Medicaid's Role in the Health Benefits Exchange: A Road Map for States

Examines issues for integrating Medicaid into the administration, operation, and coverage continuum of insurance exchanges. Discusses eligibility, enrollment, and outreach; contracting, standards, and requirements; benefits design; and infrastructure

HHS Proposed Rules on Exchange Implementation Requirements

Highlights provisions of the new regulations and commentary on state health insurance exchanges that clarify or amplify the 2010 healthcare reform or offer insight into federal guidance or consensus on their establishment, functions, and other issues

The Role of the Basic Health Program in the Coverage Continuum: Opportunities, Risks and Considerations for States

Outlines issues for offering subsidized coverage to those eligible for insurance exchange subsidies by using federal dollars that would otherwise go to those subsidies, including continuity of coverage, impact on exchanges, and financial feasibility

On Lightweight Privacy-Preserving Collaborative Learning for IoT Objects

The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. This paper considers the design and implementation of a practical privacy-preserving collaborative learning scheme, in which a curious learning coordinator trains a better machine learning model based on the data samples contributed by a number of IoT objects, while the confidentiality of the raw forms of the training data is protected against the coordinator. Existing distributed machine learning and data encryption approaches incur significant computation and communication overhead, rendering them ill-suited for resource-constrained IoT objects. We study an approach that applies independent Gaussian random projection at each IoT object to obfuscate data and trains a deep neural network at the coordinator based on the projected data from the IoT objects. This approach introduces light computation overhead to the IoT objects and moves most workload to the coordinator that can have sufficient computing resources. Although the independent projections performed by the IoT objects address the potential collusion between the curious coordinator and some compromised IoT objects, they significantly increase the complexity of the projected data. In this paper, we leverage the superior learning capability of deep learning in capturing sophisticated patterns to maintain good learning performance. Extensive comparative evaluation shows that this approach outperforms other lightweight approaches that apply additive noisification for differential privacy and/or support vector machines for learning in the applications with light data pattern complexities.Comment: 12 pages,IOTDI 201

The Inverse Shapley Value Problem

For $f$ a weighted voting scheme used by $n$ voters to choose between two candidates, the $n$ \emph{Shapley-Shubik Indices} (or {\em Shapley values}) of $f$ provide a measure of how much control each voter can exert over the overall outcome of the vote. Shapley-Shubik indices were introduced by Lloyd Shapley and Martin Shubik in 1954 \cite{SS54} and are widely studied in social choice theory as a measure of the "influence" of voters. The \emph{Inverse Shapley Value Problem} is the problem of designing a weighted voting scheme which (approximately) achieves a desired input vector of values for the Shapley-Shubik indices. Despite much interest in this problem no provably correct and efficient algorithm was known prior to our work. We give the first efficient algorithm with provable performance guarantees for the Inverse Shapley Value Problem. For any constant \eps > 0 our algorithm runs in fixed poly$(n)$ time (the degree of the polynomial is independent of \eps) and has the following performance guarantee: given as input a vector of desired Shapley values, if any "reasonable" weighted voting scheme (roughly, one in which the threshold is not too skewed) approximately matches the desired vector of values to within some small error, then our algorithm explicitly outputs a weighted voting scheme that achieves this vector of Shapley values to within error \eps. If there is a "reasonable" voting scheme in which all voting weights are integers at most \poly(n) that approximately achieves the desired Shapley values, then our algorithm runs in time \poly(n) and outputs a weighted voting scheme that achieves the target vector of Shapley values to within error $\eps=n^{-1/8}.

