Girosi, Federico
Overview
Works:  29 works in 96 publications in 1 language and 2,042 library holdings 

Genres:  Conference papers and proceedings 
Roles:  Author, Editor 
Classifications:  HB1321, 304.640112 
Publication Timeline
.
Most widely held works by
Federico Girosi
Demographic forecasting by
Federico Girosi(
Book
)
10 editions published between 2003 and 2008 in English and held by 274 WorldCat member libraries worldwide
10 editions published between 2003 and 2008 in English and held by 274 WorldCat member libraries worldwide
Extrapolating evidence of health information technology savings and costs by
Federico Girosi(
Book
)
10 editions published in 2005 in English and held by 170 WorldCat member libraries worldwide
In 2003, RAND Health began a broad study to better understand the role and importance of Electronic Medical Record Systems (EMRS) in improving health and reducing healthcare costs, and to help inform government actions that could maximize EMRS benefits and increase its use. This report provides the technical details and results of one component of that study: nationallevel efficiency savings brought about by using Healthcare Information Technology (HIT). We quantify those savingswhat results from the ability to perform the same task with fewer resources (money, time, personnel, etc.) by providing a methodological framework to scale empirical evidence on the effect of HIT to the national level and to project it into the future. A key element of this framework is a projection of the rates of adoption of HIT in the inpatient setting and in the ambulatory/outpatient setting. Next, from the evidence found in our search of peerreviewed and gray literature (the body of reports and studies produced by local government agencies, private organizations, and educational facilities that have not been reviewed and published in journals or other standard research publications), we considered savings from 10 different sources (5 inpatient; 5 outpatient). Then, we compared the efficiency savings with the costs the nation has to incur in order to be able to realize those savings, using a modeling framework analogous to the one developed for the extrapolation of savings and cost data from the literature or given to us by providers. We found that savings outweigh costs by a factor of 5, which implies that, even if a large portion of savings is not realized, the ratio of benefit to cost is still larger than 1. Finally, we studied what might be the effect of those financial incentives presented to providers that lower the cost of EMRS and quicken the pace of HIT adoption. A general result that does not depend on the size of the behavioral response of physicians is that incentive programs are more likely to be costeffective if they start early and do not last long, but are sizable. The report concludes with a summary chapter. The report should be of interest to healthcare IT professionals, other healthcare executives and researchers, and officials in the government responsible for health policy
10 editions published in 2005 in English and held by 170 WorldCat member libraries worldwide
In 2003, RAND Health began a broad study to better understand the role and importance of Electronic Medical Record Systems (EMRS) in improving health and reducing healthcare costs, and to help inform government actions that could maximize EMRS benefits and increase its use. This report provides the technical details and results of one component of that study: nationallevel efficiency savings brought about by using Healthcare Information Technology (HIT). We quantify those savingswhat results from the ability to perform the same task with fewer resources (money, time, personnel, etc.) by providing a methodological framework to scale empirical evidence on the effect of HIT to the national level and to project it into the future. A key element of this framework is a projection of the rates of adoption of HIT in the inpatient setting and in the ambulatory/outpatient setting. Next, from the evidence found in our search of peerreviewed and gray literature (the body of reports and studies produced by local government agencies, private organizations, and educational facilities that have not been reviewed and published in journals or other standard research publications), we considered savings from 10 different sources (5 inpatient; 5 outpatient). Then, we compared the efficiency savings with the costs the nation has to incur in order to be able to realize those savings, using a modeling framework analogous to the one developed for the extrapolation of savings and cost data from the literature or given to us by providers. We found that savings outweigh costs by a factor of 5, which implies that, even if a large portion of savings is not realized, the ratio of benefit to cost is still larger than 1. Finally, we studied what might be the effect of those financial incentives presented to providers that lower the cost of EMRS and quicken the pace of HIT adoption. A general result that does not depend on the size of the behavioral response of physicians is that incentive programs are more likely to be costeffective if they start early and do not last long, but are sizable. The report concludes with a summary chapter. The report should be of interest to healthcare IT professionals, other healthcare executives and researchers, and officials in the government responsible for health policy
