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The American Journal of Managed Care September 2019
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Managed Care for Long-Stay Nursing Home Residents: An Evaluation of Institutional Special Needs Plans
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Managed Care for Long-Stay Nursing Home Residents: An Evaluation of Institutional Special Needs Plans

Brian E. McGarry, PT, PhD; and David C. Grabowski, PhD
This study examines UnitedHealthcare’s Institutional Special Needs Plans and their association with hospital and skilled nursing facility use.
The transfer of nursing home residents to the ED and hospital has emerged as an important area of interest for policy makers. These transfers are known to be frequent,19-21 costly,22 often preventable,23-25 and potentially associated with negative health outcomes.26,27 Given the silo-based payment of nursing home care relative to other healthcare services, nursing homes have the narrow interest of limiting their own costs and little financial incentive to take responsibility for broader care management or quality of care. For dually eligible nursing home residents, Medicaid pays for their long-stay nursing home care and Medicare pays for their healthcare services. State Medicaid programs do not typically pay a higher rate to help nursing homes cover enhanced care within the facility. When an adverse health event occurs, nursing homes often have the incentive to transfer sick dually eligible residents in order to limit the burden on their staff and also improve their potential standing with surveyors. Also, if an FFS Medicare beneficiary is transferred and has a 3-day qualifying inpatient stay, they can return to the nursing home as a higher-reimbursed Medicare SNF patient. Thus, nursing homes have little financial motivation to discourage transfers from the nursing home setting. Our findings suggest that the I-SNP model studied here is a potential payment and delivery innovation that can overcome these misaligned incentives to encourage increased clinical investment in the care of residents in the nursing home.

Policy makers interested in lowering the rate of hospital transfers should also consider what clinical resources are needed to help nursing homes and other providers realize these savings in a safe manner. In accordance with this need, CMS has been evaluating a series of potential interventions to address the underinvestment in clinical services in long-term care settings.27 The results of this evaluation and other prior research have suggested that both the quantity and the type of clinical staffing may be means of decreasing potentially avoidable hospitalizations.2,11,28,29 The lower rates of hospital and ED transfers observed under the I-SNP model, which utilizes advanced practice clinicians within the nursing home, are consistent with these earlier findings.

Another important element of the I-SNP model that may have contributed to the lower rates of hospital and ED use is the relaxation of the rule in FFS Medicare that beneficiaries must have a 3-day hospital stay to qualify for SNF care. Not surprisingly, in the absence of this rule, we observed higher SNF use under the I-SNP model. We were unable to differentiate between necessary and unnecessary SNF utilization, but some of this increased use was likely substituting for higher-cost inpatient care. Although many policy makers have been reticent to relax the 3-day rule in traditional Medicare FFS due to fear of increasing utilization, this flexibility is an important element of MA plans and other at-risk models (eg, accountable care organizations, bundled payment initiatives) that facilitate the delivery of skilled care in a lower-cost setting.13,30

Limitations

The adoption of the I-SNP model by a nursing home is not random. Although the I-SNP nursing homes in our sample were relatively similar to national averages in terms of for-profit ownership, they were more likely to be chain-owned and larger in size. Moreover, the type of individual who enrolls in an I-SNP may be different from an individual who does not, even though they may reside in the same nursing home. We attempted to address these selection issues by drawing nursing homes from the same state and then weighting our analyses based on observable demographic characteristics. However, other unmeasured facility and individual factors may bias our estimates. In particular, we were not able to control for particular nursing home characteristics or direct measures of resident health status. Consideration was given to adjusting our analyses for health status using a claims-based risk-adjustment model such as the Hierarchical Condition Categories.31 We chose not to use such adjustments because of the greater coding intensity that has been documented in MA, which could make the I-SNP beneficiaries appear less healthy relative to the FFS Medicare group.32 Future research with alternate measures of beneficiaries’ clinical need and complexity is needed to better understand the role of patient selection in explaining the observed patterns of clinic care use.

As we describe in the Methods section, we excluded I-SNP enrollees in nursing homes that did not meet certain criteria. Thus, our results only pertain to beneficiaries in those “mature” nursing homes and not the universe of I-SNP enrollees. Finally, limitations in our data prevent the comparison of physician, outpatient, and drug spending across the I-SNP and FFS Medicare beneficiaries.

CONCLUSIONS

A major focus in long-term care policy has been to improve access to on-site clinical care in order to rebalance medical care utilization away from the ED and inpatient settings. By providing on-site advanced practice clinicians and making the insurer financially responsible for care in and out of the nursing home, the I-SNP model tested here was shown to have lower ED and inpatient utilization and higher SNF utilization relative to FFS Medicare. Our results suggest that this I-SNP model is one potential approach to shift care to less costly clinical settings and can help inform the development and implementation of other value-based payment models in the SNF population.

Author Affiliations: Department of Health Care Policy, Harvard Medical School (BEM, DCG), Boston, MA.

Source of Funding: The Donaghue Foundation.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (DCG); analysis and interpretation of data (BEM, DCG); drafting of the manuscript (BEM, DCG); critical revision of the manuscript for important intellectual content (BEM); obtaining funding (DCG); and supervision (DCG).

Address Correspondence to: David C. Grabowski, PhD, Department of Health Care Policy, Harvard Medical School, 180 Longwood Ave, Boston, MA 02115-5899. Email: Grabowski@med.harvard.edu.
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