By Alex Card
The human brain is an extremely powerful organ, capable of processing terabits of information at speeds many times faster than the world’s most advanced supercomputers—and with considerably less environmental impact.
But, as any brain owner will tell you, it’s not a perfect machine. Our grey matter encodes far more information than we can comprehend or reliably recall from long-term storage. We can only hold in our working memory about seven concepts at once. For tasks requiring the analysis of massive datasets—like those produced by billions of healthcare encounters every year—computers undoubtedly offer a more useful sort of intelligence.
In a recent Definitive Healthcare special report, we asked 135 healthcare providers and executives about how their organizations use artificial intelligence (AI) and machine learning (ML) to improve care outcomes, develop more efficient workflows, and more. We also asked about plans for future AI/ML use, as well as the challenges their organizations have faced implementing this technology.
Our survey revealed interesting developments around the latest AI applications in healthcare—but before I dig into those, let’s pin down what intelligence means when computers are doing the “thinking”.
What is artificial intelligence?
Artificial intelligence is a technology that simulates natural intelligence, often for the purpose of learning, problem-solving, or decision-making. In patient care, AI may perform tasks once handled by a provider or other healthcare professional, like analyzing clinical data, delivering insights to physicians, or controlling medical systems. In the broader industry, life science developers can use AI to uncover insights from unstructured data, identify patients for clinical trials, and accelerate drug discovery and development.
The term “AI” covers a broad swath of technologies, including augmented intelligence, robotic automation, natural language processing, predictive analytics, and machine learning.
Machine learning is a type of AI that uses algorithms and data to replicate the way humans learn, incorporating new information to synthesize or improve a computer’s “understanding” of a concept. With the assistance of ML, healthcare professionals can more easily develop drugs, identify and treat disease, and manage administrative processes.
With the definitions out of the way, let’s explore the four big healthcare AI trends our report uncovered.
1. AI/ML is used most for workflow improvement and image detection
AI is more popular than ever in healthcare, but most organizations are still primarily relying on good, old-fashioned human intelligence to get their work done. Around 38% of survey respondents reported that their organizations currently use AI/ML, with the highest usage rates in the Southeast and Southwest U.S.
Among healthcare organizations employing AI/ML today, more than half are using this technology for process and workflow improvement. Around 27% are using AI/ML for computer-aided image detection of certain disease states. A quarter of respondents say they’re using the tech to suggest more impactful care options.
While the human brain and eye make a highly effective team for recognizing patterns and anomalies in medical imaging, AI/ML-equipped computers can process millions of images in a fraction of the time. Combined with human intelligence, AI/ML provides an additional layer of accuracy and reliability that reduces misdiagnoses and ensures fewer concerns go undetected.
2. Healthcare professionals see major potential in AI/ML for process improvement and care suggestions
About 42% of healthcare professionals plan to incorporate AI/ML into their daily operations in the next two years. This figure is nearly the same as those who reported AI plans in our 2022 study.
Those who currently leverage this technology seem ready for more—nearly three-quarters have plans to expand AI/ML usage over the same period.
Process/workflow improvement is the top area for intended AI/ML use, with nearly two-thirds of respondents reporting interest in this area. No surprise there: AI can drastically cut the time necessary to create schedules, assign physicians to appropriate patients, measure staff productivity, and distribute clinical information. This puts more time back in providers’ hands, so they can focus more on patient experiences and outcomes—and avoid being overworked to the point of burnout.
About 45% of survey respondents plan to implement AI/ML to assist with financial or revenue-related performance assistance, and another 43% say they’ll use the tech to improve care guideline consultation/suggestive care options. AI/ML systems can be trained to recognize patterns that human clinicians might miss, supporting the development of more impactful care plans and recommendations with less effort on the provider’s part.
3. Cost is keeping healthcare organizations from adopting AI
AI/ML technology simplifies all kinds of processes, but it’s far from simple to develop or maintain. For this reason, AI/ML can be fairly expensive to implement, especially on the scale of a hospital or a health system (where over a third of our respondents work).
Fittingly, about 45% of respondents said cost was their organization’s biggest hurdle to implementing and using AI. This is down from about 57% in our last survey, which might suggest that this technology is becoming more affordable.
While many AI/ML systems and tools are supported by their developers as part of a service arrangement, the guidance being provided isn’t always enough. Organizations commonly report challenges like lack of strategic direction, necessary IT infrastructure, or technical expertise. This points to a disconnect between the value of AI/ML technology and its understanding among those best suited to benefit from it.
These aren’t the only issues, however. Respondents also expressed concerns about cybersecurity and AI, lack of regulatory guidelines from the FDA around AI/ML usage, and biases within AI’s detection ability.
4. AI’s biggest potential for patients is in smarter care processes
When it comes to patient care, nearly half of healthcare professionals say AI-related improvements to workflow and care processes will have the biggest impact on patient outcomes and experiences. A slightly smaller cohort (40%) believe AI’s improvements in diagnosis accuracy will deliver the greatest benefit to patients.
About 38% of respondents think AI-based improvements to business efficiency will make the biggest difference in patient care. But more than 60% say AI will have major implications for business operations in general.
As indicated earlier, providers’ opinions on the impact of AI on patient care seem directly tied to whether they’re currently using AI/ML technology. Among all respondents, the average rating of AI’s value for patient care is 6.99 (out of a 10-point scale, with 10 representing “significant value”). For those actively using AI, the average rating is 7.41.
Don’t just take our word for it—take AI/ML for a test drive
AI hasn’t won the hearts and minds of every provider, but it’s gaining more ground every year. Most of those who currently use it are sold on its value—and they want more. Plus, many providers who haven’t yet taken the plunge are making plans to do so in the next couple of years.
If you want to dig deeper into the trends discussed here, check out the full Definitive Healthcare special report. Or to keep your finger on the pulse of emerging trends, check out predictions on 2024 healthcare trends.
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