Improving Mental Health Care Access with Technology: Addressing the Screening-to-Referral Bottleneck

AbstractIn the United States, the economic and social costs of untreated mental health conditions continue to escalate, with barriers to access due to workforce shortages, long wait times, and high referral rates. Technological innovations, including large language models and generative artificial intelligence, can most effectively bridge these gaps if they are focused on optimizing the early stages of mental health care - screening, assessment, triage, and treatment planning. By automating and standardizing initial patient assessments, these tools could empower primary care providers. Critically, technology-enabled solutions stand to bridge the research-to-practice gap when it comes to dimensional models of mental health disorders. These technologies could revolutionize real-world intakes by providing general access to leading evidence-based models of mental health case conceptualization, currently leveraged by only a small percentage of specialists. (Funded by the NSF Graduate Research Fellowship Program grant number, DGE-213989.).