Editorial: Redefining acute psychiatric care: strategies for improved inpatient experiences

illness. As a result, inpatient psychiatric services continue to demand greater economic resources than community-based alternatives, regardless of a country's level of economic development (Park et al., 2025).In this research topic collection, Iwanaga et al. employed a random forest model to identify key predictors for admission to high-acuity psychiatric units among 2,064 emergency inpatients in Japan. The most significant factors were clinical profile items-such as the necessity of inpatient treatment, the need for 24-hour care, symptom severity, specialized equipment, and medication management-rather than age or diagnosis. for adults with severe mental illness in forensic psychiatric inpatient services. The trial found Motiv8 to be feasible and acceptable, with high recruitment, retention, and satisfaction rates. While not powered to detect clinical differences, the results support a larger trial to assess effectiveness and cost-efficiency in reducing physical health inequalities for this population.In addition, Miola et al. used machine learning to predict medical hospitalizations in bipolar disorder (BD) patients after psychiatric discharge. Among 71 BD patients, those with cardiovascular, neurological, or osteo-muscular diseases were at highest risk for future medical admissions. Models using medical data were more accurate than those using psychiatric data alone. The results emphasize the importance of monitoring and managing physical health in BD patients to reduce hospitalizations. Larger, prospective studies are needed to confirm these findings and improve preventive care for this vulnerable population.Overall, this research collection has addressed several issues relating to inpatient mental health care. Findings from papers included in the collection, if incorporated into practice, will contribute to improving overall patient care.