CoMEHeRe - Co-operative Models for Evidence-based Healthcare Redistribution

Project Aim: CoMEHeRe aims to transform personal healthcare through the design, development and evaluation of novel technologies and business models for commodifying and brokering casually captured personal healthcare data (e.g. from wearable biosensors and the IoT) to state and private healthcare providers. Novel decentralised ledger technology (DLT) solutions will be developed to incentivise collection and enable secure brokering of large volumes of longitudinal biometric healthcare data, optimising preventative healthcare, helping to achieve a more efficient healthcare system, and contributing to a healthier nation.

Year	Duration	Funder
2017-19	24 months	Engineering and Physical Sciences Research Council

Project Summary

CoMEHeRe will transform the delivery of public healthcare, proposing a radical new infrastructure for the commodification of personal healthcare data to healthcare providers using a combination of AI, wearable biosensors (e.g. FitBit) and distributed ledger technology (DLT) or 'Blockchain'.

CoMEHeRe's core innovation is to incentivise the collection and enable the secure brokering of large volumes of longitudinal biometric healthcare data from individuals to public or private healthcare providers. Users are empowered through new agency and mechanisms to commodify their personal healthcare data, whilst simultaneously enabling healthcare providers to optimise preventative healthcare, helping to achieve a more efficient healthcare system, and contributing to a healthier nation.

CoMEHeRe is 24 month project funded by the UKRI Digital Economy programme (£0.4m) 'Applications of DLT' call. CoMEHeRe is developing prototype technical infrastructure that fuses AI and DLT to securely collect, make sense of, and broker access to personal healthcare data. Simultaneously CoMEHeRe explores sustainable socio-economic models for engagement on that platform for mutual benefit of users and healthcare providers.

Working in close collaboration with commercial partner BioBeats, CoMEHeRe has already deployed a secure data collection infrastructure across >100 Surrey students during the January 2018 exam period. New deep learning AI techniques have been developed to analyse this data, and draw inferences between biosensor data and acute stress events (such as exams) with the ability to infer these with accuracies of over 70%. Furthermore, a follow-up UKPRP funding application is being prepared to extend CoMEHeRe to address the increases in prevalence of non-communicable diseases (NCDs), including diabetes, obesity, and cardiovascular disease.