Gamification and Microlearning for Engagement With Quality Improvement (GAMEQI): A Bundled Digital Intervention for the Prevention of Central Line-Associated Bloodstream Infection.

Central line-associated bloodstream infections (CLABSIs) cause major patient harm, preventable through attention to line care best practice standards. The objective was to determine if a digital self-assessment application (CLABSI App), bundling line care best practices with social gamification and in-context microlearning, could engage nurses in CLABSI prevention. Nurses caring for children with indwelling central venous catheters in 3 high-risk units were eligible to participate. All other units served as controls. The intervention was a 12-month nonrandomized quality improvement study of CLABSI App implementation with interunit competitions. Compared to the preceding year, the intervention group (9886 line days) CLABSI rate decreased by 48% ( P = .03). Controls (7879 line days) did not change significantly. In all, 105 unique intervention group nurses completed 673 self-assessments. Competitions were associated with increased engagement as measured by self-assessments and unique participants. This model could be extended to other health care-associated infections, and more broadly to process improvement within and across health care systems.

The Little Schmidy Pediatric Hospital Fall Risk Assessment Index: A diagnostic accuracy study.

BACKGROUND: Falls are among the most common potentially preventable adverse events. Current pediatric falls risk assessment methods have poor precision and accuracy. OBJECTIVE: To evaluate an inpatient pediatric fall risk assessment index, known as the Little Schmidy, and describe characteristics of pediatric falls. DESIGN: Retrospective case control and descriptive study. The dataset included 114 reported falls and 151,678 Little Schmidy scores documented in medical records during the 5-year study period (2007-2011). SETTING: Pediatric medical and surgical inpatient units of an academic medical center in the western United States. PARTICIPANTS: Pediatric hospital inpatients <25 years of age. METHODS: Nurses used the 5-item, 7-point Little Schmidy to assess fall risk each day and night shift throughout the patient's hospitalization. Conditional fixed-effects logistic regressions were used to examine predictive relationships between Little Schmidy scores (at admission, highest prior to fall, and just prior to fall) and the patient's fall status (fell or not). The sensitivity and specificity of different cut-off scores were explored. Associations between Little Schmidy scores and patient and hospitalization factors were examined using multilevel mixed-effects logistic regression and multilevel mixed-effects ordinal logistic regression. RESULTS: Little Schmidy scores were significantly associated with pediatric falls (p<0.005). Maximal performance was achieved with a 4-item, 4-point, Little Schmidy index (LS4) using a cut-off score of 1 to indicate fall risk with sensitivity of 79% and specificity of 49%. Patients with an LS4 score ≥1 were 4 times more likely to fall before the next assessment than patients with a score of 0. LS4 scores indicative of fall risk were associated with age ≥5 years, neurological diagnosis, multiple hospitalizations, and night shift, but not with sex, length of hospital stay, or hospital unit. Of the 114 reported falls, 64% involved a male patient, nearly one third (32%) involved adolescents (13-17 years), most resulted in no (59%) or mild (36%) injury, and most (54%) were related to diagnosis or clinical characteristics. For 60% of the falls, fall precautions had been implemented prior to the fall. CONCLUSIONS: The revised 4-item Little Schmidy, the LS4, predicts pediatric falls when administered every day and night shift, but identifies most patients (65%) as being at risk for fall. Strategies for improving the accuracy and efficiency of the assessments are proposed. Further research is needed to develop more effective pediatric fall prevention strategies tailored to patient's age, diagnosis, and time of day.