For community chronic care
APTAnalytics
We find the signal in the noise
Capture. Clean. Transform.
Capture. Clean. Transform.

Thanks to data sets collected from millions of patients, and advancements in mathematical modelling, data analysts are gaining the ability to investigate complex human behaviours and diseases in ways that were previously impossible. Through advanced analytics, we can leverage a colossal legacy of patient treatments to better understand disease correlations, apply this knowledge to optimise treatment, and provide more reliable predictive models for future interventions.

Refine. Redefine
Refine. Redefine

We’re increasing efficiency, improving outcomes, and enabling cost savings - driving forward the shift to value-based care. Most significantly, we are able to examine the data from such wide-ranging angles – across different rates of disease progression, different demographics – that we can refine solutions down to an individual level. Disease mitigation is not ‘one size fits all’, and value-based healthcare must reflect this. Population health management begins at the individual level.

Data is crucial for delivering high-quality clinical care. However, few physicians get the right type of data at the right time. We strive to provide effective, actionable analytics programmes to support critical clinical decision-making at the point of care.

We're doing something different.

There are two conventional ways of modelling healthcare and life sciences data.

Mechanistic Modeling

Differential equation-based mechanistic modeling of biological systems (also known as Systems Biology) is designed for longitudinal data analysis and can provide detailed insights which are extremely useful in R&D applications.

These are, however, arduous to develop, even harder to deploy for clinical applications and are limited by our current understanding of disease mechanisms and biology.

Statistical Modeling

Statistical & machine learning-based models are not inherently designed for sparse longitudinal data with frequent discontinuities (often a characteristic of healthcare data), and require assumptions based on the applicable data.

They are, however, easier to develop, easy to deploy, and while they provide a simple overview of associations, they are limited in applications focused on predictions of rates of disease progression over time.

The best of both worlds
The best of both worlds

Holmusk has developed a proprietary modeling platform that takes the best of these two modeling techniques - combining the concepts of differential equation-based models and machine learning approaches. Coupled with this is a deep understanding of disease pathophysiology, the mechanisms of drug action, and data from published literature.

Our unique team comprises experts in physiology, modeling methodologies and computational algorithms; adept at large-scale distributed computing and software and technology platform development.

Next-generation modelling platform

Our methodologies enable predictive algorithms for individual health management, that scale to population level - whilst accommodating heterogeneous chronic disease and comorbidity progressions.

Let us tell you more.

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