The Technology of Health - Healthcare article
By 2025, the global healthcare predictive analytics market is forecast to be worth $7.8bn, with investments in the space growing by 21% over the next five years.
But, just how close are we to leaping into this brave new world? And is society ready for it?
The forces driving the future of healthcare
For Naël Dabbagh, general manager for the Middle East, North East Africa, Turkey and Central Asia at GE Healthcare, the role of digitisation should not be understated when it comes to realising the precision health dream.
He paints a picture of big data, predictive analytics, and automated, cross-network data-sharing working together to help clinicians and healthcare systems deliver the diagnostics, treatment and monitoring required to make this an actuality.
Dabbagh believes healthcare technology firms are driving development in this burgeoning space.
“Covid-19 has really accelerated the adoption of predictive and preventative healthcare technology,” he explains, “and what would have previously taken the industry years to implement is now taking months.”
He points to the recent launch of Edison™ Health Services, an open, extendable, modern architecture for the development and deployment of AI-based digital applications in healthcare. Applications targeting clinical, operational, and financial outcomes in healthcare can easily be developed using these tools.
Applications developed using Edison Health Services can be deployed quickly and securely in the cloud, on premises (e.g. via GE Healthcare’s Edison HealthLink appliance), or directly onto smart imaging devices like x-rays, CTs and ultrasound machines.
Putting the technology into practice
Dabbagh explains that the volume of medical data being generated is exploding, with the average hospital creating 50 petabytes of data per year, coming from a vast array of sources - devices, medical images, electronic medical records, operational and financial metrics and more. And yet, less than 3 percent of the data generated is actionable, tagged or analyzed.
To transform this data in order to enable personalized, precision medicine, new AI-based tools are needed to aggregate, standardise, and make sense of this data quickly.
“The data in health systems and in social domain, exists in disparate forms, in different locations and across different systems, leading to siloed and fragmented information,” Dabbagh explains. “This makes it challenging to contextualize the patients’ information as well as horizontal knowledge base within health systems, acquired over decades of work and bring it to bear at the point of care. They try to cobble together these systems, but healthcare partners want to be able to leverage data, software and analytics across their entire business, bringing together a connected, digital enterprise,” he adds.
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