Discharging a patient without the whole picture can lead to costly missteps. Too often, patients are discharged from emergency departments and other acute settings to high-cost, unnecessary post-acute care because providers lack full visibility to guide the best next step.
Home health services may provide a more cost-effective and outcome-driven solution for specific individuals than traditional discharges. By optimizing the site of service with AI-driven decision-support, providers can ensure that patients receive the right level of care, whether in a post-acute facility or at home with support, while avoiding unnecessary expenses and improving health outcomes.
Patients Have Distinct Care Needs, Requiring Streamlined Decisions During Pivotal Moments
When a provider determines where to refer an individual for follow-up care, individuals often fit into three categories:
- Facility-based post-acute care means that an individual with high care needs would benefit from long-term facility stays.
- Home care with support means an individual has low support needs and would be a good fit for home care.
- “In between” means an individual has varying contributing factors that could make them a good fit for either facility care or home-based care. However, since the decision is not entirely straightforward, the individual could be at risk for potentially unnecessary and costly care.
Individuals who are in the “in between” category traditionally fall into this gap where greater decision support is needed. With emerging decision support technology, providers can easily analyze clinical, functional and social factors to guide decision-making on post-acute care, including information on:
- Patient mobility tools, such as the use of a walker or wheelchair
- History of memory issues or confusion
- Medication management history and ability to manage at home
With such a wide variety of data points to manage for each patient, providers and value-based care organizations will need tools that maximize efficiency with zero data entry required.
These tools help providers assess the best discharge option, ensuring patients receive the most suitable care setting while alleviating financial strain on the healthcare system. Organizations need actionable, AI-driven insights proven to reduce unnecessary SNF and IRF utilization in real time.
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Data Supports Home Health as an Effective Alternative
A study of 1.6 million patients found that skilled nursing facility (SNF) and home health discharges resulted in equivalent readmission rates. This suggests that for many patients, home-based care can achieve the same quality as institutional settings when appropriate, at times at a lower cost and with greater patient comfort.
As hospitals and emergency departments seek to optimize discharge planning, weighing cost and clinical outcomes is essential. AI tools offer tailored, evidence-based site-of-care recommendations in real time by rapidly analyzing clinical, functional and social data.
When lower-acuity settings, such as home health, offer the same benefits as more intensive facilities, providers can improve efficiency while maintaining quality care. Accountable care organizations can also reduce excess SNF and inpatient rehabilitation facility stays with early and appropriate referrals to palliative care and hospice.
Moving Toward Smarter, Cost-Conscious Care
The healthcare industry is shifting toward site-of-care optimization, emphasizing lower-cost settings like home health, ambulatory infusion centers and physician offices. By leveraging innovative, AI-driven placement tools and prioritizing site-of-care optimization, healthcare organizations can improve patient outcomes, reduce unnecessary admissions and ease the financial burden on the system.
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