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Readmitted patients are often clinically unstable at the time of transfer, AI ensures the transition to skilled nursing facilities goes smoothly.
Following a hospitalization, many patients require skilled post-acute care to support recovery, improve functional status, or manage chronic illness, and skilled nursing facilities (SNFs) are the most common setting for this critical care (1). More than five million patients are transferred from hospitals to SNFs annually (2), and in 2018, 20% of all hospitalized fee-for-service (FFS) Medicare beneficiaries were discharged to a SNF representing 2.2 million SNF stays (3). But despite the prevalence of SNF admissions, they frequently presage avoidable rehospitalizations and adverse events.
The rate of 30-day readmissions to hospitals from SNFs is high. Close to one in five patients are rehospitalized within 30 days of transfer to a SNF, and a substantial percentage of patients are rehospitalized within just two days of initial SNF admission (4). Hospital readmissions following discharge to SNFs are also extremely costly. Total Medicare FFS spending on SNF services was $28.5 billion in 2018 (3), and hospital readmissions from SNFs have been directly attributed to more than four billion dollars (5).
However, studies have shown that hospital readmissions from SNFs disproportionately occur for preventable conditions. Readmitted patients are often clinically unstable at the time of transfer, and as many as two-thirds of these readmissions are estimated to be potentially avoidable (1).
AI can help healthcare organizations to accurately identify the patients most likely to be discharged to a SNF and ensure the transition goes smoothly—reducing hospital readmissions and improving health outcomes. With predictive analytics, organizations are able to proactively target high-risk patients with individually-tailored interventions before they are transferred to SNFs. These interventions can include intensive monitoring during the first 48 hours of SNF admission, specialist consultation follow-ups, and better pre-discharge evaluation of care needs (4).
