In our recent webinar, Real-World AI Applications in Healthcare Today, held in partnership with HLTH, we examined the role of AI in healthcare, including how it drives innovation and enhances patient outcomes.
Led by a panel of experts spanning health tech, provider, and pharma organizations, the discussion revealed how healthcare organizations are leveraging AI to streamline processes, refine product development, and enhance service delivery.
As Grace Vinton, a digital health influencer and panel moderator, succinctly put it, “AI is not just a pipe-dream tool but a legit game-changer in delivering superior patient outcomes and accelerating medical research and engagement.”
Click below to watch the entire webinar.
Pressed for time? Continue scrolling for a few clips and excerpts from this session.
Leveraging AI in healthcare to boost internal workflows
One of the most immediate impacts of AI is its ability to streamline internal workflows. Craig Richardville, Chief Digital & Information Officer at Intermountain Health, shared insights on how AI integrates into their operations:
“Artificial intelligence actually fits under a larger program that I have that’s called intelligent automation…when we look at AI and the newer version with generative AI, that all fits within intelligent automation,” Richardville said.
Richardville highlighted specific applications like ambient listening, which significantly reduces administrative burdens:
“We have a product out there called ambient listening. It basically listens to the conversation that’s happening between the physician and the patient and then summarizes that conversation into a medical note,” Richardville said.
AI as a catalyst for innovation and improved patient care
AI’s ability to streamline current processes is no secret – but it’s also a powerful tool for driving innovation and boosting patient care. Abby Staible, Sr. Director of Digital Health at AstraZeneca, emphasized how her company uses AI to identify patients with complex biomarkers:
“We’re identifying very, very specific biomarkers, that you can’t just build a model based on clinical trial data or even data we purchase…you need extremely rich data, and real-world data, to be able to build these algorithms and then validate them in clinical practice and deploy them to identify these patients,” Staible explained.
Will Shapiro, Chief of AI & VP, Data Science at Flatiron Health, discussed how their mission leverages AI to enhance cancer care:
“At Flatiron, our mission is to improve and extend lives by learning from the experience of every person with cancer and AI is a critical tool there that directly enables us to deliver on that,” Shapiro said.
Future applications of AI in healthcare
Looking ahead, AI promises seemingly limitless applications in healthcare. In the panel, Richardville detailed the technology’s upcoming role in diagnosing and managing chronic conditions:
“I’d like to be able to see how we can be more proactive on prevention and keep people healthy, as well as the early detection piece,” Richardville said.
Staible also shared her vision for AI in the next decade:
“I think it’s the precision medicine, identifying patients with leveraging biomarkers, genetic testing, clinical notes and clinical data, building algorithms to identify those patients and using those same algorithms for both clinical trials as well as identifying patients earlier for progression of disease,” Staible said.
Ethical and regulatory considerations
Before implementing an AI solution, it’s critical to understand its ethical and regulatory nuances. Staible underscored the importance of patient consent and data governance:
“At the core of it is the patient owns the data, and if the patient is sitting inside of the health system, the health system is then the steward of the patient’s data – so getting patients’ consent, then also the consent to share data at the health system level, and then the consent to share data with your third-party technology partner that potentially is stewarding the data is incredibly important,” Staible cautioned.
Shapiro stressed the importance of safeguarding protected health information (PHI):
“I think it’s a critical aspect whenever we’re using something like an LLM or generative AI tools…there’s quite considerable security review, and of course, PHI is one of the main things that we’re thinking about in working with these tools,” Shapiro explained.
Challenges in implementing AI within healthcare
AI has boundless potential in healthcare, but it most certainly isn’t without its challenges. Rajiv Haravu, SVP of Product Management at IMO Health, pointed out the necessity of having a diverse, skilled team:
“To create algorithms that have their basis in machine learning, you need a lot of people with very distinct skill sets…It takes a lot of people to make an artificial intelligence solution come to life,” Haravu said.
Haravu also named cost as a potential obstacle:
“[The] second challenge is there’s a lot of talk about large language models, but they are very resource hungry beasts…they cost a lot of money to run. And as somebody who’s in product management, how do I fashion a cost effective solution to provide my customer? So that is a challenge that we have to deal with,” Haravu explained.
Richardville added that prioritization is crucial for overcoming some of these challenges:
“One [challenge] is really making sure that you prioritize the work based upon the vision of the company, the mission, the strategic values, the initiatives, because there’s just so much opportunity out there in this space,” Richardville stressed.
