AI Can Streamline Vertical ED Care—If Clinicians Still Own the Flow Decision

The waiting room is crowded and the next open bed is occupied. A patient with a stable-looking complaint may start care in a chair. Another patient may look just as stable and be the one who cannot wait. The question is who can be assessed safely without a bed, and who must move into monitored space now.

An AI-supported vertical pathway can sort triage signals into a proposed flow lane. It cannot own the clinical decision. Let the model narrow the queue, then make the nurse and physician decision visible and reversible.

A 2025 single-center study gives emergency departments a useful starting point, not a plug-in recipe. The study reduced average length of stay after implementing an ML-assisted vertical pathway, but its value is in the guardrails around the model as much as the minutes saved. The full study is available in Journal of Personalized Medicine.

Vertical care is a flow decision, not a discharge shortcut

Vertical processing means assessing and treating a patient without assigning that patient to a traditional ED bed. The patient may be evaluated in a chair or another designated area, receive appropriate testing and treatment, and then return to the waiting area or move to a bed when the clinical plan requires it. That is a space-and-sequence decision, not a lower standard of care. The 2025 study describes a vertical pathway in which all patients remained under the care of their assigned emergency physician, even when the initial assessment occurred outside a standard bed. The study’s methods and setting are described in the article.

The risk appears when an operational label starts doing clinical work. “Vertical candidate” can quietly become “low risk.” Those are different statements. A patient may start in a chair and still need a monitored bed, serial reassessment, or rapid escalation.

Patients in a waiting room are not static. Emergency-department safety work emphasizes periodic reassessment to identify status changes and reduce patients leaving before evaluation. AHRQ’s Patient Safety Network describes a waiting-room reassessment program built around that problem. An AI recommendation does not replace that loop.

What the 2025 study actually showed

The Mayo Clinic Arizona team trained a nonlinear machine-learning model on triage data from 49,350 ED encounters. The model estimated whether an incoming patient was suitable for vertical processing. The team combined that estimate with a patient-flow framework and implemented the resulting protocol during a 13-week prospective trial. The pathway prioritized Emergency Severity Index 4 and 5 patients, selected some ESI 3 complaints, and expanded eligibility during periods of saturation. These details appear in the study abstract and full methods.

After implementation, average length of stay fell by 10.75 minutes, or 4.15%. Adjusted estimates placed the reduction between 7.5 and 11.9 minutes. The study reported no adverse change in its measured quality outcomes, including 72-hour ED revisits and hospitalization rates. Those results are encouraging, but the thresholds may not transfer safely. It was a single-center, before-and-after intervention. Its earlier training period and local staffing, bed flow, triage practice, and physician assignment limit direct transfer. The study reports its design choices.

Borrow the method, not the cutoff. Build the pathway around your own patient mix, staffing model, chair space, reassessment process, and escalation routes. A model trained in one department can support a pilot; it is not a waiver of local validation.

Build the local guardrail before you route a patient

Start with a one-page decision rule that a triage nurse and attending can read during a crowded shift. It should answer five questions.

What is the model allowed to recommend? Limit it to a flow proposal, such as “consider vertical assessment.” It must not become a diagnosis, acuity downgrade, or disposition recommendation. The 2026 All-EM Consensus Statement on Artificial Intelligence says emergency physicians retain authority for patient-care decisions.

Who can say no? Name the role that can move a patient from the vertical lane to a bed or monitored space. Give that person authority to override without waiting for vendor support or a committee vote. Record the reason as a safety signal.

What inputs make the recommendation invalid? Define missing or stale vital signs, incomplete triage, communication barriers, new symptoms, and any change noticed during a waiting-room check. A missing score must not look like a low-risk score. The fallback is explicit: reassess, escalate, or use the standard pathway.

When does the clock start again? Set a reassessment interval for patients who remain outside a bed. A named team should own it, with the time visible on the tracking board and tied to escalation. A model can prioritize the next check; it cannot make an unmonitored patient safe. AHRQ’s waiting-room safety resource supports reassessment as a core control.

What stops the pathway? Write the pause rule before go-live. Triggers may include unexpected escalations, a changed recommendation rate, a broken triage feed, a new documentation template, or a software update that changes inputs. The NIST AI Risk Management Framework treats trustworthiness as part of design, development, use, and evaluation. Deployment begins the monitoring work.