Neural networks for signal processing V : proceedings of the 1995 IEEE Workshop : fifth in a series of workshops organized
by the IEEE Signal Processing Society Neural Networks Technical Committee by IEEE Workshop on Neural Networks for Signal Processing(
Book
)
10 editions published in 1995 in English and held by 54 WorldCat member libraries worldwide
10 editions published in 1995 in English and held by 54 WorldCat member libraries worldwide
Priors, stabilizers and basis functions: from regularization to radial, tensor and additive splines by
Federico Girosi(
Book
)
5 editions published in 1993 in English and held by 6 WorldCat member libraries worldwide
We had previously shown that regularization principles lead to approximation schemes, as Radial Basis Functions, which are equivalent to networks with one layer of hidden units, called Regularization Networks. In this paper we show that regularization networks encompass a much broader range of approximation schemes, including many of the popular general additive models, Breiman's hinge functions and some forms of Projection Pursuit Regression. In the probabilistic interpretation of regularization, the different classes of basis functions correspond to different classes of prior probabilities on the approximating function spaces, and therefore to different types of smoothness assumptions. In the final part of the paper, we also show a relation between activation functions of the Gaussian and sigmoidal type
5 editions published in 1993 in English and held by 6 WorldCat member libraries worldwide
We had previously shown that regularization principles lead to approximation schemes, as Radial Basis Functions, which are equivalent to networks with one layer of hidden units, called Regularization Networks. In this paper we show that regularization networks encompass a much broader range of approximation schemes, including many of the popular general additive models, Breiman's hinge functions and some forms of Projection Pursuit Regression. In the probabilistic interpretation of regularization, the different classes of basis functions correspond to different classes of prior probabilities on the approximating function spaces, and therefore to different types of smoothness assumptions. In the final part of the paper, we also show a relation between activation functions of the Gaussian and sigmoidal type
A theory of networks for approximation and learning by
Tomaso Poggio(
Book
)
7 editions published between 1989 and 1994 in English and held by 5 WorldCat member libraries worldwide
Learning an inputoutput mapping from a set of examples, of the type that many neural networks have been constructed to perform, can be regarded as synthesizing an approximation of a multidimensional function, that is solving the problem of hypersurface reconstruction. From this point of view, this form of learning is closely related to classical approximation techniques. This paper considers the problems of an exact representation of the approximation of linear and nonlinear mappings in terms of simpler functions of fewer variables. Kolmogorov's theorem concerning the representation of functions of several variables in terms of functions of one variable turns out to be almost irrelevant in the context of networks for learning. We develop a theoretical framework for approximation based on regularization techniques that leads to a class of threelayer networks that we call Generalized Radial Basis Function (GRBF), since they are mathematically related to the wellknown Radial Basis Functions, mainly used for strict interpolation tasks. GRBF networks are not only equivalent to generalized splines, but are also closely related to pattern recognition methods such as Parzen windows and potential functions and to several neural network algorithms, such as Kanerva's associative memory, backpropagation and Kohonen's topology preserving map. They also have an interesting interpretation in terms of prototypes that are synthesized and optimally combined during the learning stage. The paper introduces intriguing analogies with neurobiological data
7 editions published between 1989 and 1994 in English and held by 5 WorldCat member libraries worldwide
