Evaluation of a Fall Risk Prediction Tool Using Large-Scale Data.

Area of Science:

• Nursing
• Patient Safety
• Health Informatics

Background:

• Hospital-acquired falls pose a significant risk to patient safety and increase healthcare costs.
• Effective fall prevention strategies are crucial for improving inpatient care outcomes.
• Existing fall risk assessment methods may lack precision or consistent application.

Purpose of the Study:

• To develop and implement a novel fall risk prediction tool for nursing staff.
• To evaluate the effectiveness of the implemented tool in reducing inpatient falls.
• To analyze the impact of the tool on fall probability using institutional data.

Main Methods:

• Development and implementation of a fall risk prediction tool.
• Comparative analysis of inpatient fall rates before and after tool implementation.
• Statistical adjustment using a large dataset of 573,216 records from 25,039 patients across 24 general wards.

Main Results:

• The implementation of the fall risk prediction tool was associated with a reduction in inpatient falls.
• The odds ratio for the probability of falling was 0.79 (95% CI: 0.69-0.91, p < 0.001).
• The study adjusted for institutional data, providing a robust assessment of the tool's impact.

Conclusions:

• The developed fall risk prediction tool appears effective in reducing the risk of falls among inpatients.
• Further research is needed to fully understand nurse adherence and the impact on nursing practice behaviors.
• The findings support the integration of predictive tools into nursing workflows to enhance patient safety.