- YouGov survey finds just 12% of NHS staff and private healthcare workers would be comfortable with ‘big tech’ firms carrying out analysis of anonymised patient data
- Only 17% trust multinational ‘big tech’ companies to handle NHS patient data in a confidential manner
- This is despite widespread support for analysis, which can enable quicker diagnosis and solve some of the UK’s greatest healthcare problems
NHS staff are uncomfortable with multinational ‘big tech’ companies analysing anonymised patient data, according to a new survey.
A YouGov poll of 1,027 healthcare professionals, commissioned by Sensyne Health, found that 81% support the analysis of anonymised data to enable quicker diagnosis and more effective treatments while 71% believe this analysis can help solve some of the greatest healthcare challenges in the UK, such as cardiovascular disease.
But just 12% of NHS staff and private healthcare workers said they would be comfortable with a multinational ‘big tech’ company which pays little tax in the UK carrying the analysis out. Only 17% said they would trust multinational ‘big tech’ companies to handle the data in a confidential manner.
In comparison, 80% believe the UK should have a domestic capability in AI and health data analysis so it doesn’t need to be outsourced to other countries or multinational companies.
More than 8 in 10 (85%) say the NHS should receive a fair share of any financial gains made from subsequent medical discoveries, with 87% explicitly calling on the Government to step in and ensure that both the NHS and UK taxpayers benefit from discoveries and gains resulting from any analysis.
The findings also highlight the significant benefits that anonymised analysis and data-driven technology can bring – from reducing workloads for doctors and nurses, to helping patients better manage their conditions. This is particularly important as the NHS heads into winter, with pressures mounting on hospitals and trusts.
The survey found:
- Three-quarters (75%) said insights from the analysis of anonymised NHS patient data could lead to quicker diagnosis and more effective treatments;
- More than half (53%) believe it could help reduce the workload of doctors and nurses;
- 76% believe increased use of data-driven technology, such as diabetes management apps, would lead to more accurate monitoring of symptoms and better management of conditions;
- 58% say greater use of data-driven technology could lead to reduced cost for the NHS due to time saving from less duplication of data recording and use of tech-driven prompts.
The scale of opportunity is also shown by the findings. While 73% would recommend that their patients use data-driven technology if it could help them better manager their condition, just 36% say their patients are currently making use of digital products, including diabetes management systems and health-tracking wearable devices.
It comes after the Government announcement in August this year that it will invest £250 million into an ‘AI Lab’ which will focus on improving patient diagnosis by applying artificial intelligence to health records.
Rachel Power, Chief Executive of The Patients Association, commented:
“From these survey results, NHS staff appear to have a good grasp of the benefits that can be achieved for patients by the effective use of patient data. We support the use of data in medical research and to improve the planning and delivery of care, provided it is done carefully and within the current legal framework.
"It is important that full information should be available to patients both about the benefits of sharing their data and the methods used to store it, share it and keep it secure. Patients and the public must feel confident that their data will be used appropriately and kept secure.”
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Notes for Editors
About the research
For the research, YouGov surveyed a representative sample of 1,027 healthcare professionals between 23rd and 31st October 2019. The survey was carried out online. The survey included 768 NHS employees and 258 from private practice. The figures have been weighted.