Learning an inputoutput mapping from a set of examples, of the type that many neural networks have been constructed to perform, can be regarded as synthesizing an approximation of a multidimensional function, that is solving the problem of hypersurface reconstruction. From this point of view, this form of learning is closely related to classical approximation techniques. This paper considers the problems of an exact representation of the approximation of linear and nonlinear mappings in terms of simpler functions of fewer variables. Kolmogorov's theorem concerning the representation of functions of several variables in terms of functions of one variable turns out to be almost irrelevant in the context of networks for learning. We develop a theoretical framework for approximation based on regularization techniques that leads to a class of threelayer networks that we call Generalized Radial Basis Function (GRBF), since they are mathematically related to the wellknown Radial Basis Functions, mainly used for strict interpolation tasks. GRBF networks are not only equivalent to generalized splines, but are also closely related to pattern recognition methods such as Parzen windows and potential functions and to several neural network algorithms, such as Kanerva's associative memory, backpropagation and Kohonen's topology preserving map. They also have an interesting interpretation in terms of prototypes that are synthesized and optimally combined during the learning stage. The paper introduces intriguing analogies with neurobiological data
Parallel and deterministic algorithms for MRFs : surface reconstruction and integration by
Massachusetts Institute of Technology(
Book
)
5 editions published in 1989 in English and Undetermined and held by 4 WorldCat member libraries worldwide
In recent years many researchers have investigated the use of Markov random fields (MRFs) for computer vision. The computational complexity of the implementation has been a drawback of MRFs. In this paper we derive deterministic approximations to MRFs models. All the theoretical results are obtained in the framework of the mean field theory from statistical mechanics. Because we use MRFs models the mean field equations lead to parallel and iterative algorithms. One of the considered models for image reconstruction is shown to give in a natural way the graduate nonconvexity algorithm proposed by Blake and Zisserman
5 editions published in 1989 in English and Undetermined and held by 4 WorldCat member libraries worldwide
In recent years many researchers have investigated the use of Markov random fields (MRFs) for computer vision. The computational complexity of the implementation has been a drawback of MRFs. In this paper we derive deterministic approximations to MRFs models. All the theoretical results are obtained in the framework of the mean field theory from statistical mechanics. Because we use MRFs models the mean field equations lead to parallel and iterative algorithms. One of the considered models for image reconstruction is shown to give in a natural way the graduate nonconvexity algorithm proposed by Blake and Zisserman
Extensions of a theory of networks for approximation and learning : outliers and negative examples by
Federico Girosi(
Book
)
4 editions published in 1990 in English and held by 4 WorldCat member libraries worldwide
4 editions published in 1990 in English and held by 4 WorldCat member libraries worldwide
Estimating the global impact of improved diagnostic tools for the developing world(
Book
)
1 edition published in 2007 in English and held by 4 WorldCat member libraries worldwide
This research brief summarizes research assessing how higherquality and moreaccessible clinical diagnostic tests could improve health outcomes in the developing world for a number of common diseases
1 edition published in 2007 in English and held by 4 WorldCat member libraries worldwide
This research brief summarizes research assessing how higherquality and moreaccessible clinical diagnostic tests could improve health outcomes in the developing world for a number of common diseases
Continuous stochastic cellular automata that have a stationary distribution and no detailed balance by
Tomaso Poggio(
Book
)
2 editions published in 1990 in English and held by 3 WorldCat member libraries worldwide
Abstract: "Marroquin and Ramirez (1990) have recently discovered a class of discrete stochastic cellular automata with Gibbsian invariant measure that have a nonreversible dynamic behavior. Practical applications include more powerful algorithms than the Metropolis algorithm to compute MRF models. In this paper we describe a large class of stochastic dynamical systems that has a Gibbs asymptotic distribution but does not satisfy reversibility. We characterize sufficient properties of a subclass of stochastic differential equations in terms of the associated FokkerPlanck equation for the existence of an asymptotic probability distribution in the system of coordinates which is given. Practical implications include VLSI analog circuits to compute coupled MRF models."
2 editions published in 1990 in English and held by 3 WorldCat member libraries worldwide
Abstract: "Marroquin and Ramirez (1990) have recently discovered a class of discrete stochastic cellular automata with Gibbsian invariant measure that have a nonreversible dynamic behavior. Practical applications include more powerful algorithms than the Metropolis algorithm to compute MRF models. In this paper we describe a large class of stochastic dynamical systems that has a Gibbs asymptotic distribution but does not satisfy reversibility. We characterize sufficient properties of a subclass of stochastic differential equations in terms of the associated FokkerPlanck equation for the existence of an asymptotic probability distribution in the system of coordinates which is given. Practical implications include VLSI analog circuits to compute coupled MRF models."
