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 interprofessional 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 performance, 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. Effectiveness 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.

Learning Reactive Power Control Polices in Distribution Networks Using Conditional Value-at-Risk and Artificial Neural Networks

Scalable coordination of photovoltaic(PV)inverters,considering the uncertainty in PV and load in distribution networks(DNs),is challenging due to the lack of real-time communications.Decentralized PV inverter setpoints can be achieved to address this issue by capitalizing on the abundance of data from smart utility meters and the scalable architecture of artificial neural networks(ANNs).To this end,we first use an offline,centralized data-driven conservative convex approximation of chance-constrained optimal power flow(CVaR-OPF)in which conditional value-at-risk(CVaR)is used to compute reactive power setpoints of PV inverter,taking into account PV and load uncertainties in DNs.Following that,an artificial neural network(ANN)controller is trained for each PV inverter to emulate the optimal behavior of the centralized control setpoints of PV inverter in a decentralized fashion.Additionally,the voltage regulation performance of the developed ANN controllers is compared with other decentralized designs(local controllers)developed using model-based learning(regressionbased controller),optimization(affine feedback controller),and case-based learning(mapping)approaches.Numerical tests using real-world feeders corroborate the effectiveness of ANN controllers in voltage regulation and loss minimization.