Artificial intelligence (AI) is transforming healthcare at an astonishing rate, particularly in areas like patient experience, data management, and clinical efficiency. Keeping pace with emerging use cases is challenging—and exciting.
Our recent webinar, The art of the possible: AI in healthcare and identity management, provided a compelling look at how AI is reshaping the future of healthcare. With insights from industry leaders, we discussed AI’s practical applications, challenges, and potential in healthcare. Below, we highlight the key themes and encourage you to watch the webinar to experience the full scope of the conversation.
The expanding AI landscape and its impact on patient experience
AI is not just one technology but a suite of advanced tools, including machine learning, deep learning, natural language processing (NLP), generative AI, and medical robotics. These technologies are evolving rapidly, with AI investments in healthcare projected to jump from $12 billion in 2024 to $19 billion by 2027.
Michael Parris, former Chief Data and Analytics Officer at Texas Health Resources, had this to say about its growth: “AI is moving faster than most of us can fully grasp. From generative AI to medical robotics, it’s not a question of if AI will change healthcare, but how and when.”
Parris noted the rise of multimodal AI, which combines different data types—like video, speech, and text—to generate more accurate insights. This capability is crucial in healthcare, where precise predictions and decisions can mean life or death.
One of the most exciting prospects of AI in healthcare is its ability to enhance the patient experience. Parris discussed how AI can streamline patient navigation, reduce handoffs, and help patients make more informed decisions. He emphasized how AI can integrate a patient’s medical history, location, and insurance details to personalize their care journey.
“When it comes to patient experience, AI is doing what healthcare has always aspired to do: provide a personalized approach without adding more cost to the system. We’re talking about creating a seamless consumer journey, something that used to be extremely labor-intensive.”
AI is particularly helpful for patients with chronic conditions or complex treatment paths. For example, AI-powered tools can guide patients on where to go for urgent care or help them schedule follow-up appointments, considering their insurance coverage and previous treatments.
Identity management is the critical backbone of AI
As Parris succinctly put it, “Without identity, there is no experience.” He emphasizes the essential role of identity management in enabling AI to function effectively in healthcare. With patient data spread across multiple systems—like EMRs, imaging centers, and specialist offices—healthcare organizations must consolidate patient identities to deliver cohesive care.
Nick Orser, Senior Director of Product Marketing at Verato, echoed this sentiment: “AI can only be as good as the data it’s built on. If we can’t trust the identity data we’re working with, every AI application—whether it’s patient navigation or predictive analytics—will fall short.”
Healthcare master data management (hMDM) is crucial to connecting these fragmented systems. It works with AI, ensuring it has a comprehensive, accurate picture of the patient to work with. This allows AI to process and interpret data holistically, reducing errors and improving care outcomes.
But doesn’t AI work all on its own? Many assume this is the case—that AI, particularly generative AI, is the genie in the bottle, but it is not. And it’s important to manage those expectations.
Generative AI and managing expectations
Generative AI, particularly large language models (LLMs), has garnered massive attention recently. But while generative AI offers exciting possibilities, such as automating documentation and enhancing patient interactions, it’s just one piece of a broader AI portfolio.
As Parris cautions, “Gen AI is another tool in the toolbox. It’s not the magic solution to all healthcare challenges.” He stresses the need to manage expectations and focus on building a portfolio of AI capabilities that can address specific problems with clear ROI.
Orser expands on this, saying, “When we look at the potential of generative AI, we have to be clear that it’s about augmenting existing processes, not replacing them. The real value comes when we apply it in the right contexts—like using it for automating repetitive tasks or helping summarize clinical notes, rather than expecting it to overhaul complex decision-making processes.”
Real-world AI—from ambient listening to medical robotics
Parris shared fascinating real-world examples of how AI is already making an impact. For instance, Texas Health Resources implemented ambient listening technology in primary care offices, allowing physicians to focus more on their patients and less on documentation. This resulted in physicians saving an hour and a half each day and seeing an additional patient per day on average.
“What excites me most is how AI frees up doctors to be doctors again,” shared Paris. “Imagine walking into an office, and the physician isn’t glued to a computer but actually listening to you. That’s what AI is enabling.”
Paris also touches on the potential of medical robotics, a frontier he believes could revolutionize surgery in the next five to ten years. With AI-enabled robots performing certain surgical tasks, surgeons could step in only when anomalies arise, leading to more efficient and precise operations.
Governance and ethics challenges
As AI becomes more embedded in healthcare, ethical concerns around bias and governance are front and center. Parris emphasizes the importance of diverse data sets to ensure AI models don’t perpetuate existing biases. He warns, “If we build AI on biased historical data, we’re going to see biased outcomes. It’s that simple.”
Moderator Andy De raised another critical issue: the ethical dilemmas that AI might face in real-world scenarios, such as split-second decisions in life-threatening situations. “The problem is not just hallucinations in generative AI—it’s the blind spots we don’t even know exist,” he explains. Healthcare organizations must work diligently to identify and mitigate these risks while ensuring patient safety and equity.
“One of the most critical aspects of implementing AI in healthcare is ensuring that the systems we build can explain their decision-making,” says Orser. It’s not just about trust, but about accountability. Patients and clinicians alike need to know why AI made a particular recommendation or decision.”
The future of AI in healthcare
Looking ahead, Parris predicts that healthcare’s back-office functions would be among the first to experience AI-driven disruption, with generative AI automating tasks such as billing, coding, and revenue cycle management. He anticipates that AI will replace a significant portion of these roles over time, allowing healthcare organizations to operate more efficiently.
“You’re going to see entire departments streamlined by AI,” says Parris. “What used to take 10 people will take two or three. It’s not just about cost savings; it’s about increasing accuracy and speeding up processes.”
However, AI will not replace human clinicians; instead, it will augment their capabilities. As Dé put it, “Doctors who use AI will replace doctors who don’t.”
Building a culture of AI with thoughtful implementation
Successfully implementing AI requires a cultural shift within healthcare organizations. Paris outlined several best practices for fostering innovation with AI, including cross-departmental collaboration, clear governance structures, and a focused approach to AI projects. “You can’t do everything. Pick two or three AI projects that align with your organization’s goals and go deep on those. Trying to do it all at once is a recipe for failure.”
Building a governance framework and ensuring that AI initiatives are ethically sound and aligned with the organization’s risk tolerance are also essential.
Orser emphasized, “It’s crucial that healthcare organizations take an incremental approach to AI adoption. Start small, learn from those implementations, and then expand. By focusing on areas that deliver clear ROI—like administrative tasks—you can build momentum for broader adoption.”
The future of AI in healthcare is undoubtedly bright, but as the webinar speakers emphasizes, its success will depend on thoughtful, strategic implementation. Healthcare organizations must build a solid foundation of clean, accurate data, particularly through identity management, and take a portfolio approach to AI that leverages the right tools for the right problems.
Watch the entire webinar to learn more about how AI transforms healthcare and hear more insights from industry experts. The conversation offers deep insights into how healthcare organizations can harness the power of AI to deliver better care, streamline operations, and create a more personalized patient experience. And please reach out for a demo of our hMDM to see how it will revolutionize your organization.
