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Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
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A1

Introduction

Recent health care payment reforms aim to improve the alignment of Medicare payment strategies with goals to improve the quality of care provided, patient experiences with health care, and health outcomes, while also controlling costs. These efforts move Medicare away from the volume-based payment of traditional fee-for-service models and toward value-based purchasing, in which cost control is an explicit goal in addition to clinical and quality goals (Rosenthal, 2008). Specific payment strategies include pay-for-performance and other quality incentive programs that tie financial rewards and sanctions to the quality and efficiency of care provided, and risk-based alternative payment models (APMs) such as bundled (episode-based) payments and accountable care organizations (ACOs) in which health care providers are held accountable for both the quality and cost of the care they deliver (Burwell, 2015; Rosenthal, 2008). In this report, these types of strategies, including both incentive programs and APMs, will be referred to broadly as “value-based payment” (VBP). The Patient Protection and Affordable Care Act of 2010 (ACA) prompted widespread adoption of VBP at the federal level by directing the Centers for Medicare & Medicaid Services (CMS) to implement payment reforms in the Medicare program and by establishing a number of tools CMS can use to achieve VBP goals.

OVERVIEW OF THE MEDICARE PROGRAM

Medicare is the government’s health care program for individuals age 65 and older, those with permanent kidney failure (end-stage renal disease [ESRD]), and some individuals with long-term disability (Medicare.gov,

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
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n.d.-a). Medicare beneficiaries must be U.S. citizens or permanent legal residents. Medicare consists of four programs:

  • Part A, the hospital insurance program that pays fee-for-service for inpatient hospital care, skilled nursing facility care, hospice care, and home health care;
  • Part B, the medical insurance program that pays fee-for-service for outpatient care (physician services), home health care, durable medical equipment, and some preventive services;
  • Part C, or Medicare Advantage (MA), are insurance programs run by Medicare-certified private companies that cover all the benefits and services covered under Part A and Part B, often include Part D pharmaceutical drug coverage, and may also cover additional benefits and services at extra cost; and
  • Part D, the pharmaceutical drug reimbursement program that is also run by Medicare-approved private companies and for which Medicare pays approximately 75 percent of the cost (CMS, 2015h; MedPAC, 2014).

Persons under age 65 years receiving Social Security disability insurance benefits1 or who have permanent kidney failure (ESRD) and qualify for Social Security benefits also qualify for Medicare. Those with amyotrophic lateral sclerosis (ALS; Lou Gehrig’s disease) qualify for Medicare immediately upon receiving Social Security disability insurance and persons with ESRD receive Medicare benefits on the fourth month of dialysis treatment or the month the patient enters a qualifying hospital for a kidney transplant. All others receive coverage after a 24-month waiting period.

As with private health insurance, Medicare premiums and care are not free (Medicare.gov, n.d.-c). However, Medicare Part A is premium-free for Medicare beneficiaries 65 years of age or older who qualify for Social Security (requiring 40 quarters of work in which a threshold amount of Social Security taxes were paid).2 Part A premiums for those 65 years or older who do not meet the Social Security eligibility cost up to $407 per month in 2015. Monthly Medicare Part B premiums generally cost $104.90 (in 2015) but higher-income individuals pay more, up to $335.70 in 2015. Help with the cost of Medicare premiums is available to low-income beneficiaries meeting specific eligibility requirements through Medicaid. These beneficiaries are

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1 Persons under age 65 years receiving certain Railroad Retirement Board disability benefits also may qualify.

2 Government employees who did not pay into Social Security but paid Medicare payroll taxes and those who receive railroad retirement benefits also qualify for premium-free Part A at age 65 years.

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
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frequently referred to as “dual-eligibles.” Persons who chose to enroll in MA (Medicare Part C) plans pay the Part B premium and any additional premium costs imposed by the plan provider. Medicare Part D premium costs vary based on level of coverage and the specific provider chosen by the beneficiary.

As with private insurance, Medicare Parts A and B include deductibles and coinsurance, and Part D also includes an out-of-pocket threshold ($4,700). Many Medicare beneficiaries have some sort of supplemental coverage for cost-sharing expenses under Medicare. Some enrollees in Parts A and B purchase what is known as Medicare Supplement Insurance (Medigap) policy, sold by private companies. In 2010, 14 percent of Medicare beneficiaries had no supplemental coverage (Cubanski et al., 2015).

Medicare is financed through beneficiary premiums, federal general revenue, and payroll taxes (Medicare.gov, n.d.-b). In 2014, Medicare benefit payments totaled $597 billion, among which 45 percent was for Part A benefits, 44 percent was for Part B benefits, and 11 percent was for Part D benefits (CBO, 2015).

In 2012, the program covered more than 37 million Americans among whom 30.3 million were 65 years of age or older and 6.9 million were disabled and under 65 years of age (CMS, 2013). The health status of Medicare beneficiaries, even within those who began Medicare coverage on the basis of age, varies widely. Medicare coverage is the same for all Medicare beneficiaries, regardless of the basis for original enrollment. That is, a 40-year-old beneficiary enrolled due to ESRD provisions or on the basis of Social Security Disability determination and who requires hospitalization for any Medicare-covered condition receives the same coverage at the same costs as an 85-year-old beneficiary who enrolled on the basis of age 20 years prior.

CURRENT STATUS OF VALUE-BASED PAYMENT IN MEDICARE

The ACA and subsequent legislation, including the Improving Medicare Post-Acute Care Transformation Act of 2014 (IMPACT Act) and the Medicare Access and CHIP (Children’s Health Insurance Program) Reauthorization Act of 2015 require CMS to implement VBP programs for Medicare inpatient hospital care, ambulatory care, health plans, and post-acute care. Currently, there are eight VBP programs in Medicare, with two post-acute care programs in proposal or planning.3 These programs are summarized below and in Table A1-1. Appendix AA contains more detailed descriptions of the programs.

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3 This report does not discuss innovation models conducted under the CMS Innovation Center and other demonstration programs, such as the Maryland all-payer model, the Nursing Home Value-Based Purchasing Demonstration, and the Bundled Payments for Care Improvement (BPCI) Initiative.

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
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TABLE A1-1 Summary of Medicare Value-Based Payment Programsa

Program Incentive Design, Including Maximum Adjustment Measures
Hospital
Readmissions
Reduction
Programb
–1% 2013
–2% 2014
–3% 2015
Excess readmissions for AMI, HF, PN, COPD, total hip or knee arthroplasty
Hospital-Acquired Condition (HAC) Payment Reductione Top 25% worst performing hospitals receive a reduction of 1% of all discharge payments AHRQ Patient Safety Indicator 90
CDC NHSN infection measuresf
Hospital Value-Based Purchasingh Only hospital program to offer sanctions and rewards; it is a zero-sum program;
1% in 2013, increasing 0.25% each year until 2%
Clinical process measures (i.e., measures related to getting appropriate treatments in a timely manner)

Patient experience (taken from the Hospital Consumer Assessment of Healthcare Providers and Suppliers Survey)

Clinical outcomes (30-day mortality for AMI, HF, and PN, as well as certain patient safety measures from AHRQ PSI 90 Composite and CDC NHSN CLABSI)

Efficiency (Medicare spending per beneficiary [MSPB])
Medicare Shared Savings Programk ACO expenditures above/below benchmarks
Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
×
Risk-Adjustment Included Comments
Demographic characteristics, clinical comorbidities, patient frailty In FY 2016, an estimated 78% of hospitals will be penalized, and 1.2% of hospitals will be penalized the maximum rate of 3%.c The average hospital penalty among penalized hospitals is estimated to be –0.63%, totaling approximately $428 million.d
Age, sex, comorbidities, complicationsg In FY 2015, more than 700 hospitals received payment reductions under the HAC reduction program.
Clinical and efficiency measures: demographics and comorbidities

Patient experience: education, self-rated health, response percentile, primary language other than English, age, service line (maternity/surgical/medical), interactions (surgical line* age, maternity line* age)i

MSPB: price-standardized
For FY 2015, 74% of hospitals had payment adjustments (bonuses or penalties) of less than 0.5%; only 8 percent of hospitals received bonuses of 0.5% or greater, and 18 percent of hospitals received penalties of 0.5% or greater.j
Demographics; case-mix; disease severity For performance year 2014, 92 MSSP ACOs held spending to $806 million below their benchmarks, resulting in $341 million in payments to the ACOs and a net savings of $465 for the Medicare Trust Funds. No ACOs owed losses.l
Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
×
Program Incentive Design, Including Maximum Adjustment Measures
Physician Value-Based Modifierm Budget neutral; rewards and penalties

Category 1 (have met minimum reporting requirements): Value modifier calculated using CMS quality tiering methodology (or, in 2015, groups could choose a neutral modifier)

Category 2 (have not met minimum reporting requirements): fixed negative adjustment of –1% in 2015 and –2% in 2016
Quality: composite score covering six domains (effective clinical care; person and caregiver-centered experience and outcomes; community/population health; patient safety; communication and care coordination; and efficiency and cost reduction) n

