Team Effectiveness in Patient Health Management: An Overview of Reviews

Background: The desire to improve the quality of health care for an aging population with multiple chronic diseases is fostering a rapid growth in inter-professional team care, supported by health professionals, governments, businesses and public institutions. However, the weight of evidence measuring the impact of team care on patient and health system outcomes has not, heretofore, been clear. To address this deficiency, we evaluated published evidence for the clinical effectiveness of team care within a chronic disease management context in a systematic overview. Methods: A search strategy was built for Medline using medical subject headings and other relevant keywords. After testing for perform- ance, the search strategy was adapted to other databases (Cinhal, Cochrane, Embase, PsychInfo) using their specific descriptors. The searches were limited to reviews published between 1996 and 2011, in English and French languages. The results were analyzed by the number of studies favouring team intervention, based on the direction of effect and statistical significance for all reported outcomes. Results: Sixteen systematic and 7 narrative reviews were included. Diseases most frequently targeted were depression, followed by heart failure, diabetes and mental disorders. Effective- ness outcome measures most commonly used were clinical endpoints, resource utilization (e.g., emergency room visits, hospital admissions), costs, quality of life and medication adherence. Briefly, while improved clinical and resource utilization endpoints were commonly reported as positive outcomes, mixed directional results were often found among costs, medication adherence, mortality and patient satisfaction outcomes. Conclusions: We conclude that, although suggestive of some specific benefits, the overall weight of evidence for team care efficacy remains equivocal. Further studies that examine the causal interactions between multidisciplinary team care and clinical and economic outcomes of disease management are needed to more accurately assess its net program efficacy and population effectiveness

Foraging Through Prediction

To survive, an animal must use sensory events to predict the presence of mates, food, danger, and various other stimuli that are important for its survival and procreation. Although reliable prediction is critical, it is not understood how such prediction is carried out by nervous systems. We present a model which utilizes diffuse neuromodulatory systems to implement a predictive version of a Hebbian rule, and embed this rule in a feasible neural architecture. The predictive model suggests a unified way in which neuromodulatory influences are used to bias actions and control learning. When required to forage in a stochastic environment, the model captures the strategies seen in the behavior of bees and a number of other animals. It further suggests that predictive rules for synaptic plasticity offer a simple framework which is nevertheless more powerful than correlational accounts. Introduction Any animal presented with a real environment must have a means to react adaptively to that ..

Team Effectiveness in Patient Health Management: An Overview of Reviews

Background: The desire to improve the quality of health care for an aging population with multiple chronic diseases is fostering a rapid growth in inter-professional team care, supported by health professionals, governments, businesses and public institutions. However, the weight of evidence measuring the impact of team care on patient and health system outcomes has not, heretofore, been clear. To address this deficiency, we evaluated published evidence for the clinical effectiveness of team care within a chronic disease management context in a systematic overview. Methods: A search strategy was built for Medline using medical subject headings and other relevant keywords. After testing for perform- ance, the search strategy was adapted to other databases (Cinhal, Cochrane, Embase, PsychInfo) using their specific descriptors. The searches were limited to reviews published between 1996 and 2011, in English and French languages. The results were analyzed by the number of studies favouring team intervention, based on the direction of effect and statistical significance for all reported outcomes. Results: Sixteen systematic and 7 narrative reviews were included. Diseases most frequently targeted were depression, followed by heart failure, diabetes and mental disorders. Effective- ness outcome measures most commonly used were clinical endpoints, resource utilization (e.g., emergency room visits, hospital admissions), costs, quality of life and medication adherence. Briefly, while improved clinical and resource utilization endpoints were commonly reported as positive outcomes, mixed directional results were often found among costs, medication adherence, mortality and patient satisfaction outcomes. Conclusions: We conclude that, although suggestive of some specific benefits, the overall weight of evidence for team care efficacy remains equivocal. Further studies that examine the causal interactions between multidisciplinary team care and clinical and economic outcomes of disease management are needed to more accurately assess its net program efficacy and population effectiveness

Discriminant analysis of body surface potential maps for classification of non-Q wave infarction

