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Publications of Note

ACOs Serving Racial and Ethnic Minorities Lag in Quality
Using data from Medicare and a national survey of accountable care organizations (ACOs), researchers found having a higher proportion of minority patients was associated with worse scores on 25 of 33 quality performance measures, two disease composite measures, and an overall quality composite measure. These ACOs were similar to others in most observable characteristics and capabilities, including provider composition, services, and clinical capabilities. Because ACOs with a high share of minority patients may struggle with quality performance under ACO contracts, particularly in the early years of participation, the authors recommend policymakers consider refining ACO programs to encourage participation by providers serving minority populations and reward performance appropriately. V. A. Lewis, T. Fraze, E. S. Fisher et al., “ACOs Serving High Proportions of Racial and Ethnic Minorities Lag in Quality Performance,” Health Affairs, Jan. 2017 36(1):57–66. 

Alternative Quality Contract Improves Quality in Lower- and Higher-Income Areas
To study how quality and spending differs in areas of lower and higher socioeconomic status, researchers examined the performance of providers in Massachusetts before and after they entered into the Alternative Quality Contract, a value-based payment model launched by Blue Cross Blue Shield of Massachusetts. The model rewards physicians for improving quality while holding them accountable for medical spending. Comparing changes in process measures, outcome measures, and spending from 2006 to 2012, they found quality improved for all enrollees in the Alternative Quality Contract after their provider organizations entered into the contract. Process measures improved 1.2 percentage points per year more among enrollees in areas with lower socioeconomic status than among those in areas with higher socioeconomic status. Outcome measure improvement and spending were no different between the subgroups. The results suggest that pay-for-performance incentives could focus providers on improving quality for more disadvantaged populations, the researchers say. Z. Song, S. Rose, M. E. Chernew et al., “Lower- Versus Higher-Income Populations in the Alternative Quality Contract: Improved Quality and Similar Spending,” Health Affairs, Jan. 2017 36(1):74–82. 

Mobile Health Apps Not Benefiting High-Need, High-Cost Populations
Researchers who evaluated 137 apps highly rated by consumers and recommended by experts for helping high-need, high-cost patients manage their health found that few addressed the needs of high-cost patients. They also found that consumers’ ratings were poor indications of clinical utility or usability and that most apps did not respond appropriately when a user entered potentially dangerous health information. They noted that data privacy and security will continue to be major concerns in the dissemination of mobile health apps. K. Singh, K. Drouin, L. P. Newmark et al., “Many Mobile Health Apps Target High-Need, High-Cost Populations, But Gaps Remain," Health Affairs, Dec. 2016 35(12):2310–8. 

At One Health System, High-Cost Patients Cluster Into Five Distinct Groups
Using data on patients who use the Cleveland Clinic Health System for nearly all of their care, researchers found those in the highest decile of spending accounted for 60 percent of total costs. These patients clustered into five groups: 1) those who were ambulatory and had no hospital admissions (cancer diagnoses were prevalent in this group); 2) patients who had a median of two surgeries (care complications from surgery were common); 3) critically ill patients who spent a median of four days in the intensive care unit (cardiac diseases were prevalent); 4) patients who used the health system frequently and had a median of two admissions, three emergency department visits, and 29 outpatients visits (psychiatric disorders were common); and 5) a mixed utilization group with a median of one hospital admission and one ED visit. The majority of patients (55%) were in the frequent care cluster. Given the heterogeneity of high-cost patients, the authors suggest that interventions designed to reduce costs go beyond targeting those with frequent hospital admissions. N. S. Lee, N. Whitman, N. Vakharia et al., “High-Cost Patients: Hot-Spotters Don’t Explain the Half of It,” Journal of General Internal Medicine, Jan. 2017 32(1):28–34. 

More Refined Approach to Assessing Impact of Social Determinants Needed
In a commentary, an author argues for a more nuanced approach to assessing the influence on health of social determinants such as income, education, and housing. Sorting through the relative effect of social, biological, and medical care factors on health depends on the variation being explained and the time period studied, he says. Social factors may offer an explanation for geographic or racial differences in health outcomes at a single point in time, while variation over time may be linked to advances in medical science and technology. In addition, many social variables are correlated with one another making it difficult to determine the best allocation of scarce resources to achieve the greatest benefit. V. R. Fuchs, “Social Determinants of Health: Caveats and Nuances,” Journal of the American Medical Association, Jan. 2017 317(1):25–6. 

Safety-Net Hospitals Face Barriers But Employ Fewer Strategies to Reduce Readmissions
Researchers found that despite reporting more barriers to reducing readmissions, safety-net hospitals were less likely to use readmission-reduction strategies, which may explain why they have higher rates of readmissions and penalties under the Hospital Readmissions Reduction Program. Compared with other hospitals, safety-net hospitals were more likely to report patient-related barriers, including lack of transportation and homelessness. Yet they were less likely to use electronic tools to share discharge summaries or verbally communicate with outpatient providers, track readmissions by race and ethnicity, or enroll patients in post-discharge programs. The researchers also found high-performing safety-net hospitals were more likely to use several readmission strategies. J. F. Figueroa, K. E. Joynt, and X. Zhou, “Safety-Net Hospitals Face More Barriers Yet Use Fewer Strategies to Reduce Readmissions,” Medical Care, March 2017 55(3):229–35. 

