2345_sejda-DYJ.pdf - Chapter 12 References Dunn J.B L...

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Unformatted text preview: Chapter 12 References Dunn, J.B., L. Gaines, J.C. Kelly, C. James, and K.G. Gallagher. 2015a. The significance of Li-ion batteries in electric vehicle life-cycle energy and emissions and recycling's role in its reduction. Energy & Environmental Science 8(1):158-168. doi:10.1039/C4EE03029J. Available at: . (Accessed: February 27, 2018). Dunn, J.B., Z. Qin, S. Mueller, H. Kwon, M. Wander, and M. Wang. 2015b. Carbon Calculator for Land Use Change from Biofuels Production (CCLUB), Users' Manual and Technical Documentation (No. ANL/ESD/12-5 Rev. 2). Argonne National Laboratory (ANL). Available at: . (Accessed: February 26, 2018). Durack, P.J. and S.E. Wijffels. 2010. Fifty-year Trends in Global Ocean Salinities and their Relationship to Broad-scale Warming. Journal of Climate 23(16):4342–4362. doi:10.1175/2010JCLI3377.1. Available at: . (Accessed: February 27, 2018). Duveneck, M.J., R.M. Scheller, M.A. White, S.D. Handler, and C. Ravenscroft. 2014. Climate Change Effects on Northern Great Lake (USA) Forests: A Case for Preserving Diversity. Ecosphere 5(2):23. doi:10.1890/ES13-00370.1. Available at: . (Accessed: June 20, 2016). Easton, M., M. Gibson, A. Beer, M. Barnett, C. Davies, Y. Durandet, S. Blacket, X. Chen, N. Birbilis, T. Abbot. 2012. The Application of Magnesium Alloys to the Lightweighting of Automotive Structures. Sustainable Automotive Technologies 2012 pp. 17-23. Available at: . (Accessed: February 15, 2017). Eckel. S.P., K. Berhane, M.T. Salam, E.B. Rappaport, W.S. Linn, T.M. Bastain, Y. Zhang, F. Lurmann, E.L. Avol, and F.D. Gilliland. 2011. Residential Traffic-related Pollution Exposure and Exhaled Nitric Oxide in the Children’s Health Study. Environmental Health Perspectives 119:1472–1477. doi:10.1289/ehp.1103516. Available at: . (Accessed: February 27, 2018). Ehrenberger, S. 2013. Life Cycle Assessment of Magnesium Components in Vehicle Construction. German Aerospace Centre e.V. Institute of Vehicle Concepts. Stuttgart, Germany. Available at: . (Accessed: February 27, 2018). EIA (Energy Information Administration). 2006. Annual Energy Outlook 2006 with Projections to 2030. U.S. Department of Energy, U.S. Energy Information Administration. Available at: . (Accessed: February 27, 2018). EIA. 2009. Top 100 Oil and Gas Fields of 2009. U.S. Department of Energy, U.S. Energy Information Administration. Available at: rrent/pdf/top100fields.pdf. (Accessed: April 20, 2015). EIA. 2011a. Annual Energy Outlook 2011. DOE/EIA-0383. April 2011. U.S. Department of Energy, U.S. Energy Information Administration, Office of Integrated and International Energy Analysis: 12-16 TABLE 1 3– 1 0 There was little change in results for survey-based measures in MA over the last year Measures collected through the HOS Star rating year Improving or maintaining physical health Improving or maintaining mental health Monitoring physical activity Reducing the risk of falling Improving bladder control 2018 67% 85% 53% 57% 45% 2019 68 83 53 57 45 Measures collected through CAHPS® Star rating year Influenza vaccination rates Getting needed care Getting appointments Customer service Rating of quality of care Rating of plan Care coordination 2018 73% 84% 78% 90% 87% 87% 86% 2019 72 84 79 91 87 87 86 Note: MA (Medicare Advantage), HOS (Health Outcomes Survey), CAHPS® (Consumer Assessment of Healthcare Providers and Systems Survey®). Year 2018 star ratings were released in October 2017; year 2019 star ratings were released in October 2018. Source: MedPAC analysis of CMS star data and enrollment reports. of other measures that improved is less reliable because they are based on contract-level medical record sampling or contract-level surveys. Those measures had incremental improvements, including colorectal cancer screening and eye exams for diabetics (in addition to the HOS measure of maintaining or improving mental health), which each improved by 5 percent. Control of blood sugar among diabetics improved by 4 percent. Among local PPOs, the six measures that improved were the osteoporosis management measure (by 50 percent); the body mass index (BMI)–recording measure (also based on medical record sampling), colorectal cancer screening, eye exams for diabetics, and blood sugar control among diabetics (each by 8 percent); and the kidney disease–monitoring measure (by 5 percent). Of the 23 star measures in 2019 that allow for HMO results