Prediction of emergency department patient disposition decision for proactive resource allocation for admission.

We investigate the capability of information from electronic health records of an emergency department (ED) to predict patient disposition decisions for reducing "boarding" delays through the proactive initiation of admission processes (e.g., inpatient bed requests, transport, etc.). We model the process of ED disposition decision prediction as a hierarchical multiclass classification while dealing with the progressive accrual of clinical information throughout the ED caregiving process. Multinomial logistic regression as well as machine learning models are built for carrying out the predictions. Utilizing results from just the first set of ED laboratory tests along with other prior information gathered for each patient (2.5 h ahead of the actual disposition decision on average), our model predicts disposition decisions with positive predictive values of 55.4%, 45.1%, 56.9%, and 47.5%, while controlling false positive rates (1.4%, 1.0%, 4.3%, and 1.4%), with AUC values of 0.97, 0.95, 0.89, and 0.84 for the four admission (minor) classes, i.e., intensive care unit (3.6% of the testing samples), telemetry unit (2.2%), general practice unit (11.9%), and observation unit (6.6%) classes, respectively. Moreover, patients destined to intensive care unit present a more drastic increment in prediction quality at triage than others. Disposition decision classification models can provide more actionable information than a binary admission vs. discharge prediction model for the proactive initiation of admission processes for ED patients. Observing the distinct trajectories of information accrual and prediction quality evolvement for ED patients destined to different types of units, proactive coordination strategies should be tailored accordingly for each destination unit.

The quality of randomized controlled trials in major anesthesiology journals.

Increased attention has been directed at the quality of randomized controlled trials (RCTs) and how they are being reported. We examined leading anesthesiology journals to identify if there were specific areas for improvement in the design and analysis of published clinical studies. All RCTs that appeared between January 2000 and December 2000 in leading anesthesiology journals (Anesthesiology,Anesthesia & Analgesia,Anaesthesia, and Canadian Journal of Anaesthesia) were retrieved by a MEDLINE search. We used a previously validated assessment tool, including 14 items associated with study quality, to determine a quality score for each article. The overall mean weighted quality score was 44% +/- 16%. Overall average scores were relatively high for appropriate controls (77% +/- 7%) and discussions of side effects (67% +/- 6%). Scores were very low for randomization blinding (5% +/- 2%), blinding observers to results (1% +/- 1%), and post-beta estimates (16% +/- 13%). Important pretreatment clinical predictors were absent in 32% of all studies. Significant improvement in the reporting and conduct of RCTs is required and should focus on randomization methodology, the blinding of investigators, and sample size estimates. Repeat assessments of the literature may improve the adoption of guidelines for the improvement of the quality of randomized controlled trials.