Proceedings of the 1995 IEEE workshop by
Institute of Electrical and Electronics Engineers(
Book
)
3 editions published in 1995 in English and Undetermined and held by 3 WorldCat member libraries worldwide
3 editions published in 1995 in English and Undetermined and held by 3 WorldCat member libraries worldwide
Extensions of a Theory of Networks for Approximation and Learning: Dimensionality Reduction and Clustering by
Tomaso Poggio(
Book
)
6 editions published in 1990 in English and held by 3 WorldCat member libraries worldwide
Learning an inputoutput mapping from a set of examples, of the type that many neural networks have been constructed to perform, can be regarded as synthesizing an approximation of a multidimensional function. From this point of view, this form of learning is closely related to regularization theory. The theory developed in Poggio and Girosi (1989) shows the equivalence between regularization and a class of threelayer networks that we call regularization networks or Hyper Basis Functions. These networks are not only equivalent to generalized splines, but are also closely related to the classical Radial Basis Functions used for interpolation tasks and to several pattern recognition and neural network algorithms. In this note, we extend the theory by defining a general form of these networks with two sets of modifiable parameters in addition to the coefficients C sub alpha: moving centers and adjustable normweights. Moving the centers is equivalent to taskdependent clustering and changing the norm weights is equivalent to taskdependent dimensionality reduction. (KR)
6 editions published in 1990 in English and held by 3 WorldCat member libraries worldwide
Learning an inputoutput mapping from a set of examples, of the type that many neural networks have been constructed to perform, can be regarded as synthesizing an approximation of a multidimensional function. From this point of view, this form of learning is closely related to regularization theory. The theory developed in Poggio and Girosi (1989) shows the equivalence between regularization and a class of threelayer networks that we call regularization networks or Hyper Basis Functions. These networks are not only equivalent to generalized splines, but are also closely related to the classical Radial Basis Functions used for interpolation tasks and to several pattern recognition and neural network algorithms. In this note, we extend the theory by defining a general form of these networks with two sets of modifiable parameters in addition to the coefficients C sub alpha: moving centers and adjustable normweights. Moving the centers is equivalent to taskdependent clustering and changing the norm weights is equivalent to taskdependent dimensionality reduction. (KR)
Some extensions of the kmeans algorithm for image segmentation and pattern classification by
J. L Marroquin(
Book
)
3 editions published in 1993 in English and held by 3 WorldCat member libraries worldwide
The same state variables can be used to define algorithms for determining adaptively the optimal number of centers for clouds of data with spacevarying density. Some examples of the application of these extensions are also given."
3 editions published in 1993 in English and held by 3 WorldCat member libraries worldwide
The same state variables can be used to define algorithms for determining adaptively the optimal number of centers for clouds of data with spacevarying density. Some examples of the application of these extensions are also given."
Models of noise and robust estimates by
Federico Girosi(
Book
)
2 editions published in 1991 in English and held by 2 WorldCat member libraries worldwide
In this paper we show that, for a class of functions V, using these robust estimators corresponds to assuming that data are corrupted by Gaussian noise whose variance fluctuates according to some given probability distribution, that uniquely determines the shape of V."
2 editions published in 1991 in English and held by 2 WorldCat member libraries worldwide
In this paper we show that, for a class of functions V, using these robust estimators corresponds to assuming that data are corrupted by Gaussian noise whose variance fluctuates according to some given probability distribution, that uniquely determines the shape of V."
