Brad Porter, Global Chief Executive, Orion Health.
We all know someone with a messy desk—dog-eared papers lying about, a collection of coffee cups dotted around and brightly colored sticky notes for decoration.
Sure, the information floating around might hold value to the desk owner, but it’s unlikely to be held in much regard by anyone else. In IT speak, we call this “siloed” information.
Imagine If the person with a messy desk asked you to pick up a specific document from a random stack of paper on their desk. Could you confidently do so without ruining their personal organization system or filing through nearly every page on the desk first?
The current state of global health data is akin to a messy desk. Siloed and disconnected data means many health providers, and we, as patients, are losing out on capabilities made possible by harnessing our own information.
Recently, I wrote about my perception that healthcare is not broken; rather, it needs dire attention. Similarly, healthcare’s messy desk problem simply needs to be organized.
The volume of healthcare data is more than we can comprehend. It makes up 30% of all the data in the world and is predicted to be growing faster than any other data type, being tipped to comprise 36% of the world’s data by 2025. Of that data, up to 97% is going unused.
Each time a patient fills in a paper form with basic information their healthcare provider should already have, they’re adding another unnecessary piece of paper to the desk, literally and figuratively. Hospitals will already have this information, but can the right clinicians access it in a timely or digital manner? Unfortunately not.
While we’re slowly managing to digitize much of healthcare processes, in many cases, we have not yet been successful in linking this information together and connecting it in ways that benefit patients or clinicians.
A 2022 report by Elsevier titled “Clinician of the Future” surveyed nearly 3,000 practicing doctors and nurses worldwide. Sixty-nine percent reported being overwhelmed with the current volume of patient data, and the same amount predicted the widespread use of digital health technologies would become an even more challenging burden to them in the future—the primary issue being poorly connected health systems.
Digital health advancements should relieve the burden, not add to it.
Artificial intelligence (AI) and large language models (LLMs) like ChatGPT have taken the world by storm, touted as an answer to help industries cut through their mountainous data and improve productivity and efficiency. What could it look like for us to appropriately harness AI to our advantage in healthcare?
While as many as 75% of health system executives believe generative AI has reached a turning point in the healthcare industry, only 6% reported having an AI strategy for their organization. For AI to be truly transformative, foundationally, it needs the ability to access the right information at speed via centralized platforms.
AI can network, connect and link our mountainous health data into a workable database that positively impacts how clinicians and patients experience healthcare. And for AI to work with purpose, it needs to be seamlessly embedded into clinical workflows.
Cleveland Clinic has been working at the forefront of this change, launching its Discovery Accelerator center two years ago, where it employed the AI intelligence of IBM’s Watson. Watson mines through huge amounts of data to provide physicians with a more efficient treatment experience. By holding the ability to analyze thousands of medical papers at incredible speed, Watson can successfully inform treatment plans to clinicians who can use this edge to best treat their patients.
A 2022 study conducted by McKinsey and Harvard researchers found that the healthcare industry could save up to $360 billion annually after AI is widely adopted, and that’s just in the U.S. Inadvertent savings like these that do not come at the cost of clinician or patient experience mean money can be invested into the many other struggling areas of healthcare.
AI has the ability to unify the right data and unlock it from the pieces of paper that plague hospitals, but only if the foundational layers are set correctly.
Getting this right when you’re dealing with legacy systems and existing mountains of data is not a small project. It’s rare for health providers to have the internal expertise to make informed data platform decisions, so as a first step, I recommend getting knowledgeable, trusted advisors onboard for guidance through this process.
This should consider what your current data management challenges are. Are you struggling with interoperability or managing multiple data sources? Do you need to modernize your data structures, improve your data quality, extract further value from your data, visualize insights or find a path to support AI and machine learning?
Chances are you might be facing all of the above, and you wouldn’t be alone. The big shift is in the fact that the technology underlying data management has changed, and so your approach should be reconsidered. This is about a shift from static data warehouses towards “data lakes” that can ingest any type of data and offer much greater adaptability.
This shift also enables data management decisions to be broken down into manageable chunks in a “learn as you go” approach, which is much less daunting than in the past when extensive scoping was required for careful execution in building inflexible systems.
It took some trial and error for offices and working professionals to adjust to almost entirely digital work. But with cloud technology, collaborative online calendars and document drives, encounters with an aforementioned messy desk are now becoming few and far between.
I hope that 12 months from now, paper records in healthcare will also be a relic of the recent past. I envision a world that has the foresight to connect data in a way that better serves the health of all of us.
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