Navigating the Digital Health Ethics Landscape: A framework for understanding ethical principles for digital healthExecutive summary We have created an interactive version of the executive summary that you can use to click-through to the sections of the guidelines you would like to read more about: Introduction Within a couple of decades, digitally-enabled practices have become prolific across our work and lives and are the norm. These transformations have largely been driven and controlled by commercial organisations, however there is increased interest and participation from health and medical research charities which see the potential benefit to patients. Charities hold a unique position of trust, and therefore have a mandate to embody best practice in the use of digital technologies and any interactions these may have with people’s data. To enable this, the AMRC commissioned DataKind UK to develop an ethics framework for members to reference when developing and deploying digital products and services. The result being this paper, as well as a guide for enacting this framework when collaborating with industry partners through a series of questions: ‘Navigating the Digital Health Ethics Landscape: Questions for charities to ask digital technology company partners’. There are a wealth of existing ethical principles that are to some extent applicable, but not specific to, the digital health work of charities. Hence, these various frameworks were collated and relevant aspects from across all of them were developed into this single framework. Understanding the context The first step in the process was to characterise the environment in which health and medical research charities work when undertaking digital health research. This can be represented as the sectors they might interact with: Nine core principles Existing ethical principles in each of these areas were identified and parsed to tease out consistencies across them that are relevant to charities developing health technologies. Nine key concepts materialised: Beneficence Do work that is to the benefit, not detriment of people. The benefits of the work should outweigh the potential risks. Non-maleficence Avoid harm. This is closely related to beneficence. Autonomy Enable people to make choices. This requires people to have sufficient knowledge and understanding to decide. Justice The benefits and risks should be distributed fairly. Explicability Transparency around how and why digital health solutions generate the outcomes they do. Particularly relevant to AI, for which the assumptions, working and outputs should be explicable. Sustainability (financial and operational) Minimise risk of developing digital products and services which users become dependent on but cannot be sustained. Open research Commitment to make research freely open and accessible for reuse. Community mindedness Willingness to collaborate within the digital health community, such as sharing platforms applicable across medical conditions. Proportionality Being proportionate to the relevant risk and potential benefit. Home The need How it was developed Using the framework Glossary Appendices Appendix D: Ethical principles reviewed within the workshop Principle Explainers Example Sources Beneficence Do work that is to the benefit, not detriment of people. The benefits of the work should outweigh the potential risks 1,2 Non-maleficence Avoid harm. This is closely related to beneficence 1,2 Autonomy Enable people to make choices. This requires people to have sufficient knowledge and understanding to decide 1,2 Justice Be fair— the benefits and risks should be distributed fairly 1,2 Explicability Explanation about the outputs of algorithmic models as well as the working and assumptions in the models themselves. 2 Open research Commitment to make research freely open and accessible for reuse 3 Community mindedness Willingness to collaborate within the health and medical research community which can involve using common platforms. It requires an awareness of the eco-system in which the charity works within and can cross over specific medical conditions. 3 Proportionality Being proportionate to the relevant risk and potential benefit 4,5 Financial & operational sustainability Minimise harm of developing/deploying digital technology products and services which produce service user dependencies and cannot be sustained 5,3,6 Evidence-driven Evaluate your technology/data analysis 7,6,8 Innovation Support for taking risks- Commitment to creating an environment that enables experimentation 7 Know your limitations Ensure robust practices and work within your skill set 8 Diversity, equality & inclusion Explore the impact and utility of tech/data developments on individuals, groups and society at large 9 Source references Bioethics Principles Floridi, Luciano & Cowls, Josh & Beltrametti, Monica & Chatila, Raja & Chazerand, Patrice & Dignum, Virginia & Lütge, Christoph & Madelin, Robert & Pagallo, Ugo & Rossi, Francesca & Schafer, Burkhard & Valcke, Peggy & Vayena, Effy. (2018). An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Link here Various authors (launched 2016 and updated on website) Principles for Digital Development. Link here Public Health England, HSC Public Health Agency, Public Health Wales, NHS Scotland (2017) Public Health Ethics In Practice. A background paper on public health ethics for the UK Public Health Skills and Knowledge Framework Link here a) Definition taken from Pandemic Flu Link here Department Of Health and Social Care (2019) Code of Conduct For Data Driven Health and Care Technology. Link here Seven charity CEO’s and AMRC CEO (2018) Promoting More Effective Partnerships In Digital Health: A discussion document The Academy Of Medical Sciences (2018) Our Data-Driven Future In Healthcare. People and partnerships at the heart of health related technologies. Link here Department For Digital, Culture, Media, Sport (updated 2018) Data Ethics Framework. Link here Machine Intelligence Garage (2018) Ethics Framework. Link here Manage Cookie Preferences