Clinical Impact of an Analytic Tool for Predicting the Fall Risk in Inpatients: Controlled Interrupted Time Series

Abstract Background: Patient falls remain a common cause of harm in acute-care hospitals worldwide. Despite the availability of a considerable body of literature on risk factors of inpatient falls, it is a difficult, complex, and common problem requiring a great deal of nurses’ time, attention, and effort in practice. With recent rapid expansion of health care predictive analytic application along with growing availability of electronic health record data, a patient-level electronic analytic tool for predicting fall risk was developed.Objectives: The purpose of this study was to explore how an electronic analytics tool for predicting fall risk affect patient and process outcomes. Methods: A controlled interrupted time series (CITS) experiment was conducted in 12 medical-surgical nursing units at a public hospital between May 2017 and April 2019. The intervention was the provision of patient-level risk predictions generated by an analytic tool using routinely obtained from the hospital’s electronic health record system. The primary outcome was the fall rate, and secondary outcomes included fall-related injuries and predefined process indicators. Results: During the study there were 42,476 admissions, while 707 falls and 134 fall injuries occurred. Allowing for differences in the patients’ characteristics and baseline process outcomes, the fall rate was significantly low in the intervention group (1.79 vs. 2.11, t = 2.13, P = .038). The CITS analysis revealed that the immediate reduction was 29.73% in the intervention group (z = –2.06, P = .039) and 16.58% in the control group (z = –1.28, P = .200), but no ongoing effect. The injury rates did not differ significantly (0.42 vs. 0.31, t = –1.54, P = .131). Patient-level-adjusted logistic regression showed a significant group effect on falls. Process outcomes related to multifactorial interventions including risk-targeted interventions increased significantly in the intervention group over time.Conclusion: The effectiveness of IN@SIGHT was supported only by the before-after comparison. However, it demonstrated the potential to contribute to improvement of patient outcomes, leading to positive changes in process outcomes over time. Further research is needed for this new approach.