More operating rooms mean more patients, more coordination, and more risk of last-minute cancellations. When UAMS expanded from 28 to 42 ORs, leadership needed a way to absorb the volume without a proportional increase in staff. The answer wasn’t more nurses. It was a smarter pre-op process—powered by AI.
Here’s how they did it.
The Challenge: Manual Pre-Op Processes Limit Growth
Like many hospitals, UAMS relied on a pre-admission testing (PAT) process built around manual workflows. That approach created several operational challenges:
- Nurses called patients and played phone tag.
- Chart reviews were done across multiple systems by hand.
- Every patient went through the same workflow regardless of risk level—a one-size-fits-all approach that consumed enormous amounts of clinical bandwidth.
The operational math didn’t work. A single nurse could complete roughly eight patient calls per day, but chart review alone often took 2-3x as long as the actual conversation with the patient.
The downstream consequences were real. When risks weren’t identified until close to the surgical date, cancellations became inevitable—and costly. As Dr. Tammy Jones, Chief Nursing Officer and Associate Vice Chancellor for Patient Care Services and Clinical Operations at UAMS, explained during a recent Becker’s webinar: “If you’re within that 72-hour window, the odds of backfilling that OR time go way down. You end up with a double loss—the patient experience suffers and the institution loses valuable operating room capacity.”
The Strategy: Use AI to Identify Risk and Automate Pre-Op Workflows
UAMS partnered with Qventus and our Perioperative Care Coordination Solution to redesign the pre-operative workflow around three core capabilities:
- Automated chart review. AI continuously mines patient records across clinical notes, labs, external records, and health information exchanges—surfacing more than 170 specific diagnoses and risk factors without requiring a nurse to manually search through charts.
- Risk-based patient stratification. Lower-risk patients are routed through an automated fast-track pathway. Higher-risk patients get focused clinical attention earlier in the process—when there’s still time to intervene.
- AI-powered patient communication. Patients receive automated, conversational outreach via text or voice to complete pre-op questionnaires, confirm medical history, schedule optimization appointments, and get reminders leading up to surgery. The AI introduces itself as such from the first interaction, and patients can opt out at any time.
The result is a pre-op process that moves faster, catches more risks earlier, and frees nurses to spend their time on the patients who actually need them.
More Capacity, Fewer Cancellations, No New Headcount
The UAMS team has seen measurable improvements across all three of the metrics that matter most in perioperative operations: nurse productivity, surgical cancellation rates, and patient preparedness.
Perhaps most significantly, UAMS has been able to absorb increased surgical volume without a proportional increase in RN and APRN headcount. The automation of routine pre-op steps—patient outreach, questionnaire completion, record retrieval, appointment coordination—has expanded what the existing team can handle.
By automating routine steps in the pre-op process, nurses were able to spend more time on complex patients who needed clinical expertise. As Dr. Stewart explained, “We want our nurses working at the top of their license. This allows them to focus on patient care rather than administrative work.”
The results show the impact of automation:
- 50% increase in PAT nurse capacity, from 75 to 135 patients per day
- 25% reduction in surgical cancellations
- 60% reduction in manual processing time per fax
Patient engagement has been a notable surprise. The AI concierge tool has driven thousands of patient interactions, including extended back-and-forth conversations where patients ask detailed questions and provide nuanced medical information. One patient exchanged 75 messages with the AI assistant. And adoption has been strong even among older patients—Dr. Alvin Stewart, Medical Director, Urology Center, and Anesthesiologist at UAMS, noted that patients over 50 were engaging with the system at higher rates than younger cohorts.
What Made Implementation Work
UAMS didn’t flip a switch. The rollout was deliberate, beginning with a single surgical specialty before expanding across the program. A few principles guided their approach:
- Frontline staff shaped the workflows. Nurses, anesthesiologists, and surgeons were involved from day one—not just as end users, but as contributors to how the system was configured and refined.
- Transparency about AI reduced resistance to it. Leadership openly addressed staff concerns about AI early and often. Running parallel workflows during the transition gave teams a safety net and built confidence in the new process.
- Iteration was built into the process. Weekly meetings between UAMS clinical staff and the Qventus team created a continuous feedback loop—allowing the system to be refined in real time rather than set-and-forgotten.
- Implementation felt individualized, not templated. The solution was configured to UAMS’s specific workflows, clinical pathways, and legal requirements—rather than a one-size-fits-all rollout.
A Replicable Model for Perioperative Growth
The UAMS story is instructive for any hospitals navigating the intersection of surgical growth, staffing constraints, and care quality. AI-enabled pre-op coordination isn’t about replacing clinical judgment—it’s about removing the administrative burden that prevents clinicians from exercising it.
When risks are surfaced earlier, there’s time to optimize. When lower-risk patients move through automated pathways, nurses can focus on the patients who need them most. When patients are engaged earlier and more consistently, they arrive prepared and cases proceed as planned.
For hospitals trying to grow surgical volume without growing their challenges, that’s not a small thing.
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