predict the daily number of patients visiting the Emergency Department (ED) of a Korean hospital.MethodsData were collected from the hospital information system database. In order to develop a forecasting model, we used, 2 years of data from January 2007 to December 2008 data for the following 3 consecutive months were processed for validation. To establish a Forecasting Model, calendar and weather variables were utilized. Three forecasting models were established: 1) average; 2) univariate seasonal auto-regressive integrated moving average (SARIMA); and 3) multivariate SARIMA. To evaluate goodness-of-fit, residual analysis, Akaike information criterion and Bayesian information criterion were compared. The forecast accuracy for each model was evaluated via mean absolute percentage error (MAPE).ResultsThe multivariate SARIMA model was the most appropriate for forecasting the daily number of patients visiting the ED. Because it's MAPE was 7.4%, this was the smallest among the models, and for this reason was selected as the final model.ConclusionsThis study applied explanatory variables to a multivariate SARIMA model. The multivariate SARIMA model exhibits relativelyhigh reliability and forecasting accuracy. The weather variables play a part in predicting daily ED patient volume.

ObjectivesThis study was conducted to determine whether or not newly proposed high-resolution activity features could provide a superior analytic foundation compared to those commonly used to assess transitions in children's activities, under circumstances in which the types of courses attended exert different situational effects on activity levels.MethodsFrom 153 children at a local elementary school, 10 subjects with attention deficit hyperactivity disorder (ADHD) and 7 controls were recruited. Their activity data was collected using an actigraph while they attended school. Ratios of partitioned activity ranges (0.5-2.8 G) during the entire activity were extracted during three classes: art, mathematics, and native language (Korean). Extracted activity features for each participant were compared between the two groups of children (ADHD and control) using graphs and statistical analysis.ResultsActivity distributions between ADHD and control groups for each class showed statistically significant differences spread through the entire range in art class compared to native language and mathematics classes. The ADHD group, but not the control group, experienced many significantly different intervals (> 50%) having low to very high activity acceleration regions during the art and languages courses.ConclusionsClass content appears to influence the activity patterns of ADHD children. Monitoring the actual magnitude and activity counts in a wide range of subjects could facilitate the examination of distributions or patterns of activities. Objective activity measurements made with an actigraph may be useful for monitoring changes in activities in children with ADHD in a timely manner.

Admitting department was an independent risk factor for alerts and alert overrides. Strategies to reduce alerts and alert overrides should consider the admitting department.