Search

Inpatient falls in medical-surgical units remain frequent, clinically serious, and difficult to prevent using periodic risk assessment alone. Static scales can support bedside awareness but may miss rapidly changing patient conditions. Existing approaches often fail to integrate nursing narratives, medication burden, mobility scores, bed-exit alarm activity, and room-level environmental hazards. These signals are usually documented in separate systems and are not continuously synthesized into fall risk estimates. This article proposes a multimodal deep learning model to predict the probability of an inpatient fall within the next 24 hours. The model is designed for medical-surgical units and uses both structured and unstructured clinical inputs. The proposed architecture uses a late-fusion design with a clinical text encoder for nursing progress notes and a structured-feature subnetwork for medication burden, mobility assessment scores, alarm logs, and environmental indicators. A final risk-scoring layer would generate a dynamic probability estimate suitable for clinical decision support. Conceptually, the model would produce an updated fall risk score that reflects subtle language cues, recent medication changes, impaired mobility, repeated bed-exit activity, and modifiable room hazards. The score would support continuous surveillance rather than replacing nursing judgment. A multimodal deep learning model could help shift inpatient fall prevention from episodic screening toward continuous, data-driven monitoring. Silent validation and careful workflow integration would be essential before clinical activation.