Sach Analytics For Inpatient Operations
veLOSity
Reduce Length Of Stay & Excess Bed Days
Proactively managing and reducing length of stay is the driving factor for creating capacity without increasing labor cost
Decrease Length Of Stay
Increase Bed Capacity
Decrease Excess Bed Days
Up to 0.5 day
decrease in Length Of Stay
Up to 40% decrease
in excess bed days
Up to 40% earlier
in potential discharge ready identification
Up to 95% accuracy
in key predictions
72 hours ahead
Sach Analytics identifies future demand capacity mismatch and recommends solution
Predictive models provides future visibility with action oriented intelligence to get the right patients discharged or moved to long term care facility up to 30% earlier
admission volume demand by hour by specialty or unit
& identify highest potential patients ready for discharge
bed capacity by hour by unit
patient specific LOS at time of admission
Visually see
demand vs.
capacity
mismatch
AI / ML models continuously learn, adapt and predict
Each stakeholder in bed management and inpatient unit can see their future demand and future available capacity
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Ensure each stakeholder sees the global picture from their own perspective to drive action
Identification of high potential discharge patients by unit
Identification of long stay patients predicted at time of admission
AI / ML algorithms utilize operational data, clinical data, diagnostic data and physician behavior data to identify patients that have the highest probability of being discharged
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Custom views for stakeholders in bed management, inpatient units and care management drive focused prioritized action to get patient discharged
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Visually see a rolling 7 day prediction of your hospital's entire bed flow value stream
Up to 97% accuracy across 7 days
Drawing from all operational patient flow data, Sach Analytics AI engines continually learn and adjust the predictions for admissions by hour, discharges by hour, and demand per specialty.
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Using this future intelligence bed managers and inpatient managers can prioritize patients, plan resource allocation and make effective patient placement decisions to reduce LOS.