In an ideal scenario, a hospital’s patient flow would be fully optimized.
Patient admission patterns would be almost as predictable as the guest arrival patterns in hotels, with their scheduled check-in and check-out times. Patients would be discharged as soon as they are cleared to do so, staff allocated in advance to the units where they were needed most, and units opened and closed according to actual admissions and discharges. All staff could access the same up-to-date data to coordinate care, get ahead of possible barriers, and make strategic decisions. Patients would receive timely care, while staff would be empowered to focus on delivering it.
Unfortunately, as many health systems experience daily, this scenario rarely matches reality. Patient demand varies and fluctuates. Unlike hotels, hospitals cannot dictate patient arrival and departure times. New patients are admitted before existing patients are discharged, creating throughput issues. Discharges are delayed by missing lab and test orders or lack of care team alignment, and units reach full capacity, forcing patients to board in the ED and PACU. Nursing leaders work tirelessly each shift to distribute strained staffing resources to the locations where they think they’re needed most, with a limited view of patient care needs. As a result, some patients stay longer than necessary while others wait for care, and nursing staff experience burnout due to high patient loads and excessive work hours, impacting care delivery.
Achieving the ideal scenario is possible. Health systems have the expertise to optimize patient flow, but they need the right technology capabilities to effectively support their efforts. Unlike EHRs, purpose-built, AI-powered solutions can provide staff the predictive insights and workflow automation essential to driving proactive coordination across the care continuum.
Read on about AI solutions, including iQueue for Inpatient Flow, at Becker’s Hospital Review.