Improving access to and effectiveness of mental health care for personality disorders:the guideline-informed treatment for personality disorders (GIT-PD) initiative in the Netherlands

Evidence-based treatment for patients suffering from personality disorders (PDs) is only available to a limited extend in the Netherlands. Consequently, most patients receive non-manualized, unspecialized care. This manuscript describes the background, rationale and design of the Guideline-Informed Treatment for Personality Disorders (GIT-PD) initiative. GIT-PD aims to provide a simple, principle-driven, ‘common-factors’ framework for the treatment of PDs. The GIT-PD framework integrates scientific knowledge, professional expertise and patient experience to design a good-enough practice, based on common factors. It offers a basic framework including general principles, a structured clinical pathway, a basic professional stance, interventions focused on common factors, and team and organizational strategies, based on common features of evidence-based treatments and generic competences of professionals. The GIT-PD initiative has had a large impact on the organization of treatment for PDs in the Netherlands. For countries with an interest in improving their health care system for PDs, it could serve as a template that requires only limited resource

Impact of gameplay vs. reading on mental models of social-ecological systems: a fuzzy cognitive mapping approach

Climate change is a highly complex social-ecological problem characterized by system-type dynamics that are important to communicate in a variety of settings, ranging from formal education to decision makers to informal education of the general public. Educational games are one approach that may enhance systems thinking skills. This study used a randomized controlled experiment to compare the impact on the mental models of participants of an educational card game vs. an illustrated article about the Arctic social-ecological system. A total of 41 participants (game: n = 20; reading: n = 21) created pre- and post-intervention mental models of the system, based on a "fuzzy cognitive mapping" approach. Maps were analyzed using network statistics. Both reading the article and playing the game resulted in measurable increases in systems understanding. The group reading the article perceived a more complex system after the intervention, with overall learning gains approximately twice those of the game players. However, game players demonstrated similar learning gains as article readers regarding the climate system, actions both causing environmental problems and protecting the Arctic, as well as the importance of the base- and mid-levels of the food chain. These findings contribute to the growing evidence showing that games are important resources to include as strategies for building capacity to understand and steward sustainable social-ecological systems, in both formal and informal education

“Stickier” learning through gameplay: an effective approach to climate change education

As the impacts of climate change grow, we need better ways to raise awareness and motivate action. Here we assess the effectiveness of an Arctic climate change card game in comparison with the more conventional approach of reading an illustrated article. In-person assessments with control/reading and treatment/game groups (N = 41), were followed four weeks later with a survey. The game was found to be as effective as the article in teaching content of the impacts of climate change over the short term, and was more effective than the article in long-term retention of new information. Game players also had higher levels of engagement and perceptions that they knew ways to help protect Arctic ecosystems. They were also more likely to recommend the game to friends or family than those in the control group were likely to recommend the article to friends or family. As we consider ways to broaden engagement with climate change, we should include games in our portfolio of approaches

The Least-core and Nucleolus of Path Cooperative Games

Cooperative games provide an appropriate framework for fair and stable profit distribution in multiagent systems. In this paper, we study the algorithmic issues on path cooperative games that arise from the situations where some commodity flows through a network. In these games, a coalition of edges or vertices is successful if it enables a path from the source to the sink in the network, and lose otherwise. Based on dual theory of linear programming and the relationship with flow games, we provide the characterizations on the CS-core, least-core and nucleolus of path cooperative games. Furthermore, we show that the least-core and nucleolus are polynomially solvable for path cooperative games defined on both directed and undirected network

False-Name Manipulation in Weighted Voting Games is Hard for Probabilistic Polynomial Time

False-name manipulation refers to the question of whether a player in a weighted voting game can increase her power by splitting into several players and distributing her weight among these false identities. Analogously to this splitting problem, the beneficial merging problem asks whether a coalition of players can increase their power in a weighted voting game by merging their weights. Aziz et al. [ABEP11] analyze the problem of whether merging or splitting players in weighted voting games is beneficial in terms of the Shapley-Shubik and the normalized Banzhaf index, and so do Rey and Rothe [RR10] for the probabilistic Banzhaf index. All these results provide merely NP-hardness lower bounds for these problems, leaving the question about their exact complexity open. For the Shapley--Shubik and the probabilistic Banzhaf index, we raise these lower bounds to hardness for PP, "probabilistic polynomial time", and provide matching upper bounds for beneficial merging and, whenever the number of false identities is fixed, also for beneficial splitting, thus resolving previous conjectures in the affirmative. It follows from our results that beneficial merging and splitting for these two power indices cannot be solved in NP, unless the polynomial hierarchy collapses, which is considered highly unlikely