Convergence rates of approximation by translates by
Federico Girosi(
Book
)
2 editions published in 1992 in English and held by 2 WorldCat member libraries worldwide
Abstract: "In this paper we consider the problem of approximating a function belonging to some function space [phi] by a linear combination of n translates of a given function G. Using a lemma by Jones (1990) and Barron (1991) we show that it is possible to define function spaces and functions G for which the rate of convergence to zero of the error is O(1/[square root of n]) in any number of dimensions. The apparent avoidance of the 'curse of dimensionality' is due to the fact that these function spaces are more and more constrained as the dimension increases. Examples include spaces of the Sobolev type, in which the number of weak derivatives is required to be larger than the number of dimensions
2 editions published in 1992 in English and held by 2 WorldCat member libraries worldwide
Abstract: "In this paper we consider the problem of approximating a function belonging to some function space [phi] by a linear combination of n translates of a given function G. Using a lemma by Jones (1990) and Barron (1991) we show that it is possible to define function spaces and functions G for which the rate of convergence to zero of the error is O(1/[square root of n]) in any number of dimensions. The apparent avoidance of the 'curse of dimensionality' is due to the fact that these function spaces are more and more constrained as the dimension increases. Examples include spaces of the Sobolev type, in which the number of weak derivatives is required to be larger than the number of dimensions
On the relationship between generalization error, hypothesis complexity, and sample complexity for radial basis functions by
Partha Niyogi(
Book
)
3 editions published in 1994 in English and held by 2 WorldCat member libraries worldwide
In this paper, we bound the generalization error of a class of Radial Basis Function networks, for certain well defined function learning tasks, in terms of the number of parameters and number of examples. We show that the total generalization error is partly due to the insufficient representational capacity of the network (because of its finite size) and partly due to insufficient information about the target function (because of finite number of samples). We make several observations about generalization error which are valid irrespective of the approximation scheme. Our result also sheds light on ways to choose an appropriate network architecture for a particular problem
3 editions published in 1994 in English and held by 2 WorldCat member libraries worldwide
In this paper, we bound the generalization error of a class of Radial Basis Function networks, for certain well defined function learning tasks, in terms of the number of parameters and number of examples. We show that the total generalization error is partly due to the insufficient representational capacity of the network (because of its finite size) and partly due to insufficient information about the target function (because of finite number of samples). We make several observations about generalization error which are valid irrespective of the approximation scheme. Our result also sheds light on ways to choose an appropriate network architecture for a particular problem
A connection between GRBF and MLP by Minoru Maruyama(
Book
)
2 editions published between 1991 and 1992 in English and held by 2 WorldCat member libraries worldwide
Multiscale GRBF networks, on the other hand, can approximate MLP networks with a similar number of parameters."
2 editions published between 1991 and 1992 in English and held by 2 WorldCat member libraries worldwide
Multiscale GRBF networks, on the other hand, can approximate MLP networks with a similar number of parameters."
Forecasting global temperature variations by neural networks by
Takaya Miyano(
Book
)
2 editions published in 1994 in English and held by 1 WorldCat member library worldwide
Global temperature variations between 1861 and 1984 are forecast using regularization networks, multilayer perceptrons and linear autoregression. The regularization network, optimized by stochastic gradient descent associated with colored noise, gives the best forecasts. For all the models, prediction errors noticeably increase after 1965. These results are consistent with the hypothesis that the climate dynamics is characterized by lowdimensional chaos and that the it may have changed at some point after 1965, which is also consistent with the recent idea of climate change. (MM)
2 editions published in 1994 in English and held by 1 WorldCat member library worldwide
Global temperature variations between 1861 and 1984 are forecast using regularization networks, multilayer perceptrons and linear autoregression. The regularization network, optimized by stochastic gradient descent associated with colored noise, gives the best forecasts. For all the models, prediction errors noticeably increase after 1965. These results are consistent with the hypothesis that the climate dynamics is characterized by lowdimensional chaos and that the it may have changed at some point after 1965, which is also consistent with the recent idea of climate change. (MM)
