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Objective: To complement the previously illustrated method to measure resource use in Medicare Advantage (MA) using Encounter data and provide technical details and SAS code to validate Encounter data and implement resource use measures in MA. Data Sources: 2015-2018 MA Encounter, Medicare Provider Analysis and Review (MedPAR), Healthcare Effectiveness Data and Information System (HEDIS), and Traditional Medicare (TM) claims data. Study Design: Secondary data analysis. Data Collection/Extraction Methods: We select MA contracts with high data completeness ([less than or equal to]10% missing hospital stays in Encounter data and [less than or equal to][+ or -]10% difference in ambulatory and emergency department visits between Encounter and HEDIS data). We randomly sample TM beneficiaries with a similar geographic distribution as MA enrollees in the selected contracts. We develop standardized prices of services using TM payments, and we measure MA resource use for inpatient, outpatient, Part D, and hospice services. Principal Findings: We report identifiers/names of contracts with high data completeness. We provide SAS code to manage Encounter data, develop standardized prices, and measure MA resource use. Conclusions: Greater use and validation of Encounter data can help improve data quality. Our results can be used to inform studies using Encounter data to learn about MA performance. KEYWORDS data validation, Encounter data, Medicare Advantage, resource use, standardized prices What is known on this topic * Medicare Advantage (MA) Encounter data are the counterpart of Traditional Medicare (TM) claims, but they have missing records and do not include payment information. * Prior work assessed the completeness of 2015 Preliminary Encounter data, checking the data against other data sources. * Prior work illustrated a method to measure MA resource use for inpatient and outpatient care, using pilot 2015 Encounter data from MA enrollees with cancer. What this study adds * We identify MA contracts with highly complete Encounter data between 2015 and 2018. * We refine the previously illustrated method to measure MA resource use and expand the method to include Part D and hospice services. * We provide technical details and SAS code to implement the calculation of MA resource use, including validation of Encounter data and development of standardized prices., 1 | INTRODUCTION Encounter data are an important data source to examine care provision in Medicare Advantage (MA). However, a concern exists that Encounter data have missing records. (1,2) Lack [...]

Objective: To check the completeness of Medicare Advantage (MA) Encounter data and to illustrate a process to measure resource use among MA enrollees using Encounter data. Data sources: 2015 Preliminary MA Encounter, Medicare Provider Analysis and Review (MedPAR), Healthcare Effectiveness Data and Information System (HEDIS), and 2013 Traditional Medicare (TM) claims data. Study design: Secondary data analysis. Data collection/extraction methods: We calculated the percentage of each contract's total hospitalizations in Encounter data after identifying total inpatient stays from Encounter and MedPAR data. We constructed each contract's ambulatory visits and emergency department (ED) visits per 1000 enrollees using Encounter data and compared those visit counts with the counts from HEDIS. We defined high data completeness as having less than 10% missing hospital stays and less than [+ or -]10% difference in ambulatory and ED visits between Encounter and HEDIS data. We used TM payments as standardized prices of services to examine resource use among MA enrollees with cancer in the contracts with high data completeness. Principal findings: We identified 83 of 380 MA contracts with high data completeness. Total resource use per enrollee with cancer in the 83 contracts was $14,715 in 2015. Service-specific resource use was $5342 for inpatient care, $5932 for professional services and $3441 for outpatient facility services. These represent what an MA enrollee with cancer would have cost on average if MA plans paid providers at TM payment rates, holding the observed utilization constant. Conclusions: Checking the completeness of Encounter data is an important step to ensure the validity of research on MA resource use. Using Encounter data to measure MA resource use is feasible. It can compensate for the lack of payment information in Encounter data. It will be important to identify and refine ways to best use Encounter data to learn about care provision to MA enrollees. KEYWORDS data assessment, Encounter data, Medicare Advantage, resource use, standardized prices, What is known on this topic * The existing literature has examined care utilization and spending on specific services in Medicare Advantage (MA). * Evidence comparing resource use in MA [...]

The Next Generation Accountable Care Organization (NGACO) model (active during 2016–21) tested the effects of high financial risk, payment mechanisms, and flexible care delivery on health care spending and value for fee-for-service Medicare beneficiaries. We used quasi-experimental methods to examine the model’s effects on Medicare Parts A and B spending. Sixty-two ACOs with more than 4.2 million beneficiaries and more than 91,000 practitioners participated in the model. The model was associated with a $270 per beneficiary per year, or approximately $1.7 billion, decline in Medicare spending. After shared savings payments to ACOs were included, the model increased net Medicare spending by $56 per beneficiary per year, or $96.7 million. Annual declines in spending for the model grew over time, reflecting exit by poorer-performing NGACOs, improvement among the remaining NGACOs, and the COVID-19 pandemic. Larger declines in spending occurred among physician practice ACOs and ACOs that elected population-based payments and risk caps greater than 5 percent.

Objectives. To examine variation in risk-adjusted reinsurance payments across Part D plans, analyze its implications for the program, and explore options to reduce reinsurance payments.Data/Study Design. 2007-2015 Part D Plan Payment and Premium data; 2010-2013 Part D Prescription Drug Event data; and 2013 Part D Plan Formulary Files.Principal Findings. Risk-adjusted reinsurance payments varied widely across plans at a given out-of-pocket (OOP) premium. The variance in risk-adjusted reinsurance in common OOP premium ranges increased between 2010 and 2015. High risk-adjusted reinsurance payments were negatively correlated with use of utilization management tools for high-cost drugs.Conclusions. Growing reinsurance payments shrink plans' liability for managing drug spending for high-cost enrollees, creating plan moral hazard, and making OOP premiums a noisy signal of plans' total costs.Key Words. Part D reinsurance, competitive bidding, plan moral hazard, Medicare Part D benefits are delivered through private plans--stand-alone Prescription Drug Plans (PDPs) or Medicare Advantage Prescription Drug Plans (MA-PDs). Medicare subsidizes about 75 percent of Part D spending through [...]