Feature Artificial Intelligence

Data, data everywhere: the challenges of personalised medicine

BMJ 2017; 359 doi: https://doi.org/10.1136/bmj.j4546 (Published 11 October 2017) Cite this as: BMJ 2017;359:j4546
  1. Stephen Armstrong, freelance journalist
  1. London, UK
  1. stephen.armstrong{at}me.com

Can public trust in health record sharing be regained? Will clinicians end up frazzled data scientists? Stephen Armstrong examines healthcare’s wrestling match with big data

“One of the most important resources held by the UK health system is the data generated by the 65 million people within it,” wrote John Bell, regius professor of medicine at Oxford University, in the government’s life sciences industrial strategy published in August.1

“The development of platforms to enable de-identified health data to be appropriately used to research and develop technologies would be of great benefit to patients, to those managing the NHS, and to researchers attempting to develop new therapies or improve NHS care,” continued Bell, who is also chairman of the Office for the Strategic Coordination of Health Research, which coordinates the research of the National Institute for Health Research and the Medical Research Council.

NHS records, he argued, are uniquely suited to helping develop powerful algorithms that could transform healthcare and seed an “entirely new industry” in diagnostics based on artificial intelligence (AI).1 Bell’s strategic goal for the NHS is to develop 50 separate programmes in collaboration with industry over the next five years, including large scale data analysis.

And yet—at a panel discussion organised by Stanford Medicine and the Royal College of Physicians that asked, “Are data and analytics the new medicine?” and just five days after Bell’s strategy was published—experts from both sides of the Atlantic warned that serious problems lie ahead.

Issues with patient trust and technology

“Personalised medicine isn’t overhyped,” said Jem Rashbass, national director for disease registration and cancer analysis at Public Health England.

“It is spectacular when it works. It’s important to find and identify the right people—and to do that you need molecular data and clinical data sets. But we have issues with patient trust, security, and technology to …

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