Every healthcare AI vendor talks about accuracy as if it were one thing. If it can pass a medical exam, then it must be right for your health system. Right?
Not necessarily. There’s a reason the growth of administrators in health systems has far outpaced the growth of physicians. Health systems’ biggest cost drivers aren’t necessarily problems they would ask a clinician to fix.
Specialty care is where health systems are feeling the sharpest pressures; it’s where the processes are most dense, margins are razor-thin, and keeping volume moving is just as much about coordination as it is the care itself. It also drives much of the procedural volume and revenue that health systems depend on, which makes operational friction especially costly.
Clinical-grade AI can do a lot of things well in a health system. Today’s models can process medical imaging, draft clinical notes, and even predict health risks. But when it comes to the operational processes that throttle revenue, an AI only built to pass a medical exam can be rendered useless fairly quickly.
A model can be clinically sound and still bungle the logic that drives a health system’s operational engine. Health systems are being choked by the web of rules that govern their specialty operations, and they’re being sold AI designed for diagnostics to solve it.
For that, health systems need operational accuracy.
While clinical accuracy asks whether an AI agent gets the facts straight across patients, context, and medical questions, operational accuracy asks a completely different set of questions: is it correct for your organization, with your payer mix, scheduling rules, and referral logic? Those are two separate standards, and only one of them shows up in your P&L.
Operational accuracy, on the ground
A patient calls about an orthopedic issue and needs a subspecialty referral. The routing has to be right, or the visit is wasted. The surgeon takes post-op follow-ups only with a physician assistant, not the MD. Prior authorization requirements change depending on the payer. The follow-up sits inside a narrow ninety-day window that cannot slip, the on-call provider assignment changed this morning, and the caller, at 5:30 in the morning before anyone is at the front desk, is more comfortable in Spanish. Get any one of those wrong and the patient hangs up, the appointment never gets booked, care is delayed, and revenue walks out the door.
A clinically accurate AI can know everything about that patient’s condition and still fail every variable above.
Health system leaders see this gap every day, even if the market hasn’t named it yet. It’s why pilots look great in demos and then break as soon as they deploy into the messier parts of an ambulatory network, and why an otherwise dependable AI suddenly falls apart on specialty referrals.
The front door is the first place it shows up. Patients wait far too long on hold just to get through to somebody. A recent MGMA poll found front-end issues accounted for 23% of the biggest revenue cycle leaks. According to another MGMA poll, front office support staff turnover reached 40% in 2022, and replacing that staff is costly: new hires still need 60 to 90 days before they are efficient enough to schedule without creating more downstream problems. Meanwhile, the patients who are sick of waiting have already moved on.
The result is only 34.8% of referral scheduling attempts ending with an appointment.
Taken together, health systems are spending more to hire, train, and replace operational staff while converting only a fraction of the demand already at their front door. The challenge is turning referrals into appointments, appointments into volume, and volume into growth.
No amount of clinical accuracy can solve that, which is why operational accuracy matters so much.
Adopting a new set of criteria for accuracy
Operational accuracy means understanding how work actually gets done inside a health system. It’s something we’ve spent a lot of time building at Assort Health.
An AI has to understand all of the rules that live in the system but are not always written down, like when a patient needs a different specialist, when prior authorization is required, when a follow-up has to be slotted into a narrow window, and when a scheduling decision has downstream implications for revenue and capacity. It also has to make the experience easier for people on both ends of the phone.
That level of nuance comes from production data, knowledge of real workflows, and a lot of time spent on understanding the millions of edge case scenarios that choke health system phone lines every day.
At Assort, operational accuracy is the standard we build to. The edge cases other platforms are still failing months after go-live are the ones we built for on day one.
Getting the clinical facts straight is table stakes in today’s market. Whether or not an AI works inside the realities of your health system is the bar that should matter most.
####
At the Becker's 11th Annual IT + Revenue Cycle Conference: The Future of AI & Digital Health, taking place September 14–17 in Chicago, healthcare executives and digital leaders from across the country will come together to explore how AI, interoperability, cybersecurity, and revenue cycle innovation are transforming care delivery, strengthening financial performance, and driving the next era of digital health. Apply for complimentary registration now.