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Thomas G. Mcguire - One of the best experts on this subject based on the ideXlab platform.
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sample selection for medicare Risk Adjustment due to systematically missing data
Health Services Research, 2018Co-Authors: Savannah L Bergquist, Thomas G. Mcguire, Timothy J Layton, Sherri RoseAbstract:Objective To assess the issue of nonrepresentative sampling in Medicare Advantage (MA) Risk Adjustment. Data sources Medicare enrollment and claims data from 2008 to 2011. Data extraction Risk Adjustment predictor variables were created from 2008 to 2010 Part A and B claims and the Medicare Beneficiary Summary File. Spending is based on 2009-2011 Part A and B, Durable Medical Equipment, and Home Health Agency claims files. Study design A propensity-score matched sample of Traditional Medicare (TM) beneficiaries who resembled MA enrollees was created. Risk Adjustment formulas were estimated using multiple techniques, and performance was evaluated based on R2 , predictive ratios, and formula coefficients in the matched sample and a random sample of TM beneficiaries. Principal findings Matching improved balance on observables, but performance metrics were similar when comparing Risk Adjustment formula results fit on and evaluated in the matched sample versus fit on the random sample and evaluated in the matched sample. Conclusions Fitting MA Risk Adjustment formulas on a random sample versus a matched sample yields little difference in MA plan payments. This does not rule out potential improvements via the matching method should reliable MA encounter data and additional variables become available for Risk Adjustment.
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deriving Risk Adjustment payment weights to maximize efficiency of health insurance markets
Journal of Health Economics, 2018Co-Authors: Timothy J Layton, Thomas G. Mcguire, Richard C Van KleefAbstract:Risk-Adjustment is critical to the functioning of regulated health insurance markets. To date, estimation and evaluation of a Risk-Adjustment model has been based on statistical rather than economic objective functions. We develop a framework where the objective of Risk-Adjustment is to minimize the efficiency loss from service-level distortions due to adverse selection, and we use the framework to develop a welfare-grounded method for estimating Risk-Adjustment weights. We show that when the number of Risk adjustor variables exceeds the number of decisions plans make about service allocations, incentives for service-level distortion can always be eliminated via a constrained least-squares regression. When the number of plan service-level allocation decisions exceeds the number of Risk-adjusters, the optimal weights can be found by an OLS regression on a straightforward transformation of the data. We illustrate this method with the data used to estimate Risk-Adjustment payment weights in the Netherlands (N = 16.5 million).
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setting health plan premiums to ensure efficient quality in health care minimum variance optimal Risk Adjustment
World Scientific Book Chapters, 2017Co-Authors: Jacob Glazer, Thomas G. McguireAbstract:Risk Adjustment refers to the practice of paying health plans a premium per person (or per family) based on a formula using Risk adjusters, such as age or gender, and weights on those adjusters. One role of Risk Adjustment is to make sure plans have an incentive to accept all potential enrollees. Another role, at least as important in our view, is to lead health plans to choose the efficient level of quality of care for the various services they offer. Most of the research and policy literature on Risk Adjustment focuses on the first problem. This paper proposes a new way to calculate weights in a Risk Adjustment formula that contends with both problems. For a given set of adjusters, we identify the weights that minimize the variance in plan predictable health care costs that are not explained by Risk Adjustment (addressing the access problem), subject to the payments satisfying conditions for an optimal Risk adjuster (making sure plans provide the efficient quality). We call the formula minimum variance optimal Risk Adjustment (MVORA). © 2002 Elsevier Science B.V. All rights reserved.
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Risk Adjustment simulation plans may have incentives to distort mental health and substance use coverage
Health Affairs, 2016Co-Authors: Ellen Montz, Randall P Ellis, Sherri Rose, Timothy J Layton, Alisa B Busch, Thomas G. McguireAbstract:Under the Affordable Care Act, the Risk-Adjustment program is designed to compensate health plans for enrolling people with poorer health status so that plans compete on cost and quality rather than the avoidance of high-cost individuals. This study examined health plan incentives to limit covered services for mental health and substance use disorders under the Risk-Adjustment system used in the health insurance Marketplaces. Through a simulation of the program on a population constructed to reflect Marketplace enrollees, we analyzed the cost consequences for plans enrolling people with mental health and substance use disorders. Our assessment points to systematic underpayment to plans for people with these diagnoses. We document how Marketplace Risk Adjustment does not remove incentives for plans to limit coverage for services associated with mental health and substance use disorders. Adding mental health and substance use diagnoses used in Medicare Part D Risk Adjustment is one potential policy step tow...
