Artificial intelligence is changing healthcare, but not in the flashy, futuristic way most people expect. Some of the most urgent issues in the system today come from paperwork and manual processes that were never designed to support the pace or complexity of modern care.
Nurses are expected to chart every detail, meet billing requirements, and stay compliant, all while tending to some of the most vulnerable people in the healthcare system. That pressure affects everything. It leads to burnout, patients receiving less attention than they deserve and millions of dollars of missed reimbursements.
At Qatalyst Health, we didn’t set out to build an AI company. We started with a simple question: why are we still relying on manual processes that slow everything down? As we worked to build a platform to make documentation easier to manage, it became clear that AI was the right tool to solve the frustrating problem that was draining time and money from long-term care facilities.
Streamlining Documentation and Reimbursement with AI
In long-term care, reimbursement depends on accurate, detailed documentation, but the process of capturing that information is fragmented and time-consuming. Most notes are handwritten or dictated under pressure, and even subtle differences in phrasing can change how care is categorized and billed.
Our platform uses AI to interpret these notes in context. Instead of scanning for keywords, it analyzes the full meaning of each sentence to determine what qualifies for reimbursement. That matters because clinical documentation directly influences patient classification. For example, describing someone as “walking with assistance” signals a different level of dependency than “independent ambulation,” and that distinction affects how much support the facility is reimbursed for providing.
By turning unstructured language into structured data, our system helps facilities submit cleaner claims, reduce costly errors, and avoid delays. What used to take hours of manual review can now be handled in minutes. That saves time and gives care teams more space to do the work that actually matters.
Extending AI to Admissions and Facility Readiness
Improving documentation was just one part of the problem. Admissions is another area where facilities often struggle with limited information and high stakes. Many intake decisions are made quickly, based on fragmented referrals or incomplete notes, leaving staff to fill in the gaps while trying to prepare for a new arrival.
We designed our platform to help teams make those decisions with greater clarity. It analyzes incoming referrals to flag potential behavioral risks, estimate medication needs, and project the expected length of stay. With that information upfront, administrators can better match residents with the appropriate level of care and allocate resources more effectively from day one.
This gives care teams the context they need to plan ahead, support their staff, and create smoother transitions for residents and families alike.
Building Credibility with Healthcare Partners
Healthcare is a risk-averse industry for good reason. When a product fails, the consequences aren’t just technical—they can be permanent and life-altering for individuals, families, and entire communities. Add to that the complexity of privacy regulations, sensitive data, and deeply embedded systems, and it’s clear why many organizations hesitate to adopt new technology.
That’s why trust has to come first. Many facilities have been burned by vendors who promised transformation but failed to deliver anything useful. In healthcare, potential doesn’t mean much if it doesn’t translate into outcomes. Features sound good in a pitch, but what teams really need is a solution that works under pressure and holds up in real workflows.
You have to show up consistently. You have to listen before offering advice. And you have to prove through tangible, day-to-day results that your product actually makes the job easier for the people doing the work.
When we first launched Qatalyst Health, we didn’t focus on selling. We focused on learning and we partnered with early adopters to understand where other systems had missed the mark. Their feedback reshaped how we approached documentation, admissions, and onboarding from the ground up.
Once that trust is earned, everything moves faster. Word-of-mouth matters in healthcare, especially in long-term care where networks are tight-knit. But you can’t shortcut your way to credibility. You have to earn it by delivering what you say you will.
Advancing AI in Healthcare Beyond Documentation
AI’s role in healthcare is quickly expanding from data extraction to decision support, with tools that can analyze context and recommend next steps. We’re already seeing movement in that direction with systems designed to triage patient risk, anticipate readmission, and suggest care plan adjustments using real-time data.
As models become more powerful and AI agents more autonomous, these tools will be able to support clinical decisions with greater precision and speed. But that kind of progress only matters if it’s rooted in what teams actually need. The real opportunity lies in building systems that are responsive, explainable, and easy to integrate into existing workflows.
Building AI Products That Actually Work in Healthcare
Innovation in healthcare doesn’t begin with technology—it begins with observation. The most effective products I’ve seen come from founders who take the time to understand where people are overwhelmed, not just where trends are pointing. The real opportunity comes from noticing the moments that create daily friction and asking, how can this be easier? When a process is slow, inconsistent, or prone to error, that’s usually a signal worth following.
At Qatalyst Health, we focused on the parts of healthcare that are often overlooked because they’re so ingrained in daily routines that they’re easy to ignore. Documentation, reimbursement, and admissions may not seem cutting-edge, but improving them took thoughtful design and a deep understanding of how care teams actually work. Every feature we built came from watching how teams worked, where they struggled, and what they wished was easier.
AI has the potential to support healthcare in meaningful ways, but only if it fits into the real work already happening. The best tools are the ones that make care easier to deliver and operations easier to manage. That kind of impact comes from paying attention to what people need, then building with clarity and purpose.
Andrew Nye, a recent UofSC graduate, started his entrepreneurial path in college by co-founding a hedge fund while also running his own consulting business, where he handled financial modeling for several equity raises, convertible note issuances, and buyouts. One project introduced him to the challenges of healthcare reimbursement, which inspired him to start Qatalyst Health and build better software for the long-term care industry.
