LTSS Is Where Care Happens. AI Is How States Bring It Into Full View

September 25, 2026

Andy Allison, Vice President, Consulting and Client Services and Yasin Turkcan, Senior Vice President, Product & Innovation, Gainwell Technologies

For the fastest-growing corner of Medicaid, oversight has often been periodic, manual, and retrospective. AI makes it continuous, connected, and preventive, protecting scarce dollars and the members who depend on care.

Long-term services and supports (LTSS) represent one of Medicaid’s most important commitments: helping older adults and people with disabilities live with dignity in their own homes. In 2023, 87.1% of Medicaid LTSS users received home- and community-based services (HCBS), and 63.8% of Medicaid LTSS expenditures were for HCBS, maintaining HCBS as the largest and fastest-growing area of program spending.¹ That growth is a sign of success. It also creates a distinct oversight challenge, making it the hardest to monitor.

Unlike hospital care, LTSS happens in living rooms, kitchens, bedrooms, and bathrooms. Services are authorized in fragmented, often handwritten, care plans. Providers bill per visit or per item, in settings that don’t allow for real-time audits. The result is a program that delivers deeply personal care but gives states a blind spot—one that’s getting more expensive to ignore.

With roughly $900 billion in reduced federal Medicaid funding on the horizon, states face hard choices about optional services like HCBS. Yet these are exactly the services older adults and people with disabilities rely on to stay in their homes and out of hospitals or long-term post-acute care facilities. The pressure for states to maintain these services will be intense. Every dollar lost to fraud or waste is a dollar that can’t protect access.

Two Doors, One System of Trust

Fraud and waste enter LTSS through two doors, and traditional prevention methods haven’t worked well. The first door is who gets into the program and stays in. That’s determined through enrollment, assessment, and revalidation. The second door is what gets authorized, delivered, and billed. States have historically watched each one with periodic reviews and manual reconciliation. AI and integrated interoperable data now make it possible to watch both continuously.

Door 1: The Right Providers in, the Wrong Ones Out

In April 2026, CMS directed states to undertake swift, risk-based provider revalidation aimed squarely at HCBS, personal care, home health, durable medical equipment, and non-emergency medical transport, including providers without a National Provider Identifier. Many of these are the LTSS provider types that have lived in Medicaid’s blind spot.

This leaves an opportunity that’s greater than compliance. Revalidation is the moment to shift from a five-year checkbox to something continuous, where enrollment becomes the beginning, not the end, of program integrity. When provider data and external signals are evaluated monthly, emerging risk surfaces early, and those signals feed back into the lifecycle to determine when revalidation should happen and what level of scrutiny is warranted.

Done well, monthly provider screening improves the experience as much as it improves program integrity. By verifying a provider once and regularly refreshing the record, enrollment moves faster, directories become more accurate, and qualified providers spend less time on administrative hurdles. That keeps them in the program and protects network adequacy. Cross-state visibility strengthens the whole system when a continuously synchronized provider record more quickly surfaces sanctions and adverse actions across participating states.

Door 2: Matching Authorized, Delivered, and Billed Care

The second open door for FWA reduction is where oversight of LTSS services breaks down. Unlike the digital matchup possible in medical care between very well-established diagnoses and procedures, states often can’t digitally reconcile what a written LTSS care plan authorized, what services were delivered, and what was ultimately billed. That information sits across disconnected systems.

Care plans are more complex than a single medical diagnosis, and they’re multi-dimensional in ways that yield even more complexity than second or third medical diagnoses. An LTSS care plan will often accommodate individuals with both multiple medical diagnoses and multi-dimensional LTSS needs. Much of this complexity is conveyed in prose, not just coding systems and numeric indices.

Traditional tools weren’t built for this complexity. Duplicate-claim checks, static thresholds, and post-payment reviews remain necessary, but they can’t scale to the nuance of home-based care. Here’s where AI changes the equation. Instead of periodic audits that catch problems months after payment, AI reviews claims, care plans, and visit data as they come in. It connects information that used to sit in separate systems, surfaces patterns a manual review would miss, and flags issues before dollars go out the door. Oversight shifts from retrospective cleanup to real-time protection—so states can defend their budgets and the members who count on care.

The next generation of oversight goes further with digitized care plans deeply integrated into medical records and needs assessments, cross-program pattern analysis, longitudinal member-utilization modeling, and near-real-time anomaly detection. Oversight evolves from comprehensive retrospective analysis, generating connected operational intelligence that catches misalignment upstream. As always, prevention delivers far more impact than pay-and-chase recovery, and especially with LTSS, FWA prevention comes right along with program improvement and elevated member outcomes.

Protecting People, Not Just Dollars

This opportunity matters beyond the balance sheet because prevention over pay-and-chase recoveries means fewer improper payments and fewer disruptions to legitimate care. AI frees case workers and reviewers from manual reconciliation so they can focus on the highest-risk, highest-need cases, with humans firmly in the loop and accountable for decisions. It also enables them to interpret, shape, and guide AI-enabled analytic investigation to directly inform improvements in policy, payment, care coordination, and delivery of services.

That’s the real promise of a single, trusted, continuously updated view into both LTSS providers and the services they provide to members—a system of trust Medicaid has never quite had. As budgets tighten and federal scrutiny rises, the states that treat this as connected transformation instead of an exercise in compliance will simultaneously protect their dollars and their members.

Although LTSS has long been Medicaid’s blind spot, it doesn’t have to stay that way. For state Medicaid directors, the practical takeaway is straightforward: apply AI-driven oversight to LTSS the same way you’d protect any other high-value, high-growth part of your program. The tools exist today. The stakes have never been clearer. Contact us to learn more.