Artificial intelligence for reducing involuntary admissions: Opportunities and challenges

Deborah ALUH1,2, Graça CARDOSO1, Barbara PEDROSA1, Ugn? GRIGAIT?1, Manuela SILVA1, José Miguel CALDAS-DE-ALMEIDA1

1Lisbon Institute of Global Mental Health, Comprehensive Health Research Centre (chrc). Nova Medical School. Nova University of Lisbon, Lisboa, Portugal
2Clinical Pharmacy and Pharmacy Management, University of Nigeria Nsukka. , Nsukka, Nigeria

Artificial intelligence (AI) has garnered significant attention in various fields, and its potential to revolutionize mental health care is undeniable. One area where AI shows promise is in improving the quality of care by reducing involuntary admissions in mental health care. Involuntary admissions refer to situations where individuals are admitted to psychiatric facilities against their will, often due to concerns about their safety or the safety of others. These admissions are typically guided by mental health professionals who assess the risk and make decisions based on their expertise. AI algorithms can be used to analyze large amounts of data from various sources, such as electronic health records and social media, to identify potential indicators of a mental health crisis. This can enable early intervention and preventive measures, reducing the likelihood of involuntary admissions. Moreover, the utilization of advanced predictive models in healthcare analysis can allow hospitals to forecast increased demand for emergency care and plan accordingly. This is crucial, as an excessively high patient-to-staff ratio can contribute to involuntary admissions.

However, the use of AI to reduce involuntary admissions also presents significant challenges. AI systems are only as good as the data they are trained on, and if the datasets used to develop AI algorithms are biased, the resulting system may perpetuate inequalities in mental health care. Another main concern is the ethical implications associated with relying on AI systems in such critical decision-making processes. The complexity of mental health conditions and the subjective nature of certain symptoms make it challenging for AI algorithms to accurately capture the nuances of individual experiences. Human oversight and clinical judgment should, therefore, continue to play a crucial role in the decision-making process, with AI serving as a supportive tool rather than a replacement for human expertise.