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Big Medical Data Use in Primary Care: an ethnographic, socio-technical, investigation of challenges and opportunities

Periodic Reporting for period 1 - BIMEDA (Big Medical Data Use in Primary Care: an ethnographic, socio-technical, investigation of challenges and opportunities)

Reporting period: 2015-08-01 to 2017-07-31

Big medical data analytics is a new and unique opportunity for national health systems to reduce costs and improve population health management. Processing and analysis of vast amounts of medical histories from electronic health records can provide researchers, clinicians, policy makers and private health companies with invaluable insights into health and illness. New treatments, medication regimens and medical technologies can then be developed based on more accurate cost/benefit analyses. Importantly, it constitutes national health systems’ engines of economic growth. However, there are also social, legal and ethical concerns about personal health information, around issues of informed consent, data security and algorithmic healthcare provision.

BIMEDA aimed to elaborate a theoretical framework for critically analysing big data analytics social, technical and ethical challenges in primary care, via mapping of the controversy about the initiative by NHS England in the UK, qualitative study of organisations conducting big primary care data analytics, GPs who obtain informed consent and citizens who opt out from such big data initiatives.

The three main objectives were:
1. Map controversy around open primary care data and informed consent of
2. Identify challenges in:
Technical maintenance of big primary care datasets
Anonymisation of electronic patient records for big data analytics
Analysis of big primary care datasets
Integration of primary care datasets with national healthcare databases
Obtaining informed consent by GPs for individual patients to upload their health record to big primary care databases
3. Identify why individual patients opt out from big primary care databases
1. Developed theoretical framework to critically analyse social, technical and ethical challenges of big data analytics in healthcare, particularly in the context of national health systems
2. Trained in primary care data and electronic health records for research, standards & interoperability, public trust & engagement in research, data mining, digital social network analysis & data visualisation, ethnography & participant observation, ethics & research in external organisations and systematic reviews, plus short courses on career development and wider transferable skills
3. Collected ethnographic data from 2 organisations that collect and analyse data from GP records (e.g. medical statisticians, epidemiologists, data architects, database administrators, data analysts); GPs using clinical information systems to maintain electronic patient records and have processed patient requests to opt out from big healthcare databases; citizens who have opted out
4. Published in 4 peer-reviewed scientific journals. 2 more under review or in preparation. Main concepts and results presented at 9 national and international events
5. Organised 2-day open international symposium “Digital Healthcare: social logics, ethics and politics of data and technology provision” at the University of Nottingham
6. Prepared monograph proposal on socio-technical and ethical challenges of big data in healthcare
7. Developed academic research and teaching material, freely accessible through the project’s website, with more than 400 scientific publications, 800 news articles and stories plus videos related to and big data in healthcare
8. Support and advice to early-career academic researchers on preparing applications

Main results:
1. UK has developed extensive research programmes and databases from big health data, placing it at the centre of an expanding global scientific community in medicine, biochemical and pharmaceutical research
2. Academic organisations and databases that conduct health data-based research face considerable challenges impacting on sustainability of valuable scientific research, e.g. training and retaining scientists and technical staff, growing requirements in secure computational infrastructure, prohibitive costs of datasets
3. Time, energy and expertise is needed to curate datasets from data providers and healthcare organisations in primary and secondary care that are highly contextual and vary in coding and standardisation practices
4. Information governance regulations for data access and use are perceived as adequate for protection of data from potential misuse and abuse, but create bureaucracy that causes considerable time and cost restrictions
5. Work is needed to enhance transparency and inform patients-citizens about data-sharing choices
6. In a political economic environment where conscripted marketisation of datasets is promoted and legislated for scientific, clinical and economic advancement of society, the main challenge for regulators is to foster an environment that nurtures organisations capable, credible, sustainable and responsible in use of public datasets
7. Need transparent processes to enforce validity and auditability of database operations (academic and/or private), particularly exhaustiveness of data collected and linked and level of profiling of patients-citizens
8. The state-run programme was resisted and eventually scrapped after public outcry and before any data extraction. The normative assumptions of data size, speed of innovation and discourses of citizenship and wealth it mobilised, although promising disruptive research and economies of scale for the realisation of value from NHS data, were not adopted by all
9. The public remains largely supportive of responsible scientific research for the benefit of the society, but patients-citizens who opted out and some GPs resisted the programme’s appropriation of knowledge and potential exploitation of financial rewards by the fe
It will be impossible to fully realise potential benefits of big data analytics from electronic health records to save lives, improve quality of life and reduce costs in healthcare without a national policy/regulatory environment predicated on a thorough understanding of technical, social and ethical challenges posed by this kind of observational research. This project contributes to such debates by providing a framework for examining these challenges. It facilitates the adoption of an objective and down-to-earth approach to development of national big data programmes in healthcare that will not only minimise public resistance to exploitation of these public assets, but also facilitate their expansion across other data-driven industries and sectors, locally and internationally. Importantly, it fosters public participation in discussions on what use of big data holds for the future of healthcare and increases the relevance of social science research for policy-making. Dissemination activities ensure that the framework and its main concepts will continue to be communicated to relevant scientific, clinical, policy, industrial and civil liberties stakeholders.