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Code of conduct for data-driven health and care technology
Today we have some truly remarkable data-driven innovations, apps, clinical decision support tools supported by intelligent algorithms, and the widespread adoption of electronic health records.
Combining these developments with data-sharing across the NHS has the potential to improve diagnosis, treatment, experience of care, efficiency of the system and overall outcomes for the people at the heart of the NHS, public health and the wider health and care system.
Innovators in this field come from sectors that are not necessarily familiar with medical ethics and research regulation, and who may utilise data sets and processing methods that sit outside existing NHS safeguards.
Our responsibility as an internationally trusted health and care system is to use all the tools at our disposal to improve the quality and safety of care, including data-driven technologies, in a safe, ethical, evidenced and transparent way.
For this reason, we have developed our 10 principles in a code of conduct to enable the development and adoption of safe, ethical and effective data-driven health and care technologies.
In addition to this online feedback, the Department of Health and Social Care and NHS England began an extensive period of engagement with industry experts, academics, regulators and patient representative organisations over the last quarter of 2018.
The code is designed to recognise that, while data-driven health and care technologies will undoubtedly deliver huge benefits to patients, clinicians, carers, service users and the system as a whole, it is our duty as NHS England and central government to capitalise on these opportunities responsibly.
If we do not think about issues such as transparency, accountability, liability, explicability, fairness, justice and bias, it is also possible that the increasing use of data-driven technologies, including AI, within the health and care system could cause unintended harm.
The code of conduct clearly sets out the behaviours we expect from those developing, deploying and using data-driven technologies, to ensure that all those in this chain abide by the ethical principles for data initiatives developed by the Nuffield Council on Bioethics: The code tackles a number of emerging ethical challenges associated with the use of data-driven technologies in the NHS and the wider health and care system.
When used as part of an overarching strategy it will help to create a trusted environment that supports innovation of data-driven technologies while being: Understand who specifically the innovation or technology will be for, what problems it will solve for them and what benefits they can expect.
Keep systems safe by safeguarding data and integrating appropriate levels of security into the design of devices, applications and systems, keeping in mind relevant standards and guidance.
Having a clear hypothesis about how the technology or innovation will contribute to that, for example through: Have a clear value proposition and make a business case highlighting outputs, outcomes, benefits and key performance indicators (KPIs).
If using patient data or accessing NHS patients in order to conduct healthcare research to either develop a proof of concept or test a digital health tool (sometimes referred to as health technology assessment), conform to the UK Policy Framework for Health and Social Care Research.
The DAQ was developed by a group of subject matter experts across several specialist organisations and regulatory bodies with the aim to provide a clear reference point for current regulation, standards and best practice relevant to apps in health and social care.
If the patient data planning to be used has been anonymised in line with the ICO’s code of conduct on anonymisation and meets the requirements of the common law duty of confidentiality, ethical review from the HRA will not be needed.
If planning to use identifiable patient data in the development and/or testing of the technology, ensure that there is appropriate consent to access the data or some other legal basis, such as approval under section 251 of the NHS Act 2006 and Health Service (Control of Patient Information) Regulations 2002.
Since May 2018 the national data opt-out allows people to opt out of their confidential patient information being used for purposes beyond their individual care and treatment.
By 2020 any health and care organisation that processes and/or disseminates data that originates with the health and adult social care system in England is required to be in compliance with the national data opt-out policy.
good data flow map identifies the data assets (data at rest) and data flows (exchanges of data) that enable the relevant objective or initiative to be delivered.
Where data flow mapping identifies instances where data is processed by a data processor on behalf of a data controller, a legally binding written data processing contract is required.
The data flow map will then influence the DPIA as the vehicle by which proposed flows of personal identifiable data are governed, and the controls developed to ensure lawful processing.
The vast majority of data processing in a health and social care context will involve special categories of data and it is therefore recommended that a full DPIA is carried out.
NHS Digital currently hosts a range of data, clinical and interoperability standards for the health and social care network: Other data, clinical and interoperability standards: The data used must be well understood and reviewed for accuracy and completeness.
If the data provided for the AI to learn is limited to certain demographic categories or disease areas, this could potentially limit the applicability of the AI in practice as its ability to accurately predict could be different in other ethnic groups.
Achieving transparency of algorithms that have a higher potential for harm or unintended decision-making, can ensure the rights of the data subject as set out in the Data Protection Act 2018 are met, to build trust in users and enable better adoption and uptake.
Work collaboratively with partners, specify the context for the algorithm, specify potential alternative contexts and be transparent on whether the model is based on active, supervised or unsupervised learning.
These standards inform technology developers and evaluators about which types of evidence should be expected, taking into account the functions and intended use of the product (as specified in principles 1 and 2) and its overall economic impact.
All organisations that have access to NHS patient data and systems – including NHS trusts, primary care and social care providers, and commercial third parties – must complete the toolkit to provide assurance that they are practising good data security and that personal information is handled appropriately.
The Department for Digital, Culture, Media and Sport has published a code of practice for consumer ‘internet of things’ (IoT) security, which sets out practical steps that will help manufacturers to improve the security of consumer IoT products and services and ensure products are secure by design.
It will give the Medicines and Healthcare Products Regulatory Agency (MHRA) increased oversight of connected medical devices, and will improve the cyber security of diagnostic equipment (and other connected medical devices) in the longer term.
The foundation of any commercial structure should be to ensure that the terms of the engagement fairly allocate the benefits between the parties based on their respective contributions, roles, responsibilities, risks and costs.
We want to hear from patients, the public and partners whether the 5 guiding principles published on page 46 of Sector Deal 2 are the right basis for the more detailed national policy framework that we will publish.
Financial value can be realised for the NHS through numerous models, such as simple royalties, free or reduced payments for products, equity shares in the business and improved data sets that can be offered (at a price) to others.
Technology providers derive significant value from the NHS beyond access to unique data sets – through medical and clinical involvement, test beds and pilots – and this value should be captured within the commercial arrangement.
AI, ML and Big Data in Healthcare
AI has multiple impacts across the entire healthcare industry, but they can typically be categorized as aiding with one or more of the following.
AI-powered clinical decision support tools could provide physicians with suggestions based on hard data, but it would be down to physicians and their patients to take this data and to decide together on the best way to proceed.
Artificial intelligence goes hand-in-hand with machine learning, natural language processing and other technologies, all of which can be combined to process the huge amounts of big data that we create on a daily basis.
After all, we’ll all become patients at some point in our lives, and AI has the potential to usher in a new era of health care in which we’re all treated with personalized health care plans based on data and not just the results of clinical trials.
- On 26. februar 2021
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