Abstract: Background:The US Food and Drug Administration’s Sentinel Initiative is well positioned to support pragmatic clinical trials. FDA-Catalyst combines direct contact with health plan members and/or pr...

Abstract: Objective:Few individuals with mood disorders have access to evidence-based collaborative chronic care models (CCMs) because most patients are seen in small-group practices (<20 providers) with lim...

Abstract: INTRODUCTION: As clinical trials test efficacy rather than effectiveness of medications, real-world effectiveness data often vary from clinical trial data. Given the recent market entry of multiple...

Abstract: Objective:To determine whether a 6-week Positivity Program could impact employee cardiovascular inflammation, blood sugars, cortisol, dehydroepiandrosterone (DHEA), and/or life satisfaction.Methods:Pre- and post-study blood draw and life satisfaction questionnaire tracked changes in 10 cardiovascula

Abstract: BACKGROUND: As new biosimilar and follow-on insulins enter the market, more data are needed on safety, effectiveness, and patterns of use for these products to inform prescriber and patient decisio...

Abstract: Employers are increasingly focused on addressing the behavioral health issues of their employees and families. The high prevalence of behavioral health disorders in employees and their dependents has increased total health and disability costs as well as impacted productivity. These circumstances have stimulated the behavioral health focus and prompted new innovative strategies to address it. Ultimately, employers need a healthy and productive workforce in today’s increasingly competitive global economy.

Abstract: Although cloud service providers have deployed numerous large-scale cloud data centres world-wide, research in performance modelling for cloud data centres are still in its infancy. A precise model of a cloud data centre can help the service providers improve their service quality, capacity planning, load balance and reduce operation costs. Most studies in literature focused on modelling hypervisor based cloud, typically IaaS. With the growing popularity of containers in cloud service providers; there is a need to develop performance models specifically for these systems. In this paper, a novel cloud analytic model (CAM) for container-based cloud data centre was proposed. The interactive stochastic models are used to analyse the performance of the system in terms of mean job delay and probability of job rejection. Finally, a container emulation framework, ConSim, was developed and tested against the analytic model. Experimental development using real data were compared with theoretical calculation.

Abstract: Opioid use and misuse are urgent health issues. Previous studies suggest that opioid use increases healthcare resource use but severity adjustment is lacking. The objective of this study was to evaluate the severity-adjusted cost difference between opioid users and non-users among patients with conservatively managed degenerative joint disease of the spine within a large commercial health plan population in the United States. A retrospective observational study was performed using a national commercial database covering 531,819 patients aged 18–64 years with non-surgically managed cervical or lumbar degenerative spine disease during 2015–6. Patients were grouped based on whether there was evidence for an opioid prescription. Costs for the opioids themselves were excluded. Severity adjustment, on an ascending integer scale from 1 to 4, was performed based on member demographics, clinical comorbidities, disease progression indicators, and complications. Median episode costs for patients given opioids were approximately twice that for patients not given opioids after severity adjustment. For patients with episodes in both years and stable severity, patients with new prescriptions for opioids in 2016 doubled their median 2015 costs, and patients who had opioids discontinued in 2016 had a 60% cost reduction. Episode costs showed a nearly linear increase based on the length of time taking opioids, as well as with a higher average daily dose. Cost increases with opioids were broad across service categories even when comparing within the same severity-adjusted episodes of care. The data suggest a clinically and statistically significant increase in episode costs associated with opioid use for degenerative joint disease of the spine, both within and between patients, and higher costs with a longer duration of opioid use as well as with higher daily dosages. Given the health consequences surrounding the overuse of opioids, concerted efforts to move towards a non-opioid pain control strategy are needed.

Abstract: Embodiments of the disclosure provide a method of assessing scalability of a computing infrastructure performed by a scalability server, the scalability server comprising a processor to execute computer executable instructions stored on a non-transitory computer readable medium, so that when the instructions are executed, the server performs the method comprising: (a) receiving growth data from one or more client devices, the growth data including growth projection of subunits of an organization; (b) receiving application and infrastructure information from a database, the application and infrastructure information including a list of application and infrastructure resources of the computing infrastructure; (c) determining scalability of the computing infrastructure using the growth data and the application and infrastructure information; (d) monitoring real-time performance of the computing infrastructure; and (e) determining a priority of infrastructure components to be upgraded using the scalability of the computer infrastructure, the real-time performance of the computing infrastructure, the growth data, and the application and infrastructure information.

Abstract: An embodiment, of the disclosure provides a method for using a reusable dynamic object in a runtime environment. The method includes: (a) configuring, using an object dictionary, properties of the dynamic object; (b) setting a persistence state for the dynamic object; (c) setting a hierarchy state for the dynamic object; (d) establishing a create data buffer, a read data buffer, an update data buffer, and a delete data buffer; and (e) instantiating the dynamic object at runtime, wherein the object dictionary includes an object structure, a logical to physical mapping, a persistence configuration, and object relationships for a plurality of dynamic objects, and the create data buffer, the read data buffer, the update data buffer, and the delete data buffer execute data persistence mechanisms based on the persistence configuration of the dynamic object.

Abstract: A method for profiling a dataset includes: querying, by a data profiler executed on a distributed computing system, a metadata storage to obtain table information; allocating, by the data profiler, system resources based on the obtained table information; profiling, by the data profiler, the dataset to obtain profiling results, wherein profiling the dataset includes shuffling and repartitioning data blocks of the dataset with respect to a plurality of nodes of the distributed computing system, and computing aggregates based on the shuffled and repartitioned data blocks; and outputting, by the data profiler, the profiling results.

Abstract: This project was undertaken to reduce unneeded variation among practicing primary care clinicians participating in an accountable care organization (ACO) and to raise quality and reduce costs. This real-world, quasi-controlled experiment compared ACO target improvements between 3 participating geographic regions and members within the ProHealth ACO against nonparticipating regions and members. The authors used a novel care standardization initiative to engage participating providers. This was a 2-year longitudinal study with 6 rounds of serially measured provider care decisions and customized individual and group improvement feedback. Participating providers cared for online patient simulations as they would actual patients, and their care decisions were scored against evidence-based guidelines. This approach generated significant increases in evidence-based quality scores (+27%) and reductions in unneeded testing (-55%) in the patient simulations. Improvements in the online simulated patients correlated with improvements in patient-level ACO quality measures, which showed gains above and beyond the quasi-control group. Reductions calculated for spending on unneeded tests and specialist referrals exceeded $4.8 million. This study found that supporting practicing physicians in ACOs with evidence-based feedback significantly improved care and cost-efficiency.

Abstract: Metformin is recommended as first-line treatment for type 2 diabetes (T2D). A disadvantage of metformin is the possibility of gastrointestinal adverse effects in some patients. Many T2D pa...

Abstract: BACKGROUND: The approval of new oral disease-modifying drugs (DMDs), such as fingolimod, dimethyl fumarate (DMF), and teriflunamide, has considerably expanded treatment options for relapsi...

Abstract: While the prevalence of cardiovascular risk factors has decreased in the United States in recent years, cardiovascular disparities by sex and race persist. Among the factors contributing to these disparities is the physical environment in which individuals live. Neighborhood characteristics, ranging from air pollution exposure to residential segregation, have been found to be related to cardiovascular health (CVH) and stroke risk. Through the use of cross-sectional, longitudinal, and analytic regression modeling, we are gaining clarity about the relationship between an individual's external environment and CVH. Moreover, differences in CVH vary by sex and/or race within the same neighborhood. The mechanism by which these disparities exist is still being explored. In this review, we examine the literature that has accumulated regarding how external environments and community factors affect individuals and populations by race and sex.