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deriving Risk Adjustment payment weights to maximize efficiency of health insurance markets
National Bureau of Economic Research, 2016Co-Authors: Timothy J Layton, Thomas G. Mcguire, Richard C Van KleefAbstract:Risk Adjustment of payments to health plans is fundamental to regulated competition among private insurers, which serves as the basis of national health policy in many countries. To date, estimation and evaluation of a Risk Adjustment model has been a two-step process. In a first step, the Risk-Adjustment payment weights are estimated using statistical techniques, generally ordinary-least squares, to maximize some statistical objective such as the R-squared; then, in a second step, the Risk Adjustment model is evaluated, usually with simulation methods, but without an explicit framework describing the objective of the model. This paper first develops such a framework and then uses it to replace the two-step “estimate-then-evaluate” approach with a one-step “estimate-to-maximize-the-objective” approach. We assume that the objective of Risk Adjustment is to minimize the loss from service-level distortions due to adverse selection incentives, and we derive expressions for the service-level distortions as a linear function of the Risk Adjustment payment weights. We show that when the number of Risk adjustor variables exceeds the number of decisions plans make about service allocations, incentives for service-level distortion can always be eliminated. Under these circumstances the welfare maximizing payment weights can be found with a constrained least-squares regression where the constraints are the conditions under which plan actions achieve efficiency. We illustrate this method with the data used to estimate Risk Adjustment payment weights in the Netherlands (N=16.5 million). When the number of “services” exceeds the number of available Risk adjustors, however, it is not possible to eliminate incentives for service-level distortion. In this case, a regression on transformed data produces the (second-best) payment weights that minimize welfare loss.
Mathew J Reeves - One of the best experts on this subject based on the ideXlab platform.
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the relationship between the c statistic of a Risk Adjustment model and the accuracy of hospital report cards a monte carlo study
Medical Care, 2013Co-Authors: Peter C Austin, Mathew J ReevesAbstract:BACKGROUND: Hospital report cards, in which outcomes following the provision of medical or surgical care are compared across health care providers, are being published with increasing frequency. Essential to the production of these reports is Risk-Adjustment, which allows investigators to account for differences in the distribution of patient illness severity across different hospitals. Logistic regression models are frequently used for Risk Adjustment in hospital report cards. Many applied researchers use the c-statistic (equivalent to the area under the receiver operating characteristic curve) of the logistic regression model as a measure of the credibility and accuracy of hospital report cards. OBJECTIVES: To determine the relationship between the c-statistic of a Risk-Adjustment model and the accuracy of hospital report cards. RESEARCH DESIGN: Monte Carlo simulations were used to examine this issue. We examined the influence of 3 factors on the accuracy of hospital report cards: the c-statistic of the logistic regression model used for Risk Adjustment, the number of hospitals, and the number of patients treated at each hospital. The parameters used to generate the simulated datasets came from analyses of patients hospitalized with a diagnosis of acute myocardial infarction in Ontario, Canada. RESULTS: The c-statistic of the Risk-Adjustment model had, at most, a very modest impact on the accuracy of hospital report cards, whereas the number of patients treated at each hospital had a much greater impact. CONCLUSIONS: The c-statistic of a Risk-Adjustment model should not be used to assess the accuracy of a hospital report card.
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the relationship between the c statistic of a Risk Adjustment model and the accuracy of hospital report cards a monte carlo study
Medical Care, 2013Co-Authors: Peter C Austin, Mathew J ReevesAbstract:Background Hospital report cards, in which outcomes following the provision of medical or surgical care are compared across health care providers, are being published with increasing frequency. Essential to the production of these reports is Risk-Adjustment, which allows investigators to account for differences in the distribution of patient illness severity across different hospitals. Logistic regression models are frequently used for Risk-Adjustment in hospital report cards. Many applied researchers use the c-statistic (equivalent to the area under the receiver operating characteristic curve) of the logistic regression model as a measure of the credibility and accuracy of hospital report cards.
Peter C Austin - One of the best experts on this subject based on the ideXlab platform.
