Using AI for early,
accurate intervention.

How it works

Metadvice’s seamless integration with electronic medical records (EMRs) enables key capabilities that streamline workflow, increase health system capacity and support clinicians’ interactions with patients.

The Metadvice platform works in three steps:

By stratifying patient groups, Metadvice ensures the efficient allocation of resources. This strategic approach not only delivers timely and precise healthcare, it also increases capacity for early intervention – essential in chronic disease management.

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Neural networks in action

The current situation

  • ~50% of patients are not treated to guidelines
  • Even when treated to guidelines ~50% don’t derive optimum clinical benefit

The Metadvice approach

Our neural networks are first trained to meet clinical guidelines. Once trained, they then assess the clinical responses of patients, first using synthetic data, and then using real-world data. From this assessment, the neural networks are able to determine in whom the guidelines work best, and in whom they are less effective. Using digital twin approaches, we are able to identify the most appropriate treatment options for individual patients.

Neural networks development

Clinical module

Continuous
learning

Training to guidelines

Trained by data science & medical experts
using large complex synthetic datasets and small real-world datasets

1a. Training

refine

1b. Testing

refine

1c. Validation

Medically knowledgeable neural networks able to provide a
response with ~98% accuracy to guidelines

Provide actionable insights for the optimum treatment pathway

Determine the typical course of action for an individual according to guidelines

Transfer learning

Uses real-world evidence to assess
the most effective treatment pathways for different subgroups

2b. Digital twin cohorts

2a. Advanced learning

Identifies the appropriate course of action for subgroups
where guidelines do not achieve target outcomes

Continuous
learning

Training to guidelines

Trained by data science & medical experts
using large complex synthetic datasets and small real-world datasets

1a. Training

refine

1b. Testing

refine

1c. Validation

Medically knowledgeable neural networks able to provide a
response with ~98% accuracy to guidelines

Provide actionable insights for the optimum treatment pathway

Determine the typical course of action for an individual according to guidelines

Transfer learning

Uses real-world evidence to assess
the most effective treatment pathways for different subgroups

2b. Digital twin cohorts

2a. Advanced learning

Identifies the appropriate course of action for subgroups
where guidelines do not achieve target outcomes

Comorbidity management

Cardiometabolic disease

Neural networks clinical modules

Diabetes

Cardiovascular disease

Chronic kidney disease

Uses real world evidence to assess most effective treatment pathways based on comorbidities

Synthesises combined outputs and prioritises treatment pathways, supported by statistical analysis

Advanced
learning
Digital twin
cohorts

Stratifies patients by overall clinical outcomes

Provides a nuanced approach for patients with comorbidities

Neural networks able to provide optimal treatment recommendations based on individual profile

Neural networks clinical modules

Diabetes

Cardiovascular disease

Chronic kidney disease

Uses real world evidence to assess most effective treatment pathways based on comorbidities

Synthesises combined outputs and prioritises treatment pathways, supported by statistical analysis

Advanced
learning
Digital twin
cohorts

Stratifies patients by overall clinical outcomes

Provides a nuanced approach for patients with comorbidities

Neural networks able to provide optimal treatment recommendations based on individual profile

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Training to guidelines

1a. Training

refine

1b. Testing

refine

1c. Validation

Provide actionable insights for the optimum treatment pathway

Transfer learning

2b. Digital twin cohorts

2a. Advanced learning

Determine the typical course of action for an individual according to guidelines

Neural networks clinical modules

Diabetes

Cardiovascular disease

Chronic kidney disease

Uses real world evidence to assess most effective treatment pathways based on comorbidities

Advanced learning

Stratifies patients by overall clinical outcomes

Synthesises combined outputs and prioritises treatment pathways, supported by statistical analysis

Digital twin cohorts

Provides a nuanced approach for patients with comorbidities

Neural networks able to provide optimal treatment recommendations based on individual profile