Longitudinal Modeling May Enhance Efforts to Predict Spending
Researchers found using trajectory modeling to predict changes in patients’ health care spending over time was superior to more conventional methods including defining high-cost patients as those in the top fifth percentile of spending. The latter collapses an entire year’s spending into a single static variable and may include patients who have extremely high, but short-lived, health care spending. The group-based trajectory approach identifies patients with similar longitudinal patterns using a variety of information, such as socioeconomic and clinical characteristics, utilization data, and prescription records. The predictive value of using this data was only slightly inferior to using the proprietary Johns Hopkins ACG methodology, a tool for identifying high-risk patients, the researchers found. Because trajectory modeling identified more potentially high-cost patients (31% vs. 5%), it may be important to identify subgroups that have modifiable costs, they say. J. C. Lauffenburger, J. M. Franklin, A. A. Krumme et al., “Longitudinal Patterns of Spending Enhance the Ability to Predict Costly Patients: A Novel Approach to Identify Patients for Cost Containment,” Medical Care, Jan. 2017 55(1):64–73.  

Systematic Approaches Needed to Reduce Wasteful Spending
The authors of this commentary argue that focusing on improving care for high-need, high-cost patients may be less effective at reducing wasteful spending than systematically discouraging low-value services for all patients and encouraging more efficient use of specialty care resources. A narrow focus on high-need, high-cost patients may also entrench the status quo by supporting provider structures developed under fee-for-service incentives. They outline three approaches for incentivizing systems-focused strategies to limit wasteful care: bundled payment programs that place episodes of care under budgets; allowing provider consolidation to the point that a single organization provides the bulk of care in each market; or ensuring that small providers can participate in accountable care contracts for the majority of their patients, as physician groups have demonstrated an aptitude for reducing spending in several categories. They caution that advancement of the first two approaches may quash the latter in its infancy. J. M. McWilliams and A. L. Schwartz, “Focusing on High-Cost Patients—The Key to Addressing High Costs?New England Journal of Medicine, March 2017, 376(9):807–9. 

Potential Benefits and Pitfalls of the Merit-Based Incentive Payment System Outlined
In a commentary, Commonwealth Fund–affiliated authors highlight potential intended and unintended consequences of the Merit-Based Incentive Payment System (MIPS), which links physician reimbursement to performance. It may encourage physicians to choose the combinations of measures and improvement activities that produce the most beneficial payment adjustments, producing little actual quality improvement. The variety of options available to providers for reporting their performance data may also make it difficult to compare providers. Evaluating MIPS solely on the basis of measured quality, payment adjustments, and financial effects may be insufficient, the authors say. To ensure the program’s success, they suggest CMS monitor the system’s effects on access to care, clinician morale, patient experience, practice consolidation, migration to advanced alternative payment models, and progress toward innovative care models and team-based care. E. C. Schneider and C. J. Hall, “Improve Quality, Control Spending, Maintain Access—Can the Merit-Based Incentive Payment System Deliver?New England Journal of Medicine, published online Jan. 18, 2017. 

Risk Adjustment Should Factor in Variation in Diagnostic Practices
To enhance the accuracy of risk adjustment and ensure that payments to providers and insurers reflect the health of patients and not the regional differences in providers’ diagnostic practices, the authors of this commentary recommend adjusting risk scores to account for providers who tend to diagnose more aggressively. To develop such an approach, they examined changes in the measured health of Medicare beneficiaries who moved between different areas of the country and found 50 percent of geographic variation in reported health status was due to place-specific differences in diagnostic practices. The authors suggest using place-specific adjustment factors. A. Finkelstein, M. Gentzkow, P. Hull et al., “Adjusting Risk Adjustment—Accounting for Variation in Diagnostic Intensity,” New England Journal of Medicine, Feb. 2017 376(7):608–10. 

Strategies for Introducing Social Risk Factors to Value-Based Payment Models
The authors of this commentary recommend three strategies for ensuring that Medicare’s value-based purchasing programs take patients’ social risk factors into account, as these may make it harder for providers to achieve high performance on quality metrics and adversely affect providers who disproportionately care for at-risk populations. The strategies include measuring and reporting quality of care for beneficiaries with social risk factors; setting high, fair quality standards for the care of all beneficiaries; and rewarding and supporting better outcomes for all beneficiaries with social risk factors. K. E. Joynt, N. De Lew, S. H. Sheingold et al., “Should Medicare Value-Based Purchasing Take Social Risk Into Account,” New England Journal of Medicine, Feb. 2017, 376(6):510–3. 

ACO-Affiliated Hospitals Achieved Greater Reductions in Rehospitalizations from Skilled Nursing Facilities
Researchers found hospitals affiliated with accountable care organizations (ACOs) were able to reduce rehospitalizations from skilled nursing facilities more quickly than other hospitals. They say the reductions suggest that ACO-affiliated hospitals are either discharging to the nursing facilities more effectively compared with other hospitals, targeting at-risk patients better, or enhancing information sharing and communication between hospitals and skilled nursing facilities. They recommend further research to investigate the precise mechanisms that underlie the reductions. U. Winblad, V. Mor, J. P. McHugh et al., “ACO-Affiliated Hospitals Reduced Rehospitalizations from Skilled Nursing Facilities Faster Than Other Hospitals,” Health Affairs, Jan. 2017 36(1):67–73. 

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