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the relationship between the c statistic of a Risk Adjustment model and the accuracy of hospital report cards a monte carlo study
Medical Care, 2013Co-Authors: Peter C Austin, Mathew J ReevesAbstract:BACKGROUND: Hospital report cards, in which outcomes following the provision of medical or surgical care are compared across health care providers, are being published with increasing frequency. Essential to the production of these reports is Risk-Adjustment, which allows investigators to account for differences in the distribution of patient illness severity across different hospitals. Logistic regression models are frequently used for Risk Adjustment in hospital report cards. Many applied researchers use the c-statistic (equivalent to the area under the receiver operating characteristic curve) of the logistic regression model as a measure of the credibility and accuracy of hospital report cards. OBJECTIVES: To determine the relationship between the c-statistic of a Risk-Adjustment model and the accuracy of hospital report cards. RESEARCH DESIGN: Monte Carlo simulations were used to examine this issue. We examined the influence of 3 factors on the accuracy of hospital report cards: the c-statistic of the logistic regression model used for Risk Adjustment, the number of hospitals, and the number of patients treated at each hospital. The parameters used to generate the simulated datasets came from analyses of patients hospitalized with a diagnosis of acute myocardial infarction in Ontario, Canada. RESULTS: The c-statistic of the Risk-Adjustment model had, at most, a very modest impact on the accuracy of hospital report cards, whereas the number of patients treated at each hospital had a much greater impact. CONCLUSIONS: The c-statistic of a Risk-Adjustment model should not be used to assess the accuracy of a hospital report card.
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the relationship between the c statistic of a Risk Adjustment model and the accuracy of hospital report cards a monte carlo study
Medical Care, 2013Co-Authors: Peter C Austin, Mathew J ReevesAbstract:Background Hospital report cards, in which outcomes following the provision of medical or surgical care are compared across health care providers, are being published with increasing frequency. Essential to the production of these reports is Risk-Adjustment, which allows investigators to account for differences in the distribution of patient illness severity across different hospitals. Logistic regression models are frequently used for Risk-Adjustment in hospital report cards. Many applied researchers use the c-statistic (equivalent to the area under the receiver operating characteristic curve) of the logistic regression model as a measure of the credibility and accuracy of hospital report cards.
Jacob Glazer - One of the best experts on this subject based on the ideXlab platform.
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setting health plan premiums to ensure efficient quality in health care minimum variance optimal Risk Adjustment
World Scientific Book Chapters, 2017Co-Authors: Jacob Glazer, Thomas G. McguireAbstract:Risk Adjustment refers to the practice of paying health plans a premium per person (or per family) based on a formula using Risk adjusters, such as age or gender, and weights on those adjusters. One role of Risk Adjustment is to make sure plans have an incentive to accept all potential enrollees. Another role, at least as important in our view, is to lead health plans to choose the efficient level of quality of care for the various services they offer. Most of the research and policy literature on Risk Adjustment focuses on the first problem. This paper proposes a new way to calculate weights in a Risk Adjustment formula that contends with both problems. For a given set of adjusters, we identify the weights that minimize the variance in plan predictable health care costs that are not explained by Risk Adjustment (addressing the access problem), subject to the payments satisfying conditions for an optimal Risk adjuster (making sure plans provide the efficient quality). We call the formula minimum variance optimal Risk Adjustment (MVORA). © 2002 Elsevier Science B.V. All rights reserved.
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Risk Adjustment as Mechanism Design
Encyclopedia of Health Economics, 2014Co-Authors: Jacob Glazer, Thomas G. McguireAbstract:Risk Adjustment of plan and provider payment pays more for the sick than for the healthy. Finding the right weights on Risk adjustor variables can be cast as a problem in mechanism design, requiring an explicit objective of Risk Adjustment policy and incorporating constraints, the most important being the response of health plans to the incentives in Risk Adjustment. We apply this perspective to the problem of encouraging plans to provide services efficiently, and to the objective of maximizing compatibility of the payment system in a managed competition context in which plans are paid partly by Risk adjusted payments and partly by premiums.
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setting health plan premiums to ensure efficient quality in health care minimum variance optimal Risk Adjustment
Journal of Public Economics, 2002Co-Authors: Jacob Glazer, Thomas G. McguireAbstract:Abstract Risk Adjustment refers to the practice of paying health plans a premium per person (or per family) based on a formula using Risk adjusters, such as age or gender, and weights on those adjusters. One role of Risk Adjustment is to make sure plans have an incentive to accept all potential enrollees. Another role, at least as important in our view, is to lead health plans to choose the efficient level of quality of care for the various services they offer. Most of the research and policy literature on Risk Adjustment focuses on the first problem. This paper proposes a new way to calculate weights in a Risk Adjustment formula that contends with both problems. For a given set of adjusters, we identify the weights that minimize the variance in plan predictable health care costs that are not explained by Risk Adjustment (addressing the access problem), subject to the payments satisfying conditions for an optimal Risk adjuster (making sure plans provide the efficient quality). We call the formula minimum variance optimal Risk Adjustment (MVORA).
