Waiting months to see a doctor? It doesn’t have to be that way. |
Waiting months to see a doctor? It doesn’t have to be that way.
The average American now waits 31 days to see a specialist — the longest wait on record. Dermatology averages 36.5 days, up 50 percent since 2004. Cardiology has risen 74 percent over the same period, to 33 days. Rheumatology stretches to six months — and in some parts of the country, to one year.
The queue for specialist care is longer than it has ever been, and it is getting worse.
We often characterize these numbers as an access problem. But framing them that way understates the severity of the issue. When specialist wait times reach these levels across an entire population, the consequences are not merely inconvenient; they are epidemiological. Delayed specialist access is a public health crisis. We should start treating it like one.
When we discover lead in a community’s water supply, we mobilize quickly. When maternal mortality spikes in a region, we investigate. We treat these as systemic failures demanding systemic responses.
Specialist wait times have crossed that threshold. A patient with early rheumatoid arthritis who waits six months for a rheumatology appointment risks permanent joint damage before treatment begins — damage that could have been prevented with timely treatment. A patient with a changing pigmented lesion who waits 36 days to see a dermatologist may giving a chance to an early melanoma — a cancer whose prognosis depends heavily on the speed of its removal.
These are not edge cases. They are the predictable consequences of a system that forces urgent and routine needs into the same queue, with no mechanism for distinguishing between them. The Association of American Medical Colleges projects a shortfall of up to 86,000 physicians by 2036. The queue is not getting shorter.
Here is what makes this crisis particularly frustrating: It is not purely a supply problem. It is a sorting problem. Research consistently shows that roughly one-third of specialist visits are for conditions manageable in primary care. Patients who need anti-inflammatory drugs and physical therapy wait alongside patients with early inflammatory disease who need urgent intervention. We have no systematic mechanism for matching clinical urgency to access speed — the worst possible allocation of a scarce resource.
The shortage is national, but its effect is profoundly unequal. A patient at an academic medical center has institutional workarounds — curbside consults, internal referral networks, expedited pathways. A patient at a federally qualified health center in rural Mississippi has none of these.
Layer in the reality that nearly one in three Americans lack a primary care doctor — and therefore have no referral pathway into specialist care at all — and the crisis compounds. The populations with the fewest workarounds bear the greatest burden.
What this crisis needs is triage infrastructure at the population level — a systematic way to match clinical urgency to access speed. Artificial intelligence can help provide that layer.
The eConsult model, where primary care clinicians submit cases for asynchronous specialist review, has demonstrated that roughly two-thirds of referred cases can be resolved without a face-to-face visit when cases are properly prepared. Programs at institutions such as UC San Francisco have shown resolution in days rather than months.
AI can serve as the triage layer this system lacks — organizing clinical information, identifying which patients need to be seen urgently, and routing straightforward cases to faster resolution pathways. When specialists receive well-prepared cases for asynchronous review, they can provide guidance in minutes rather than scheduling visits months out.
But technology alone is not sufficient. Payment models must compensate specialists for guidance, not just for visits. We need closed-loop outcome tracking so the system learns and improves. And health systems must be willing to redesign referral pathways.
The technology exists today. What’s missing is the policy decision to treat specialist triage as infrastructure — the way we treat vaccination networks and water systems — and fund it accordingly. The Centers for Medicare and Medicaid Services could start by reimbursing specialists for asynchronous guidance at parity with in-person visits. States could mandate that referral systems track time-to-specialist as a quality metric, the way we already track hospital readmissions.
These are solvable problems, but solving them requires first acknowledging their severity. We calculate the population-level cost of medication non-adherence, missed screenings, or hospital-acquired infections. We treat these as system failures with measurable consequences. Specialist wait times produce the same kind of harm — but we track none of it, because we have decided this is “just how things work.”
We do not track how long patients wait for specialist care, or what happens to them while they wait. That silence is a choice — and it is time to make a different one.
Quentin Chu is a healthcare technologist and the founder of Quercus Health, where he is building AI infrastructure to improve patient access to specialist care. He has spent two decades in healthcare operations, technology and investment.
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