Cost: composite score covering two domains (per capita costs for all attributed beneficiaries and per capita costs for beneficiaries with specific conditions)
End-Stage Renal Disease Quality Incentive Programr –2% Quality: clinical measures (anemia management, dialysis adequacy, iron management, bone mineral metabolism, vascular access, patient satisfaction) and reporting measures
Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
×
Risk-Adjustment Included Comments
Quality measures: age, sex

Patient experience: age, education, general health status, mental health status, Medicaid status, low-income subsidy, Asian language survey (Cantonese/Korean/Mandarin/Vietnamese), survey mode (proxy helped or answered)o

Cost measures: age, sex, original reason for Medicare entitlement, disability status, Medicaid enrollment, clinical comorbidities
In 2015, 691 groups fell into Category 1 and 319 were designated to Category 2. Of Category 1 groups, 127 groups elected to have their Value Modifier calculated using quality tiering. Among these, 14 groups received upward adjustments for performance, 81 received no adjustments, 11 received negative adjustments, and 21 received no adjustment due to insufficient data to determine quality and cost performance. A total of $11.4 million was distributed from groups receiving negative adjustments to those receiving positive adjustments.p

This program expires in 2018 and will be replaced in 2019 by the Merit-based Incentive Payment System.q
Adults patients: Age, dialysis onset, body surface, body mass, comorbidities

Pediatric patients: age, dialysis method

Patient experience: survey mode; overall health; overall mental health; heart disease; deaf or serious difficulty hearing; blind or serious difficulty seeing; difficulty concentrating, remembering, or making decisions; difficulty dressing or bathing; age; sex; education; speaks language other than English at home; did someone help the patient complete the survey; total years on dialysiss

Also adjusted for volume, geographic factors, wage indext
69.1% of facilities were expected to have no payment reduction in 2012. 16.6% of facilities were expected to receive a 0.5% reduction, 6.0% a 1.0% reduction, 7.7% a 1.5% reduction, and 0.6% a 2.0% reduction.u
Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
×
Program Incentive Design, Including Maximum Adjustment Measures
Medicare Advantage (MA) (Part C)v Bonus payments or rebates are a fixed percentage (50, 65, or 70% based on Star Quality rating) Star Quality ratings
Medicare Part Dx Bonus payments or rebates for MA Part D plans

20% of the costs that are higher than expected
Star Quality Ratings (for MA Part D plans, Part D Star Rating contributes to overall plan rating) y
Skilled Nursing Facility Value-Based Purchasingaa –2% if facilities do not report quality data on three domains

Incentive program begins in 2019
For 2018, quality domains include skin integrity and changes in skin integrity; incidence of major falls; functional status, cognitive function, and changes in function or cognitive function. CMS proposed the NQF-endorsed, 30-day all-cause readmission measures for the incentive program
Home Health Value-Based Purchasingbb Incremental increase in maximum penalties or rewards of 5% in 2018, 6% in 2020, 8% in 2021 Proposed measures to cover clinical processes, clinical outcomes, patient safety, patient and caregiver experience, population/community health, efficiency, and cost reduction

NOTE: ACO = accountable care organization; AHRQ = Agency for Healthcare Research and Quality; AMI = acute myocardial infarction; CDC = Centers for Disease Control and Prevention; CMS = Centers for Medicare & Medicaid Services; CLABSI = Central Line-associated Bloodstream Infection; COPD = chronic obstructive pulmonary disease; FY = fiscal year; HCC = hierarchical condition categories; HF = heart failure; MSSP = Medicare Shared Savings Program; NHSN = National Healthcare Safety Network; NQF = National Quality Forum; PN = pneumonia; PSI = patient safety indicator.

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
×
Risk-Adjustment Included Comments
Quality measures: CMS–HCC model, which includes age, sex, clinical comorbidities, Medicaid status, disabled status, and working aged status

Patient experience: age, education, general health status, mental health status, survey mode (proxy helped or answered), Medicaid status, low-income subsidy, and Chinese language survey w
Age, education, general health status, mental health status, survey mode (proxy helped or answered), Medicaid status, low-income subsidy, and Chinese language survey z Nearly 75% of plans pay a portion of their profits to Medicare each year under risk corridors; between 2010 and 2012, total annual payments ranged between $900 million and $1 billion

SOURCES:

a The information in this table is derived from the material in Appendix AA. The interested reader is referred there for more complete information.

b Unless indicated otherwise, the information in this row is from CMS, 2014d.

c Boccuti and Casillas, 2015.

d Boccuti and Casillas, 2015.

e CMS, 2014b.

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
×

f CMS, 2015e.

g HHS, 2014b.

h Unless indicated otherwise, the information in this row is from MLN, 2013.

i HCAHPS, 2013.

j GAO, 2015.

k Unless indicated otherwise, the information in this row is from CMS, 2014c.

l CMS, 2015i.

m Unless indicated otherwise, the information in this row is from CMS, n.d.-c.

n CMS, 2015b.

o CMS, 2015a.

p CMS, n.d.-a.

q CMS, 2015k.

r Unless indicated otherwise, information in this row is from CMS, 2015c.

s CMS, 2015f.

t MedPAC, 2015c.

u Mullin, 2012.

v Unless indicated otherwise, information in this row is from MedPAC, 2015b.

w CMS, 2015g.

x Unless indicated otherwise, information in this row is from MedPAC, 2014.

y Medicare.gov, n.d.-d.

z CMS, 2015g.

aa Unless indicated otherwise, information in this row is from CMS, 2015j.

bb Unless indicated otherwise, information in this row is from HHS, 2015.

Medicare Value-Based Payment Programs for Hospital Inpatient Care

Hospital Readmissions Reduction Program

The Hospital Readmissions Reduction Program (HRRP) requires CMS to reduce a share of the base operating payments to acute care hospitals paid under the Inpatient Prospective Payment System (IPPS) that have the highest readmission rates (CMS, 2014d). For fiscal years (FY) 2013 and 2014, CMS adopted measures to calculate excess readmissions for three conditions: acute myocardial infarction (AMI), heart failure, and pneumonia. In FY 2014, CMS refined the measure to account for planned readmissions, and in FY 2015, CMS expanded the program to include excess readmissions from two additional conditions: chronic obstructive pulmonary disease (COPD) and total hip arthroplasty or total knee arthroplasty. For FY 2013, the maximum reduction was 1 percent of a hospital’s base operating payment; for FY 2014, the maximum reduction was 2 percent; and for FY 2015, the maximum reduction is 3 percent (CMS, 2014d).

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
×

Hospital-Acquired Condition Payment Reduction

The Hospital-Acquired Condition Payment Reduction program requires the Secretary of Health and Human Services to reduce payments to acute care hospitals paid under the IPPS based on their performance on select risk-adjusted hospital-acquired condition quality measures beginning in FY 2015 (discharges beginning October 1, 2014) (CMS, 2015e). Performance measures include the Agency for Healthcare Research and Quality Patient Safety Indicator 90 and the Centers for Disease Control and Prevention (CDC) National Healthcare Safety Network (NHSN) infection measures. The top 25 percent worst performing hospitals receive a payment reduction of 1 percent for all discharges in those hospitals (CMS, 2014b).

Hospital Value-Based Purchasing

The Hospital Value-Based Purchasing program is a pay-for-performance program also for acute care hospitals paid under the IPPS and implemented by CMS beginning FY 2013 (MLN, 2013). In this program, hospitals are eligible for either incentive payments (rewards) or penalties for their performance; it is the only Medicare hospital incentive program that offers both rewards and sanctions. In FY 2013, performance measures included clinical process measures and patient experience measures. CMS added clinical outcome measures to the total performance measures in FY 2014. In FY 2015, CMS also added an efficiency measure, Medicare spending per beneficiary (MSPB). Clinical and efficiency measures are currently risk adjusted for patient demographics and comorbidities; MSPB is also price standardized (MLN, 2013).

Maximum rewards and penalties can equal up to a total of 1 to 2 percent of a hospital’s base operating payment: 1 percent in FY 2013 and increasing in 0.25 percent increments annually to 2 percent in FY 2017 and future years (MLN, 2013). The program is a zero-sum program, so the total incentive payments must equal the total reduced payments (penalties).

Medicare Value-Based Payment Programs for Ambulatory Care

Medicare Shared Savings Program

The Medicare Shared Savings Program (MSSP) is a key payment and delivery system reform program of the ACA, implemented beginning in 2014 (CMS, 2015l). MSSP reforms payments to ACOs and aims to motivate delivery and organizational reforms that improve care coordination across providers, as well as the quality and efficiency of care (CMS, 2015l).

