The Merit-based Incentive Payment System: Pearson’s Chi-Square and Categorical Dependent Variable Models Analyzed for Domains—Effective Clinical Care and Efficiency/Cost Reduction

Background: Following the 2015 repeal of the Sustainable Growth Rate formula, the US Centers for Medicare & Medicaid Services’ formula under which physicians were reimbursed, two payment systems were put in place to incentivize physicians, one of which was the Merit-based Incentive Payment System (MIPS). MIPS emphasizes high-quality care that is accessible, affordable, and supports a healthier population. Objectives: This research aims to measure characteristics of MIPS relevant to National Quality Strategy (NQS) domains, quality measure types, and clinical specialties; categorize MIPS with NQS domains and quality measure types by MIPS specialty types; and quantify the relationship between MIPS specialties, measure types, and two NQS domains, Effective Clinical Care (ECC) and Efficiency/Cost Reduction (E/CR), for years 2017 through 2020. Methodology: The Pearson’s chi-square test examined distributions of the analyzed categorical variables. The Categorical Dependent Variable Method examined the association between the dependent and independent variables. Results: The Pearson’s chi-square test showed statistically significant distributions between ECC and E/CR when analyzed with the types of quality measures. There were more process measures (93.81% vs 89.64% [P=.000]) in 2018 versus 2017. This changed minutely with significantly less process measures (93.75% vs 93.81% [P=.000]) in 2019 versus 2018. Finally, measure types changed minutely but significantly with less process measures (93.81% vs 93.75% [P=.000]) in 2020 versus 2019. The regression model showed that ECC was significantly associated with outcome measures through all analyzed years of this research. Conclusion: The above findings show scope for including additional outcome measures, given its importance in MIPS. There is potential to increase the percentage allocation for reporting more outcome measures in quality. This re-allotment infers reporting more outcome measures aligning with priority outcome measures (PROMs). Re-allocating the incentive formula to report more outcome measures aligned with PROMs shows potential to increase reporting of more outcome measures under MIPS.

Air-Wake Prediction Based Air-Vehicle Recovery Aids

This report demonstrates the capability of the forward prediction of the properties of the arriving wind at a vessel for time intervals adequate to significantly aid in the recovery of a wide range of air vehicles onto vessels. For craft with flight decks sited in the fore part of the vessel it is adequate to simply predict the arriving wind. For the more difficult task of recovery to stern areas behind superstructure it is also necessary to predict either the explicit properties of the turbulent air-wake or else to predict some quality measure for the aid of recovery under the prevailing conditions. The approach is able to relate the trends in the short-term statistical properties of fluctuating airflow over the flight deck to the trends in the predicted arriving wind.

Fourier Ring Correlation and Anisotropic Kernel Density Estimation Improve Deep Learning Based SMLM Reconstruction of Microtubules

Single-molecule super-resolution microscopy (SMLM) techniques like dSTORM can reveal biological structures down to the nanometer scale. The achievable resolution is not only defined by the localization precision of individual fluorescent molecules, but also by their density, which becomes a limiting factor e.g., in expansion microscopy. Artificial deep neural networks can learn to reconstruct dense super-resolved structures such as microtubules from a sparse, noisy set of data points. This approach requires a robust method to assess the quality of a predicted density image and to quantitatively compare it to a ground truth image. Such a quality measure needs to be differentiable to be applied as loss function in deep learning. We developed a new trainable quality measure based on Fourier Ring Correlation (FRC) and used it to train deep neural networks to map a small number of sampling points to an underlying density. Smooth ground truth images of microtubules were generated from localization coordinates using an anisotropic Gaussian kernel density estimator. We show that the FRC criterion ideally complements the existing state-of-the-art multiscale structural similarity index, since both are interpretable and there is no trade-off between them during optimization. The TensorFlow implementation of our FRC metric can easily be integrated into existing deep learning workflows.