Abstract
OBJECTIVES: To assess whether a fall-prevention clinical decision support (CDS) approach using electronic analytics that stimulates risk-targeted interventions is associated with reduced rates of falls and injurious falls. MATERIALS AND METHODS: The CDS intervention included a machine-learning prediction algorithm, individual risk-factor identification, and guideline-based prevention recommendations. After a 5-month plan-do-study-act quality improvement initiative, the CDS intervention was implemented at an academic tertiary hospital and compared with the usual care using a pretest (lasting 24 months and involving 23 498 patients) and posttest (lasting 13 months and involving 17 341 patients) design in six nursing units. Primary and secondary outcomes were the rates of falls and injurious falls per 1000 hospital days, respectively. Outcome measurements were tested using a priori Poisson regression and adjusted with patient-level covariates. Subgroup analyses were conducted according to age. RESULTS: The age distribution, sex, hospital and unit lengths of stay, number of secondary diagnoses, fall history, condition at admission, and overall fall rate per 1000 hospital days did not differ significantly between the intervention and control periods before (1.88 vs 2.05, respectively, P = .1764) or after adjusting for demographics. The injurious-falls rate per 1000 hospital days decreased significantly before (0.68 vs 0.45, P = .0171) and after (rate difference = -0.64, P = .0212) adjusting for demographics. The differences in injury rates were greater among patients aged at least 65 years. CONCLUSIONS: This study suggests that a well-designed CDS intervention employing electronic analytics was associated with a decrease in fall-related injuries. The benefits from this intervention were greater in elderly patients aged at least 65 years. TRIAL REGISTRATION: This study was conducted as part of a more extensive study registered with the Clinical Research Information Service (CRIS) (KCT0005378).