3 ways to lower readmission rates
Neil Smiley, Founder and CEO, Loopback Analytics
This October, the Centers for Medicare & Medicaid Services will publish the Skilled Nursing Facility 30-Day All-Cause Readmission Measure (SNFRM) on Skilled Nursing Compare. The SNFRM estimates the risk-standardized rate of unexpected readmissions within 30 days for patients with fee-for-service Medicare who were inpatients at PPS, critical access, or psychiatric hospitals for any cause or condition.
Long-term care facility performance on readmission rates is a big deal. Public reporting of readmission rates impacts the facility's reputation for quality. Long-term care facilities with higher than expected readmission rates will be subject to financial penalties starting October 2018. In addition, hospital referral sources are actively narrowing their post-acute care networks and cutting facilities that have high readmission rates.
In markets where there are bundled payments, post-acute care facilities are finding that lower acuity patients are no-longer being sent to SNFs, and patients that are referred to SNF are higher average acuity than in the past. Even as acuity levels increase, there is intense pressure to reduce post-acute length of stay and reduce readmissions.
Long-term care facilities must effectively utilize data to manage the readmission challenge:
1. Get a handle on your readmission data
2. Participate in a data sharing network
3. Leverage analytics
Get a handle on your readmission data
Many long-term care providers manually collect statistics to report readmission rates to hospital referral sources. However, in spite of the considerable effort involved, the reporting is often of little value. Differences in timing, variations in measurement methodology and gaps in visibility after discharge from post-acute care facilities can result in profoundly misleading results.
A better alternative to track readmission performance is to leverage analytics based on third-party claims data such as CMS Standard Analytical Files (SAF). Data dashboards can provide a risk-adjusted view of post-acute care provider's performance on readmissions by referral source and condition, as well as the readmission rates of other competitors in their market.
As hospitals take steps to narrow their post-acute care networks, post-acute care facilities should be proactive in leveraging timely analytics to ensure their readmission performance is being fairly represented on a risk adjusted basis. Hospitals may not consider the relative acuity of patient referrals as they compare one facility to next. To the extent there are readmission issues, a well framed data-driven action plan can boost the confidence of referral sources and keep a post-acute care provider in the preferred referral network.
Participate in a data sharing network
Post-acute care facilities cannot solve the readmission problem alone. Participation in a data sharing network with hospital referral sources and other post-acute care providers that comprise the post-acute care continuum is essential to providing effective care coordination. Many readmissions are the result of failed hand-offs and communication problems. Data sharing networks help network participants to work together in identifying and correcting the root causes that lead to readmissions.
For example, in Austin, TX, the majority of post-acute care providers have connected to a real-time data network enabled by the Loopback Analytics. Data from multiple providers and care settings are linked through a Master Patient Index (MPI) into a common continuity of care record. The data network enables hospitals and post-acute care providers that care for the same patients to receive real-time alerts based on member defined parameters. When readmissions do occur, all network participants can reference a common source of data to support collaborative problem solving.
The level of patient acuity in long-term care facilities will likely continue to rise as patients that were previously cared for in hospitals are now in post-acute facilities, and patients that were previously in post-acute facilities are going home earlier. Long-term care facilities will need to leverage data from network partners, along with predictive analytics to identify patients of rising risk to balance the competing demands of readmission risk management, bundle costs and patient safety. Better data provides the path to help ensure the right patient is in right care setting for the right amount time.
Healthcare as a whole, and post-acute care in particular, is increasingly under pressure to lower readmissions by improving clinical outcomes while simultaneously lowering the total cost of care. With comprehensive data, network participation and targeted analytics, leading long-term care organizations are embracing this challenge as a defining opportunity in the transition to value-based care.
Neil Smiley is the CEO of Loopback Analytics.