Until Dr. Shiri Sharvit, clinical director at Eleos Health, began embracing artificial intelligence in her work as a practicing mental health clinician, her partner was a post-it note. But these Post-it notes sometimes got lost or spilled.
then came Eleans, an artificial intelligence solution that improves workflow for behavioral health clinicians, he said. Although she has adopted it in her own work as a part-time physician, she experienced challenges in getting others to use it.
“I felt that when I tried to communicate it to my colleagues or when I gave training on how to incorporate technology into clinical practice on behalf of the American Psychological Association, people still didn’t get it,” he said. Sharvit has been invited to speak several times for the American Psychological Association.
This prompted Sharvit and Katherine C. Kellogg, professor of management and innovation at MIT Sloan School of Management, to write a paper on how AI can be implemented in mental health clinician workflows, as well as the challenges and the solutions to do it.
They felt that doctors have historically been left out of the conversation about AI technologies in mental health, Kellogg said.
“We believe that much research has been done on the societal implications of AI and how organizations can adopt AI to improve quality, reduce costs and increase revenue,” Kellogg said. “But very little research has been done on the benefits of AI technologies for frontline providers, what challenges they face in their daily work, and how healthcare leaders and technology developers can help address these challenges.”
the peer reviewed article, published in Frontiers in Psychology on September 6, focuses on three types of AI solutions: automation technologies, patient engagement technologies, and clinical decision support technologies. Automation technologies computerize structured or semi-structured tasks, such as the selection or administration of digital surveys. Patient engagement technologies are solutions like conversational agents or chatbots that can provide scripted counseling or therapy. Clinical decision support technologies use machine learning algorithms to make predictions based on past data. Examples of conditions it can predict include depression and anxiety, stroke, and suicidal ideation.
But using these AI technologies is not without its challenges, the researchers admitted. Difficulties in implementing AI include fear of being replaced, lack of evidence-based evaluation studies, and concerns about a negative impact on the doctor-patient relationship.
“Physicians right now don’t have many use cases or role models using AI in their practice,” Sharvit said. “We wanted to give them a little more confidence to explore digital tools, explaining that they are meant to augment and enhance their practice and complement it. They will not be replaced.”
There are also solutions to some of these challenges, such as exposing clinicians to potential failures, training them in computational thinking, and involving them in the technology development process. If some aspects of the AI technology are not accurate, developers should work with clinicians to determine how they can be improved.
But more research is needed, Kellogg said. She is identifying leading organizations using AI in mental health to determine what the best practices are. However, because she will soon begin these studies, she cannot name the organizations yet. She added that while clinicians should be the focal point when it comes to developing AI solutions, there needs to be collaboration between others in the field, including technology developers, C-suite leaders and researchers.
“When you all work together, just don’t forget about these frontline doctors,” Kellogg said. “They are the crucial linchpin for making these technologies work in the real world.”
For Sharvit, the difference between using AI and post-it notes at work is like the “difference between navigating from one place to another with maps and having Google Maps or Waze on my phone.” AI decreases her stress during sessions with clients, improves her note-taking and helps her remember conversations from previous sessions, she said.
“It’s that big of a difference,” Sharvit said. “Because, first of all, I have an AI partner. I’m not alone in the session.”
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