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
×

To calculate shared savings and losses, CMS first establishes a benchmark for each performance year based on 3 years of per-beneficiary costs for traditional, fee-for-service Medicare Part A and Part B for the beneficiaries enrolled in the ACO. The benchmark is also adjusted at the beginning of each agreement period for “beneficiary characteristics and such other factors as the Secretary [of Health and Human Services] determines appropriate” (CMS, 2014c; MLN, 2014). This risk adjustment is currently performed using the CMS–hierarchical condition categories model originally developed for MA, and includes certain demographic characteristics, case-mix, and disease severity (CMS, 2014c, n.d.-d; MedPAC, 2015a). The benchmark reflects the expected per-beneficiary costs for the performance period.

At the end of each performance period, CMS compares MSSP ACOs’ actual spending to the calculated benchmark. As of January 1, 2015, 404 ACOs covering 7.92 million Medicare beneficiaries in 49 states; Washington, DC; and Puerto Rico entered into a Shared Savings Program agreement with CMS (CMS, 2015d).

Physician Value-Based Modifier

The Physician Value-Based Modifier is a budget-neutral pay-for-performance program required by the ACA and established by CMS beginning in 2015. Under this program, physicians can receive incentive payments for high-quality, efficient care and penalties for poor performance (CMS, n.d.-c).

CMS divides eligible physicians into two categories based on whether they meet minimum reporting requirements using the Physician Quality Reporting System (Category 1) or not (Category 2). In general, physicians in Category 1 are subject to value modifier payment adjustments based on the quality and cost of the care they provided during the performance period, while those in Category 2 are subject to a value modifier payment set at a fixed downward adjustment (CMS, 2015b, n.d.-c). Quality measures are adjusted for patient demographics (age and sex), and cost measures are adjusted for patient demographics (e.g., age, sex, original reason for Medicare entitlement, disability status, Medicaid enrollment) and clinical comorbidities (CMS, n.d.-c). The program is budget neutral; total upward adjustments for Category 1 must equal total downward adjustments for Categories 1 and 2 combined (CMS, 2015b). In other words, funds from the worst performing physician groups are redistributed to the best performing groups.

End-Stage Renal Disease Quality Improvement Program

The End-Stage Renal Disease Quality Improvement Program is authorized under the Medicare Improvements for Patients and Providers Act,

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
×

which requires CMS to reduce payments to outpatient dialysis facilities treating patients with ESRD based on quality of care (CMS, 2015c). Beginning in 2012, CMS reduced the bundled payment rate to ESRD facilities that perform poorly by up to 2 percent. CMS groups its quality measures into two groups: clinical measures, which reflect a facility’s clinical performance, and reporting measures, which assess whether facilities have met reporting requirements (CMS, 2015c). CMS then calculates both an achievement score and an improvement score for each clinical measure (except the CDC NHSN Bloodstream Infection in Hemodialysis Outpatients measure, which is given only an achievement score) (CMS, 2014a). Facilities that meet a minimum total performance score will receive full payment, while those that fall under it may receive a reduction between 0.5 percent and 2.0 percent (CMS, 2014a, n.d.-b).

Medicare Value-Based Payment Programs for Health Plans4

Medicare Advantage/Part C

As described in the previous section, MA or Medicare Part C is the insurance program that covers the Part A and Part B benefits, typically offers Part D prescription drug coverage, and may offer additional benefits and services at additional cost (MedPAC, 2015b). Compared to traditional, fee-for-service Medicare (i.e., Part A and Part B), MA plans can limit providers, provide supplemental benefits (e.g., additional coverage or reduced cost sharing), and charge a premium for the supplemental benefits (MedPAC, 2015b). In 2015, roughly 30 percent of Medicare beneficiaries were enrolled in MA plans (MedPAC, 2015b). Medicare pays private insurance companies to run the insurance programs. In 2014, these payments totaled $159 billion. Plans with higher-quality ratings have bonus payments added to their benchmark through the Medicare Five-Star Rating System. Payments are also risk adjusted for patient characteristics (MedPAC, 2015b).

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4 The committee included Medicare Part C and Part D, because the study sponsor, the Office of the Assistant Secretary for Planning and Evaluation of the Department of Health and Human Services, included them as relevant payment models in its presentation to the committee at the first meeting (Epstein, 2015), and thus the program is of interest to them. Additionally, the committee considers Part C and Part D to have important design features through which quality and cost performance affect payment and market share. As described in more detail below, Part C and Part D are both risk-sharing models of payment, which necessitates consideration of risk adjustment for the capitation amount or global spending target, and also include other value-based payment mechanisms, such as bonus payments (Part C) and risk corridors (Part D).

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
×

Medicare Part D

Medicare Part D is a reimbursement program for pharmaceutical drugs. In calendar year (CY) 2013, 35.7 million Americans were enrolled in Medicare Part D, and in FY 2014, expenditures totaled $73.3 billion (HHS, 2014a). Although CMS administers Part D, the individual plans are run by Medicare-approved private insurance plans that submit annual bids to CMS to cover expected benefit and administrative costs (MedPAC, 2015d). These plans are paid through several mechanisms. CMS pays plans direct subsidies, which take the form of a monthly prospective payment and cover approximately 75 percent of an enrollee’s premium (MedPAC, 2015d). CMS then subtracts the cost of an enrollee’s premium (calculated as the difference between a plan’s bid for basic benefits and the national average bid) from the risk-adjusted payment to calculate the final direct subsidy payment. For low-income enrollees, Medicare pays plans an additional low-income subsidy to cover most of the cost sharing and premiums (i.e., costs above the direct subsidy otherwise paid for by the enrollee out of pocket) (MedPAC, 2015d). Medicare also pays plans through two risk-sharing mechanisms: individual reinsurance and risk corridor adjustments.

Medicare Value-Based Payment Programs for Post-Acute Care

Skilled Nursing Facility Value-Based Purchasing

The IMPACT Act requires CMS to implement a quality-reporting program for Skilled Nursing Facilities (SNFs) and the Protecting Access to Medicare Act of 2014 authorizes an SNF incentive program (CMS, 2015j). Beginning 2018 and in accordance with the IMPACT Act, SNFs will be required to report quality data on three quality domains to CMS or face a payment reduction of 2 percent (CMS, 2015j).

Home Health Value-Based Purchasing

CMS proposed a Home Health Value-Based Purchasing model and included program details in the CY 2016 Home Health Prospective Payment Final Rule (HHS, 2015). Under this program, home health agencies would be subject to upward or downward payment adjustments based on quality measured over 5 performance years. Proposed maximum adjustments would increase incrementally from 5 percent in 2018 and 2019, to 6 percent in 2020, and 8 percent in 2021 and 2022 (HHS, 2015). In its proposal, CMS identified possible quality measures covering clinical processes, clinical outcomes, patient safety, patient and caregiver experience, population/community health, and efficiency and cost reduction for use in

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
×

the program. Additionally, CMS sought public comment on constructing the initial set of quality measures for the program (HHS, 2015).

Future Directions for Medicare Value-Based Payment

VBP is a key goal of the Department of Health and Human Services (HHS) and is likely to be taken up more widely in the future. In 2015, Secretary of HHS Sylvia Burwell announced three primary strategies by which HHS aims to achieve VBP goals (Burwell, 2015). These strategies build on the initiatives described throughout this section as well as a number of demonstration and pilot programs, and include using financial incentives, implementing delivery system and organizational reforms that promote better care coordination across providers and settings, and improving the information available to both providers and patients to help them make informed decisions (Burwell, 2015). Whether VBP and these strategies are successful at improving quality and patient experiences and reducing costs in the long run remains to be seen. However, based on early results, policy makers, health care researchers, advocates, and other stakeholders have begun to raise concerns about potential unintended consequences VBP may have on health disparities.

POTENTIAL UNINTENDED CONSEQUENCES OF VALUE-BASED PAYMENT ON VULNERABLE POPULATIONS AND HEALTH DISPARITIES

Impact of Value-Based Payment on Providers Serving Vulnerable Populations

A wide range of stakeholders representing government, academia, providers, advocates, and others have raised concerns that some of Medicare’s VBP programs, especially the HRRP, may be disproportionately penalizing hospitals serving the most vulnerable patients. This concern is grounded in part in an understanding of health outcomes as emerging from the interaction between patients and the health care system rather than being properties of either in isolation. When outcomes depend on both provider and consumer, provider inputs may differ according to consumer needs, with implications for provider reimbursement. In other words, resources required to care for patients may differ depending on the patient’s life circumstances, symptoms, needs, and abilities to interact with the health care system, and whether a health system’s processes and programs support these patient differences (Batalden et al., 2015; Loeffler et al., 2013). Because providers serving vulnerable populations are likely to have fewer resources to begin with (e.g., lower operating margins, fewer board-certified

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
×

physicians) and because more vulnerable and complex patients may require more resources to achieve certain health outcomes, providers serving these patients may be more likely to fare poorly on quality rankings and receive financial penalties and less likely to receive incentive payments (Chien et al., 2007; Joynt and Rosenthal, 2012; Ryan, 2013).

