Title: Civic Data Identity Partnership
Funder: Engineering and Physical Sciences Research Council

Abstract: The use of health data in research is well recognised as a potential driver of economic development in areas including machine learning, edge computing and internet of things, digital healthcare, genomics, and wellbeing. Public health, wider society and the $8tn global healthcare market face diminishing returns on late, expensive, imprecise and unfairly distributed care. As the US struggles with health system reforms moving from "volume to value" the UK has the data and governance to measure health needs and care value with sufficient resolution to create new supply chains of health and care. For example, NHS primary care data are sufficiently detailed to train algorithms in predicting needs and personalising notifications to help patients manage medications when they have more than one condition. Such advances in predictive, preventive, personalised care need social contractsand currencies for experimenting with algorithms in supply chains across different providers. The goal of the Civic Data Identity Partnership (CDIP) is to create a platform for patients and clinicians to manage their data and interactions with digital health solutions, such as predictive algorithms. By building the CDIP using Distributed Ledger Technology (DLT) the platform will take advantage of the secure, decentralised properties of DLT to ensure privacy, identity and trust based on an open technology. The use of DLT in health data management has been considered but not yet resolved, with approaches often being led by technology enthusiasts rather than driven by user need, which must include the views of citizens, the NHS, research organisations and industry. Over the course of two years the project will take a multi-disciplinary approach to research the potential of the CDIP platform based on four key objectives: -CDIP Platform: providing the infrastructure for the management of predictive algorithms ensuring a provenance trail and validation record are maintained on the ledger -Security and Trust: Developing a model that can be trusted by patients, clinicians and researchers to generate insight and learning on the efficacy of digital health interventions that demonstrates the efficacy of how apps can be used -Consent: Building a clear and transparent governance platform to support patient consent so access to data can become easier. The ability to review who has access to data and how it is being used is increasingly important at an increasingly granular level. -Efficacy and Reputation: There is a need to develop a model that can be trusted by patients, clinicians and researchers to generate insight and learning on the efficacy of digital health interventions that demonstrates the efficacy of how apps can be used, not just a rating in an app store. As algorithmic models are developed in interaction with data there is a ledger of use and outcomes that can be recorded, used and tested.CDIPs have the potential to transform the ways in which: citizens think about their personal data; and how public and private organisations use the data for improved safety, better management of complex care pathways and transparency of consent for use of data in health research.

Lay abstract: The key objective for the programme is to investigate the how a Distributed Ledger Technology will support health improvement through the use of the CDIP platform. Key CDIP components are: CDIP Platform: Building the infrastructure for the management of predictive algorithms ensuring a provenance trail and validation record are maintained on the ledger - this will lay the foundation for a future of real time predictive personal healthcare agents/avatars that can transact data through the CDIP ledger. The ledger will provide full transparency on how data has been used and related to outcomes through the transaction log; Security and Trust: Developing a model that can be trusted by patients, clinicians and researchers to generate insight and learning on the efficacy of digital health interventions. This model should demonstrates the efficacy of health apps, not just a rating in an app store. It will also remove intermediaries (third parties) from the system, putting the data subject in control of the data and in a direct transactional relationship with data consumers and algorithm developers, exploiting the key property of DLT of decentralisation and trust. Finally it can provide a single source of identity for individuals and organisations based on public key cryptography to which other identifiers can be mapped, which is maintained in the ledger; Consent: Building a clear and transparent governance platform to support patient consent so the flow of personal health data can become easier. This includes the ability to review who has access to data and how it is being used is increasingly important at an increasingly granular level. However, consent choices need to be informed by questions of reputation and capability, and an understanding of the provenance of any individual digital health intervention. A solution for managing organisational and individual app reputation will be essential for the CDIP platform. Efficacy and Reputation: There is a need to develop a model that can be trusted by patients, clinicians and researchers to generate insight and learning on the efficacy of digital health interventions that demonstrates the efficacy of how apps can be used, not just a rating in an app store. As algorithmic models are developed in interaction with data there is a ledger of use and outcomes that can be recorded, used and tested. For example, this framework can help inform a citizen about the need to discuss a questionable test result or prescription. The record of this interaction can then be shared with SME a to develop a medication behaviour and mental health wellbeing app to support the interaction of the multiple care pathways of an individual. SME b may develop a physical activity package delivered through a mix of online and face to face care for citizens with diabetes. The ledgers of interactions can then be used to support continuous, externally validated improvement of both companies offerings, providing clinicians and patients with an ideal digital health economy where the strength of each intervention can be measured more accurately and provide both patients and clinicians with the right information to select the appropriate combination intervention for their current needs.

Research activity codes: 2.4 SURVEILLANCE AND DISTRIBUTION;7.4 RESOURCES AND INFRASTRUCTURE (DISEASE MANAGEMENT);8.5 RESOURCES AND INFRASTRUCTURE (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE