MACRA Palliative Care Quality Measure Development-Testing Summary Report: Measure Name: Receiving Desired Help for Pain.

Palliative care has expanded rapidly in the past 20 years, especially in the ambulatory (office) setting, and there is growing consensus regarding the need to systematically measure and incentivize high-quality care. The Centers for Medicare & Medicaid Services entered a cooperative agreement with the American Academy of Hospice and Palliative Medicine (AAHPM) as part of the Medicare Access and CHIP Reauthorization Act of 2015 to develop two patient-reported measures of ambulatory palliative care experience: Feeling Heard and Understood and Receiving Desired Help for Pain. Under contract to AAHPM, RAND Health Care researchers developed and tested both measures over a three-year project period. Researcher efforts included identifying, developing, testing, and validating appropriate patient-reported data elements for each measure; developing and fielding a survey instrument to collect necessary data in a national beta field test with 44 ambulatory palliative care programs; and collecting and analyzing data about measure reliability and validity to establish measure performance and final specifications. Further, the authors elicited provider and program perspectives on the use and value of the performance measures and their implementation and elicited the perspectives of patients from racial and ethnic minorities to understand their experience of ambulatory palliative care and optimal approaches to measurement. In this study, the authors present results from their test of the Receiving Desired Help for Pain performance measure, which they demonstrate to be a reliable and valid measure that is ready for use in quality improvement and quality payment programs.

MACRA Palliative Care Quality Measure Development-Testing Summary Report: Measure Name: Feeling Heard and Understood.

Palliative care has expanded rapidly in the past 20 years, especially in the ambulatory (office) setting, and there is growing consensus regarding the need to systematically measure and incentivize high-quality care. The Centers for Medicare & Medicaid Services entered a cooperative agreement with the American Academy of Hospice and Palliative Medicine (AAHPM) as part of the Medicare Access and CHIP Reauthorization Act of 2015 to develop two patient-reported measures of ambulatory palliative care experience: Feeling Heard and Understood and Receiving Desired Help for Pain. Under contract to AAHPM, RAND Health Care researchers developed and tested both measures over a three-year project period. Researcher efforts included identifying, developing, testing, and validating appropriate patient-reported data elements for each measure; developing and fielding a survey instrument to collect necessary data in a national beta field test with 44 ambulatory palliative care programs; and collecting and analyzing data about measure reliability and validity to establish measure performance and final specifications. Further, the authors elicited provider and program perspectives on the use and value of the performance measures and their implementation and elicited the perspectives of patients from racial and ethnic minorities to understand their experience of ambulatory palliative care and optimal approaches to measurement. In this study, the authors present results from their test of the Feeling Heard and Understood performance measure, which they demonstrate to be a reliable and valid measure that is ready for use in quality improvement and quality payment programs.

Higher Moments for Optimal Balance Weighting in Causal Estimation.

We expand upon a simulation study that compared three promising methods for estimating weights for assessing the average treatment effect on the treated for binary treatments: generalized boosted models, covariate-balancing propensity scores, and entropy balance. The original study showed that generalized boosted models can outperform covariate-balancing propensity scores, and entropy balance when there are likely to be nonlinear associations in both the treatment assignment and outcome models and when the other two models are fine-tuned to obtain balance only on first-order moments. We explore the potential benefit of using higher-order moments in the balancing conditions for covariate-balancing propensity scores and entropy balance. Our findings showcase that these two models should, by default, include higher-order moments and focusing only on first moments can result in substantial bias in estimated treatment effect estimates from both models that could be avoided using higher moments.

Nonparametric Estimation of Population Average Dose-Response Curves using Entropy Balancing Weights for Continuous Exposures.

Weighted estimators are commonly used for estimating exposure effects in observational settings to establish causal relations. These estimators have a long history of development when the exposure of interest is binary and where the weights are typically functions of an estimated propensity score. Recent developments in optimization-based estimators for constructing weights in binary exposure settings, such as those based on entropy balancing, have shown more promise in estimating treatment effects than those methods that focus on the direct estimation of the propensity score using likelihood-based methods. This paper explores recent developments of entropy balancing methods to continuous exposure settings and the estimation of population dose-response curves using nonparametric estimation combined with entropy balancing weights, focusing on factors that would be important to applied researchers in medical or health services research. The methods developed here are applied to data from a study assessing the effect of non-randomized components of an evidence-based substance use treatment program on emotional and substance use clinical outcomes.

Optimally Balanced Gaussian Process Propensity Scores for Estimating Treatment Effects.

Propensity scores are commonly employed in observational study settings where the goal is to estimate average treatment effects. This paper introduces a flexible propensity score modeling approach, where the probability of treatment is modeled through a Gaussian process framework. To evaluate the effectiveness of the estimated propensity score, a metric of covariate imbalance is developed that quantifies the discrepancy between the distributions of covariates in the treated and control groups. It is demonstrated that this metric is ultimately a function of the hyperparameters of the covariance matrix of the Gaussian process and therefore it is possible to select the hyperparameters to optimize the metric and minimize overall covariate imbalance. The effectiveness of the GP method is compared in a simulation against other methods of estimating the propensity score and the method is applied to data from Dehejia and Wahba (1999) to demonstrate benchmark performance within a relevant policy application.