Several studies have shown that larger hospitals, teaching hospitals, and safety-net hospitals, which traditionally serve more disadvantaged patients, are more likely to rank poorly on quality measures and therefore are more likely to be penalized under Medicare VBP programs (Berenson and Shih, 2012; Gilman et al., 2014, 2015; Joynt and Jha, 2013; Rajaram et al., 2015). An analysis by the Medicare Payment Advisory Commission (MedPAC) found that not only were hospitals serving the most low-income patients more likely to be penalized under the HRRP, but also their average penalty was double that of those serving the fewest low-income patients (MedPAC, 2013). Studies have also shown that hospitals serving more racial and ethnic minorities and those in metropolitan areas may be more likely to be penalized under Medicare VBP reforms (Shih et al., 2015; Williams et al., 2014). Other studies have found that these types of hospitals would similarly rank poorly and be more likely to be penalized under potential expansions of Medicare VBP programs to include other conditions (Ly et al., 2010; Sjoding and Cooke, 2014). Likewise, earlier studies found that hospitals serving greater proportions of racial and ethnic minorities were more likely to have low quality rankings, less likely to be eligible for bonus payments, and more likely to be penalized (Karve et al., 2008; Mehta et al., 2008). An analysis of the impact of implementing pay-for-performance in primary care in Massachusetts found that primary care practices with more vulnerable populations would receive less per practice compared to practices with fewer vulnerable patients (Friedberg et al., 2010).

Studies have also looked at the neighborhoods in which providers are located. Blustein and colleagues (2010) found that neighborhood resources (poverty, unemployment, health care provider shortages, and low educational achievement) were associated with hospital performance on health care process measures proposed for Medicare’s Hospital Value-Based Purchasing Program. Specifically, hospitals located in areas with fewer college graduates in the workforce or higher levels of chronic poverty and those located in counties that were partly or entirely designated health professional shortage areas had significantly worse performance scores for AMI and HF. Chien and colleagues (2012) found that in the Integrated Healthcare Association’s pay-for-performance program provider organizations located in neighborhoods with higher socioeconomic status (SES) were more likely to have above average performance rankings.

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
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Impact of Value-Based Payment on Health Disparities

That providers serving vulnerable populations may be disproportionately penalized under Medicare’s VBP programs has raised concerns that these programs have the potential to increase health disparities (Casalino et al., 2007; Friedberg et al., 2010; Ryan, 2013). If providers serving vulnerable populations are likely to have fewer resources to begin with and providers serving these patients may be more likely to receive financial penalties and less likely to receive incentive payments, as is suggested above, value-based purchasing programs may be taking resources from the organizations who need it most (Chien et al., 2007; Ryan, 2013). In so doing, value-based purchasing would widen the resource gap between providers serving vulnerable populations and those serving patients who are better off (Chien et al., 2007). Moreover, because more vulnerable patients may need more resources to achieve certain health outcomes, widening the resource gap may also lead to widening health disparities (Bhalla and Kalkut, 2010; Ryan, 2013). Two studies of the Medicare Premier Hospital Quality Incentive Demonstration found no evidence that pay-for-performance widened racial disparities in performance (clinical processes or outcomes) (Epstein et al., 2014), nor did incentives widen disparities between hospitals serving more poor patients compared to those serving fewer poor patients (Jha et al., 2010). However, because hospital participation in the demonstration program was voluntary, effects may not be generalizable.

Improving Value-Based Payment to Address Unintended Consequences

While the impact of value-based purchasing strategies on providers serving vulnerable populations and on health disparities continues to be monitored both under Medicare and more widely, and because more VBP programs are being implemented and existing programs are expanding, some methods have been proposed to improve these payment programs to address the potential unintended consequences on vulnerable populations and disparities. Chief among methods proposed to improve VBP to address these unintended consequences is accounting for differences in patient characteristics when measuring quality and calculating payments, sometimes referred to as risk adjustment or payment adjustment. Most emerging VBP strategies recognize that differences in patient characteristics may impact health care outcomes and costs independently of variations in the provision of care, and that these must be accounted for when measuring quality and calculating payments (Rosenthal, 2008). Currently, and as detailed in the Medicare payment program descriptions earlier in the chapter in Table A1-1 and in Appendix AA, patient characteristics included in these adjustments typically include only certain demographic and

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
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clinical characteristics (e.g., age, sex, and clinical comorbidities). If patient characteristics beyond demographic and clinical information contribute to differences in underlying risk that cause differences in health care outcomes and costs, certain policy makers, researchers, health care providers, and other stakeholders have proposed that these other characteristics should also be accounted for when measuring quality and calculating payments (Boozary et al., 2015; Feemster and Au, 2014; Fiscella et al., 2014; Girotti et al., 2014a; Jha and Zaslavsky, 2014; Joynt and Jha, 2013; Lipstein and Dunagan, 2014). Specific characteristics proposed for inclusion when calculating payments include SES and other social determinants of health (e.g., race or ethnicity, health literacy, and English language proficiency).

Accounting for Social Risk Factors in Value-Based Payment

The primary method proposed to account for social risk factors in value-based payment has been to include them in risk adjustment of performance measures used as the basis for payment. To that end, it is important to separate two different methods—risk adjustment and payment adjustment. Risk adjustment primarily aims to improve measurement accuracy, such as for the purposes of quality assessment and public reporting, but becomes a method of payment adjustment when measures that are risk adjusted are used as the basis for payment. In other words, risk adjustment can include social factors for the purposes of measurement accuracy without affecting payment. Similarly, payment adjustment can be done by basing payment on measures that are risk adjusted or through other methods, such as directly funding programs to improve the quality of care for disadvantaged patients (Berenson and Shih, 2012). However, because recent discussions about including SES and other social determinants of health in risk adjustment occurs in the latter context of value-based purchasing, these two issues have been conflated, proposed adjustments have implications for health equity and fairness of provider reimbursement, and the proposal has controversial.

Critics of including social factors in risk adjustment argue that what may appear as differences by social groups may be genuinely attributed to quality differences and not the social factors themselves. In this case adjusting for the social factor would obscure genuine disparities and make it more difficult to hold those providing lower-quality care accountable (Jha and Zaslavsky, 2014; Kertesz, 2014; Krumholz and Bernheim, 2014; O’Kane, 2015). They further argue that so doing implicitly accepts a lower standard for vulnerable patients (Bernheim, 2014; Jha and Zaslavsky, 2014). This would not only enable lower quality care for disadvantaged persons, but would also reduce incentives for improvement (Bernheim, 2014; Kertesz, 2014). Additionally, critics note that social factors account for very

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
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little variance in performance measurement, so including social factors in risk adjustment models would not substantially change quality rankings (Bernheim, 2014; Krumholz and Bernheim, 2014). Finally, they suggest that other ways of accounting for social factors such as directly funding programs for vulnerable patients, providing incentives based on improvement and not achievement, adjusting payment rather than performance measurement, and phasing in penalties to disadvantaged providers more slowly may be more appropriate (Bernheim, 2014).

Proponents argue that certain social factors lie outside the control of providers and thus hospitals should not be accountable for them (Jha and Zaslavsky, 2014; Joynt and Jha, 2013; Pollack, 2013; Renacci, 2014). In this way of thinking, social factors are confounders masking true performance, and adjusting for them provides more accurate measurement (Fiscella et al., 2014; Jha and Zaslavsky, 2014). If this is the case, risk adjusting for social factors would ensure that hospitals are being fairly assessed and that providers caring for more disadvantaged patients are not punished precisely for caring for these patients (Girotti et al., 2014b). Indeed, if serving disadvantaged patients results in disproportionate penalties, this may disincentivize providers from caring for them (Joynt and Jha, 2013). Others also raise concerns that because disproportionate penalties will further reduce the already limited resources of providers serving greater shares of disadvantaged patients with even fewer financial resources, quality in these providers will likely worsen (Grealy, 2014; Ryan, 2013), and the organizations could potentially fail, leaving fewer providers to care for disadvantaged patients (Lipstein and Dunagan, 2014). In both cases, this would widen disparities.

Operating under the assumption that social factors do impact health care quality and efficiency outcomes independently of variations in the provision of health care, a small number of analyses have included SES and other social determinants of health in risk adjustment of provider performance profiles to estimate the effect of including social factors in measuring quality, but findings have been mixed. Three studies found that including these social determinants had no impact on risk adjustment models, and thus hospital rankings (Blum et al., 2014; Eapen et al., 2015; Keyhani et al., 2014). One study found that including social determinants had little impact on most providers’ quality scores, but a substantial impact on a few (Zaslavsky and Epstein, 2005). Five studies found that including SES and other social determinants substantially altered provider quality rankings (Fiscella and Franks, 1999, 2001; Franks and Fiscella, 2002; Maney et al., 2007; Nagasako et al., 2014). One study found that including patient characteristics in adjusting payments rather than quality measures would reduce payment disparities (Damberg et al., 2015). Similarly, several studies have found that inclusion of SES in predictive models improves the models’

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
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predictive ability (Amarasingham et al., 2010; Fleming et al., 2014; Han et al., 2012; Kansagara et al., 2011).

