AI predicts admissions 24 hours out so beds, staff, and resources are ready when patients arrive
Their ER was on ambulance diversion 22% of the time — meaning ambulances were being routed to other hospitals because they had no beds. Every diversion cost an estimated $12,000 in lost revenue. Patients boarding in the ER for 8+ hours waiting for inpatient beds. Staff morale was at rock bottom. Two experienced charge nurses had left for outpatient clinics.
This 3-hospital network served 180,000 ER visits annually. The fundamental problem was predictability: they couldn't predict tomorrow's admissions well enough to prepare beds, staff, and resources in advance. Every day was reactive — scrambling to find beds, calling in staff last-minute, and running elective surgery schedules that conflicted with emergency demand. The budget for additional beds was zero.
We built an admission prediction model that forecasts patient volume by acuity level, 24-48 hours in advance. The system integrates ER census data, historical admission patterns, seasonal trends, flu surveillance data, and even community event calendars. Bed management algorithms optimize discharge timing and room turnover. Our overnight team monitors real-time census data and alerts charge nurses to emerging capacity issues before the morning rush.
Monday morning used to be chaos. Now we know by Sunday night how many beds we'll need, which units will be tight, and which patients are likely to discharge. We went from reacting to anticipating.— Chief Operating Officer
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