Today’s issue is about the impact of AI on a struggling workforce. As a physician, it’s a difficult conversation to have and one that requires some honest reflection. But if emergency medicine has taught me anything, it is that we don’t shy away from difficult conversations. Before we get into it, if you enjoy reading the newsletter, I invite you to subscribe and tell a friend.

Sam

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For decades, discussions about the healthcare workforce have centered on a familiar concern: physician shortages. The proposed solutions have been equally familiar. Expand medical school enrollment. Increase residency positions. Improve retention. Reduce burnout.

Then generative AI arrived. Most discussions about AI in medicine focus on whether AI will replace physicians. That debate generates headlines, conference presentations, and endless social media arguments.

A more important question is emerging:

Could AI reduce the amount of physician involvement required for a healthcare system to function?

The question shifts the conversation away from physician growth and toward workforce capacity, access to care, and healthcare economics.

The Shortage Is Real

The United States currently has approximately 1.08 million licensed physicians, according to the Federation of State Medical Boards. Only a subset actively provide direct patient care. Meanwhile, workforce projections continue to worsen.

The Health Resources and Services Administration (HRSA) projects a shortage of approximately 141,000 full-time equivalent physicians by 2038. The Association of American Medical Colleges (AAMC) projects a shortage of up to 86,000 physicians by 2036.

The exact number matters less than the trend. Every major workforce analysis points in the same direction: demand for physician services is growing faster than physician supply.

The cause is known. By 2030, every member of the Baby Boomer generation will be older than 65. Older adults consume substantially more healthcare resources than younger populations. They require more specialty care, more medications, more procedures, and more hospitalizations.

At the same time, physician training remains extraordinarily slow. A physician entering independent practice often requires:

Four years of medical school
Three to seven years of residency
Additional fellowship training in many specialties

Burnout adds additional pressure. According to national surveys conducted by the AMA, Mayo Clinic, Stanford Medicine, and the University of Colorado, physician burnout remains elevated despite modest improvement from pandemic peaks. Documentation requirements, prior authorizations, inbox management, quality reporting, regulatory compliance, and electronic health record tasks consume increasing amounts of physicians’ time.

The Workforce Is Already Changing

While physician workforce growth remains modest, the nurse practitioner workforce continues to expand rapidly. According to HRSA and the American Association of Colleges of Nursing, approximately 39,000 new nurse practitioners complete training each year. Combined U.S. MD and DO programs graduate roughly 29,000 physicians annually.

The Bureau of Labor Statistics projects workforce growth from 2024 to 2034 at:

Physicians 3%
Nurse Practitioners 35%

More than 430,000 nurse practitioners are currently licensed in the United States, and many states now permit full practice authority, allowing NPs to evaluate patients, diagnose conditions, prescribe medications, and manage treatment without physician collaboration.

Viewed independently, none of these trends is particularly surprising. Physician shortages have been discussed for years. NP workforce growth has been underway for decades. Full practice authority continues to expand. What makes the current moment different is the arrival of increasingly capable clinical AI systems.

AI Changes the Equation

Most conversations about AI begin with a very high standard.

Can AI diagnose as accurately as a physician?
Can AI independently manage patients?
Can AI replace physicians?

Healthcare systems facing workforce shortages may already be asking: Can AI safely reduce the amount of physician involvement required to deliver care?

That’s a much lower bar. If you step back, this is already what many health systems are purchasing AI to accomplish. They’re not buying AI to independently run cardiology services or replace surgeons. They are buying AI to:

Reduce unnecessary referrals
Reduce unnecessary consultations
Reduce specialist workload
Reduce documentation burden
Reduce physician time per patient
Improve triage and risk stratification

The goal is not physician replacement. The goal is to expand the reach of scarce physician resources. That distinction sits at the center of this entire discussion.

The Consultation Bottleneck

The AANP’s 2024 National NP Practice Survey provides an interesting glimpse into how care is actually delivered. Seventy percent of nurse practitioners report referring patients to specialists as part of their practice. More than three-quarters of primary care NPs report consulting specialist physicians as part of their clinical workflow.

These figures don’t represent consultation rates per patient encounter. They describe practice patterns. But they illustrate an important point: even in states with full practice authority, patient care frequently relies on referral, consultation, and escalation. Modern healthcare remains fundamentally collaborative.

This raises an interesting possibility.

What if AI simply reduces the frequency with which physician expertise is needed?

Consider a hypothetical primary care practice where nurse practitioners seek physician consultation in 20% of patient encounters. The exact percentage is illustrative rather than a measured national rate, but the concept is familiar to anyone who has collaborated with APPs. The physician serves as an escalation resource for uncertainty, complexity, and high-risk decisions.

Now imagine that each NP has access to an AI system capable of:

Reviewing records before visits
Generating differential diagnoses
Retrieving guidelines
Identifying medication interactions
Highlighting red flags
Suggesting management pathways

The AI doesn’t replace the NP or the physician. It just helps the NP resolve more uncertainty before escalating the case.

Suppose physician consultation rates fall from 20% of encounters to 10% while maintaining equivalent morbidity, mortality, patient safety, and quality outcomes. A physician who previously supported five clinicians might support ten. A specialist who previously reviewed twenty escalated cases each day might review ten.

The workforce implications are difficult to ignore.

What Would Have To Be True?

We don’t have evidence that an AI-supported NP is equivalent to a physician. The more interesting question is what evidence would be required before healthcare systems, regulators, payers, and patients began treating such a model as acceptable.

Historically, debates about the authority of NP practice have focused on comparisons between NPs and physicians. Researchers have examined quality metrics, utilization patterns, referral rates, costs, patient satisfaction, and outcomes.

AI introduces a new variable into that debate. An AI-assisted model would need to demonstrate equivalent:

Mortality
Hospitalization rates
Complication rates
Malpractice rates
Patient satisfaction
Quality metrics

All while reducing physician consultation volume. Importantly, AI does not need to prove that it performs at the physician level. It only needs to demonstrate that it helps identify which patients truly require physician expertise.

Most physicians naturally ask whether AI can practice medicine as well as a physician. Health systems instead ask whether AI can safely reduce the number of situations that require physician involvement.

The Benchmark Problem

The thought experiment becomes even more compelling when viewed through the lens of healthcare access.

A rural hospital unable to recruit a neurologist is not choosing between neurologist care and NP-plus-AI care. It may be choosing between NP-plus-AI care and no local neurology care at all.
An understaffed primary care clinic may not be choosing between physician-led care and NP-led care. It may be choosing between NP-led care and months-long waits for appointments.

In these settings, the benchmark is no longer ideal physician staffing. It’s whether patients receive timely care at all.

Healthcare has repeatedly adopted technologies that expand the reach of scarce expertise. Telemedicine enabled a single specialist to cover multiple hospitals. PACS systems expanded the reach of radiologists. Electronic health records made patient information available across large networks. AI may ultimately prove to be another tool that expands the reach of limited physician resources.

Conclusion

The central question is not whether AI can become a physician. It is whether AI can reduce the amount of physician involvement required to achieve acceptable outcomes.

Healthcare systems facing workforce shortages, growing demand, and uneven access to care may increasingly evaluate models that expand the reach of physician expertise rather than models that replicate it. If AI can safely reduce consultation volume, improve triage, and help clinicians resolve uncertainty before escalation, the effects on workforce capacity could be substantial.

Healthcare systems, regulators, payers, and physicians themselves may eventually be forced to answer a difficult question:

How much physician involvement is actually necessary to achieve acceptable outcomes?

Ashoo Review: AI in Medicine

NP + AI = MD?