From Blind Spots to Better Outcomes: How SNF Data Transparency Strengthens LTSS

This article was originally featured in MedCity News.

Long-stay skilled nursing facility (SNF) members represent one of the most clinically complex populations served through Medicaid long-term services and supports (LTSS) and remain a foundational component of many state LTSS programs. These members often live with multiple chronic conditions, functional limitations, and ongoing clinical needs that require coordination across providers and care settings.

At the same time, avoidable hospital admissions and readmissions remain a major cost driver within LTSS programs. Nationally, potentially preventable inpatient stays account for an estimated $33.7 billion in hospital costs annually. As Medicaid agencies and managed care organizations (MCOs) continue advancing value-based care strategies, many are looking for new ways to better identify member risk, strengthen care coordination, lower costs, and improve outcomes.

Drawing on their respective experiences from the payer and provider perspectives, Anna Keith, Vice President of LTSS Product & Strategy at Centene Corporation, and Phyllis Wojtusik, RN, Executive Vice President of Value-Based Care at Real Time Medical Systems, shared their perspectives on how greater transparency into long-stay SNF populations can help health plans and providers reduce avoidable hospital utilization while strengthening LTSS performance.

The Long-Stay SNF Blind Spot

Long-stay SNF members occupy a unique position within the healthcare continuum. They do not fit neatly within acute care models, yet they are often overlooked in discussions around community-based LTSS because they reside in institutional settings. As a result, responsibility for coordinating their care is often distributed across health plans, nursing facilities, providers, and state stakeholders, creating fragmented accountability and reducing opportunities for early intervention.

Many organizations continue to rely on retrospective claims data and periodic Minimum Data Set (MDS) assessments to understand this population. While valuable, these sources provide only a delayed view of a member’s condition.

As Anna Keith explains: “Most health plans still depend heavily on claims and MDS data. While those are important sources, they’re often weeks or even months behind what’s actually happening with the member. By the time a claim appears, the hospitalization has already occurred, while an MDS assessment may not capture a clinical decline that began weeks earlier.”

According to Phyllis Wojtusik, one challenge is that long-stay SNF populations have historically received less attention than their post-acute counterparts despite their complexity and cost. “There’s been so much focus on value-based care for post-acute populations that long-stay members often get overlooked. The challenge isn’t a lack of commitment from health plans or providers. It’s that organizations haven’t historically had access to timely, actionable information that helps them take action before a member’s condition worsens.”

As a result, opportunities to identify risk, coordinate care, and prevent avoidable hospitalizations may be missed.

What Data Transparency Looks Like in Practice

Effective SNF data transparency goes well beyond basic utilization reporting. It incorporates admissions per thousand, readmission timing and causes, case-mix trends, and other operational and clinical measures that help organizations evaluate current performance and emerging risk.

Equally important are clinical indicators that can signal early changes in condition, including fall-risk trends, changes in activities of daily living (ADLs), weight loss and nutrition concerns, medication-related issues, infection indicators, immunization status, and member acuity.

Individually, these indicators may seem routine. Together, they reveal patterns that can signal increased risk long before a hospitalization occurs.

“Maybe one indicator doesn’t tell you anything, but three or four indicators together begin to reveal a pattern. Claims data alone can’t provide that perspective. If we want to move toward a preventive and proactive care model, we need information that helps us recognize those patterns before they become problems,” states Keith.

The goal is not simply more reporting, but actionable intelligence that supports earlier intervention and performance improvement.

Wojtusik emphasizes the role technology plays in making these insights usable at scale. “When you have 40, 50, or 60 complex members in a facility, no one person can maintain a complete picture of every clinical change. Technology helps bring those signals together and identify where action is needed most, allowing care teams to focus their attention where it can have the greatest impact.”

Together, these capabilities create a shared understanding of emerging trends and potential concerns across health plans, providers, and nursing facilities.

Turning Insight into Action

Having data is one thing. Acting on it is another.

When care managers, service coordinators, and facility clinicians can identify subtle changes in condition earlier, they can intervene before issues escalate into hospitalizations or emergency department visits. Whether the warning signs involve infection, declining mobility, poor nutrition, changes in behavior, or increasing care needs, earlier intervention can help stabilize members in place and reduce unnecessary transfers.

“Once someone is hospitalized, they rarely come back exactly where they were before. If we can recognize those early warning signs and intervene sooner, we have a much better opportunity to preserve function, reduce disruption, and support a better experience for the member,” Keith explains.

The goal is not only to reduce hospital utilization, but also to help members avoid the functional decline that often accompanies an acute-care stay.

Timely information also supports stronger care transitions, more effective discharge planning, and helps identify members who may be appropriate for community-based care when clinically feasible.

For Wojtusik, another important benefit is ensuring care remains aligned with member goals and preferences. “Technology can help identify when someone is entering a decline that may not be reversible. That creates an opportunity for meaningful goals-of-care conversations and a better understanding of what matters most to that individual and their family.”

Better Outcomes Through Shared Accountability

Data alone does not improve outcomes. The real value comes from how organizations apply those insights to create alignment and drive action across stakeholders.

When health plans, MCOs, and SNFs operate from a shared source of truth, they gain a common understanding of member risk, utilization patterns, and opportunities for improvement. This creates greater accountability around hospitalizations, readmissions, quality measures, member stability, and successful community transitions.

As Keith explains, “Historically, many payer-provider relationships have been transactional. But when everyone can see the same information and establish a common understanding of where opportunities exist, you can build true accountability around outcomes.”

Wojtusik agrees. “If you clear the decks and put the member first, health plans and providers can really come together around this goal. Transparent data helps everyone understand what’s happening, what the member wants, and what steps need to be taken to support the best path forward.”

For Medicaid programs, the impact can be substantial. Preventable inpatient utilization remains one of the largest drivers of healthcare spending among long-stay SNF populations, making quality improvement and cost reduction inherently connected. As Keith notes, “Quality and cost don’t have to compete with each other. When you improve quality, better outcomes follow, and cost savings become a natural result.”

She adds, “For years we’ve managed long-term nursing facility populations looking through the rearview mirror. Real-time data transparency allows us to look through the windshield. We can identify risk sooner, plan proactively, and work together to prevent avoidable hospitalizations before they happen.”

Technologies that aggregate and analyze real-time SNF clinical data, including platforms such as Real Time’s AI-driven solution, are helping organizations incorporate these insights into day-to-day decision-making and value-based care strategies.

As Keith concludes, “Collaboration, shared transparency of data, and proactive engagement. That’s what drives the outcomes we’re looking for.”

You may view this article on the MedCity News website, here.