2019 Risk Adjustment

Praise for the first edition: "DNP students may struggle with data management, since their projects are not research but quality improvement, and this book covers the subject well. I recommend it for DNP students for use during their capstone projects." Score: 98, 5 Stars -- Doody's Medical Reviews This unique text and reference—the only book to address the full spectrum of clinical data management for the DNP student—instills a fundamental understanding of how clinical data is gathered, used, and analyzed, and how to incorporate this data into a quality DNP project. The new third edition is updated to reflect changes in national health policy such as quality measurements, bundled payments for specialty care, and Advances to the Affordable Care Act (ACA) and evolving programs through the Centers for Medicare and Medicaid Services (CMS). The third edition reflects the revision of 2021 AACN Essentials and provides data sets and other examples in Excel and SPSS format, along with several new chapters. This resource takes the DNP student step-by-step through the complete process of data management, from planning through presentation, clinical applications of data management that are discipline-specific, and customization of statistical techniques to address clinical data management goals. Chapters are brimming with descriptions, resources, and exemplars that are helpful to both faculty and students. Topics spotlight requisite competencies for DNP clinicians and leaders such as phases of clinical data management, statistics and analytics, assessment of clinical and economic outcomes, value-based care, quality improvement, benchmarking, and data visualization. A progressive case study highlights multiple techniques and methods throughout the text. New to the Third Edition: New Chapter: Using EMR Data for the DNP Project New chapter solidifies link between EBP and Analytics for the DNP project New chapter highlights use of workflow mapping to transition between current and future state, while simultaneously visualizing process measures needed to ensure success of the DNP project Includes more examples to provide practical application exercises for students Key Features: Disseminates robust strategies for using available data from everyday practice to support trustworthy evaluation of outcomes Uses multiple tools to meet data management objectives [SPSS, Excel®, Tableau] Presents case studies to illustrate multiple techniques and methods throughout chapters Includes specific examples of the application and utility of these techniques using software that is familiar to graduate nursing students Offers real world examples of completed DNP projects Provides Instructor’s Manual, PowerPoint slides, data sets in SPSS and Excel, and forms for completion of data management and evaluation plan

Prominent voices have called for a better way to measure, predict, and adjust for social factors in healthcare and population health. Local area characteristics are sometimes framed as a proxy for patient characteristics, but they are often independently associated with health outcomes. We have developed an “artificially intelligent” approach to risk adjustment for local social determinants of health (SDoH) using random forest models to understand life expectancy at the Census tract level. Our Local Social Inequity score draws on more than 150 neighborhood-level variables across 10 SDoH domains. As piloted in Ohio, the score explains 73 percent of the variation in life expectancy by Census tract, with a mean squared error of 4.47 years. Accurate multidimensional, cross-sector, small-area social risk scores could be useful in understanding the impact of healthcare innovations, payment models, and SDoH interventions in communities at higher risk for serious illnesses and diseases; identifying neighborhoods and areas at highest risk of poor outcomes for better targeting of interventions and resources; and accounting for factors outside of providers’ control for more fair and equitable performance/quality measurement and reimbursement.

Risk Adjustment, Risk Sharing and Premium Regulation in Health Insurance Markets: Theory and Practice describes the goals, design and evaluation of health plan payment systems. Part I contains 5 chapters discussing the role of health plan payment in regulated health insurance markets, key aspects of payment design (i.e. risk adjustment, risk sharing and premium regulation), and evaluation methods using administrative data on medical spending. Part II contains 14 chapters describing the health plan payment system in 14 countries and sectors around the world, including Australia, Belgium, Chile, China, Columbia, Germany, Ireland, Israel, the Netherlands, Russia, Switzerland and the United States. Authors discuss the evolution of these payment schemes, along with ongoing reforms and key lessons on the design of health plan payment. Provides a conceptual toolkit that describes the goals, design and evaluation of health plan payment systems in the context of policy paradigms, such as efficiency, affordability, fairness and avoidance of risk selection Brings together international experience from many different countries that apply regulated competition in different ways Delivers a practical toolkit for the evaluation of health plan payment modalities from the standpoint of efficiency and fairness

The Risk Adjustment Coding and HCC Guide brings together hard-to-find information about risk adjustment (RA) coding and hierarchical condition categories (HCCs) in a new comprehensive resource that explains this complex reimbursement methodology. Now your organization will have a guide that provides both the big picture and the fine detail needed to document, code, and report essential information so that accurate risk levels are assigned and appropriate reimbursement received.

The October 2019 Global Financial Stability Report (GFSR) identifies the current key vulnerabilities in the global financial system as the rise in corporate debt burdens, increasing holdings of riskier and more illiquid assets by institutional investors, and growing reliance on external borrowing by emerging and frontier market economies. The report proposes that policymakers mitigate these risks through stricter supervisory and macroprudential oversight of firms, strengthened oversight and disclosure for institutional investors, and the implementation of prudent sovereign debt management practices and frameworks for emerging and frontier market economies.

Explore different measures of ex-post risk-adjusted performance measurement and learn to choose the correct one In the newly revised Second Edition of Practical Risk-Adjusted Performance Measurement, accomplished risk and investment expert Carl R. Bacon delivers an insightful, accessible, and real-world guide to ex-post risk measurement. The author bridges the gap between theory and practice, showing you how to apply the former to the latter without introducing unnecessary mathematical complexity. The book describes the fundamentals of risk in the asset management context and the descriptive statistics used to describe it. It builds on that foundation with detailed examinations of concepts like regression, drawdown, and partial moments, before moving on to topics like fixed income risk and Prospect Theory. With helpful additions that include recently developed measures of risk, supplementary explanatory sections, and six brand-new chapters, this book also offers: A practical classification of all ex-post risk measures and how they connect to one another An explanation of how risk-adjusted performance measures impact performance fees A discussion of risk measure dashboard designs Instructions on how appraisal measures should be used for manager selection Perfect for portfolio managers, asset owners, risk controllers, and investment performance analysts, Practical Risk-Adjusted Performance Measurement is an indispensable resource for anyone looking for a hands-on exploration of the buy-side, asset management perspective.