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AIMS AND OBJECTIVES: The purpose of this study was to compare the experience of a new clinical model with traditional clinical teaching and examine the effects of evidence-based practice strategies among staff and student nurses. BACKGROUND: This provides an innovative approach to nursing student clinical learning that emphasised the academic-clinical partnership with the use of a new model called the Evidence-based Clinical Academic Partnership (ECAP) model. The model incorporates three main components (a) unit transformation into an innovative hybrid version of a dedicated education unit (hDEU); (b) Evidence-in-Action (EIA) rounding; and (c) the cognitive apprenticeship theoretical framework. DESIGN: This pilot study used a mixed-method, quasi-experimental design. METHODS: The quantitative portion included a pre-test, post-test non-randomised quasi-experimental design using self-reported survey data. The qualitative methodology used was a hermeneutic phenomenological approach to data interpretation of three focus groups with staff nurses and unit leaders. SQUIRE 2.0 guidelines were followed (Ogrinc et al., 2016). CONCLUSIONS: The themes that emerged emphasised relationships and the partnership with this innovative approach to clinical teaching. The staff nurses emphasised the need for a collaborative approach and having the presence of the academic faculty member as a way to support the teaching and learning aspects with students. RELEVANCE TO CLINICAL PRACTICE: This study did provide significant contributions to the development of an innovative clinical model and highlighted the importance of the academic-clinical partnership with the education of undergraduate nursing students. The study results provided insight to the ways the hDEU framework may be strengthened, such as increased communication and partnership in the implementation of the ECAP model. Implementing curricular change to include innovative clinical models within a nursing programme is vital in this time of healthcare transformation.

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BACKGROUND AND OBJECTIVE: Identification of subgroups may be useful to understand the clinical characteristics of ICU patients. The purposes of this study were to apply an unsupervised machine learning method to ICU patient data to discover subgroups among them; and to examine their clinical characteristics, therapeutic procedures conducted during the ICU stay, and discharge dispositions. METHODS: K-means clustering method was used with 1503 observations and 9 types of laboratory test results as features. RESULTS: Three clusters were identified from this specific population. Blood urea nitrogen, creatinine, potassium, hemoglobin, and red blood cell were distinctive between the clusters. Cluster Three presented the highest blood products transfusion rate (19.8%), followed by Cluster One (15.5%) and cluster Two (9.3%), which was significantly different. Hemodialysis was more frequently provided to Cluster Three while bronchoscopy was done to Cluster One and Two. Cluster Three showed the highest mortality (30.4%), which was more than two-fold compared to Cluster One (14.1%) and Two (12.2%). CONCLUSION: Three subgroups were identified and their clinical characteristics were compared. These findings may be useful to anticipate treatment strategies and probable outcomes of ICU patients. Unsupervised machine learning may enable ICU multi-dimensional data to be organized and to make sense of the data.

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BACKGROUND: A pressure ulcer is injury to the skin or underlying tissue, caused by pressure, friction, and moisture. Hospital-acquired pressure ulcers (HAPUs) may not only result in additional length of hospital stay and associated care costs but also lead to undesirable patient outcomes. Intensive care unit (ICU) patients show higher risk for HAPU development than general patients. We hypothesize that the care team's decisions relative to HAPU risk assessment and prevention may be better supported by a data-driven, ICU-specific prediction model. OBJECTIVE: The aim of this study was to determine whether multiple logistic regression with ICU-specific predictor variables was suitable for ICU HAPU prediction and to compare the performance of the model with the Braden scale on this specific population. METHODS: We conducted a retrospective cohort study by using the data retrieved from the enterprise data warehouse of an academic medical center. Bivariate analyses were performed to compare the HAPU and non-HAPU groups. Multiple logistic regression was used to develop a prediction model with significant predictor variables from the bivariate analyses. Sensitivity, specificity, positive predictive values, negative predictive values, area under the receiver operating characteristic curve (AUC), and Youden index were used to compare with the Braden scale. RESULTS: The total number of patient encounters studied was 12,654. The number of patients who developed an HAPU during their ICU stay was 735 (5.81% of the incidence rate). Age, gender, weight, diabetes, vasopressor, isolation, endotracheal tube, ventilator episode, Braden score, and ventilator days were significantly associated with HAPU. The overall accuracy of the model was 91.7%, and the AUC was .737. The sensitivity, specificity, positive predictive value, negative predictive value, and Youden index were .650, .693, .211, 956, and .342, respectively. Male patients were 1.5 times more, patients with diabetes were 1.5 times more, and patients under isolation were 3.1 times more likely to have an HAPU than female patients, patients without diabetes, and patients not under isolation, respectively. CONCLUSIONS: Using an extremely large, electronic health record-derived dataset enabled us to compare characteristics of patients who develop an HAPU during their ICU stay with those who did not, and it also enabled us to develop a prediction model from the empirical data. The model showed acceptable performance compared with the Braden scale. The model may assist with clinicians' decision on risk assessment, in addition to the Braden scale, as it is not difficult to interpret and apply to clinical practice. This approach may support avoidable reductions in HAPU incidence in intensive care.

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This article describes the initiatives of doctorate of nursing practice (DNP)-prepared nurses in a large healthcare system supporting the DNP competencies as outlined by the American Association of Colleges of Nursing. The goal of this group was to demonstrate the impact of DNP education on the roles for nurse administrators, advanced practice nurses, and educators in a large health system. Exemplars profile nurse administrators, clinical nurse specialists, and a nurse educator.

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