The healthcare technology industry is learning the real value of medical data


The author is the co-founder and head of research and development at Qure.ai, an AI developer for medical images

In a company with medical artificial intelligence, the quality of your algorithms – and therefore the value of your company – depends on your access to data. The healthcare technology industry is in some ways similar to the advertising and internet search industries in that respect: it quickly learned that data is immensely valuable.

However, on the internet, most of the user-generated data is used to train algorithms that promote consumption, commerce, or engagement. Health data vary widely – it can be used for the global public good. It can help us track epidemics and prevent them from spreading, discover new drugs and diagnostics, and advance medical research that can help us live healthier, longer lives.

Therefore, it is imperative that our health technology industry accept this difference.

Data has always been the currency of science: like a form of silver that is distributed, it enables us to evaluate experiments, test hypotheses, discover side effects and continuously improve medical practice. However, with the advent of AI, data has become gold.

About 20 US health systems have recently been identified Data company named Truveta, Raise $ 200 million to leverage the value of their combined health records. In 2018, pharmaceutical company Roche valued US cancer patient data at nearly $ 2 billion through its acquisition of Flatiron Health.

Hospitals and diagnostic laboratories are a rich source of this type of health data for AI developers. Their databases of images and medical records are fodder for machine learning algorithms. These healthcare institutions usually obtain the patient’s consent to the use of their data via a blanket “research use”, which is a condition for the use of the medical service.

But this sweeping transfer of the value of data from individuals to healthcare providers and then to industry has not escaped the public eye. When it comes to sharing medical records, there is a general distrust – not just of the private sector, but of public institutions like the UK’s NHS.

And what about the health data we lose from our wearable devices? Smartphones are used by 6 billion people worldwide and are quickly reaching the next billion users. Body-worn sensors and smartphone cameras that track heart rate and rhythm, blood oxygen content, breathing rate, cardiovascular and metabolic status, as well as digital symptom checkers are not far away.

In places with underdeveloped and underfunded health systems, this digital version of health care could reach people whom the traditional doctor-centered version has not served well enough.

Pooja Rao

For example, in 2021, tuberculosis was the first medical disease to earn an award World Health Organization recommendation that “software programs can be used in place of human readers to interpret X-rays”. Tuberculosis is not a first-world disease. Digital health is now likely to quickly invade densely populated middle-income countries, aided by the huge gap between health demand and supply, unprepared regulators, and abundant 4G data.

We are about to enter an era where more health data is generated by individuals through their phones and wearables than by health care workers who maintain electronic medical records in hospital databases.

Imagine wanting an evidence-based answer to a health question like “What causes my migraines?” or “What are the side effects of this new birth control pill for women of my age and ethnicity?” The answer is probably already in the data streams flowing through our health apps and wrist-worn sensors.

We need to make this data a commons so that society can benefit from it.

The amount of open data available for digital health is a good predictor of the number of startups, independent developers, research, and innovative software products that will emerge. As of 2016, publicly funded efforts have done dozen of open radiology records available. Five years later, we have 150 Food and Drug Administration-cleared AI radiology products, most of which were developed by startups rather than established industrial companies.

Health data is most meaningful (and valuable) when aggregated, preferably on a large scale. However, individual ownership of health data and consent to its use are inviolable. In order to reconcile these two principles, we need digital health tools that enable individuals to meaningfully and explicitly give or withdraw consent to the use of personal health data.

We need more experimentation with digital health data trusts and cooperatives designed for user-generated streaming of health data. Data federations Promises of decentralization, transparency, meaningful revocable consent and a share in the benefits for people who contribute data, thereby creating incentives for more shared use.

Due to data protection and security regulations, we need mandates for data portability, which enable the control and storage of our health data. Above all, companies that want to benefit from digital health need to think longer-term and give more of the value of health data back to the people who generate it.


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