Crossing Borders: An Online Interdisciplinary Course in Health Informatics for Students From Two Countries.

A cross-countries and interprofessional novel approach for delivering an international interdisciplinary graduate health informatics course online is presented. Included in this discussion are the challenges, lessons learned, and pedagogical recommendations from the experiences of teaching the course. Four professors from three different fields and from three universities collaborated in offering an international health informatics course for an interdisciplinary group of 18 US and seven Norwegian students. Highly motivated students and professors, an online technology infrastructure that supported asynchronously communication and course delivery, the ability to adapt the curriculum to meet the pedagogy requirements at all universities, and the support of higher administration for international collaboration were enablers for success. This project demonstrated the feasibility and advantages of an interdisciplinary, interprofessional, and cross-countries approach in teaching health informatics online. Students were able to establish relationships and conduct professional conversations across disciplines and international boundaries using content management software. This graduate course can be used as a part of informatics, computer science, and/or health science programs.

Implementing common data elements across studies to advance research.

Challenges arise in building the knowledge needed for evidence-based practice partially because obtaining clinical research data is expensive and complicated, and many studies have small sample sizes. Combining data from several studies may have the advantage of increasing the impact of the findings or expanding the population to which findings may be generalized. The use of common data elements will allow this combining and, in turn, create big data, which is an important approach that may accelerate knowledge development. This article discusses the philosophy of using common data elements across research studies and illustrates their use by the processes in a developmental center grant funded by the National Institutes of Health. The researchers identified a set of data elements and used them across several pilot studies. Issues that need to be considered in the adoption and implementation of common data elements across pilot studies include theoretical framework, purpose of the common measures, respondent burden, teamwork, managing large data sets, grant writing, and unintended consequences. We describe these challenges and solutions that can be implemented to manage them.

Information technology as a tool to facilitate the academic accreditation process.

The site visit is a crucial component of the academic accreditation process. Many nursing education programs have adopted electronic processes for document management and course delivery. In this article, the authors describe reasons for using an electronic resource room, changes made in document management systems, methods to provide electronic documentation access to site evaluators, and ways to link decisions directly to supporting evidence. Significant advantages were realized from the electronic resource room.

Descriptive data analysis.

This 13th article of the Basics of Research series is first in a short series on statistical analysis. These articles will discuss creating your statistical analysis plan, levels of measurement, descriptive statistics, probability theory, inferential statistics, and general considerations for interpretation of the results of a statistical analysis.

Intra-rater/inter-rater reliability of the air transport minimum data set.

INTRODUCTION: The Air Transport Minimum Data Set consists of time-related terms proposed for use during rotor-wing transport. Reliable data collection is vital for monitoring safety, providing medical care, and meeting regulatory standards and financial constraints. The purpose of this research was to conduct a pilot study to investigate the intra-rater and inter-rater reliability of the ATMDS. SETTING: Three communication specialists at a rotor-wing air transport service in rural Nebraska participated. METHODS: This prospective study examined time data elements recorded during 11 rotor wing transports. At approximately 2 weeks, subjects again collected time data while listening to audio recordings of their original flights and while listening to four additional flights conducted by a colleague. RESULTS: Because of the small sample size, descriptive statistics rather than correlation coefficients were used to evaluate reliability. Data for both inter-rater and intra-rater reliability varied greatly across terms. As with previous research, Time Arrive Bedside and Time Depart Bedside were particularly problematic. Calculated times had less agreement than times for specific events. CONCLUSIONS: The study results were limited by sample size but showed the need for clarification of definitions for specific terms. Further investigation is needed related to how time intervals are calculated.

Evaluation of user interface satisfaction of a clinical outcomes database.

Interface design and usability testing are important skills for the informatics nurse. This study used the Questionnaire for User Interaction Satisfaction (QUIStrade mark) to evaluate a nurse practitioner outcomes database used to track client variables for evaluating clinical outcomes and for implementing a process improvement project. Five nursing faculty and students were instructed in the use of the database and subsequently evaluated the usability or "ease of use" of the interface (data entry screens). The mean on individual QUIS ratings ranging from 3.0 to 8.0 on a 9-point scale, although most mean ratings were moderately high. User comments identified specific areas for improvement in the interface. Discovering problem areas and modifying the screen design prior to deployment may increase the usability of the database and decrease user frustration.

Modeling and teaching techniques for conceptual and logical relational database design.

This paper proposes a series of techniques to be used in teaching database design. Common ERD notations are discussed. The authors developed an ERD notation, adapted from the Unified Modeling Language, which facilitates student learning of the database design process. The paper presents a specific step by step process for representing the ERD components as tables and for normalizing the resulting set of tables.

Language validation of the air transport minimum data set: time-related terms.

INTRODUCTION: Transport times, such as time of call, are an essential part of the patient record. The purpose of this study was to validate a previously proposed minimum data set of time-related terms. METHODS: A stratified sample of 508 nurses, physicians, paramedics, pilots, and communication specialists was selected to participate in the validation survey. Subjects indicated their agreement/disagreement with the proposed terms and their definitions on a scale of 1 (low) to 3 (high). In addition, subjects indicated whether they currently collect the data elements or could do so easily. Finally, subjects said whether they were willing to release aggregate data for benchmarking purposes. RESULTS: One-hundred-eighteen subjects (23.2%) responded to the survey with usable data. Agreement to include the terms (level 3) ranged from 71.2% to 95.8%. Agreement with the proposed definition ranged from 72.9% to 95.8%. Seventy-eight of the respondents were willing to release all the data elements. CONCLUSION: Fourteen of the 19 terms are recommended for inclusion in a minimum data set for rotor-wing transport. Most persons expressed willingness to release data for benchmarking efforts.

The use of aggregate data for measuring practice improvement.

Practice improvements are much needed in health care but are difficult to implement and to measure. Without records that include aggregate data, outcomes cannot be compared. Aggregate data are defined as data not limited to one patient, but data that are tracked across time, across organizations, across patient populations, or across some other variable. There are several issues that complicate the gathering of aggregate data, including data availability, format, management and definition, as well as the processes of trending and comparison to a standard benchmark.