Several systems have been developed to monitor and feedback information about a patient's responses to psychotherapy as a method of enhancing patient outcome. Feedback is generated from decision rules based on a patient's expected level of progress. Those patients who do not make expected levels of progress or whose progress in therapy is less than adequate are referred to as signal-alarm cases. Research has shown that feedback based on rationally-derived identification procedures increased the duration of treatment and improved outcomes for patients identified as potential treatment failures (signal-alarms). This paper compared two identification methods: a rationally-derived method based on clinical judgments about poor progress, and an empirical method based on statisticallyderived expected recovery curves. The concordance of these two methods was examined with regards to detecting signal-alarm cases. Results suggested that the empirically-derived method was more accurate in identifying patients who actually deteriorated. It was able to identify 100% of the cases that had deteriorated at termination, with 85% being identified by the time they had had three treatment sessions. However, the rationally-derived method was faster at identifying signal cases and more likely to identify the most seriously disturbed cases as potential treatment failures. Future directions for research in quality management were identified.

Psychotherapy outcome can be enhanced by early identification of potential treatment failures before they leave treatment. In adults, compelling data are emerging that provide evidence that an early warning system that identifies potential treatment failures can be developed and applied to enhance outcome. The present study reports an analysis of early warning algorithms to identify treatment failures among child/adolescent patients (ages 3-18). The progress of 300 patients who had completed treatment was analyzed to see if algorithms could identify those children who ultimately had a negative outcome. Results indicated that the rationally derived method had a 77% success rate for identifying child/adolescent patients who were reliably worse or had deteriorated by the time that therapy was terminated.

IntroductionTransesophageal echocardiography (TEE) is a well-established method of evaluating cardiac pathology. It has many advantages over transthoracic echocardiography (TTE), including the ability to image the heart during active cardiopulmonary resuscitation. This prospective simulation study aims to evaluate the ability of emergency medicine (EM) residents to learn TEE image acquisition techniques and demonstrate those techniques to identify common pathologic causes of cardiac arrest.MethodsThis was a prospective educational cohort study with 40 EM residents from two participating academic medical centers who underwent an educational model and testing protocol. All participants were tested across six cases, including two normals, pericardial tamponade, acute myocardial infarction (MI), ventricular fibrillation (VF), and asystole presented in random order. Primary endpoints were correct identification of the cardiac pathology, if any, and time to sonographic diagnosis. Calculated endpoints included sensitivity, specificity, and positive and negative predictive values for emergency physician (EP)-performed TEE. We calculated a kappa statistic to determine the degree of inter-rater reliability.ResultsForty EM residents completed both the educational module and testing protocol. This resulted in a total of 80 normal TEE studies and 160 pathologic TEE studies. Our calculations for the ability to diagnose life-threatening cardiac pathology by EPs in a high-fidelity TEE simulation resulted in a sensitivity of 98%, specificity of 99%, positive likelihood ratio of 78.0, and negative likelihood ratio of 0.025. The average time to diagnose each objective structured clinical examination case was as follows: normal A in 35 seconds, normal B in 31 seconds, asystole in 13 seconds, tamponade in 14 seconds, acute MI in 22 seconds, and VF in 12 seconds. Inter-rater reliability between participants was extremely high, resulting in a kappa coefficient across all cases of 0.95.ConclusionEM residents can rapidly perform TEE studies in a simulated cardiac arrest environment with a high degree of precision and accuracy. Performance of TEE studies on human patients in cardiac arrest is the next logical step to determine if our simulation data hold true in clinical practice.

Carbapenemase-producing Enterobacteriaceae (CPE) are of increasing prevalence worldwide, and long-term acute care hospitals (LTACHs) have been implicated in several outbreaks in the United States. This prospective study of routine screening for CPE on admission to a LTACH demonstrates a high prevalence of CPE colonization in central Virginia.

Human trafficking is associated with a variety of adverse health and mental health consequences, which should be accurately addressed and documented in electronic health records.