Civic Data Identity Partnership

Lead Research Organisation: University of Manchester
Department Name: School of Health Sciences


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.

Planned Impact

UK healthcare has a rich longitudinal patient record that exists in very few places across the world - it is a national asset, enabling research that cannot be done easily anywhere else. Releasing the latent value of these data will generate a vibrant health and life sciences industry, based in the UK, developing businesses and local economies. The outcomes of this research on CDIP will potential benefit four key groups: Patients, the NHS, the pharmaceutical industry and digital technology companies. Patients will benefit by having better access and control of their personal data, independent of the source, alongside being more informed around consent for the use of their data in algorithms and apps that they may use to support their health. This will provide increased trust and understanding of their care. This could lead to better compliance, safety and outcomes. Additionally they will be able to participate in wider research opportunities. Citizens who provide data extend a personal ledger with interactive avatars composed of algorithmic supply chains that can be managed and evaluated by both the care system and the patients. This system will support the sharing of the reputation and reliability of different algorithm and data models to enable greater transparency and safety in the combination of different interventions for individual patient pathways. The NHS needs to have the appropriate governance and ability to measure the efficacy and safety of digital interventions. In particular, with the predicted rise in the use of machine learning the CDIP platform could provide an opportunity to support enforcement of an ethical framework for AI. Currently digital SMEs have no clear interface to engage with the patients and public sector data to develop relevant algorithms. At the same time, regulators are calling for algorithm validation and more transparent/governable interfaces with data. CDIP would provide a key platform for addressing these challenges of access and validation. The pharmaceutical industry is also under-served by UK data assets because of the lack of public trust in the institutions that supply data and because citizens see no return on the investment the industry is prepared to make in accessing data. Research and innovation is being suffocated through the lack of a data supply. By making the link between data, research and patient outcomes more transparent it should support the growth in the available of data, the quality of the data collected and improve access to patients who consent to be approached for research. Further opportunities for investigation include the use of the platform to help identify consented citizens for clinical trials recruitment along with mixed levels of micro-billing for sharing and use of data based on participation. For example, more drug interventions are being developed with a digital component to support better compliance and management in combination with AI. Future healthcare, built on deeper connection with patients/citizens and more predictive information, will depend on supply chains of analytics that can manage the validity and provenance of the information that supports decisions, as available through the CDIP platform.


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Description User group workshops 
Form Of Engagement Activity A formal working group, expert panel or dialogue
Part Of Official Scheme? No
Geographic Reach Regional
Primary Audience Public/other audiences
Results and Impact During September and December 2020, the research team spoke with 30 members of the general public, recruited from a range of health research charities and patient and public involvement groups in addition six PhD students as part of a pilot discussion group. Participants aged between 20 and 76 years took part in a series of five online focus group discussion groups which lasted between ninety minutes and two-hours. Each focus group included discussion based on at least two of the aforementioned scenarios which were rotated between discussion groups in order to ensure equal exposure. The focus groups were held via the videoconferencing tool Zoom as this phase of fieldwork took place during the pandemic with social distancing policies in place, which significantly impacted upon opportunities for more interactive workshop activities.

Discussion groups were capped at a maximum of six in order to maximise opportunities for participants to express their views. Three researchers were also involved in the facilitation of the focus groups in order that a researcher was readily available to address any technological issues at any point during the discussions. Participants were also encouraged to use the chat facility during focus group discussions to comment or raise concerns whilst another participant was speaking or if they felt unable or uncomfortable with verbal interaction. Chat comments were moderated by a researcher and any points raised via this facility were introduced to verbal discussions throughout the duration of the focus group.

The pilot was conducted to test if materials, timing and format of the focus group designs were appropriate and accessible to a wider public audience. The pilot revealed that, despite considerable effort to produce easy-to-understand information (short animation, beginner level technological introduction into basic concepts) about DLTs, participants spent the majority of time questioning and trying to understand how blockchain technology worked, rather than discussing design requirements for the CDIP platform and scenario use-cases. Consequently, we omitted the animation and reformatted the materials so that they highlighted the features of DLTs (such as transparency, immutability or decentralisation) rather than attempting to articulate a beginner's guide to understanding blockchain technology more widely. We also introduced stakeholder perspectives to trigger a deeper exploration of the topics based on the previous work [xx]. The restructuring of the focus group format and presentation proved to be much more successful in generating useful points for further deliberation and debate between focus group participants. The final focus group format included an overview of the project aims and objectives, a short description of the current health data sharing context and presentation of two use case scenarios. After the presentation of each user story scenario, we asked participants the following questions:
1. What are major benefits and drawbacks of this use case example?
2. What choices and controls would you like to have over your own health data?
3. What information is important for you to know before using a platform like this?
Year(s) Of Engagement Activity 2020