Measure the failure modes, not just the minutes

Length of stay is easy to display and easy to overvalue. Pair it with unexpected transfers from vertical care, escalation after reassessment, return visits, left-without-being-seen events, time to initial clinician assessment, and cases in which the recommendation conflicted with bedside judgment. Use the same definitions before and after launch.

Review measures by shift, complaint group, age band, language needs, and other locally relevant groups when the sample is large enough to interpret. The 2026 emergency-medicine consensus statement calls for validation and monitoring across diverse populations and settings. Its recommendations are stated in the consensus document.

Add a short case review. Choose cases where the model was followed and rapid escalation followed, where it was overridden, and where it produced no recommendation. Ask: What did the system see? What did the clinician see? What did the workflow make easy or hard?

For a practical rehearsal, run a short simulation before launch. Give the team a crowded waiting room, a patient whose vital signs change, a recommendation that conflicts with the bedside picture, and an unavailable model. The team should be able to state the fallback in 30 seconds. Chet’s Medium discussion of silent AI failure in emergency medicine makes the same operational point: a score that keeps appearing can still be unreliable if its inputs, population, or workflow have changed. You can rehearse the human handoff and override steps with EM-Sim, especially when your department needs a repeatable training scenario rather than a policy document.

Dr. Chet's Take

I have spent 25 years in emergency medicine, in HEMS, and in the Army National Guard. Crowding changes the shape of risk. It does not remove it. A vertical pathway can be a good clinical operation when it brings the right patient to a clinician sooner and keeps the patient visible while the work continues. The Mayo study is useful because it treated flow as a designed process instead of a collection of hallway habits. It measured throughput and quality together. That is the right instinct.

That being said, the model is not the hard part. The hard part is what happens after the recommendation. I have watched a “fast track” become a place where patients disappear from the team’s mental map. That is not an algorithm problem. That is ownership failure. If the score says vertical and the patient looks wrong, the score loses. If nobody owns the next reassessment, the pathway is not ready. Local validation will uncover problems a vendor dashboard cannot see.

If you are leading an emergency department, start with one narrow pathway and one named override owner. Run the failure cases before you celebrate the length-of-stay number. Make the fallback visible on the tracking board. A flow tool earns its place by making clinical work safer, not merely faster.

Key Takeaways

• Use AI to propose a vertical-care lane, not to make a diagnosis, acuity downgrade, or disposition decision.
• Treat the 2025 Mayo study as a method to test locally, not as permission to copy its cutoffs.
• Keep a named nurse or physician in control of reassessment, escalation, and override.
• Track safety events, missing recommendations, subgroup performance, and workflow failures alongside length of stay.
• Write and rehearse the pause rule before the tool goes live.

FAQ

Can AI decide which ED patients should go to a vertical-care pathway?

It can support a flow recommendation after local testing, with clinical authority kept by the treating team. The 2025 study supports a pilot method, not automatic transfer of the decision to software. Read the study.

What should happen when an AI flow recommendation conflicts with the bedside picture?

Override it, move the patient to the standard or monitored pathway, and record the reason as a safety signal. “No score” must not appear as “low risk.”

How often should patients in the ED waiting room be reassessed?

Set an interval that matches the patient mix, staffing, and risk controls, with a named owner and escalation route. The safety feature is a visible process that can detect change before the patient is lost in the queue. AHRQ explains the purpose of periodic reassessment.

If you're an emergency physician (or any clinician treating patients daily) trying to understand how AI will actually impact your clinical practice — not just the hype — I put together a free practical guide. You can download it here: AI in EM Survival Guide.

Sources

• Hodgson NR, et al. Artificial Intelligence-Assisted Emergency Department Vertical Patient Flow Optimization. Journal of Personalized Medicine, 2025.
• American College of Emergency Physicians and partner organizations. All-EM Consensus Statement on Artificial Intelligence, 2026.
• Agency for Healthcare Research and Quality. Improving patient safety in the ED waiting room.
• National Institute of Standards and Technology. AI Risk Management Framework, 2023.
• Chet Shermer. When an AI Model Fails Silently: Detection and Response Protocols for Emergency Physicians.
• Related simulation training: EM-Sim.
• Books by Dr. Shermer.