Predictive Analytics Could Help States More Effectively Target HCBS and SDoH Services in Medicaid | Deloitte US has been added to Bookmarks.
By Jim Hardy, specialist executive, State Health Transformation Services, Deloitte Consulting LLP
State Medicaid agencies and managed care organizations (MCOs) have flexibility to offer optional or traditionally non-covered services to beneficiaries under the presumption that these services might improve the health and functionality of beneficiaries and reduce spending in hospitals, nursing homes, and other institutions. Two examples of non-covered services in Medicaid are:
HCBS coverage has been provided for many years, while SDoH coverage is in its early stages. What have we learned from HCBS programs so far? While these programs are popular with beneficiaries and advocates, enrollment growth has not led to a corresponding decline in the use of institutional care.1 I could envision this happening with SDoH services as well if we don’t target those services to the people who are most likely to benefit from them. My take is that predictive analytics could help Medicaid departments and MCOs more accurately target these services by using a state’s resources more efficiently and effectively.
A brief history of home and community-based care
Congress created the HCBS program in 1983 through section 1915(c) of the Social Security Act. HCBS waivers and new HCBS state plan options now exist in every state and provide services to about 4.6 million people at a cost of nearly $83 billion a year in state and federal funds.2 Many waivers are specific to certain populations, such as individuals with physical disabilities who need assistance with accomplishing daily activities, or beneficiaries who have specific diseases. Seniors and adults who have physical disabilities make up more than half of all waiver enrollment, followed by people with intellectual or developmental disabilities.
The waivers allow states to provide non-Medicaid covered services to targeted populations and allow them to limit the number of beneficiaries who can receive those services. But states must demonstrate that providing HCBS services would be less expensive than moving the beneficiaries into institutionalized care. States and CMS have been committed to re-balancing long-term spending by shifting more spending to less costly HCBS programs.
From one perspective, this re-balancing has already occurred. In fiscal year 2008, HCBS accounted for 43 percent of long-term care spending, and by fiscal year 2016, that percentage had increased to 57 percent.
These statistics are often cited to promote the success of states’ re-balancing initiatives. Upon closer inspection however, it becomes clear that the shift in long-term care services is the result of HCBS spending increases—rather than a corresponding decrease in institutional spending. In other words, while HCBS now accounts for a larger percentage of long-term care spending in Medicaid, it has not driven up overall long-term care spending.
Spending growth in HCBS and institutional long-term care
Why hasn’t the growth in HCBS led to lower nursing home use?
Increased HCBS spending reflects more people receiving services, people using more services, and rising costs for those services.
So why hasn’t the use of institutional care fallen? Possible explanations include:
Another factor that I think is important—and which could help explain why institutionalization costs haven’t decreased as HCBS spending has increased—is that existing assessment tools typically aren’t effective at identifying members who are most likely to need nursing home care. These tools tend to have an eligibility focus rather than a risk focus. They help Medicaid programs determine whether an individual meets the criteria for being admitted to an institutional setting. However, they don’t assess whether the individual is at a high risk of being admitted into an institution if HCBS services are unavailable.
In 2017, more than 700,000 beneficiaries were on HCBS waiting lists across 40 states—up eight percent from the prior year.3 The fact that so many people are on the waiting list for HCBS services suggests that eligibility criteria might not be a good indicator of institutionalization risk.
Predictive analytics might help better target benefits
Some SDoH initiatives incorporate predictive analytics, which helps identify characteristics that are likely to be associated with the use of health care services. This can help MCOs and Medicaid agencies more effectively determine which beneficiaries should get referred to services. It also can help states decide which services to cover, and what other community resources might serve as effective interventions.
States might consider whether predictive analytics could be incorporated into HCBS programs to direct benefits to the people who are most likely to use institutionalized care. This could both effectively target HCBS spending and reduce institutional care. This technology could become part of the initial assessment to holistically determine the individual’s risk of institutionalization. Analytics might also be used to decide which beneficiaries should be promoted from waiting lists, or to direct HCBS resources more appropriately.
State Medicaid programs might want to look at the reasons institutional care costs have not been offset by growth in HCBS spending and use new tools to increase the value of HCBS programs. States that implement SDoH programs might also want to determine how to ensure spending is aimed at individuals who are most at risk of a decline in health status so that they can leverage the lessons learned from the HCBS experience.
1. CMS/medicaid.gov, Medicaid Expenditures for Long-Term Services and Supports in FY 2016 (https://www.medicaid.gov/medicaid/hcbs/index.html)
2. Medicaid Home and Community-Based Services Enrollment and Spending, April 4, 2019, Kaiser Family Health Foundation (https://www.kff.org/medicaid/issue-brief/medicaid-home-and-community-based-services-enrollment-and-spending/)
3. Key Questions About Medicaid Home and Community-Based Services Waiver Waiting Lists, April 4, 2019, Kaiser Family Health Foundation (https://www.kff.org/medicaid/issue-brief/key-questions-about-medicaid-home-and-community-based-services-waiver-waiting-lists/)
Jim is Deloitte Consulting LLP’s Medicaid Advisory Services lead. Previously Pennsylvania’s Medicaid director, he has more than 20 years of Medicaid, health policy, reimbursement and rate development experience. Recently, Jim assisted in developing a state Medicaid care management strategy and long-term care reform strategy; assisted states with coverage initiatives; and led a hospital payment reform initiative for quality incentives and to reduce payment for avoidable re-admissions.