Integrating an Academic Electronic Health Record: Challenges and Success Strategies.

Technology is increasing the complexity in the role of today's nurse. Healthcare organizations are integrating more health information technologies and relying on the electronic health record for data collection, communication, and decision making. Nursing faculty need to prepare graduates for this environment and incorporate an academic electronic health record into a nursing curriculum to meet student-program outcomes. Although the need exists for student preparation, some nursing programs are struggling with implementation, whereas others have been successful. To better understand these complexities, this project was intended to identify current challenges and success strategies of effective academic electronic health record integration into nursing curricula. Using Rogers' 1962 Diffusion of Innovation theory as a framework for technology adoption, a descriptive survey design was used to gain insights from deans and program directors of nursing schools involved with the national Health Informatics & Technology Scholars faculty development program or Cerner's Academic Education Solution Consortium, working to integrate an academic electronic health record in their respective nursing schools. The participants' experiences highlighted approaches used by these schools to integrate these technologies. Data from this project provide nursing education with effective strategies and potential challenges that should be addressed for successful academic electronic health record integration.

Optimization of a highly standardized carboxyfluorescein succinimidyl ester flow cytometry panel and gating strategy design using discriminative information measure evaluation.

The design of a panel to identify target cell subsets in flow cytometry can be difficult when specific markers unique to each cell subset do not exist, and a combination of parameters must be used to identify target cells of interest and exclude irrelevant events. Thus, the ability to objectively measure the contribution of a parameter or group of parameters toward target cell identification independent of any gating strategy could be very helpful for both panel design and gating strategy design. In this article, we propose a discriminative information measure evaluation (DIME) based on statistical mixture modeling; DIME is a numerical measure of the contribution of different parameters towards discriminating a target cell subset from all the others derived from the fitted posterior distribution of a Gaussian mixture model. Informally, DIME measures the "usefulness" of each parameter for identifying a target cell subset. We show how DIME provides an objective basis for inclusion or exclusion of specific parameters in a panel, and how ranked sets of such parameters can be used to optimize gating strategies. An illustrative example of the application of DIME to streamline the gating strategy for a highly standardized carboxyfluorescein succinimidyl ester (CFSE) assay is described.