Previous Recommendations for Accounting for Social Risk Factors in Medicare Payment Programs

In light of this debate, two expert panels have previously examined whether to include social risk factors in risk adjustment for Medicare payment models and offered recommendations. In its June 2013 Report to the Congress, MedPAC recommended that CMS use two methods of adjustment, one for public reporting (i.e., quality measurement) and another for financial incentives. Readmissions rates for public reporting would remain unadjusted for socioeconomic disparities so as not to mask potential disparities in quality of care. However, when calculating penalties, hospitals would be compared not to all other hospitals as is currently done, but to hospitals with a similar patient mix (MedPAC, 2013). Their methodology would not only reduce the number of penalties to hospitals serving the most poor, but also the size of the penalty.

The National Quality Forum (NQF) is a nonprofit, membership-based organization that endorses standards for performance measurement. In 2013, NQF convened an expert panel, including representatives of health care providers, advocacy groups, government, industry, and academia to make recommendations about including SES and other social factors in risk adjustment for performance measures. In 2014, the panel released a technical report reversing NQF’s previous position to exclude “sociodemographic factors”5 in risk adjustment of performance measures used in “accountability applications” (i.e., as a basis of payment or public reporting). The panel recommended that sociodemographic factors should be included in risk adjustment if there is a conceptual relationship between a given factor and specific quality metrics as well as empirical evidence of that association (NQF, 2014). It also mentioned that the performance metric should specify risk adjustment methods to include the factor (NQF, 2014). Congress has also taken up the issue. Two bills proposed that CMS risk adjust readmissions measures used in the HRRP for patient SES and other related measures.6,7 Additionally, while authorizing the establishment of several VBP programs in Medicare, the IMPACT Act also required the Secretary

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5 Socioeconomic factors are defined as “a variety of socioeconomic (e.g., income, education, occupation) and demographic factors (e.g., race, ethnicity, primary language)” (NQF, 2014, p. 4).

6 Establishing Beneficiary Equity in the Hospital Readmission Program Act. H.R. 4188. 113th Congress (2014).

7 Hospital Readmissions Program Accuracy and Accountability Act of 2014. S. 2501. 113th Congress (2014).

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
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of HHS submit a report to Congress by October 2016 that assesses the impact of SES on quality and resource use in Medicare using measures such as poverty and rurality from existing Medicare data. The IMPACT Act also required a report to Congress by October 2019 on the impact of SES on quality and resource use in Medicare using measures (e.g., education and health literacy) from other data sources. It also required qualitative analysis of potential SES data sources and secretarial recommendations on obtaining access to necessary data on SES and accounting for SES in determining payment adjustments (Epstein, 2015).

STATEMENT OF TASK

As input to the analyses to be included in the 2016 and 2019 reports to Congress, HHS, acting through the Office of the Assistant Secretary for Planning and Evaluation, asked the Institute of Medicine (IOM) to convene an ad hoc committee to provide a definition of SES for the purposes of application to Medicare quality measurement and payment programs; to identify the social factors that have been shown to impact health outcomes of Medicare beneficiaries; and to specify criteria that could be used in determining which social factors should be accounted for in Medicare quality measurement and payment programs. Furthermore, the committee will identify methods that could be used in the application of these social factors to quality measurement and/or payment methodologies. Finally, the committee will recommend existing or new sources of data and/or strategies for data collection. The committee’s work will be conducted in phases and produce five brief reports. Details of the statement of task and the sequence of reports can be found in Box A1-1. In this first report, the committee will focus on the definition of SES and other social factors relevant to the health outcomes of Medicare beneficiaries. Reports will be released every 3 months, addressing each item in the statement of task in turn. It is important to note that the committee has been tasked with providing recommendations only in the fourth report.

Interpreting the Statement of Task

The statement of task for this report includes several key words that drove the committee’s work. The statement of task refers to identifying “SES factors” that “have been shown” to “impact” “health outcomes” of “Medicare beneficiaries.” This project is intended to provide very practical and targeted input to HHS and Congress as they consider whether to adjust Medicare payment programs for social risk factors. This project builds on decades of research assessing the social determinants of health; it does not reinvent or redefine that field of scholarship. The committee is narrowly

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
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Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
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focused on how social risk factors affect health care use and outcomes of a specific group of people—Medicare beneficiaries—in response to encounters with the health care system, not how social factors affect health status generally.

As will be defined in Chapter 2, the committee identified five social risk factors that are conceptually likely to be of importance to health outcomes of Medicare beneficiaries: socioeconomic position; race, ethnicity, and cultural context; gender; social relationships; and residential and community context. Although an independent risk factor and not a social factor, the committee included health literacy as another important factor, because it is specifically mentioned in the IMPACT Act and thus is of interest to Congress, and because it is affected by social factors. Additionally, although the statement of task specifies only examining the impact of these social risk factors on “health outcomes,” it also specifies that the social risk factors should be targeted “for the purpose of application to quality, resource use, or other measures used for Medicare payment programs.” Thus, given the importance that Medicare VBP programs has placed on this broader set of measures and given that Medicare applies these measures when calculating payments, the committee interpreted “health outcomes” as encompassing measures of health care use, health care outcomes, and resource use. Hence, the committee included two domains of health care use measures (health care utilization and clinical processes of care) and one measure of resource use (costs) in the literature search. In addition to health (clinical care) outcomes, the committee also included related outcomes of patient experience and patient safety.

Figure A1-1 illustrates the committee’s conceptual framework. The framework illustrates the primary hypothesized relationships by which social risk factors may directly or indirectly affect health care use, health care outcomes, and resource use outcomes among Medicare beneficiaries. In the figure, dotted arrows represent feedback mechanisms and bold lettering highlights social risk factors plus health literacy and the domains included in the expanded definition of “health outcomes” that are at issue in this report. The framework is not intended to illustrate the entire universe of potential causes and risks.

The conceptual framework applies to all Medicare beneficiaries, including disabled beneficiaries and beneficiaries with ESRD, because although the committee acknowledges that the Medicare population is heterogeneous (even among beneficiaries age 65 and older), the committee expects the effect of social risk factors to be similar for all Medicare subpopulations (beneficiaries with disabilities, those with ESRD, and older adults). Any variation in the effect of social risk factors among disabled Medicare beneficiaries under age 65, Medicare beneficiaries age 65 and older, and beneficiaries with ESRD is considered to fall within a continuous spectrum of effects. The committee

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
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Image
FIGURE A1-1 Conceptual framework of social risk factors for health care use, outcomes, and cost.
NOTE: This conceptual framework illustrates primary hypothesized conceptual relationships.
Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
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will revisit this assumption in subsequent reports. It is important to note that disabled Medicare beneficiaries are systematically different from persons with disabilities more generally, because in order to be eligible for federal disability benefits, a person must be unable to work, have a low income, and meet certain medical criteria (SSA, n.d.). As such, they are by definition a more socially vulnerable group for which social risk factors may be particularly salient, similar to older adults. Finally, Medicare coverage and the quality measures used to asses health care and health outcomes do not differ for Medicare beneficiaries by origin of entitlement (i.e., whether an individual qualified because of disability, age, or ESRD), except for certain measures of ESRD care and outcomes, and thus the health outcomes in the framework are also equally applicable.

Current Medicare quality measures fall within each of the domains embraced by the committee in the expanded definition of “health outcomes”—health care use, health care outcomes, and resource use outcomes. The committee expects that quality measures will change over time, but developed a framework that will remain stable regardless of the specific measures used to assess the theoretical constructs. Thus, it is important to note that what Medicare currently considers a quality “outcome” may not necessarily align with the committee’s definition of a health care outcome. For example, Medicare and health care quality experts frequently consider readmissions to be an outcome of care. However, in the committee’s conceptual framework, readmissions are more theoretically consonant as a measure of utilization that is given a quality interpretation. Table A1-2 contains examples of Medicare quality measures currently in use in each of the health care use and outcome domains.

COMMITTEE PROCESS AND OVERVIEW OF THIS REPORT

The committee comprises expertise in health disparities, social determinants of health, risk adjustment, Medicare programs, health care quality, clinical medicine, and health services research. Appendix F contains biographical sketches for the committee members. The committee will meet five times over 12 months and issue five brief, consensus reports. The committee met in open, public session at its first meeting to discuss the charge to the committee with the leadership of the Office of the Assistant Secretary for Planning and Evaluation. In the next (and final) chapter of this report, the committee presents the results of a literature search to identify those social risk factors that have been shown to influence health care use, costs, and health care outcomes.

