Most hospitals have no shortage of dashboards but by the time a daily report shows the emergency department (ED) backing up, the beds are already full and discharges have already slipped.
Leaders at San Antonio, Texas-based University Health say the answer wasn’t replacing their EHR or building more beds. It was layering predictive intelligence on top of the systems they already had. In five of the nine months since going live, the Level I trauma center cut average length of stay by half a day even as admissions and ED visits climbed steadily year over year.
During a webinar hosted by Becker’s Healthcare, Bill Phillips, executive vice president and COO of University Health and Ashley Schutz, the system’s director of IT quality assurance, discussed how the organization partnered with LeanTaaS and to implement its iQueue for Inpatient Flow platform to turn early ED signals into hospitalwide action.
Here are four key takeaways from their discussion.
1. Enhancing the EHR
University Health’s EHR excelled at frontline documentation but fell short when leaders wanted to see house-wide movement without digging for data or waiting on reports. Rather than swap systems, the team layered iQueue on top of the EHR to surface the information that mattered most, predicted discharges, ED arrivals by acuity and real-time bed availability.
The approach also created an unexpected feedback loop. Early on, the system revealed that staff weren’t consistently documenting barriers to discharge in the EHR. Once nurses could see those gaps in daily huddles, documentation improved which sharpened predictions.
“We don’t need 20 dashboards to show us this,” Ms. Schutz said. “We really only need about six data points to be able to start making changes.”
2. Aligning operations and IT
University Health ran two parallel work groups: an operational team spanning nurses, physicians, case managers, ED leaders, environmental services, transport and the float pool and an equally large IT team covering EHR analysts, interface engineers, staffing systems and the field engineers building the command center.
Ms. Schutz, who has a background in both IT and operations, bridged the two groups — speeding up troubleshooting and ensuring the data was validated before staff ever saw it. Mr. Phillips said the team never fielded a single “I don’t believe that data” question after iQueue’s go-live and the trust translated into immediate action.
The system launched on desktops first, then consolidated house supervisors, case managers, EVS and bed placement into a physical command center shortly after.
3. No extra beds
Like many capacity-limited systems, University Health had tried converting PACU space and old units into extra beds. “You open the beds, you fill the beds,” Mr. Phillips said. “You’re really not fixing the problem.”
Instead, Mr. Phillips made a commitment to his board: with the right investment, the system could cut average half a day from length of stay within a year. They hit that goal in five of the first nine months.
The improvement held even as adult admissions and ED volumes continued to rise. Other gains followed, with sharp declines in ED boarding and hold times, higher HCAHPS scores and a left-without-being-seen rate that now stays below 2% and often falls below 1%.
“When a promise turns into a reality, that’s just a no-brainer,” Mr. Phillips said.
4. Making predictability operational
Nine months in, the platform’s discharge forecasts remain accurate within roughly five patients per day, inspiring friendly competition as units that beat their targets are celebrated during morning huddles.
The predictions have even outperformed clinician judgment. When the team compared LeanTaaS’ estimated discharge dates against those providers entered in the EHR, the model proved more accurate. “Of course I believe it,” one physician told Ms. Schutz, “because the system has access to all of our data, a lot more than our human brains can compute at any given time.”
That predictability now shapes daily routines well beyond discharges. The system automatically flags candidates for University Health’s hospital-at-home program, has driven up use of its transition-of-care lounge and helps teams position EVS and transport staff before demand hits.
Looking ahead, University Health plans to expand the platform to its infusion centers, ORs and three hospitals now in development, with the goal of improving throughput and patient access systemwide.
“I could put numbers up on a screen all day,” Ms. Schutz said. “But if they’re not feeling the relief from that, it’s not sinking in.”
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