Evaluation of a computer-based intervention to enhance metabolic monitoring in psychiatry inpatients treated with second-generation antipsychotics.

WHAT IS KNOWN AND OBJECTIVE: Second-generation antipsychotics (SGAs) play an important role in the pharmacologic management of various psychiatric conditions. Use of these medications has been associated with metabolic complications. Adherence to guideline-recommended monitoring is suboptimal. We evaluated the effect of a computerized physician order entry (CPOE) pop-up alert designed to improve rates of laboratory metabolic monitoring of patients treated with SGAs on a University Hospital inpatient psychiatry unit. METHODS: A single-centre, retrospective chart review was performed in which patient demographics and SGA drug and laboratory data were extracted from the CPOE database. We assessed the number of orders for appropriate metabolic monitoring data for patients admitted within a 6-month period before or after the alert implementation. RESULTS AND DISCUSSION: Pre-alert (n = 171) and post-alert (n = 157) groups were similar with respect to age, length of stay, sex, race and comorbidities. Following alert implementation, significant increases in monitoring both random (92.4% vs. 100%) and fasting (46.8% vs. 70%) glucose levels as well as random (28.7% vs. 74.5%) and fasting (18.7% vs. 59.9%) lipid panels (all P ≤ 0.001) were observed. The number of patients with both a fasting glucose level and fasting lipid panel available for monitoring increased from 12.9% to 47.8% (P < 0.0001). Significantly more post-alert laboratory orders were submitted at the same time as the SGA drug order (P < 0.0001), suggesting that the alert itself had a direct influence on the ordering of metabolic monitoring labs. WHAT IS NEW AND CONCLUSIONS: Implementation and use of an electronic pop-up alert in an inpatient psychiatric unit significantly improved rates of ordering fasting blood glucose and lipid levels for inpatients treated with SGAs. Overall rates remain suboptimal, suggesting a need for additional strategies to further improve metabolic monitoring.

Uncertainties analysis of doses resulting from chronic inhalation of plutonium at the Mayak production association.

A method is presented to determine the uncertainties in the reported dose due to incorporated plutonium for the Mayak Worker Cohort. The methodology includes errors generated by both detection methods and modeling methods. To accomplish the task, the method includes classical statistics, Monte Carlo, perturbation, and reliability groupings. Uncertainties are reported in percent of reported dose as a function of total body burden. The cohort was initially sorted into six reliability groups, with "A" being the data set that the investigators are most confident is correct and "G" being the data set with the most ambiguous data. Categories were adjusted based on preliminary calculation of uncertainties using the sorting criteria. Specifically, the impact of transportability (the parameter used to describe the transport of plutonium from the lung to systemic organs) was underestimated, and the structure of the sort was reorganized to reflect the impact of transportability. The finalized categories are designated with Roman numerals I through V, with "I" being the most reliable. Excluding Category V (neither bioassay nor autopsy), the highest uncertainty in lung doses is for individuals from Category IV-which ranged from 90-375% for total body burdens greater than 10 Bq, along with work histories that indicated exposure to more than one transportability class. The smallest estimated uncertainties for lung doses were determined by autopsy. Category I has a 32-38% uncertainty in the lung dose for total body burdens greater than 1 Bq. First, these results provide a further definition and characterization of the cohort and, second, they provide uncertainty estimates for these plutonium exposure categories.