The literature search was conducted by a professional librarian available to committees of the IOM. The committee limited its search to studies on patients in the United States, and to review articles published from

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
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TABLE A1-2 Health Care Use and Outcome Domains and Example Medicare Quality Measures

Health Care Use or Outcome Domain Example Medicare Quality Measures
Health Care Use
Clinical Processes of Care
  • HbA1c testing for beneficiaries with diabetes
  • Influenza and pneumonia vaccination
  • Primary percutaneous coronary intervention received within 90 minutes of hospital arrival for patients with AMI
  • Blood cultures performed in the emergency department prior to initial antibiotic received in hospital for patients with pneumonia
  • Anemia management for kidney disease patients
Utilization
  • Hospital admissions for COPD exacerbations
  • Heart failure admissions
  • 30-day readmissions after hospital discharge for AMI, heart failure, pneumonia, COPD, or total hip arthroplasty/total knee arthroplasty
Outcomes
Resource Use (costs)
  • Medicare spending per beneficiary
Health (Clinical Care)
  • Diabetes composite quality indicator (controlled diabetes, short- and long-term diabetes complications, lower-extremity amputation for diabetes)
  • 30-day mortality after hospital discharge for AMI, heart failure, or pneumonia
Patient Safety
  • AHRQ Patient Safety Indicator composite (pressure ulcer, iatrogenic pneumothorax, central venous catheter-related bloodstream infections, postoperative hip fracture, perioperative pulmonary embolism or deep vein thrombosis, postoperative sepsis, postoperative wound dehiscence, accidental puncture or laceration)
  • Central line-acquired bloodstream infection
  • Catheter-acquired urinary tract infection
  • Surgical site infection
  • Incidence of major falls for post-acute care patients
Patient Experience
  • Communication with nurses
  • Communication with doctors
  • Getting timely appointments, care, and information
  • Getting information from Part D drug plan
  • Helpful, courteous, and respectful office staff
  • Responsiveness of hospital staff
  • Care coordination
  • Pain management
  • Communication about medications
  • Cleanliness and quietness
  • Overall rating of hospital
  • Overall rating of Medicare Advantage health plan

NOTE: AHRQ = Agency for Healthcare Research and Quality; AMI = acute myocardial infarction; COPD = chronic obstructive pulmonary disease.

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
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1995–2015 and original research published from 2005–2015. The searches included both searches targeting publications relating to Medicare beneficiaries, disabled populations, and patients with ESRD and broader searches not specifically targeting these populations. The literature search focused on social risk factors identified by the committee (as described in Appendix A2) and on health care use and outcomes such as those used in Medicare VBP programs. The relevant literature retrieved is described generally without an assessment of the quality of each individual study and with no attempt at data integration, such as in a meta-analysis. However, research that did not control for covariates and evidence pertaining to pediatric populations were not included. Because the committee expects social risk factors to affect subpopulations similarly, where variations in effect fall within a continuous range of effects, in describing the evidence, the committee did not systematically distinguish between the adult subpopulations to which articles refer. The identification and description of the literature should not be mistaken for a systematic review that uses a formal system for weighing and describing evidence, such as those used in clinical or public health guideline development.

The committee’s interpretation of the task for report one was to define SES for the purposes of application to Medicare payment programs and to identify whether there exists literature showing an influence of one or more social risk factors on one or more measures of relevant health care use or outcomes. In its findings, the committee uses the term “influence” to describe an association between a social risk factor and a health care use or outcome measure without implying a causal association. Future work of the committee will address the question of whether a specific social factor could be incorporated into Medicare payment programs, the methods to do so, and data needs to accomplish the task.

REFERENCES

Amarasingham, R., B. J. Moore, Y. P. Tabak, M. H. Drazner, C. A. Clark, S. Zhang, W. G. Reed, T. S. Swanson, Y. Ma, and E. A. Halm. 2010. An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data. Medical Care 48(11):981–988.

Batalden, M., P. Batalden, P. Margolis, M. Seid, G. Armstrong, L. Opipari-Arrigan, and H. Hartung. 2015. Coproduction of healthcare service. BMJ Qualiy and Safety.

Berenson, J., and A. Shih. 2012. Higher readmissions at safety-net hospitals and potential policy solutions. Issue Brief (Commonwealth Fund) 34:1–16.

Bernheim, S. M. 2014. Measuring quality and enacting policy: Readmission rates and socioeconomic factors. Circulation: Cardiovascular Quality Outcomes 7(3):350–352.

Bhalla, R., and G. Kalkut. 2010. Could Medicare readmission policy exacerbate health care system inequity? Annals of Internal Medicine 152(2):114–117.

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
×

Blum, A. B., N. N. Egorova, E. A. Sosunov, A. C. Gelijns, E. DuPree, A. J. Moskowitz, A. D. Federman, D. D. Ascheim, and S. Keyhani. 2014. Impact of socioeconomic status measures on hospital profiling in New York City. Circulation: Cardiovascular Quality Outcomes 7(3):391–397.

Blustein, J., W. B. Borden, and M. Valentine. 2010. Hospital performance, the local economy, and the local workforce: Findings from a US national longitudinal study. PLoS Med 7(6):e1000297.

Boccuti, C., and G. Casillas. 2015. Aiming for fewer hospital u-turns: The Medicare Hospital Readmissions Reduction Program. Menlo Park, CA: The Kaiser Family Foundation.

Boozary, A. S., J. Manchin, 3rd, and R. F. Wicker. 2015. The Medicare Hospital Readmissions Reduction Program: Time for reform. Journal of the American Medical Association 314(4):347–348.

Burwell, S. M. 2015. Setting value-based payment goals—HHS efforts to improve U.S. health care. New England Journal of Medicine 372(10):897–899.

Casalino, L. P., A. Elster, A. Eisenberg, E. Lewis, J. Montgomery, and D. Ramos. 2007. Will pay-for-performance and quality reporting affect health care disparities? Health Affairs (Millwood) 26(3):w405–w414.

CBO (Congressional Budget Office). 2015. March 2015 Medicare baseline by fiscal year. https://www.cbo.gov/sites/default/files/cbofiles/attachments/44205-2015-03-Medicare.pdf (accessed October 26, 2015).

Chien, A. T., M. H. Chin, A. M. Davis, and L. P. Casalino. 2007. Pay for performance, public reporting, and racial disparities in health care: How are programs being designed? Medical Care Research and Review 64(5 Suppl):283s–304s.

Chien, A. T., K. Wroblewski, C. Damberg, T. R. Williams, D. Yanagihara, Y. Yakunina, and L. P. Casalino. 2012. Do physician organizations located in lower socioeconomic status areas score lower on pay-for-performance measures? Journal of General Internal Medicine 27(5):548–554.

CMS (Centers for Medicare & Medicaid Services). 2013. Table 2.1—Medicare enrollment: Hospital insurance and/or supplementary medical insurance programs for total, fee-for-service and managed care enrollees as of July 1, 2011: Selected calendar years 1966–2012. https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MedicareMedicaidStatSupp/2013.html (accessed October 26, 2015).

CMS. 2014a. End-Stage Renal Disease Quality Incentive Program payment year 2016 program details. https://www.cms.gov/Medicare/quality-initiatives-patient-assessment-instruments/esrdqip/downloads/qip-details-py16.pdf (accessed October 26, 2015).

CMS. 2014b. Fiscal year 2015 results for the CMS Hospital-Acquired Condition Reduction Program and Hospital Value-Based Purchasing Program. https://www.cms.gov/Newsroom/MediaReleaseDatabase/Fact-sheets/2014-Fact-sheets-items/2014-12-18-2. html (accessed October 26, 2015).

CMS. 2014c. Medicare Shared Savings Program: Shared savings and losses and assignment methogology specifications version 3. Baltimore, MD: Centers for Medicare & Medicaid Services.

CMS. 2014d. Readmissions Reduction Program. https://www.cms.gov/Medicare/Medicarefee-for-service-payment/acuteinpatientpps/readmissions-reduction-program.html (accessed October 26, 2015).

CMS. 2015a. CAHPS for Physician Quality Reporting System (PQRS) survey, quality assurance guidelines version 1.0. http://www.pqrscahps.org/contentassets/6b04e492602b4bb58f80e3fb4e1e0676/cahps_for_pqrs_survey_qag_v1_july_2015.pdf (accessed November 19, 2015).

CMS. 2015b. Computation of the 2016 value modifier. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/PhysicianFeedbackProgram/Downloads/2016-VM-Fact-Sheet.pdf (accessed October 26, 2015).

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
×

CMS. 2015c. End-Stage Renal Disease Quality Incentive Program frequently asked questions. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/ESRDQIP/Downloads/ESRDQIPFrequentlyAskedQuestions.pdf (accessed October 26, 2015).

CMS. 2015d. Fast facts: All Medicare Shared Savings Program (Shared Savings Program) ACOs. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/sharedsavingsprogram/Downloads/All-Starts-MSSP-ACO.pdf (accessed October 26, 2015).

CMS. 2015e. Hospital-Acquired Condition Reduction Program fiscal year 2016 fact sheet. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/HAC-Reduction-Program.html (accessed October 26, 2015).

