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Why Some Home Health Providers Are Still Struggling With Low-Utilization Payment Adjustments
Following the implementation of the Patient-Driven Groupings Model (PDGM) – and during the height of the COVID-19 pandemic – home health providers struggled significantly with Low-Utilization Payment Adjustments (LUPAs).
Since then, some providers have found ways to mitigate them, and others haven’t. Broadly, LUPAs occur in fee-for-service Medicare when a provider does not meet a certain threshold of visits during a care episode. They result in a reduction in reimbursement.
McKenna noted that providers struggled with LUPAs for a variety of reasons.
“I think that providers really struggled with adjusting to both the changes in the 30-day period, and then the change in varying LUPA thresholds,” she said. “I think that it was compounded by the fact that the LUPA threshold in the HIPPS code is not finalized until your coding has been completed and finalized, and your OASIS has been reviewed and locked. That can be a few days or even longer.”
The other major headwind was the impact of the COVID-19 pandemic, Anthony D’Alonzo, division director and senior vice president of practice analytics and insights at Bayada Home Health Care, told HHCN.
Medigy Insights
Hospitals and health-tech innovators can address the persistent struggles faced by home health providers regarding low utilization payment adjustments by leveraging technology, enhancing care coordination, fostering partnerships, and implementing data-driven approaches. Integrating advanced technologies like telehealth platforms and remote monitoring systems, promoting robust care coordination systems, developing strategic partnerships, and utilizing data analytics tools can optimize resource utilization, improve patient outcomes, and enhance financial performance. Collaborative efforts among hospitals, health-tech innovators, and home health providers are crucial in transforming and sustaining home healthcare, ensuring high-quality care delivery in the face of evolving payment models.
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