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Private employers don't need formal Risk Adjustment.
INQUIRY: The Journal of Health Care Organization Provision and Financing, 2001Co-Authors: Jacob Glazer, Thomas G. McguireAbstract:This paper lays down a set of hypotheses to explain why private employers do not use formal Risk Adjustment. The theme running through these hypotheses is simple: private employers don't need formal Adjustment because they have better tools for dealing with adverse selection than formal Risk Adjustment provides. Open enrollment provisions, premium negotiations, and restricting employees' choices of health plans are mechanisms superior to formal Risk Adjustment for dealing with problems caused by adverse selection.
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optimal Risk Adjustment in markets with adverse selection an application to managed care
The American Economic Review, 2000Co-Authors: Jacob Glazer, Thomas G. McguireAbstract:It is well known that adverse selection causes distortions in contracts in markets with asymmetric information. Taxing inefficient contracts and subsidizing the efficient ones can improve market outcomes (Bruce C. Greenwald and Joseph E. Stiglitz, 1986), although regulators rarely seem to implement tax and subsidy schemes with adverse-selection motives in mind. Contracts are often complex and “incomplete,” and it is the “inefficient” elements of the contract that are difficult to verify and hence tax or subsidize. This is precisely the reason that in health insurance markets, rather than subsidizing contracts, regulators and payers contend with adverse selection by taxing and subsidizing the price paid to insuring health plans on the basis of observable characteristics of the persons joining the plan—a practice known as “Risk Adjustment.” Risk-adjusted premiums are paid to “managed-care” plans—plans that ration care by management, rather than by conventional approaches like coinsurance and deductibles, and offer a bundle of characteristics (quality, access for many services) that is fundamentally outside the scope of direct regulation. Selection-related incentives threaten the efficiency and fairness of this organization of health insurance markets by inducing plans to distort the quality of the services they offer to discourage high-cost persons from joining the plan. As managed care becomes the predominant source of health care for residents of the United States and many other countries, payers attempt to address this incentive by setting a Risk-adjusted price that pays more for more-expensive enrollees. As it is conventionally practiced, Risk Adjustment sets prices for people proportional to their expected cost based on observable characteristics. The federal Medicare program, for example, has used age, sex, welfare status, and county-of-residence adjusters to set prices to managed-care plans. To convey how what we term “conventional” Risk Adjustment works, suppose age is the Risk adjuster for a Medicare population over 65. If it is determined that the 75to 84-year-old population costs 20 percent more than the overall average in Medicare, the assumption in conventional Risk Adjustment is that the premium paid to plans for someone in this group should be 20 percent above the average. We fundamentally disagree that this is the right way to think about and do Risk Adjustment.
Timothy J Layton - One of the best experts on this subject based on the ideXlab platform.
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sample selection for medicare Risk Adjustment due to systematically missing data
Health Services Research, 2018Co-Authors: Savannah L Bergquist, Thomas G. Mcguire, Timothy J Layton, Sherri RoseAbstract:Objective To assess the issue of nonrepresentative sampling in Medicare Advantage (MA) Risk Adjustment. Data sources Medicare enrollment and claims data from 2008 to 2011. Data extraction Risk Adjustment predictor variables were created from 2008 to 2010 Part A and B claims and the Medicare Beneficiary Summary File. Spending is based on 2009-2011 Part A and B, Durable Medical Equipment, and Home Health Agency claims files. Study design A propensity-score matched sample of Traditional Medicare (TM) beneficiaries who resembled MA enrollees was created. Risk Adjustment formulas were estimated using multiple techniques, and performance was evaluated based on R2 , predictive ratios, and formula coefficients in the matched sample and a random sample of TM beneficiaries. Principal findings Matching improved balance on observables, but performance metrics were similar when comparing Risk Adjustment formula results fit on and evaluated in the matched sample versus fit on the random sample and evaluated in the matched sample. Conclusions Fitting MA Risk Adjustment formulas on a random sample versus a matched sample yields little difference in MA plan payments. This does not rule out potential improvements via the matching method should reliable MA encounter data and additional variables become available for Risk Adjustment.