CMS 2015f. Introduction to the in-center hemodialysis CAHPS survey webinar training session. https://ichcahps.org/Portals/0/ICHCAHPS_IntroTraining_February2015.pdf (accessed November 19, 2015).

CMS. 2015g. Medicare 2016 Part C & D Star Rating technical notes from Medicare Advantage and prescription drug plan CAHPS survey. http://www.ma-pdpcahps.org/content/TechnicalSpecifications.aspx (accessed November 19, 2015).

CMS. 2015h. Medicare & you 2016. Baltimore, MD: Centers for Medicare & Medicaid Services.

CMS. 2015i. Medicare ACOs provide improved care while slowing cost growth in 2014. https://www.cms.gov/Newsroom/MediaReleaseDatabase/Fact-sheets/2015-Fact-sheetsitems/2015-08-25.html (accessed October 26, 2015).

CMS. 2015j. Proposed fiscal year 2016 payment and policy changes for Medicare skilled nursing facilities. https://www.cms.gov/Newsroom/MediaReleaseDatabase/Fact-sheets/2015Fact-sheets-items/2015-04-15.html (accessed October 26, 2015).

CMS. 2015k. Proposed policy, payment, and quality provisions changes to the Medicare physician fee schedule for calendar year 2016. https://www.cms.gov/newsroom/mediareleasedatabase/fact-sheets/2015-fact-sheets-items/2015-07-08.html (accessed October 26, 2015).

CMS. 2015l. Shared Savings Program. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/sharedsavingsprogram/index.html (accessed October 26, 2015).

CMS. n.d.-a. 2015 value modifier results. https://www.cms.gov/Medicare/Medicare-Fee-forService-Payment/PhysicianFeedbackProgram/Downloads/2015-Value-Modifier-Results.pdf (accessed October 30, 2015).

CMS. n.d.-b. End-Stage Renal Disease Quality Incentive Program summary: Payment years 2014–2018. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/ESRDQIP/Downloads/ESRDQIPSummaryPaymentYears2014-2018.pdf (accessed October 26, 2015).

CMS. n.d.-c. Summary of 2015 Physician Value-Based Payment Modifier policies. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/PhysicianFeedbackProgram/Downloads/CY2015ValueModifierPolicies.pdf (accessed October 26, 2015).

CMS. n.d.-d. Table: Comparison of one- and two-sided performance-based risk models by track. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/sharedsavingsprogram/Downloads/ACO-Financial-Models-Overview.pdf (accessed November 2, 2015).

Cubanski, J., C. Swoope, C. Boccuti, G. Jacobson, G. Casillas, S. Griffin, and T. Neuman. 2015. A primer on Medicare: Key facts about the Medicare program and the people it covers. http://kff.org/report-section/a-primer-on-Medicare-what-types-of-supplemental-insurance-do-beneficiaries-have (accessed December 9, 2015).

Damberg, C. L., M. N. Elliott, and B. A. Ewing. 2015. Pay-for-performance schemes that use patient and provider categories would reduce payment disparities. Health Affairs (Millwood) 34(1):134–142.

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
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Eapen, Z. J., L. A. McCoy, G. C. Fonarow, C. W. Yancy, M. L. Miranda, E. D. Peterson, R. M. Califf, and A. F. Hernandez. 2015. Utility of socioeconomic status in predicting 30-day outcomes after heart failure hospitalization. Circulation: Heart Failure 8(3):473–480.

Epstein, A. 2015. Accounting for socioeconomic status in Medicare payment programs: ASPE’s work under the IMPACT act. Presented to the Committee on Accounting for Socioeconomic Status in Medicare Payment Programs, Washington, DC, October 6, 2015.

Epstein, A. M., A. K. Jha, and E. J. Orav. 2014. The impact of pay-for-performance on quality of care for minority patients. American Journal of Managed Care 20(10):e479–e486.

Feemster, L. C., and D. H. Au. 2014. Penalizing hospitals for chronic obstructive pulmonary disease readmissions. American Journal of Respiratory and Critical Care Medicine 189(6):634–639.

Fiscella, K., and P. Franks. 1999. Influence of patient education on profiles of physician practices. Annals of Internal Medicine 131(10):745–751.

Fiscella, K., and P. Franks. 2001. Impact of patient socioeconomic status on physician profiles: A comparison of census-derived and individual measures. Medical Care 39(1):8–14.

Fiscella, K., H. R. Burstin, and D. R. Nerenz. 2014. Quality measures and sociodemographic risk factors: To adjust or not to adjust. Journal of the American Medical Association 312(24):2615–2616.

Fleming, L. M., M. Gavin, G. Piatkowski, J. D. Chang, and K. J. Mukamal. 2014. Derivation and validation of a 30-day heart failure readmission model. American Journal of Cardiology 114(9):1379–1382.

Franks, P., and K. Fiscella. 2002. Effect of patient socioeconomic status on physician profiles for prevention, disease management, and diagnostic testing costs. Medical Care 40(8):717–724.

Friedberg, M. W., D. G. Safran, K. Coltin, M. Dresser, and E. C. Schneider. 2010. Paying for performance in primary care: Potential impact on practices and disparities. Health Affairs (Millwood) 29(5):926–932.

GAO (U.S. Government Accountability Office). 2015. Hospital value-based purchasing: Initial results show modest effects on Medicare payments and no apparent change in quality-of-care trends. Washington, DC: U.S. Government Accountability Office.

Gilman, M., E. K. Adams, J. M. Hockenberry, I. B. Wilson, A. S. Milstein, and E. R. Becker. 2014. California safety-net hospitals likely to be penalized by ACA value, readmission, and meaningful-use programs. Health Affairs (Millwood) 33(8):1314–1322.

Gilman, M., E. K. Adams, J. M. Hockenberry, A. S. Milstein, I. B. Wilson, and E. R. Becker. 2015. Safety-net hospitals more likely than other hospitals to fare poorly under Medicare’s value-based purchasing. Health Affairs (Millwood) 34(3):398–405.

Girotti, M. E., T. Shih, and J. B. Dimick. 2014a. Health policy update: Rethinking hospital readmission as a surgical quality measure. JAMA Surgery 149(8):757–758.

Girotti, M. E., T. Shih, S. Revels, and J. B. Dimick. 2014b. Racial disparities in readmissions and site of care for major surgery. Journal of the American College of Surgeons 218(3):423–430.

Grealy, M. R. 2014. Measure under consideration (MUC) comments: Letter to the National Quality Forum: Healthcare Leadership Council, December 5, 2014. http://www.hlc.org/wp-content/uploads/2014/06/HLC_Early-Public-Comment-on-Measures-UnderConsideration.pdf (accessed October 30, 2015).

Han, P. K., M. Lee, B. B. Reeve, A. B. Mariotto, Z. Wang, R. D. Hays, K. R. Yabroff, M. Topor, and E. J. Feuer. 2012. Development of a prognostic model for six-month mortality in older adults with declining health. Journal of Pain and Symptom Management 43(3):527–539.

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
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HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems). 2013. Patient-mix adjustment coefficients, applicable to the patient experience of care domain score in the Hospital Value-Based Purchasing, FY 2014 actual percentage payment summary report. http://www.hcahpsonline.org/Files/HospitalVBPPMAFY2014.pdf (accessed November 19, 2015).

HHS (U.S. Department of Health and Human Services). 2014a. 2014 Centers for Medicare & Medicaid Services Statistics. https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/CMS-Statistics-Reference-Booklet/Downloads/CMS_Stats_2014_final.pdf (accessed January 24, 2017).

HHS. 2014b. Medicare program; hospital Inpatient Prospective Payment Systems for acute care hospitals and the long-term care hospital Prospective Payment System and fiscal year 2015 rates; quality reporting requirements for specific providers; reasonable compensation equivalents for physician services in excluded hospitals and certain teaching hospitals; provider administrative appeals and judicial review; enforcement provisions for organ transplant centers; and electronic health record (EHR) incentive program. Federal Register 79(163):50094.

HHS. 2015. Medicare and Medicaid programs; CY 2016 Home Health Prospective Payment System rate update; Home Health Value-Based Purchasing model; and home health quality reporting requirements. Federal Register 80:39840.

Jha, A. K., and A. M. Zaslavsky. 2014. Quality reporting that addresses disparities in health care. Journal of the American Medical Association 312(3):225–226.

Jha, A. K., E. J. Orav, and A. M. Epstein. 2010. The effect of financial incentives on hospitals that serve poor patients. Annals of Internal Medicine 153(5):299–306.

Joynt, K. E., and A. K. Jha. 2013. A path forward on Medicare readmissions. New England Journal of Medicine 368(13):1175–1177.

Joynt, K. E., and M. B. Rosenthal. 2012. Hospital Value-Based Purchasing: Will Medicare’s new policy exacerbate disparities? Circulation: Cardiovascular Quality Outcomes 5(2):148–149.

