Keeping Humans Centered in the AI Conversation
During a session on redefining the human-AI partnership in healthcare, Matt Troup, solution principal for clinician documentation at Abridge, said that when he worked as a clinician, one of his biggest challenges was figuring out how to best use his time so he could meet both the needs of his patients and his family. AI can help clinicians transform their workloads and enable them to give both their patients and family the time they deserve.
Pallavi Ranade-Kharkar, enterprise director of research informatics and genomics at Intermountain Health, shared how her organization is giving clinicians time back to focus on patients by using AI to handle Epic in-basket messaging. The tool drafts a response based on the patient’s question or comment and backed by context in the patient’s medical record. The clinician can review that message before it’s sent.
“This intervention has simplified and streamlined workflows, and real-time savings of 20 to 30 seconds per message have occurred,” she said. “This is reducing ‘pajama time’ and helping clinicians to focus on the things they do best.”
When implementing AI, Russell Yeager, senior vice president and CIO at Encompass Health, emphasized that augmented intelligence and people need to work together. His organization focuses on real intelligence rather than AI.
“We take AI and combine it with our clinical and business intelligence to provide real intelligence to people involved in decision-making,” he said. Yeager pointed out that while AI can be autonomous when it comes to IT operations, it needs guardrails when used in patient care.
While AI brings tremendous potential to the healthcare industry, it also comes with risk. Ranade-Kharkar said that inaccuracies in data, a lack of transparency and increased security and privacy vulnerabilities are some of the biggest risks.
“At the center of healthcare is a human, a patient. Human-to-human interaction is the core of a positive patient experience. Keeping that in mind, we have to come up with guardrails to ensure the responsible use of AI,” she said, adding that identifying key performance indicators and measuring them is a big part of what AI success means.
Tracey Touma, cybersecurity business liaison at Cleveland Clinic, explained that AI governance is another important factor in AI success.
DIVE DEEPER: Data governance is a human challenge, not just a tech issue.
“We have to start there. People want that shiny new toy and to move forward quickly. AI is moving fast and furious, but we have to make sure that we have governance in place to implement AI securely and safely,” she said. “We’ve all heard about hallucinations and false positives. The data is only as good as what’s in the system. Make sure the data is good, secure and protected.”
Risk assessment and documentation should play key roles in AI governance, according to Ranade-Kharkar. Having an AI governance committee through which organizations evaluate internal and external products is the first step of AI adoption. Algorithm drift can occur and introduce bias, so it’s crucial that organizations are undergoing continuous, quality monitoring of how well their AI tools are performing. Organizations should also have an inventory of their tools, with transparency into how the vendor tested and validated algorithms originally, she added.
Intermountain Health recently migrated its electronic health record to Epic, and the organization is leaning on them to help implement and operationalize it, especially when it comes to the long list of AI tools available.
“We haven’t turned them all on yet. We’re doing it in an intentional and thoughtful way,” she said. “We’ll turn them on one by one as we’re sure we can monitor them and keep that level of quality.”
Touma emphasized the need for intention in healthcare AI adoption. Having stakeholders from across departments and business areas is important to ensure the organization isn’t missing anything. And the organization shouldn’t just buy a shiny new tool; the tool should have demonstrated cost savings or address concerns about patient experience, patient outcome or caregiving experience.
“At the end of the day we’re in business to take care of patients,” she added. “If AI can solve those three problems, then we need to know how, the impact, the cost and the business use case.”
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