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deriving Risk Adjustment payment weights to maximize efficiency of health insurance markets
Journal of Health Economics, 2018Co-Authors: Timothy J Layton, Thomas G. Mcguire, Richard C Van KleefAbstract:Risk-Adjustment is critical to the functioning of regulated health insurance markets. To date, estimation and evaluation of a Risk-Adjustment model has been based on statistical rather than economic objective functions. We develop a framework where the objective of Risk-Adjustment is to minimize the efficiency loss from service-level distortions due to adverse selection, and we use the framework to develop a welfare-grounded method for estimating Risk-Adjustment weights. We show that when the number of Risk adjustor variables exceeds the number of decisions plans make about service allocations, incentives for service-level distortion can always be eliminated via a constrained least-squares regression. When the number of plan service-level allocation decisions exceeds the number of Risk-adjusters, the optimal weights can be found by an OLS regression on a straightforward transformation of the data. We illustrate this method with the data used to estimate Risk-Adjustment payment weights in the Netherlands (N = 16.5 million).
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imperfect Risk Adjustment Risk preferences and sorting in competitive health insurance markets
Journal of Health Economics, 2017Co-Authors: Timothy J LaytonAbstract:I develop a model of insurer price-setting and consumer welfare under Risk-Adjustment, a policy commonly used to combat inefficient sorting due to adverse selection in health insurance markets. I use the model to illustrate graphically that Risk-Adjustment causes health plan prices to be based on costs not predicted by the Risk-Adjustment model (“residual costs”) rather than total costs, either weakening or exacerbating selection problems depending on the correlation between demand and costs predicted by the Risk-Adjustment model. I then use a structural model to estimate the welfare consequences of Risk-Adjustment, finding a welfare gain of over $600 per person-year.
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Risk Adjustment simulation plans may have incentives to distort mental health and substance use coverage
Health Affairs, 2016Co-Authors: Ellen Montz, Randall P Ellis, Sherri Rose, Timothy J Layton, Alisa B Busch, Thomas G. McguireAbstract:Under the Affordable Care Act, the Risk-Adjustment program is designed to compensate health plans for enrolling people with poorer health status so that plans compete on cost and quality rather than the avoidance of high-cost individuals. This study examined health plan incentives to limit covered services for mental health and substance use disorders under the Risk-Adjustment system used in the health insurance Marketplaces. Through a simulation of the program on a population constructed to reflect Marketplace enrollees, we analyzed the cost consequences for plans enrolling people with mental health and substance use disorders. Our assessment points to systematic underpayment to plans for people with these diagnoses. We document how Marketplace Risk Adjustment does not remove incentives for plans to limit coverage for services associated with mental health and substance use disorders. Adding mental health and substance use diagnoses used in Medicare Part D Risk Adjustment is one potential policy step tow...
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deriving Risk Adjustment payment weights to maximize efficiency of health insurance markets
National Bureau of Economic Research, 2016Co-Authors: Timothy J Layton, Thomas G. Mcguire, Richard C Van KleefAbstract:Risk Adjustment of payments to health plans is fundamental to regulated competition among private insurers, which serves as the basis of national health policy in many countries. To date, estimation and evaluation of a Risk Adjustment model has been a two-step process. In a first step, the Risk-Adjustment payment weights are estimated using statistical techniques, generally ordinary-least squares, to maximize some statistical objective such as the R-squared; then, in a second step, the Risk Adjustment model is evaluated, usually with simulation methods, but without an explicit framework describing the objective of the model. This paper first develops such a framework and then uses it to replace the two-step “estimate-then-evaluate” approach with a one-step “estimate-to-maximize-the-objective” approach. We assume that the objective of Risk Adjustment is to minimize the loss from service-level distortions due to adverse selection incentives, and we derive expressions for the service-level distortions as a linear function of the Risk Adjustment payment weights. We show that when the number of Risk adjustor variables exceeds the number of decisions plans make about service allocations, incentives for service-level distortion can always be eliminated. Under these circumstances the welfare maximizing payment weights can be found with a constrained least-squares regression where the constraints are the conditions under which plan actions achieve efficiency. We illustrate this method with the data used to estimate Risk Adjustment payment weights in the Netherlands (N=16.5 million). When the number of “services” exceeds the number of available Risk adjustors, however, it is not possible to eliminate incentives for service-level distortion. In this case, a regression on transformed data produces the (second-best) payment weights that minimize welfare loss.
