Metabolic Health Process Mapping for Digital Health

Metabolic Health Solutions

About

Metabolic Health Solutions (MHS) is an integrated medical technology and digital health company, commercialising low cost, metabolic measurement technology to clinically manage obesity, Type 2 Diabetes (T2DM) and other common metabolic disorders. MHS currently has CE, TGA and HSA Certification for its lead technology ECAL, and have early commercial activities in 8 Countries in Europe and Asia, including wholly owned and partner clinics. Through our clinics and international research programmes we are building an evidence-based data set of the efficacy of indirect calorimetry in a clinical setting to better manage obesity and weight-related chronic disease. We are building this knowledge into a digital health platform ENABLE, that will allow health systems and individual practitioners to benefit from this evidence informed data led metabolic lifestyle approach. The large de-identified data footprint will use machine learning and expert analysis to support enhanced care and develop effective clinical and health economic models to reverse this global epidemic of metabolic disease.

Project 

MHS previously had an iPREP team answer the question of whether digital health and machine learning was possible to deliver better metabolic health outcomes. That resulted in the company being able to understand how such a system could be developed. As part of a state innovation grant, MHS is finalising an end goal design of the commercial digital technologies to achieve that. This team will map and build the data model to bridge between the clinical practice and commercial deployment as a prelude to full commercialisation of a minimum viable product.

Benefits

The cross functional team will work in the behavioural, allied health, and data science domains to map our clinical model and the data it collects to a ‘data frame’ that can be used in a digital health implementation. This is a necessary step for commercialisation. In particular the quantification of behavioural elements like patient reported outcomes, emotional and attitudinal aspects requires a cross functional team approach.

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KHANDU OM

Murdoch University

IT

Skills: Machine and deep learning models, Python, Pandas, Numpy, Tensorflow, keras, Jupyter notebook, Matplotlib, Seaborn

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BINGYAN PANG

Curtin University 

Allied Health

Skills: Efficient, solution focus, IT competencies, extensive experience
with Microsoft Suite, SAS, Endnote (Web of Knowledge),  Articulate 360,  and WA Health specific programs (BOSSNET, iClinical Manager, IMPAX, excellent communication skills including written, verbal, and interpersonal.