Multivariate analysis was performed on 120 lead data from 159 normals (N), 233 patients with myocardial infarction (MI), and 116 patients with left ventricular hypertrophy (LVH). Optimal lead-ins (n = 5) and features were identified for the multigroup model. The discriminant functions combined measurements from the P, QRS, and ST-T waveforms and included the duration of the P wave. The diagnostic performance achieved with this model averaged 89.2%. The multiple bigroup model required 7 leads, 6 of which were identical or closely related to those selected in the multigroup model. The diagnostic performance averaged 91.3%. The most striking difference between the schemes was observed when the discriminant functions were tested on patients from the non-Q-wave-MI group producing correct classification rates of 76% and 88%, respectively.SCOPUS: cp.pinfo:eu-repo/semantics/publishe

Identification of first acute Q wave and non-Q wave myocardial infarction by multivariate analysis of body surface potential maps

Background. Patients with acute non-Q wave myocardial infarction (NQMI) appear to have more jeopardized residual myocardium at high risk for subsequent angina, reinfarction, or malignant arrhythmias than patients with acute Q wave myocardial infarction (QMI). Unfortunately, conventional electrocardiographic (ECG) criteria have limited utility in recognizing NQMI. Methods and Results. The present study combines the increased information content of body surface potential maps (BSPM) over the 12-lead ECG with the power of multivariate statistical procedures to identify a practical subset of leads that would allow improved diagnosis of NQMI. Discriminant analysis was performed on 120-lead data recorded simultaneously in 159 normal subjects and 308 patients with various types of myocardial infarction (MI) by using instantaneous voltages on time-normalized P, PR, QRS, and ST-T waveforms as well as the duration of these waveforms as features. Leads and features for optimal separation of 159 normals from 183 patients with recent or old QMI (group A) were selected. A total of six features from six torso sites accounted for a specificity of 96% and a sensitivity of 94%. All lead positions were outside the conventional electrode sites and selected features were voltages at mid-P, early and mid-QRS, and before and after the peak of the T wave. The discriminant function was then tested on 57 patients with acute NQMI (group B) and 68 patients with acute QMI (group C): Rates of correct classification were 91% and 93%, respectively. Because of the possible deterioration of the results caused by ST-T abnormalities also present in other clinical entities, a second classification model including an independent group of 116 patients with left ventricular hypertrophy (LVH) but without MI was developed. Two additional measurements were required, namely, P wave duration and a mid-QRS voltage on a lead located 10 cm below V1. Testing the model on both acute MI groups produced correct classification rates of 88% for acute NQMI and 93% for acute QMI. Group mean BSPM were plotted for the three MI groups at successive instants throughout the PQRST waveform. Typical patterns for each MI group were identified during PQRST by removing the corresponding normal variability at each electrode site from sequential MI maps. These standardized maps or discriminant maps provided information on the capability of each measurement at each electrode site and at each instant to separate each class of MI from the normal group (N). Striking similarities were observed between the three MI groups, particularly at mid-QRS and throughout ST-T. The closest resemblance was between acute NQMI and old QMI. Discriminant analysis was also performed on the 12-lead ECG: The first classification model (N versus MI) produced correct classification rates of 85% for acute QMI and 70% for NQMI. With the second model (MI versus N or LVH), correct rates were 81% and 65%, respectively. Conclusions. Diagnosis of acute NQMI and QMI (also in the presence of LVH) can be improved substantially by appropriate selection of ECG leads and features. Comparison of discriminant maps from groups A, B, and C does not support the concept of acute NQMI as a distinct ECG entity but rather as a group with infarcts of smaller size. However, pathophysiological and clinical differences between acute NQMI and acute QMI influence long-term risks and may define different therapeutic approaches.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Location and magnitude of ST changes in acute myocardial infarction by analysis of body surface potential maps

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

A framework for mesencephalic dopamine systems based on predictive Hebbian learning

We develop a theoretical framework that shows how mesen-cephalic dopamine systems could distribute to their targets a signal that represents information about future expectations. In particular, we show how activity in the cerebral cortex can make predictions about future receipt of reward and how fluc-tuations in the activity levels of neurons in diffuse dopamine systems above and below baseline levels would represent errors in these predictions that are delivered to cortical and subcottical targets. We present a model for how such errors could be constructed in a real brain that is consistent wit