What the hospital was dealing with
The hospital's operations team received a single bed-occupancy report each morning at 9:00 AM. By 11:00 AM, the data was already 2 hours stale. In the emergency ward, nurses had to phone each department individually to check if a bed had been freed — a process that took an average of 52 minutes per patient transfer.
ICU bottlenecks were discovered only after they occurred, because there was no predictive visibility into discharge timelines or incoming emergency volumes. The COO had no reliable way to make real-time staffing decisions or proactively manage department load.
How Goolk AI approached it
Goolk AI built a real-time hospital operations platform by connecting a stream processor to the hospital's existing EMR ADT (Admissions, Discharges, Transfers) message stream.
Live Bed Board: Every admission, discharge, and transfer updates the central bed board within 90 seconds. The board is accessible on any tablet, smartphone, or large ward display screen.
Predictive ICU Load Model: A lightweight ML model trained on 18 months of historical ADT data flags projected ICU pressure 45 minutes in advance, giving coordinators time to action bed preparation.
Emergency Triage Dashboard: Emergency coordinators see a live queue board showing patient acuity, assigned doctor, and estimated wait time — replacing the manual phone-call coordination process.
Staffing Intelligence: Department-wise load vs. nurse ratio alerts ensure charge nurses can request shift adjustments before understaffing occurs.
Measured results at 90 days
Clinical decision latency dropped by 40% — the average emergency room wait for a bed assignment fell from 52 to 31 minutes. Bed utilisation improved by 12% because the discharge-to-admission loop became faster.
ICU overflow events dropped by 28% within 60 days, attributed entirely to the predictive alert model allowing pre-emptive preparation. The COO reported that the first week of real-time data was "transformative" for ward round planning and staffing decisions.
ICU overflow events cost hospitals an average of ₹2–4L per event in penalties and additional resource deployment.
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What we built it with
How we delivered it
ADT stream capture & data audit
Connected Kafka listeners to the EMR HL7 v2 ADT feed. Audited 18 months of historical ADT data to identify patterns for predictive modelling.
Dashboard & predictive model build
Designed the live bed board interface. Trained and validated the ICU load forecasting model with the clinical team. Built the emergency triage queue view.
Deployment, calibration & training
Installed dashboard screens at emergency counter, ICU station, and COO office. Trained clinical coordinators and nursing charge staff.
Our operations team has live visibility for the first time. We no longer make staffing or admission decisions based on yesterday's data. The predictive ICU alert alone has prevented three crisis situations in the first month.Chief Operating OfficerSahyadri Specialty Hospitals, Pune
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Book a free clinical IT consultation. We will audit your workflows, analyze your existing systems, and give you an honest roadmap — no sales pitch, no obligation.
