Healthcare AI/ML
Patient volume forecasting across a four-hospital group
Patients cluster into specific hours while staffing rosters stayed fixed. Forecasting lets the group schedule against actual demand.
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Healthcare
An industry where accuracy and privacy are both non-negotiable. BigAI deploys on-premise, with models supporting rather than replacing clinical judgement.
Context
Data challenges specific to this industry
The barriers BigAI encounters repeatedly across companies in this sector.
Support, not substitution
In healthcare BigAI sets the boundary explicitly from day one: AI systems assist, reducing time spent searching for information and flagging cases that need attention. Clinical decisions always stay with people.
Demand peaks by hour
Patients cluster into specific windows while staffing schedules stay fixed.
Highly sensitive patient data
Cannot be sent to external services; every access must be logged.
Applications
Problem → Solution → Measurable benefit
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| Business problem | BigAI solution | Measurable benefit |
|---|---|---|
| Long waiting times<br><span class="small muted">Fixed staffing schedules</span> | Volume forecasting by hour and specialty | −58% average wait time |
| Paper records cannot be analysed<br><span class="small muted">Physical storage</span> | Vietnamese OCR digitisation into a clinical data store | Seconds record lookup time |
Case studies
Projects in this industry
Compliance
Designed to clear the approval process
Data never leaves the hospital
All models and data run on internal infrastructure with no connection to public AI services.
Clear boundaries
Models suggest and flag; clinical decisions always remain with medical staff.