The writer is co-founder and head of research and development at Qure.ai, an AI developer for medical images.
In a medical artificial intelligence company, the quality of your algorithms – and therefore the value of your business – depends on your access to data. In this, the healthcare tech industry is in some ways similar to the advertising and internet search industries: it quickly realized that data is extremely valuable.
However, on the internet, most user-generated data is used to train algorithms that encourage consumption, commerce, or engagement. Health data is very different: it can be used for the global public good. It can help us track epidemics and prevent their spread, discover new drugs and diagnostics, and advance medical research that can help us live longer and healthier lives.
It is therefore imperative that our health technology industry embraces this difference.
Data has always been the currency of science: as a form of money that, when distributed, allows us to evaluate experiments, test hypotheses, discover side effects, and continually improve the practice of data. medicine. With the emergence of AI, however, data has turned to gold.
Some 20 U.S. healthcare systems recently formed a data company called Truveta, raising $ 200 million to capitalize on the value of their combined patient records. In 2018, pharmaceutical company Roche valued cancer patient data in the United States at nearly $ 2 billion, through its acquisition of Flatiron Health.
Hospitals and diagnostic labs 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 health establishments generally ask for the patient’s consent for the use of their data via a general provision “use for research” which is a condition of use of the medical service.
But this massive transfer of the value of data from individuals to healthcare providers, and then to industry, has not escaped the general public. When it comes to sharing medical records, there is a general environment of mistrust – not only of the private sector but also of public institutions, such as the UK NHS.
And what about the health data we disclose from our wearable devices? Smartphones are used by 6 billion people around the world and are quickly reaching the next billion users. Body-worn sensors and smartphone cameras that track heart rate and rhythm, blood oxygen levels, respiratory rates, cardiovascular and metabolic status, as well as digital symptom checkers, are not not far behind.
In places where health systems are underdeveloped and underfunded, this digital version of health care could reach people that the traditional doctor-centered version has not adequately served.
For example, in 2021, tuberculosis became the first medical condition to merit a recommendation from the World Health Organization that “software programs can be used in place of human readers to interpret x-rays”. Tuberculosis is not a first world disease. Digital health now appears poised to enter densely populated middle-income countries quickly, aided by the wide gap between healthcare 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 wearable devices than by health workers who complete electronic medical records in hospital databases.
Imagine wanting an evidence-based answer to a health question such as “What causes my migraines?” Or “What side effects does this new birth control pill have in women of my age and ethnicity?” The answer is probably already there, in the data flows that circulate through our health applications and our sensors worn on the wrist.
We need to make this data a common good so that society can benefit from it.
The amount of open data available for digital health is a good predictor of the number of start-ups, independent developers, research papers and innovative software products that will emerge. Since 2016, publicly funded efforts have made dozens of open radiological datasets available. Five years later, we have 150 U.S. Food and Drug Administration approved AI radiology products, most developed by start-ups rather than historical players in the industry.
Health data is more powerful (and valuable) when aggregated, preferably at a large scale. Yet individual ownership of health data and consent to its use is inviolable. So, to reconcile these two principles, we need digital health tools that allow individuals to meaningfully and explicitly give or revoke their consent to the use of personal health data.
More stories from this report
We need more experiments with digital health data trusts and cooperatives designed for user-generated streaming health data. Data unions promise decentralization, transparency, meaningful revocable consent, and a share of the benefits for those providing data, prompting more sharing.
With privacy and security regulations, we need data portability mandates that allow control and retention of our health data. Above all, we need companies that intend to capitalize on digital health to think longer term, returning more of the value of health data to the individuals who generate it.