Kansagara, D., H. Englander, A. Salanitro, D. Kagen, C. Theobald, M. Freeman, and S. Kripalani. 2011. Risk prediction models for hospital readmission: A systematic review. Journal of the American Medical Association 306(15):1688–1698.

Karve, A. M., F. S. Ou, B. L. Lytle, and E. D. Peterson. 2008. Potential unintended financial consequences of pay-for-performance on the quality of care for minority patients. American Heart Journal 155(3):571–576.

Kertesz, K. 2014. Center for Medicare Advocacy comments on the impact of dual eligibility on MA and Part D quality scores. http://www.Medicareadvocacy.org/center-for-Medicare-advocacy-comments-on-the-impact-of-dual-eligibility-on-ma-and-part-d-quality-scores (accessed October 30, 2015, 2015).

Keyhani, S., L. J. Myers, E. Cheng, P. Hebert, L. S. Williams, and D. M. Bravata. 2014. Effect of clinical and social risk factors on hospital profiling for stroke readmission: A cohort study. Annals of Internal Medicine 161(11):775–784.

Krumholz, H. M., and S. M. Bernheim. 2014. Considering the role of socioeconomic status in hospital outcomes measures. Annals of Internal Medicine 161(11):833–834.

Lipstein, S. H., and W. C. Dunagan. 2014. The risks of not adjusting performance measures for sociodemographic factors. Annals of Internal Medicine 161(8):594–596.

Loeffler, E., G. Power, T. Bovaird, and F. Hine-Hughes (Eds.). 2013. Co-production of health and wellbeing in Scotland. http://www.govint.org/fileadmin/user_upload/publications/Co-Production_of_Health_and_Wellbeing_in_Scotland.pdf (accessed November 10, 2015).

Ly, D. P., L. Lopez, T. Isaac, and A. K. Jha. 2010. How do black-serving hospitals perform on patient safety indicators? Implications for national public reporting and pay-for-performance. Medical Care 48(12):1133–1137.

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
×

Maney, M., C. L. Tseng, M. M. Safford, D. R. Miller, and L. M. Pogach. 2007. Impact of self-reported patient characteristics upon assessment of glycemic control in the veterans health administration. Diabetes Care 30(2):245–251.

Medicare.gov. n.d.-a. Glossary—M. https://www.Medicare.gov/glossary/m.html (accessed October 26, 2015).

Medicare.gov. n.d.-b. How is Medicare funded? https://www.Medicare.gov/about-us/how-Medicare-is-funded/Medicare-funding.html (accessed October 26, 2015).

Medicare.gov. n.d.-c. Medicare 2015 & 2016 costs at a glance. https://www.Medicare.gov/your-Medicare-costs/costs-at-a-glance/costs-at-glance.html (accessed December 9, 2015).

Medicare.gov. n.d.-d. Star Ratings. https://www.Medicare.gov/find-a-plan/staticpages/rating/planrating-help.aspx (accessed November 10, 2015).

MedPAC (Medicare Payment Advisory Commission). 2013. Chapter 4: Refining the Hospital Readmissions Reduction Program. In Report to the Congress: Medicare and the health care delivery system. Washington, DC: MedPAC.

MedPAC. 2014. Explainer: Risk sharing mechanisms in Part D. http://www.medpac.gov/blog/october-2014/october-2014/2014/10/15/explainer-risk-sharing-mechanisms-in-part-d (accessed October 26, 2015).

MedPAC. 2015a. Accountable care organization payment systems. In Payment Basics. Washington, DC: MedPAC.

MedPAC. 2015b. Medicare Advantage program payment system. In Payment Basics. Washington, DC: MedPAC.

MedPAC. 2015c. Outpatient dialysis services payment system. In Payment Basics. Washington, DC: MedPAC.

MedPAC. 2015d. Part D payment system. In Payment Basics. Washington, DC: MedPAC.

Mehta, R. H., L. Liang, A. M. Karve, A. F. Hernandez, J. S. Rumsfeld, G. C. Fonarow, and E. D. Peterson. 2008. Association of patient case-mix adjustment, hospital process performance rankings, and eligibility for financial incentives. Journal of the American Medical Association 300(16):1897–1903.

MLN (Medicare Learning Network). 2013. Hospital Value-Based Purchasing Program. https://www.cms.gov/Outreach-and-Education/Medicare-Learning-Network-MLN/MLNProducts/downloads/Hospital_VBPurchasing_Fact_Sheet_ICN907664.pdf (accessed October 26, 2015).

MLN. 2014. Methodology for determining shared savings and losses under the Medicare Shared Savings Program. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/sharedsavingsprogram/Downloads/ACO_Methodology_Factsheet_ICN907405.pdf (accessed November 3, 2015).

Mullin, E. 2012. ESRD QIP: Early results out, but effect on care still unclear. Nephrology Times 5(1):21–22.

Nagasako, E. M., M. Reidhead, B. Waterman, and W. C. Dunagan. 2014. Adding socioeconomic data to hospital readmissions calculations may produce more useful results. Health Affairs (Millwood) 33(5):786–791.

NQF (National Quality Forum). 2014. Risk adjustment for socioeconomic status or other sociodemographic factors. Washington, DC: National Quality Forum.

O’Kane, M. 2015. Comment on the advance notice of methodological changes for calender year 2016 for Medicare Advantage call letter. https://www.ncqa.org/PublicPolicy/CommentLetters/MedicareAdvantage032015.aspx (accessed November 3, 2015).

Suggested Citation:"A1: Introduction." National Academies of Sciences, Engineering, and Medicine. 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: The National Academies Press. doi: 10.17226/23635.
×

Pollack, R. 2013. CMS-1599-p, Medicare program; hospital Inpatient Prospective Payment Systems for acute care hospitals and the long-term care hospital Prospective Payment System and proposed fiscal year 2014 rates; quality reporting requirements for specific providers; hospital conditions of participation; Medicare program; proposed rule (vol. 78, no. 91): Letter to the CMS Administrator Tavenner. http://www.aha.org/advocacy-issues/letter/2013/130620-cl-cms-1599p.pdf (accessed October 30, 2015).

Rajaram, R., J. W. Chung, C. V. Kinnier, C. Barnard, S. Mohanty, E. S. Pavey, M. C. McHugh, and K. Y. Bilimoria. 2015. Hospital characteristics associated with penalties in the Centers for Medicare & Medicaid Services Hospital-Acquired Condition Reduction Program. Journal of the American Medical Association 314(4):375–383.

Renacci, J. B. 2014. Letter to HHS Secretary Burwell and CMS Administrator Tavenner regarding the Medicare Hospital Readmissions Reduction Program. http://tinyurl.com/q6shyoc (accessed October 30, 2015).

Rosenthal, M. B. 2008. Beyond pay for performance—emerging models of provider-payment reform. New England Journal of Medicine 359(12):1197–1200.

Ryan, A. M. 2013. Will value-based purchasing increase disparities in care? New England Journal of Medicine 369(26):2472–2474.

Shih, T., A. M. Ryan, A. A. Gonzalez, and J. B. Dimick. 2015. Medicare’s hospital readmissions reduction program in surgery may disproportionately affect minority-serving hospitals. Annals of Surgery 261(6):1027–1031.

Sjoding, M. W., and C. R. Cooke. 2014. Readmission penalties for chronic obstructive pulmonary disease will further stress hospitals caring for vulnerable patient populations. American Journal of Respiratory and Critical Care Medicine 190(9):1072–1074.

SSA (U.S. Social Security Administration). n.d. Disability planner: How we decide if you are disabled. https://www.ssa.gov/planners/disability/dqualify5.html#&a0=-1 (accessed December 4, 2015).

Williams, Sr., K. A., A. A. Javed, M. S. Hamid, and A. M. Williams. 2014. Medicare readmission penalties in Detroit. New England Journal of Medicine 371(11):1077–1078.

Zaslavsky, A. M., and A. M. Epstein. 2005. How patients’ sociodemographic characteristics affect comparisons of competing health plans in California on HEDIS quality measures. International Journal for Quality in Health Care 17(1):67–74.

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Recent health care payment reforms aim to improve the alignment of Medicare payment strategies with goals to improve the quality of care provided, patient experiences with health care, and health outcomes, while also controlling costs. These efforts move Medicare away from the volume-based payment of traditional fee-for-service models and toward value-based purchasing, in which cost control is an explicit goal in addition to clinical and quality goals. Specific payment strategies include pay-for-performance and other quality incentive programs that tie financial rewards and sanctions to the quality and efficiency of care provided and accountable care organizations in which health care providers are held accountable for both the quality and cost of the care they deliver.

Accounting For Social Risk Factors in Medicare Payment is the fifth and final report in a series of brief reports that aim to inform ASPE analyses that account for social risk factors in Medicare payment programs mandated through the IMPACT Act. This report aims to put the entire series in context and offers additional thoughts about how to best consider the various methods for accounting for social risk factors, as well as next steps.

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