Fall Risk Assessment

Fall risk is not formally assessed. Nurses may mention a patient 'seems unsteady' in shift handoff, but there is no standardized assessment tool or documented risk score. Whether a patient gets a fall prevention intervention depends on which nurse is caring for them and what they happen to notice.

None — AI cannot predict falls, recommend prevention protocols, or monitor risk levels because no formal fall risk assessment records exist.

Fall risk assessments are completed using a standardized scale (Morse or Hendrich), but documentation is inconsistent. Some nurses record the total score without the component risk factors. Others write the assessment in narrative nursing notes rather than the designated form. The assessment exists but its completeness and location vary by unit and shift.

AI can identify patients with documented fall risk scores, but cannot analyze contributing risk factors or validate assessment accuracy because the component-level scoring details are not consistently captured.

Fall risk assessments are consistently documented in a standardized EHR form with all component scores. Each assessment captures the individual risk factors (history of falling, secondary diagnosis, ambulatory aid, IV access, gait, mental status), total score, and risk category. The quality team can reliably identify high-risk patients. But assessments are point-in-time snapshots — they are not linked to the patient's medications, mobility orders, or environmental factors.

AI can calculate unit-level fall risk profiles, identify patients with elevated scores, and flag assessments that are overdue for reassessment. Cannot correlate fall risk with medication changes, mobility status, or environmental factors because assessments are self-contained documents.

Fall risk assessments are linked to the patient's broader clinical context. Each assessment connects to current medications (sedatives, anticoagulants, antihypertensives), mobility orders, cognitive status assessments, continence status, and prior fall events. A care team can query 'show me this patient's fall risk trajectory alongside their medication changes over this admission' and see the full picture.

AI can perform multi-factor fall risk analysis — correlating assessment scores with medication profiles, post-surgical status, and cognitive changes to generate more accurate risk predictions than the assessment tool alone. Can recommend targeted prevention interventions based on the specific contributing factors.

Fall risk assessments are schema-driven entities with full relational modeling. Each assessment links to patient demographics, medication orders (with fall-risk flags), mobility assessments, cognitive evaluations, room assignment, prior fall events, active prevention interventions, and nursing care plans. An AI agent can navigate the complete risk constellation for any patient.

AI can autonomously manage fall prevention programs — continuously evaluating multi-factor risk, recommending specific interventions (bed alarms, non-slip footwear, medication review), and monitoring intervention compliance across the patient population.

Fall risk assessments are real-time dynamic risk profiles. Every clinical event that affects fall risk — a new sedative order, a change in mobility status, a room transfer, a confusion episode — updates the patient's risk score in real-time. The fall risk assessment is not a periodic nursing task but a continuously computed risk state that reflects the patient's current condition at every moment.

Fully autonomous fall prevention intelligence — continuously computing risk from all contributing factors, triggering prevention protocols, alerting staff to emerging risk, and optimizing unit-wide fall prevention resource allocation in real-time.

Ceiling of the CMC framework for this dimension.