Establishing state health insurance exchanges : implications for health insurance enrollment, spending, and small businesses(
)
1 edition published in 2010 in English and held by 0 WorldCat member libraries worldwide
The RAND Corporation's Comprehensive Assessment of Reform Efforts microsimulation model was used to analyze the effects of the Patient Protection and Affordable Care Act (PPACA) on employers and enrollees in employersponsored health insurance, with a focus on small businesses and businesses offering coverage through health insurance exchanges. Outcomes assessed include the proportion of nonelderly Americans with insurance coverage, the number of employers offering health insurance, premium prices, total employer spending, and total government spending relative to what would have been observed without the policy change. The microsimulation predicts that PPACA will increase insurance offer rates among small businesses from 53 to 77 percent for firms with ten or fewer workers, from 71 to 90 percent for firms with 11 to 25 workers, and from 90 percent to nearly 100 percent for firms with 26 to 100 workers. Simultaneously, the uninsurance rate in the United States would fall from 19 to 6 percent of the nonelderly population. The increase in employer offer rates is driven by workers' demand for insurance, which increases due to an individual mandate requiring all people to obtain insurance policies. Employer penalties incentivizing businesses to offer coverage do not have a meaningful impact on outcomes. The model further predicts that approximately 60 percent of businesses will offer coverage through the health insurance exchanges after the reform. Under baseline assumptions, a total of 68 million people will enroll in the exchanges, of whom 35 million will receive exchangebased coverage from an employer
1 edition published in 2010 in English and held by 0 WorldCat member libraries worldwide
The RAND Corporation's Comprehensive Assessment of Reform Efforts microsimulation model was used to analyze the effects of the Patient Protection and Affordable Care Act (PPACA) on employers and enrollees in employersponsored health insurance, with a focus on small businesses and businesses offering coverage through health insurance exchanges. Outcomes assessed include the proportion of nonelderly Americans with insurance coverage, the number of employers offering health insurance, premium prices, total employer spending, and total government spending relative to what would have been observed without the policy change. The microsimulation predicts that PPACA will increase insurance offer rates among small businesses from 53 to 77 percent for firms with ten or fewer workers, from 71 to 90 percent for firms with 11 to 25 workers, and from 90 percent to nearly 100 percent for firms with 26 to 100 workers. Simultaneously, the uninsurance rate in the United States would fall from 19 to 6 percent of the nonelderly population. The increase in employer offer rates is driven by workers' demand for insurance, which increases due to an individual mandate requiring all people to obtain insurance policies. Employer penalties incentivizing businesses to offer coverage do not have a meaningful impact on outcomes. The model further predicts that approximately 60 percent of businesses will offer coverage through the health insurance exchanges after the reform. Under baseline assumptions, a total of 68 million people will enroll in the exchanges, of whom 35 million will receive exchangebased coverage from an employer
Employer selfinsurance decisions and the implications of the Patient Protection and Affordable Care Act as modified by the
Health Care and Education Reconciliation Act of 2010 (ACA) by
Christine Eibner(
)
1 edition published in 2011 in English and held by 0 WorldCat member libraries worldwide
The Patient Protection and Affordable Care Act as amended by the Health Care and Education Reconciliation Act of 2010 (ACA) changes the regulatory environment within which health insurance policies on the smallgroup market are bought and sold. New regulations include rate bands that limit premium price variation, riskadjustment policies that will transfer funds from lowactuarialrisk to highactuarialrisk plans, and requirements that plans include "essential health benefits." While the new regulations will be applied to all nongrandfathered fully insured policies purchased by businesses with 100 or fewer workers, selfinsured plans are exempt from these regulations. As a result, some firms may have a stronger incentive to offer selfinsured plans after the ACA takes full effect. In this report we identify factors that influence employers' decisions to selfinsure and estimate how the ACA will influence selfinsurance rates. We also consider the implications of higher selfinsurance rates for adverse selection in the nonselfinsured smallgroup market and whether enrollees in selfinsured plans receive different benefits than enrollees in fullyinsured plans. Results are based on data analysis, literature review, findings from discussions with stakeholders, and microsimulation analysis using the COMPARE model. Overall, we find little evidence that selfinsured plans differ systematically from fully insured plans in terms of benefit generosity, price, or claims denial rates. Stakeholders expressed significant concern about adverse selection in the health insurance exchanges due to regulatory exemptions for selfinsured plans. However, our microsimulation analysis predicts a sizable increase in selfinsurance only if comprehensive stoploss policies become widely available after the ACA takes full effect and the expected cost of selfinsuring with stoploss is comparable to the cost of being fully insured in a market without rating regulations
