The global healthcare system generates more than a zettabyte (a trillion gigabytes) of data annually.
And that is only growing, as healthcare organizations continue to progress in their digital transformation.
In today’s big data-fueled landscape, one would be forgiven for thinking that this is a good thing, even a very good thing.
But in reality, the sheer volume of available data represents the industry’s biggest problem. And solving for it could bring about a revolution in patient care and delivery, as well as transform the very nature of today’s healthcare system workflows and drug discovery processes.
That’s because as is often the case, the healthcare sector’s biggest bottleneck also represents its most attractive opportunity area.
The issue hamstringing a data-fueled healthcare revolution that appears to be tantalizingly just around the corner lies in the sheer scale and hyper-fragmented nature of the zettabytes of collected health data, which are generated by a complex ecosystem of thousands of institutions each involved in the collection, transfer, and use of information about hundreds of millions, if not billions, of patients.
While it represents an as-of-yet unactivated goldmine, this health data comes in many types and from many disparate sources and is further exacerbated by the industry’s lack of universal data standards.
Still, as awareness grows of both the problem and its possibilities, the healthcare sector is increasingly turning to innovative new tools like generative artificial intelligence (AI) to bridge historic fragmentations and usher in a new, more streamlined era of patient care and operational efficiency.
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Breaking Down the Barriers of Data Fragmentation
The division and isolation of information across different healthcare systems, facilities and platforms comes from many sources including payer claims, HIEs (health information exchanges), EMRs (electronic medical records), employee surveys, disease registries, clinical data depositories, laboratory research, Medicare and Medicaid information, along with messy and unstructured data like emails, call center records and more.
And that list represents just a quick sampling of data sources — one that barely scratches the surface.
Often, health systems are dealing with mountains of paper forms and legacy data silos. After all, the industry has been one of the slowest to adapt to the digital age from an administrative standpoint, with 20th century technologies like the fax machine still being heavily relied upon.
“You’d be surprised how many healthcare providers in the country still don’t even have a website for accepting online patient payments, and that you have to call an office to speak to someone to process a credit card transaction,” Brad Garfield, managing director and head of healthcare solutions at J.P. Morgan Commercial Banking, told PYMNTS. “Looking at the end-to-end patient experience, there’s so much opportunity for improvement.”
Think about the last time you went to the doctor, and the number of forms, notes, and records that were required. Then multiply that by the number of patient visits and treatment occasions taking place around the world each day and add to it the amount of data being collected by wearable devices and health apps on a second-by-second basis.
It is overwhelming to even think about, but being able to effectively and efficiently activate the knowledge and insight trapped within this growing corpus of health data represents a revolutionary opportunity for the healthcare sector.
And AI might just be the industry’s silver bullet when it comes to doing so.
Tapping Artificial Intelligence for Real Results
“Healthcare systems produce a zettabyte of data annually, but it all sits in silos. The change-the-game opportunity AI represents in healthcare is the ability to transform, analyze, and link these fragmented data sources into actionable timely insights,” Vikas Mehta, CFO at Komodo Health, told PYMNTS.
As reported here, the healthcare sector is projected to nearly double its spending on artificial intelligence, with the amount allocated to AI and machine learning (ML) in health company budgets anticipated to be 10.5% next year, compared to 5.5% in 2022.
Still, the same industry concerns about reliability and standards that keep health systems mired in fax machines and phone calls are similarly holding back broader adoption of future-fit innovations. After all, there is little regulation around AI, and healthcare is a highly regulated industry.