Discover how Aidoc’s AI platform offers seamless end-to-end integration into a facility’s existing IT infrastructure enabling implementation of AI at scale.
Discover how Aidoc’s AI platform offers seamless end-to-end integration into a facility’s existing IT infrastructure enabling implementation of AI at scale.
Uncovering Hidden Gems in Healthcare Data with AI As the Pickaxe
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Imagine you’re a clinician at a health system, knowing that your database is packed with relevant clinical insights. You know this data can be incredibly helpful in navigating the nuances of each patient case, as there could be pertinent information that influences the treatment path you prescribe.
The challenge is that you can’t always access this data or make it actionable, and if you can it’s not likely to be within a relevant clinical timeframe. In other words, you have a goldmine of information that can enhance patient care, but lack the tools to utilize it effectively.
The obstacles to accessing this data are multifaceted and largely beyond your control. Technical barriers, such as fragmented and incompatible tech stacks, scatter data across various health systems and care providers, creating silos that hinder seamless access.
On top of these technical challenges, you’re grappling with more immediate, tangible constraints: soaring patient volumes and an overburdened workforce. These pressures exacerbate the struggle, leaving you and your care team overwhelmed and unable to harness the data’s power to enhance patient outcomes.
Technological advancements have revolutionized healthcare, making once sci-fi-like innovations like robotic surgeries, telemedicine, genomic medicine and advanced imaging a reality. These tools have simplified procedures and improved outcomes significantly.
However, despite these remarkable advancements, I was taken aback upon rereading a study conducted by Johns Hopkins quantifying the devastating impact of misdiagnosis, which is far more staggering than I had imagined. Would you believe me if I said that up to 795,000 deaths and serious medical harms (i.e. permanent disability) each year are a result of medical error in the U.S.? This would make misdiagnosis a leading cause of death, surpassing heart disease, cancer and accidents.
That brought me to this question: Could improving access to data help turn the tides?
The Untapped Data Mine
Think about the health system as a mining site. More specifically, a mine referred to as a “vertical shaft single stage hoisting mine.” (Sounds fancy, right?) Picture it: hard hats, pickaxes and maybe even a canary or two. See the diagram below of such a mine for context and its healthcare operation equivalent (with AI):
Now think about the mining operation as a healthcare network. Sitting above ground in the headframe are the operational and administrative pieces, including much of the staff. Here you have your administrative staff, your tech stack, clinicians and even your patients. Everything is accessible, within reach and running smoothly. Now you enter the proverbial miner’s cage and work your way underground. This is where you find things like EHRs, the imaging department and other specialists and proceduralists, all utilizing what’s available to them to gain deeper insights into patient cases and provide optimal care. The ore bodies (all the way at the bottom) are the data. We know that in today’s digital age, there’s an amassing of data in health systems.
To put the quantity of data into perspective, a projection from the World Economic Forum states that the amount of data that is being created in healthcare “is almost incomprehensible,” globally generating 2.3 zettabytes of data – or 2.3 trillion DVDs (remember those?) worth of data.The problem is that as providers and patients navigate the complex healthcare landscape, making numerous stops along the way to receive care, they miss vital opportunities to reach the true value of their data.
With such an overwhelming amount of data being generated, it’s no wonder that some of these valuable insights often slip through the cracks. But what if there was a way to harness this information effectively? This is where AI steps in, offering a powerful solution to mine healthcare data and uncover new opportunities for improved care within health systems.
How Does AI Work as the Pickaxe?
My proposition is that AI platforms act as the pickaxe that will make this stockpile of data accessible to clinicians and, thus, actionable. This would result in transforming care paths and, subsequently, patient outcomes.
When properly selected and implemented, an AI platform can:
Scale Across the Entire Health System: AI’s ability to integrate in various departments and systems makes it scalable, ensuring consistent and comprehensive data analysis and insight generation.
Process Data in Real Time: AI can analyze vast swaths of data in real time, providing clinicians with up-to-the-minute insights that can immediately influence treatment decisions. This real-time processing is crucial in emergency and critical care settings, where timely decisions can impact outcomes.
Analyze a Broad Spectrum of Data: AI doesn’t limit itself to a single data type, of which there are many. It can integrate and analyze EMRs, lab results, imaging studies, vitals and even patient-reported outcomes. This comprehensive approach ensures that no relevant information is overlooked.
Support Personalized Medicine: By analyzing individual patient data alongside vast datasets, AI can tailor treatments to each patient’s unique needs, moving away from a one-size-fits-all approach to further personalize care.
Enhance Operational Efficiency: AI can streamline administrative tasks, optimize resource allocation and reduce redundancies, improving the efficiency of care delivery.
Consider a case study involving adult oncology patients, where AI was used to prioritize the detection of incidental pulmonary embolism (iPE) on routine chest CT scans. In this study, AI was evaluated alongside historical controls and prospective analysis. During three distinct periods, the AI demonstrated remarkable effectiveness: it detected 91.6% of true-positive cases with a specificity of 99.7%, reducing the median detection and notification time from 7,714 minutes (routine workflow) to just 87 minutes. Additionally, the radiologists’ missed rate of iPE plummeted from 44.8% without AI assistance to a mere 2.6% with the AI tool. This significant reduction in missed cases and expedited diagnosis highlights how AI can uncover critical care opportunities that busy physicians might overlook, ultimately leading to faster, more accurate treatment paths for patients. Additionally, these sorts of findings could unearth future care opportunities.
Implemented on a wide scale, this sort of technological shift in health systems would signal a transformation in care delivery, showing the impact for AI in real-time clinical environments. It’s not just about mining the data; it’s about revolutionizing the way healthcare providers interact with and utilize this data, leading to improved patient outcomes and more efficient healthcare systems.