to be compared with local PPO results, results for 17 measures are within 1 percent of each other. Local PPOs outperform HMOs in the influenza vaccination rate (76 percent vs. 73 percent), and for five measures, HMOs show better performance. HMOs show substantially better performance than local PPOs in the osteoporosis management measure (17 percent better than local PPOs), medication reconciliation after discharge measure (10 percent better), and managing the risk of falling measure (7 percent better). Developing a method of comparing MA and FFS quality The need to be able to compare MA and FFS quality has long been recognized. The Medicare Improvements for Patients and Providers Act of 2008 includes a requirement for the Commission to conduct a study on this issue— that is, methods that could be used to compare MA and FFS quality (in addition to studying how to compare quality among MA plans). In its March 2010 report, the Commission made a number of recommendations in response to the mandate, including the following: • meaningful use standards for electronic health records should be such that those records could form the basis of quality metrics; • quality results should be collected and reported on a market area–basis for the two sectors; • the HOS should be fielded for FFS beneficiaries (rather than only MA, and only if such surveys would produce meaningful results); and • specifications for encounter data submission should be such that encounter data could be the basis for calculating patient outcome measures. Report to the Congress: Medicare Payment Policy  |  March 2019 373 Chapter 12 References Washington, D.C. Available at: . (Accessed: February 27, 2018). EIA. 2011b. Assumptions to the Annual Energy Outlook 2011. Transportation Demand Module. Washington, D.C. U.S. Energy Information Administration. Available at: . (Accessed: February 28, 2018). EIA. 2011c. Most electric generating capacity additions in the last decade were natural gas-fired. Available at: . (Accessed: June 7, 2018). EIA. 2012a. Annual Energy Outlook 2012. Early Release Overview. DOE/EIA-0383ER. Washington, D.C.: U.S. Energy Information Administration. Available at: (2012).pdf. (Accessed: February 28, 2018). EIA. 2012b. EIA’s AEO2012 includes analysis of breakthroughs in vehicle battery technology. Last revised: July 2, 2012. U.S. Energy Information Administration. Available at: . (Accessed: February 16, 2018). EIA. 2014a. How Much Carbon Dioxide is Produced when Different Fuels are Burned? Frequently Asked Questions Website. U.S. Energy Information Administration. Last revised: June 8, 2017. U.S. Energy Information Administration. Available at: . (Accessed: June 4, 2014). EIA. 2014b. International Energy Statistics, Total Primary Energy Consumption. U.S. Energy Information Administration. Available at: . (Accessed: February 28, 2018). EIA. 2014c. Market Trends: Natural Gas. Annual Energy Outlook 2014. U.S. Energy Information Administration. Available at: (2014).pdf. (Accessed: June 4, 2014). EIA. 2016a. Biodiesel Production, Exports, and Consumption. Monthly Energy Review. Available at: . (Accessed: March 24, 2018). EIA. 2016b. Hydraulic Fracturing accounts for about half of current U.S. crude oil production. U.S. Energy Information Administration. Last revised: March 15, 2016. Available at: . (Accessed: February 28, 2018). EIA. 2016c. Market Trends: Natural Gas. Annual Energy Outlook 2016. U.S. Energy Information Administration. Last Revised: September 15, 2016. Available at: N. (Accessed: February 28, 2018). EIA. 2016d. Today in Energy: Hydraulically Fractured Wells Provide Two-thirds of U.S. Natural Gas Production. Last revised: May 5, 2016. U.S. Energy Information Administration. Available at: . (Accessed: February 28, 2018). 12-17 Regarding the last point, there are many advantages to relying primarily on encounter data as the basis for evaluating quality in MA—not the least of which is the ability to compare FFS and MA results using a data source that is more likely to ensure consistency of measurement between the two sectors. Encounter reporting is a mechanism that is perhaps less subject to variation across plans in MA given the standards that apply to the submissions. Using encounter data that plans are already required to submit can substitute for other plan reporting and can address some of the weaknesses of the current quality reporting system. For example, we frequently note that plans that are new to MA tend to show poorer performance on plan-reported