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1.  Clinician Adoption Patterns and Patient Outcome Results in Use of Evidence-Based Nursing Plans of Care 
Delivery of safe, effective and appropriate health care is an imperative facing health care organizations globally. While many initiatives have been launched in a number of countries to address this need from a medical perspective, a similar focus for generating evidence-based nursing knowledge has been missing [1]. This paper reports on a collaborative evidence-based practice (EBP) research initiative that adds nursing knowledge into computerized care protocols. Here, a brief overview of the study’s aims, purpose and methodology is presented as well as results of data analysis and lessons learned. The research team examined nurses’ adoption patterns of EBP recommendations with respect to activity tolerance using four-month patient data collected from a pilot hospital. Study findings indicate a need for more focus on the system design and implementation process with the next rollout phase to promote evidence-based nursing practice.
PMCID: PMC2655848  PMID: 18693871
Nursing knowledge; evidence-based practice; nursing standard terminology; nursing dataset; information technology infrastructure; clinician adoption
2.  Predictive Modeling for the Prevention of Hospital-Acquired Pressure Ulcers 
A one-to-one case control study was conducted on a pre-existing dataset to examine a predictive model with a set of risk factors for pressure ulcer development in acute care settings. Various techniques were used to select the most relevant predictors from ten subsets of a pre-existing dataset. The predictors identified were further examined using ten additional subsets by measuring sensitivities, specificities, positive/negative predictive values, and the areas under the ROC (receiver operating characteristic) curves. The best components for identifying at-risk patients consisted of three Braden subscales and five risk factors routinely collected through electronic health records. Entering these eight predictors into the logistic regression model yielded a sensitivity of 92%, a specificity of 67%, and an area under the ROC curve of 89%. Further evaluation, however, is needed to explore the validity of the model.
PMCID: PMC1839359  PMID: 17238378

Results 1-3 (3)