Advancing Suicide-Risk Detection in Post-COVID Hospital Systems: A Three-Layer Model for Unified Screening, Training, and Follow-Up

Behind the medical urgency of COVID-19 was an overlooked consequence: a sharp rise in mental-health concerns that heightened the need for stronger tools to detect suicide risk in hospital settings. The COVID-19 pandemic intensified both psychological distress and existing gaps in hospital suicide-risk detection, exposing the longstanding inconsistencies in screening, documentation, and follow-up practices. Pre-pandemic systems often relied on nonstandardized assessments, limited EHR integration, and uneven staff training, contributing to inconsistent identification of at-risk patients. These weaknesses became more apparent during COVID-19, when increasing rates of anxiety, depression, and suicidal ideation overwhelmed existing workflows. Based on national data, multidisciplinary research, and original survey findings, this paper analyzes why prior suicide-risk frameworks failed to meet the growing need for timely assessment. To address these gaps, it proposes a three-layer model unifying EHR-based screening, structured follow-up, and continuous staff training. This framework strengthens early detection and provides a scalable, feasible pathway for improving patient safety in post-pandemic care environments.