Springhill Medical Center in Mobile, Ala., achieved a half-day reduction in average length of stay just two months after implementing predictive, AI-driven inpatient flow tools.
In a recent webinar hosted by Becker’s Healthcare and LeanTaaS, Sharon Barnicle, RHIA, CRCR, executive director of revenue cycle at the 270-bed hospital, shared how Springhill rolled out LeanTaaS’ iQueue for Inpatient Flow, cutting discharge processing times in half and reaching 90% compliance with estimated discharge dates (EDDs).
Here are four key takeaways from the discussion.
1. Predictive analytics drove timely discharge planning
Springhill lacked visibility into EDDs prior to working with LeanTaaS, which made early discharge planning nearly impossible. The iQueue’s predictive models offered real-time EDD forecasts and flagged patients likely to go home without additional care needs, a major efficiency lever for Ms. Barnicle’s team.
“The platform allows automatic identification of patients who can be discharged home without additional needs,” said Ms. Barnicle. “It alerts our team and then we can prioritize and expedite discharges to free up beds quickly.”
This also allowed ancillary departments to accelerate test scheduling for patients nearing discharge. With predictive insights highlighting patients at higher risk, case managers could better prioritize their time in the busy emergency department. This allowed them to address underlying issues, assist with discharge planning and coordinate follow-up care before a readmission occurred, ultimately reducing unnecessary returns to the hospital.
2. Replacing siloed workflows
Before AI-enabled optimization, Springhill’s nursing, case management and hospitalist teams worked in silos, often holding separate huddles without shared discharge targets. The new process centralized real-time patient data and aligned teams around dynamic EDDs during unit-level huddles.
“We had siloed care teams that lacked the necessary coordination to effectively manage discharge,” Ms. Barnicle said. “With LeanTaaS, our teams could initiate discharge planning earlier and streamline the entire process.”
3. AI-powered improvements across the care continuum
Beyond EDD compliance, staff received timely alerts for other key flow disruptors. These included alerts for patients exceeding recovery thresholds, those at risk of emergency department (ED) readmissions, and prolonged boarding times post-discharge.
One impactful example: Springhill used boarding time alerts to ensure patients weren’t waiting over 90 minutes after discharge orders.
The platform bridged gaps in collaboration, communication and real-time visibility, enabling the hospital to streamline patient flow, optimize capacity and boost operational efficiency. These improvements enhanced care coordination and patient experience while creating financial and growth opportunities.
“It became a huge force in this transformative journey for us,” Ms. Barnicle said. “It provided that comprehensive solution we needed to bridge the gaps in our collaboration, communication and real-time visibility into our internal initiatives.”
4. Improved operational outcomes
Within just two months, Springhill reduced discharge processing times from up to four hours to less than two, a 50% improvement.
With over 200 of Springhill’s staff members already on the platform, these gains translated to improved patient throughput, fewer bottlenecks in the ED and greater capacity to accommodate new patients.
Looking ahead, Springhill aims to build on its early success to achieve even greater operational efficiency. The team remains committed to continuous improvement and views its partnership with LeanTaaS as key to reaching ambitious new goals. “Without LeanTaaS’ team and their support, it would’ve been much more difficult to implement at scale,” Ms.Barnicle said. “They have been there with us every step of the way and have helped us so we can focus on our patients.”
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