1 edition published in 2011 in English and held by 0 WorldCat member libraries worldwide
The Patient Protection and Affordable Care Act as amended by the Health Care and Education Reconciliation Act of 2010 (ACA) changes the regulatory environment within which health insurance policies on the smallgroup market are bought and sold. New regulations include rate bands that limit premium price variation, riskadjustment policies that will transfer funds from lowactuarialrisk to highactuarialrisk plans, and requirements that plans include "essential health benefits." While the new regulations will be applied to all nongrandfathered fully insured policies purchased by businesses with 100 or fewer workers, selfinsured plans are exempt from these regulations. As a result, some firms may have a stronger incentive to offer selfinsured plans after the ACA takes full effect. In this report we identify factors that influence employers' decisions to selfinsure and estimate how the ACA will influence selfinsurance rates. We also consider the implications of higher selfinsurance rates for adverse selection in the nonselfinsured smallgroup market and whether enrollees in selfinsured plans receive different benefits than enrollees in fullyinsured plans. Results are based on data analysis, literature review, findings from discussions with stakeholders, and microsimulation analysis using the COMPARE model. Overall, we find little evidence that selfinsured plans differ systematically from fully insured plans in terms of benefit generosity, price, or claims denial rates. Stakeholders expressed significant concern about adverse selection in the health insurance exchanges due to regulatory exemptions for selfinsured plans. However, our microsimulation analysis predicts a sizable increase in selfinsurance only if comprehensive stoploss policies become widely available after the ACA takes full effect and the expected cost of selfinsuring with stoploss is comparable to the cost of being fully insured in a market without rating regulations
The impact of the coverage  related provisions of the Patient Protection and Affordable Care Act on insurance coverage and
state health care expenditures in Illinois : an analysis from Rand compare(
)
5 editions published in 2011 in English and held by 0 WorldCat member libraries worldwide
The Patient Protection and Affordable Care Act (ACA) contains substantial new requirements aimed at increasing rates of health insurance coverage. Because many of these provisions impose additional costs on the states, officials need reliable estimates of the likely impact of the ACA in their state. To demonstrate the usefulness of modeling for statelevel decisionmaking, RAND undertook a preliminary analysis of the impact of the ACA on five states  California, Connecticut, Illinois, Montana, and Texas  using the RAND COMPARE microsimulation model. For Illinois, the model predicts that, in 2016 (the year that all of the provisions in the ACA related to coverage expansion will be fully implemented), the uninsured rate in Illinois will fall to 3 percent; without the law, it would remain near 15 percent. The model projects that total state government spending on health care will be 10 percent higher for the combined 20112020 period because of the ACA
5 editions published in 2011 in English and held by 0 WorldCat member libraries worldwide
The Patient Protection and Affordable Care Act (ACA) contains substantial new requirements aimed at increasing rates of health insurance coverage. Because many of these provisions impose additional costs on the states, officials need reliable estimates of the likely impact of the ACA in their state. To demonstrate the usefulness of modeling for statelevel decisionmaking, RAND undertook a preliminary analysis of the impact of the ACA on five states  California, Connecticut, Illinois, Montana, and Texas  using the RAND COMPARE microsimulation model. For Illinois, the model predicts that, in 2016 (the year that all of the provisions in the ACA related to coverage expansion will be fully implemented), the uninsured rate in Illinois will fall to 3 percent; without the law, it would remain near 15 percent. The model projects that total state government spending on health care will be 10 percent higher for the combined 20112020 period because of the ACA
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