Using AI to identify in-hospital falls: a comparative analysis with incident reports and manual chart review

Falls remain a problem in hospitalised patients, especially in older adults. In-hospital falls occasionally lead to injuries. Currently, the rate of falls in hospitals is monitored using incident reports, which often leads to underreporting. Recently, natural language processing (NLP) models gained interest. In this study, an NLP model (AI algorithm) to detect falls in nursing records was evaluated.

In this retrospective study conducted in a large teaching hospital in the Netherlands, hospitalised patients aged 18 years and older were included. Data were collected from free text in nursing records; all data were manually checked for fall incidents. For comparative analysis, the AI algorithm and incident reports were used. A descriptive comparison of the three different methods is provided by using absolute counts. For analysis, descriptive statistics and an area under the curve (AUC) were calculated. For time efficiency a comparative analysis was performed.

76 fallers and 1547 non-fallers with a total of 30 615 nursing records were included. Using manual chart review, a total of 124 falls were identified. The AI algorithm showed good sensitivity (79.8%), good specificity (98.3%), excellent negative predictive value (NPV, 99.9%) and moderate positive predictive value (PPV, 16.4%). Incident reports had a sensitivity of 37.0% and a specificity of 99.9%, with a good PPV and NPV (97.9% and 99.7%, respectively). The AI algorithm had good discriminatory ability with an AUC of 0.891; incident reports had a moderate discriminatory ability with an AUC of 0.685. Utilising the algorithm, only 604 nursing records needed to be manually reviewed to identify 79.2% of all fall incidents, a higher detection rate than that achieved through incident reports (36.8% of all fall incidents). The semi-automated workflow using the AI algorithm requires significantly less time compared with incident reports, with a 92.6% reduction in time.

The AI algorithm has a good discriminatory ability to timely detect in-hospital falls. Using this technique, more falls are identified than by using incident reports, while the administrative burden of nursing personnel is reduced. Moreover, it will enable more effective, near real-time preventive measurements.