Date of Graduation

Spring 5-18-2023

Document Type


Degree Name

Doctor of Nursing Practice (DNP)


School of Nursing and Health Professions




Executive Leader DNP

First Advisor

Nicholas R. Webb, DNP, RN, ESQ

Second Advisor

Committee Member: Juli Maxworthy, DNP, Ph.D. (c), MSN, MBA, RN, CNL, CPHQ, CPPS, CHSE, FNAP, FSSH


Predicting the Risk of Falling with Artificial Intelligence


Background: Fall prevention is a huge patient safety concern among all healthcare organizations. The high prevalence of patient falls has grave consequences, including the cost of care, longer hospital stays, unintentional injuries, and decreased patient and staff satisfaction. Preventing a patient from falling is critical in maintaining a patient’s quality of life and averting the high cost of healthcare expenses.

Local Problem: Two hospitals' healthcare system saw a significant increase in inpatient falls. The fall rate is one of the nursing quality indicators, and fall reduction is a key performance indicator of high-quality patient care.

Methods: This quality improvement evidence-based observational project compared the rate of fall (ROF) between the experimental and control unit. Pearson’s chi-square and Fisher’s exact test were used to analyze and compare results. Qualtrics surveys evaluated the nurses’ perception of AI, and results were analyzed using the Mann-Whitney Rank Sum test.

Intervention. Implementing an artificial intelligence-assisted fall predictive analytics model that can timely and accurately predict fall risk can mitigate the increase in inpatient falls.

Results: The pilot unit (Pearson’s chi-square = p pp<0.001).

Conclusions: AI-assisted automatic fall predictive risk assessment produced a significant reduction if the number of falls, the ROF, and the use of fall countermeasures. Further, nurses’ perception of AI improved after the introduction of FPAT and presentation.

Included in

Nursing Commons