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

EPSRC Reference:

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

Principal Investigator:
Brown, Professor A W

Other Investigators:
Plans, Dr D Moessner, Professor K Collomosse, Professor JP

Project Partners:
AXA Group Guardtime


University of Surrey

Standard Research – NR1

30 June 2017

02 September 2018

Value (£):

EPSRC Research Topic Classifications:
Computer Sys. & Architecture Digital Signal Processing, Human-Computer Interactions Information & Knowledge Mgmt

EPSRC Industrial Sector Classifications:
Healthcare Information Technologies

Panel Date:
07 Feb 2017

Panel Name:
Distributed Ledger Tech Full Proposals Meeting


CoMEHeRe aimed to transform personal healthcare for the benefit of individuals through the use and management of biometric information created by wearable devices.

To do this it combined data from an individual’s wearables with DLT (Distributed Ledger Technologies, blockchains) and machine learning to securely store and access data to enable the individual to share and benefit from their generated information. Sharing was with state and private healthcare providers to enable more targeted, personalised patterns of treatment. Other benefits arose from the individual participating materially in new markets created through the monetisation of this data.

Recent interest in cryptocurrencies such as Bitcoin has ignited interest in DLTs and the role they play in how shared agreements are defined, managed and evolved for a variety of ecosystems and information sources typical of today’s digital economy. Indeed, the focus of attention has shifted from DLT as a technological phenomenon supporting new types of currency, e.g. bitcoin, to their likely impact in changing business and society. DLTs have the potential for rewriting conventional notions of how business transactions relate with customers, enhance transparency and trust, and create fresh opportunities for value creation and capture. In domains such as healthcare, the potential of DLTs to disrupt the status quo is clear. However, a critical research need must be addressed: how to expose the opportunities and threats, such as privacy and security from emerging business models enabled by this technological revolution.

CoMEHeRe aspired to build and assess the feasibility of the first publicly available software demonstrator to interface with insurers (AXA/PPP and its Seed Factory labs will be a partner) and the general public, using distributed ledger technologies to allow for data to be curated, hosted, and used as tradeable value by the individual’s’ choice.

To achieve this CoMEHeRe addressed a number of research challenges by utilising a novel combination of technologies, including the blockchain – a form of secure DLT – to store health evidence derived from multi-modal signals extracted from users’ wearables and the Internet of Things (IoT) sensors they interact within the environment. In addition, the project examined the potential use of Smart Contracts (simple programs) in healthcare management at the research, public policy, and individual levels. Such a use challenged many kinds of contractual, ethical and moral issues: for example if ownership is taken away from the individual, smart contracts could be made partially or fully self-executing, self-enforcing, or both, by authorities or businesses seeking to optimise for cost instead of health benefit to the individual.

The CoMEHeRe project was an 18-month research project designed to create value in an innovative application domain for DLT in healthcare. To undertake this exciting, ambitious project we built on a strategic multi-disciplinary partnership at the University of Surrey that unites world-leading research groups focused on examining the business and societal impact of applications of digital technology (CoDE), multi-modal signal processing (CVSSP), and IoT and sensor-based communications infrastructures (ICS and 5GIC). This partnership is contained within a broader delivery consortium. This includes Axa/PPP offering the application context and a basis for assessing practical impact, Guardtime providing a DLT foundation for the research work, and BioBeats delivering machine learning platform expertise. To govern this work there was an experienced Advisory Board bringing governance and guidance to ensure the project delivered meaningful results from which new research and practice can emerge. This experienced partnership has a practical record of previous work in these areas, and a broad network of relationships bringing deep support, and rapid promotion of research results.

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