Hospitals across the U.S. are struggling with rising emergency department (ED) demand, inpatient capacity constraints and longer lengths of stay.
Leaders at University Health in San Antonio, Texas, say a predictive approach to patient flow helped them address those trends. Within nine months, the system reduced length of stay by more than half a day, increased discharge volume by 16%, and cut ED boarding time in half even as patient volumes continued to climb.
During a webinar hosted by Becker’s Healthcare, Bill Phillips, executive vice president and COO of University Health and Ashley Schutz, MHA, MLS(ASCP), director of IT quality assurance at the system, discussed how the organization partnered with LeanTaaS to improve ED and inpatient throughput with iQueue for Inpatient Flow.
Here are four key takeaways from their discussion.
1. Transforming daily operations
Before adopting predictive tools, University Health primarily used retrospective dashboards to understand hospital operations. Leaders could see what happened the previous day but wanted better visibility into what was coming next.
“That’s where a true AI-based product helps you,” Mr. Phillips said. “It helps you connect your dashboards, get everybody on the same page and ultimately do the right thing at the right time for the patient.”
By implementing LeanTaaS’ iQueue for Inpatient Flow, University Health created a command center environment where teams across the hospital could see real-time predictions for ED arrivals, inpatient discharges and bed availability.
This shift enabled leaders to run earlier huddles, align staffing with expected demand and proactively address discharge barriers before they delayed patient movement.
“The sooner we had the information, the better decisions we could make and the smoother our flow went throughout the day,” Ms. Schutz said.
2. The command center model
Improving patient flow required more than technology. University Health focused heavily on aligning operational and IT teams during implementation.
Ms. Schutz led an interdisciplinary effort that included nursing leaders, physicians, case managers, environmental services staff and IT teams responsible for data integration. The goal was to ensure the system delivered accurate insights and supported frontline workflows from day one.
The approach paid off quickly. Staff began using the predictive dashboards during daily huddles to track discharge targets and identify barriers earlier in the day.
“You didn’t know what you didn’t know,” Mr. Phillips said. “When staff started seeing the data in front of them, they realized where the delays were and how to address them.”
The result was greater transparency across departments and stronger coordination around discharge planning, bed placement and staffing.
3. Relieving pressure
Like many hospitals, University Health explored a range of ED-focused interventions before concluding that improving inpatient throughput could have a bigger impact on overall patient flow. However, leaders eventually realized that ED congestion was largely driven by delays upstream in inpatient care.
“We had been tackling the ED for years,” Mr. Phillips said. “But the real issue was bed availability and length of stay.”
Once inpatient throughput improved, ED performance improved as well. The system reduced ED boarding time while also easing waiting room congestion, even as ED visit volumes continued to rise.
“At one point our ED medical director came to me and said, ‘I walked in and there were no patients waiting in the ED waiting room. I can’t believe this,’” Mr. Phillips shared.
The improvements also contributed to higher patient satisfaction scores and reduced staff strain in the ED.
4. Establishing trust in a new system
One of the biggest challenges in healthcare technology adoption is gaining trust from frontline staff. At University Health, early operational improvements helped build that confidence quickly.
Within nine months of implementation, the system reduced average length of stay by more than half a day while admissions and ED visits continued to increase.
The predictive model has also proven highly accurate. Discharge forecasts typically fall within about five patients of actual daily results, according to Ms. Schutz.
“That accuracy created excitement,” Ms. Shutz said. “Most days we actually exceed what was predicted.”
For leaders at University Health, the experience reinforced an important lesson: technology works best when paired with operational alignment and clear accountability across teams.
“Patient flow is everyone’s responsibility,” Mr. Phillips said. “It takes everyone, from EVS to physicians, to move patients efficiently through the hospital.”
Looking ahead, the organization plans to expand its collaboration with LeanTaaS into additional operational areas, including surgical and infusion services, as it continues working to improve patient access and systemwide efficiency.
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