Post-acute care has always operated under tight margins and heavy regulation, but the pressure facilities are facing now is fundamentally different. Leaders are being asked to improve patient outcomes, maintain airtight compliance, and secure full reimbursement in an environment defined by staffing shortages and unprecedented data complexity.
At the same time, the World Economic Forum tells us that healthcare is “below average” on adopting advanced AI technologies that can improve this challenge. Expectations around artificial intelligence in healthcare are rising quickly, often without a clear understanding of what AI can and cannot realistically deliver in the near term.
For years, much of the industry’s technology investment has focused on basic automation. Tools that summarize documents, extract fields, or flag missing information have helped reduce some manual effort, but they have not addressed the deeper operational challenges post-acute providers face every day. Admissions and nursing teams still spend an enormous amount of time reviewing documentation by hand, reconciling information across systems, and trying to interpret whether a referral is clinically appropriate, financially viable, and compliant with evolving regulations. That work is cognitively demanding, highly variable, and prone to error, especially when staff are stretched thin.
Why automation alone has fallen short
Reimbursement is where the cracks in this approach become most visible. Capturing the full value of a patient stay depends on accurately connecting clinical notes, hospital records, referral documents, assessments, and regulatory criteria. In most organizations, that process still relies on people stitching together information from multiple systems and sources that were never designed to work together. When details are missed or documentation is incomplete, the consequences show up as undercoding, denied claims, or increased audit exposure. These are not edge cases; they are structural risks baked into fragmented workflows.
Data fragmentation only amplifies the problem. Post-acute intake teams routinely receive information from a wide range of hospital electronic medical record systems and referral platforms, each with its own formats, terminology, and gaps. Context switching between systems slows down admissions, increases the risk of readmissions, and makes it difficult to form a clear picture of patient complexity and operational capacity.
Expecting basic automation tools to resolve these issues is unrealistic, because the challenge is not speed alone. It is interpretation, prioritization, and judgment across multiple variables at once.
The shift from automation to foresight
This is where expectations around AI need to mature.
The real value of AI in post-acute care is not how quickly it can process documents, but whether it can provide foresight. That means understanding how clinical indicators, regulatory requirements, and reimbursement rules interact, and identifying risk before it turns into a denial or an audit finding.
Agentic AI represents an opportunity for meaningful shifts in this direction. Rather than performing isolated tasks, these systems are designed to evaluate data holistically, take multi-step actions, and continuously adapt as conditions change. In practice, that capability allows organizations to move from reactive to proactive operations. Instead of discovering documentation gaps after a claim has been submitted, AI can surface high-risk reimbursement profiles early in the intake process. Instead of relying on manual reviews to ensure compliance alignment, systems can continuously assess whether required elements are present and flag inconsistencies that need attention.
Smarter intake and better use of clinical resources
Agentic AI also enables more sophisticated decision-making around patient intake and resource allocation. Evaluating a referral is not a single-variable problem – it’s a balancing act of different qualifiers:
- Clinical acuity,
- Staffing levels,
- Bed availability,
- Equipment needs, and
- Financial considerations.
When these factors are assessed independently or sequentially, delays and misalignment are inevitable. When they are evaluated together, organizations can make faster, more informed decisions about whether they can safely and sustainably care for a patient. Equally important, this approach helps protect clinical staff from being overwhelmed by administrative complexity. Lifting multi-dimensional problem solving out of manual workflows allows clinicians and care teams to do what they were trained to do: focus on patient care rather than paperwork and cross-system reconciliation. In an environment where staffing shortages show no signs of easing, that distinction matters.
Photo: Vithun Khamsong, Getty Images
Cory Evans is the CEO of Clinware. He’s built a 20+ year career centered on improving care delivery and solving systemic gaps that strengthen both patient outcomes and organizational performance.
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