Making Healthcare more efficient

Commercial Mathematics for improved planning, scheduling and predictive decision making
about us

Our team of programmers and mathematicians work alongside your service to provide meaningful insights and decision support tools that complement your current technology and scale with your service.

Scalability is a fundamental benefit of delivering solutions through the Biarri platform. Our agile team build solutions that can be rapidly prototyped, tested and rolled out across a single department or an entire service. By deploying systems that coordinate information across a complex network of health services, we can efficiently streamline work flows. We enable efficiency gains through data analytics and process optimisation helping you deliver quality health services while reducing costs.


Biarri develop mathematically based decision support engines
that are fast to run and easy to use.


Coordinate data sources and information visibility to streamline work flows. Use data to gain insight and inform decision making.


Optimise the allocation of people and resources across your health service. Reduce idle time of resources and reduce waiting time for patients.


Automate structured and repetitive tasks. Replace paper-based processing with digital information processing to get more effective returns on your assets and people.


Biarri has an extensive history of providing customised solutions to the health space.

Health Rostering Optimisation Systems

A technology platform comprised of three main stages; planning and shift construction, allocating staff to shifts, and day of operations management.

Efficient Pathology Routing

Biarri has developed a pathology routing solution to maximise fleet efficiency whilst adhering to strict sample viability constraints.

Patient Activity Forecasting

Biarri has created a simulation tool to accurately forecast in-patient and out-patient admissions to determine resource requirements for multiple hospital departments.

The Biarri Score

Biarri has built a statistical model
to determine the likelihood of emergency department patient readmission. The model is used to identify high likelihood patients and to co-ordinate early intervention programs with local primary care practices.

Patient cost prediction

Biarri has developed a statistical model to predict the final cost for a patient based on data available at the time of admission.  This model provides a stratified set of cost margin categories with associated probabilities. This is used to identify episodes of care likely to have a negative margin.