Dana Tschannen

Background and purpose: Nurse engagement in quality improvement (QI) is critical in the delivery of safe high-quality care, yet few studies have evaluated frontline nurses in this area. The purpose of this study was to identify and compare levels of self-reported QI engagement and QI competence among frontline nurses and nurse leaders. Methods: This study used a cross-sectional descriptive design. A convenience sample of frontline nurses (bedside RNs and advanced practice nurses) and nurse leaders from acute and ambulatory care sites completed the Nursing Quality Improvement in Practice (NQuIP) tool, which measures engagement and competence (knowledge, skills, and attitudes) in QI. Results: Data from 6,351 surveys completed by frontline nurses and nurse leaders representing 66 sites nationwide were analyzed. Only 52.5% of all respondents reported participating in QI. Knowledge scores were relatively high, while skills scores—especially those related to using QI tools—were low. Overall attitudes toward QI were positive. Nurse leaders scored significantly higher in engagement and competence than the frontline nurses they supervise. Conclusions: The study findings indicate that nurse engagement in QI is limited. Although nurses' knowledge levels appear to be high, their limited competency in QI-related skills may contribute to low QI engagement. Leaders must make efforts to increase nurse engagement in order to attain high-quality outcomes. Using the NQuIP tool will allow leaders to evaluate nurses' self-perceived QI competence and engagement, which will aid in identifying target areas and developing effective strategies for improvement.

Background: Hospital-acquired pressure injuries (PIs) induce significant patient suffering, inflate healthcare costs, and increase clinical co-morbidities. PIs are mostly due to bed-immobility, sensory impairment, bed positioning, and length of hospital stay. In this study, we use electronic health records and administrative data to examine the contributing factors to PI development using artificial intelligence (AI).Methods: We used advanced data science techniques to first preprocess the data and then train machine learning classifiers to predict the probability of developing PIs. The AI training was based on large, incongruent, incomplete, heterogeneous, and time-varying data of hospitalized patients. Both model-based statistical methods and model-free AI strategies were used to forecast PI outcomes and determine the salient features that are highly predictive of the outcomes.Results: Our findings reveal that PI prediction by model-free techniques outperform model-based forecasts. The performance of all AI methods is improved by rebalancing the training data and by including the Braden in the model learning phase. Compared to neural networks and linear modeling, with and without rebalancing or using Braden scores, Random forest consistently generated the optimal PI forecasts.Conclusions: AI techniques show promise to automatically identify patients at risk for hospital acquired PIs in different surgical services. Our PI prediction model provide a first generation of AI guidance to prescreen patients at risk for developing PIs.Clinical impact: This study provides a foundation for designing, implementing, and assessing novel interventions addressing specific healthcare needs. Specifically, this approach allows examining the impact of various dynamic, personalized, and clinical-environment effects on PI prevention for hospital patients receiving care from various surgical services.