Predict discharge dates and receive proactive alerts to reduce delays, optimize patient flow, and improve outcomes.
Length of Stay
(H2O-LOS)
By reducing patient length of stay by nearly 10% – up to 5x more than typical outcomes from comparable solutions – H2O-LOS delivered over $100M in savings for major hospital systems, with no increase in readmission rates.
How it works.
H2O-LOS leverages clinical and contextual data such as lab results, surgery details, medications, admission history, and patient demographics to deliver accurate, data-driven discharge predictions.
H2O-LOS predicts each patient’s likelihood of discharge within 24 and 48 hours, providing probability scores that help clinical teams determine the right day for discharge and plan next steps in care. Each prediction includes an explanation plot highlighting the key factors driving the outcome, powered by Explainable AI (XAI) for transparency and trust.
H2O-LOS provides proactive alerts for patients likely to be medically ready for discharge in the coming days, using the most recent data to ensure predictions are always accurate and up to date. Green-coded indicators highlight patients expected to be discharged, helping care teams plan ahead with confidence.
Daniel Craig Kombert, MD
Associate Vice President, Medical Affairs at Hartford HealthCare
Join top health organizations using H2O’s Length of Stay.
“The H2O-LOS prediction tool provides support to our clinical care teams in determining the potential for patient discharge readiness for our medical patients. By providing timely, data-driven insights, the tool has helped to reduce our average length of stay by 0.51 days - improving patient safety, enhancing throughput, and allowing us to effectively care for more patients across our multi-hospital system.”
H2O-LOS integrates with most EHRs so you can keep everything in one place. Automatically enroll new patients and send data to provide insights into their original chart.
Integrate with your Electronic Health Records (EHR) System
Our mobile and tablet app is launching soon, designed to give care teams near real time access to patient flow insights, discharge predictions, and alerts from anywhere in the hospital or on the move.
Coming Soon! Stay connected to patient flow with our new mobile and tablet app.
Questions about predictive models and data? Ask H2O!
Ask H2O is more than a chatbot, it’s a co-intelligence agent that seamlessly integrates with EHR data to deliver reliable answers and support clear, transparent interactions within the H2O-LOS solution. Simply ask a question about your data and receive fast, trustworthy, data-driven responses in user-friendly language.
FAQs
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H2O-LOS helps physicians and care teams identify patients who are likely medically ready for discharge by predicting discharge probability with high accuracy. It uses advanced AI to support clinical decisions, reduces length of stay, and increases patient access.
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The solution helps maintain quality of care, improve patient satisfaction, and increase access for new admissions by ensuring patients are discharged at the right time.
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By analyzing the most recent patient data and clinical indicators, the platform flags patients in your EHR who may be medically ready for discharge and provides a probability percentage. This assists doctors in making more confident and accurate discharge decisions – improving accuracy by up to 30% compared to teams not using the predictive platform.
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Reducing unnecessary inpatient days directly increases patient flow, decreases ED boarding, and improves bed availability. These efficiencies translate into better financial performance per bed and enhanced hospital capacity without additional infrastructure.
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Yes. The solution can seamlessly integrate with major EHR systems to extract relevant clinical and operational data securely. Integration ensures minimal disruption to existing workflows.
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Absolutely. The predictive models are validated using large datasets exclusively from your health system and are retrained to adapt to evolving patient populations and clinical practices.
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Hospitals typically begin seeing measurable improvements in discharge accuracy, patient flow, and length of stay metrics within 4–8 weeks of go-live, depending on the scale of implementation and data readiness.
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We are committed to protecting patient privacy and data. We comply with industry-standard data protection laws and are continually enhancing our security measures. Please contact us for additional information.