quality measures collected through HEDIS, and their ability to report improves over time. Such improvement reflects greater familiarity with the reporting system and better administration, but it often does not mean there has been any change in the quality of care. Similarly, plans with sophisticated electronic medical record systems frequently have better HEDIS results than other plans (compare, for example, the differences between plans that report based on administrative data and those that report based on medical record review for measures in which both options are possible) (Medicare Payment Advisory Commission 2018a). In contrast with measures reported based on medical record sampling, claims and encounter data (when the encounter data are complete and accurate) can provide information on the universe of beneficiaries receiving care. Such complete reporting facilitates analysis of issues such as geographic variation in quality and permits stratification by the factors that NCQA recommends (all of which are known from administrative data). In FFS, a number of quality measures are already calculated using claims data (such as mortality, readmissions, and Medicare spending per beneficiary), and such measures could also be calculated based on encounter data. Examining the Medicare Advantage star rating system In this section, we discuss the results of our detailed examination of various aspects of the MA star rating system and suggest possible ways of improving aspects of the quality measurement system. MA contracts are rated using a 5-star rating system that includes up to 46 measures of clinical quality, patient experience, and administrative performance. Measures are assigned different weights, with outcome measures more heavily weighted than process measures. A contract’s 374 The Medicare Advantage program: Status report star rating is the weighted average of the star values for the individual measures. For most measures, CMS uses what we refer to as a “tournament model” to evaluate plan performance and to group that performance into the five different star levels. Under this model, each year CMS determines new statistical “cut points” for ranking plans into the five star groups. Every year, the tournament, or competition, among plans determines which contracts fall into which star category—regardless of what the cut points might have been in the preceding year. The star rating system is intended to help beneficiaries evaluate their Medicare choices and serves as the basis of bonus payments to plans. Bonus payments take the form of a 5 percent increase in the MA benchmark (or 10 percent in some counties) for plans with an overall average rating of 4 stars or higher. In addition to the Commission’s concerns regarding unwarranted payments and inaccurate information on MA quality in many areas, we have additional concerns with the implementation of the star system. These concerns are consistent with those raised by a technical expert panel sponsored by CMS (Damberg and Paddock 2018) and are the subject of proposed changes in CMS’s recent notice of proposed rulemaking (Centers for Medicare & Medicaid Services 2018). Contract-level reporting of quality and nonrepresentative samples Wide contract configurations—that is, contracts extending across a wide, disparate geographic area—have a particular impact on quality measurement at the level of individual star measures because of the manner in which the measures are collected and reported. Of the 11 HEDIS clinical quality measures in the star system that plans report for all enrollees, 7 are based on a sample of medical records (with only a few plans reporting based on administrative data for 6 of the 7 measures). These measures constitute 65 percent of the weight of the HEDIS non-survey-based measures. Under current rules, it is sufficient for a contract to use a sample of 411 medical records to report on the 7 HEDIS measures (to obtain a sample result with a 95 percent confidence level). For measurement year 2016, the largest MA contract (with over 1 million enrollees) used a sample of 437 diabetics to determine the contract-level rate of blood sugar control among diabetics; 25 percent of the contract’s enrollment was in states with 5 or fewer enrollees in the sample of 437, and 4 percent of the contract’s enrollment was in states not represented at all in the sample. Given the extent to which the quality of medical care can vary from ...
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