NEW YORK, NY (September 30, 2024)--- A new Columbia University School of Nursing-led study explores how patients view the use of artificial intelligence (AI) to improve mental health care. The survey, which included 500 US-based adults, found 49.3% of participants considered AI to be beneficial for mental health care. Particularly, African Americans and those with lower self-rated health literacy were more likely to have this view while women were less likely to. 81.6% of participants also believed health professionals to be responsible for misdiagnosis of mental health conditions. The study, “Patient Perspectives on AI for Mental Health Care: A Cross-sectional Survey Study” was published in the Journal of Medical Internet Research (JMIR) Mental Health on September 18, 2024.
“This survey comes at a time where AI applications are ubiquitous, and patients are gaining greater access and ownership over their data. Understanding patient perceptions of if and how AI may be appropriately used for mental health care is critical,” said Natalie Benda, PhD, an assistant professor at Columbia Nursing who led the study, “Our findings can support health professionals in deploying AI tools safely.”
The researchers also found participants were most concerned about incorrect diagnoses by AI tools, application of inappropriate treatments, lack of interaction with their mental health care providers, and the negative effects of AI on confidentiality. In addition, participants said they would like visibility into and autonomy around how AI was being applied to help them understand the care process and a tool’s performance.
Based on the findings, the researchers recommend these actions for health professionals when using AI for mental health care:
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Evaluate AI tools in clinical simulation environments before larger deployment
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Promote transparency in AI’s use, communicate its accuracy, and include potential risks for patients
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Communicate how potential biases have been evaluated and mitigated
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Explain how the performance and process for a given task may change with and without AI
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Conduct studies to understand what information is needed within patient communications that incorporates patient values, supports comprehension of concepts, and fosters trust
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Increase opportunities for patient autonomy so that patients and health professionals may collaboratively make decisions
This work was supported by the National Institute for Mental Health (R41MH124581-S1). Other Columbia University study authors include Sarah Harkins and Meghan Reading Turchioe of Columbia Nursing, Pooja M. Desai, Department of Biomedical Informatics; and Zayan Reza, Mailman School of Public Health, along with Anna Zheng, Stuyvesant High School; Shiveen Kumar, Cornell University; and Alison Hermann, Yiye Zhang, Rochelle Joly, Jessica Kim, and Jyotishman Pathak, all from Weill Cornell Medicine.
About Columbia University School of Nursing
Columbia University School of Nursing is advancing nursing education, research, and practice to advance health for all. As one of the top nursing schools in the country, we offer direct-entry master’s degrees, advanced nursing, and doctoral programs with the goal of shaping and setting standards for nursing everywhere. And, as a top recipient of NIH research funding, we address health disparities for under-resourced populations and advance equitable health policy and delivery.
Through our expansive network of clinical collaborations in New York City and around the world —including our dedicated faculty practice, the ColumbiaDoctors Nurse Practitioner Group — we cultivate a culture of innovation and diversity and champion a community-centered approach to care. Across the Columbia Nursing community, we encourage active listening, big thinking, and bold action, so that, together, we’re moving health forward.
Columbia University School of Nursing is part of Columbia University Irving Medical Center, which also includes the Columbia University Vagelos College of Physicians and Surgeons, the Mailman School of Public Health, and the College of Dental Medicine.