Mollie R. Poynton

Nursing informatics competencies required of nurses in Taiwan

PURPOSE In todays workplace, nurses are highly skilled professionals possessing expertise in both information technology and nursing. Nursing informatics competencies are recognized as an important capability of nurses. No established guidelines existed for nurses in Asia. This study focused on identifying the nursing informatics competencies required of nurses in Taiwan. METHODS A modified Web-based Delphi method was used for two expert groups in nursing, educators and administrators. Experts responded to 323 items on the Nursing Informatics Competencies Questionnaire, modified from the initial work of Staggers, Gassert and Curran to include 45 additional items. Three Web-based Delphi rounds were conducted. Analysis included detailed item analysis. Competencies that met 60% or greater agreement of item importance and appropriate level of nursing practice were included. RESULTS N=32 experts agreed to participate in Round 1, 23 nursing educators and 9 administrators. The participation rates for Rounds 2 and 3=68.8%. By Round 3, 318 of 323 nursing informatics competencies achieved required consensus levels. Of the new competencies, 42 of 45 were validated. A high degree of agreement existed for specific nursing informatics competencies required for nurses in Taiwan (97.8%). CONCLUSIONS This study provides a current master list of nursing informatics competency requirements for nurses at four levels in the U.S. and Taiwan. The results are very similar to the original work of Staggers et al. The results have international relevance because of the global importance of information technology for the nursing profession.

Predicting the start week of respiratory syncytial virus outbreaks using real time weather variables

BackgroundRespiratory Syncytial Virus (RSV), a major cause of bronchiolitis, has a large impact on the census of pediatric hospitals during outbreak seasons. Reliable prediction of the week these outbreaks will start, based on readily available data, could help pediatric hospitals better prepare for large outbreaks.MethodsNaïve Bayes (NB) classifier models were constructed using weather data from 1985-2008 considering only variables that are available in real time and that could be used to forecast the week in which an RSV outbreak will occur in Salt Lake County, Utah. Outbreak start dates were determined by a panel of experts using 32,509 records with ICD-9 coded RSV and bronchiolitis diagnoses from Intermountain Healthcare hospitals and clinics for the RSV seasons from 1985 to 2008.ResultsNB models predicted RSV outbreaks up to 3 weeks in advance with an estimated sensitivity of up to 67% and estimated specificities as high as 94% to 100%. Temperature and wind speed were the best overall predictors, but other weather variables also showed relevance depending on how far in advance the predictions were made. The weather conditions predictive of an RSV outbreak in our study were similar to those that lead to temperature inversions in the Salt Lake Valley.ConclusionsWe demonstrate that Naïve Bayes (NB) classifier models based on weather data available in real time have the potential to be used as effective predictive models. These models may be able to predict the week that an RSV outbreak will occur with clinical relevance. Their clinical usefulness will be field tested during the next five years.

Modeling the distribution of Nursing Effort using structured Labor and Delivery documentation

Our study objectives included the development and evaluation of models for representing the distribution of shared unit-wide nursing care resources among individual Labor and Delivery patients using quantified measurements of nursing care, referred to as Nursing Effort. The models were intended to enable discrimination between the amounts of care delivered to patient subsets defined by attributes such as patient acuity. For each of five proposed models, scores were generated using an analysis set of 686,402 computerized nurse-documented events associated with 1093 patients at three hospitals during January and February 2006. Significant differences were detected in Nursing Effort scores according to patient acuity, care facility, and in scores generated during shift change versus non-shift change hours. The development of nursing care quantification strategies proposed in this study supports outcomes analysis by establishing a foundation for measuring the effect of patient-level nursing care on individual patient outcomes.

Communication patterns for the most serious poison center calls

Context. The communication demands faced by specialists in poison information (SPI) are unique in the health-care context. Objectives. (1) To describe SPI communication patterns for the highest risk poison exposure calls using cluster analysis, and (2) to describe variation in communication patterns or clusters. Methods. A sample of 1 year of poison exposure calls to a regional poison control center with SPIs’ perceived severity rating of major or moderate perceived was collected. Digital voice recordings were linked with medical records and were coded using the Roter Interaction Analysis System. Descriptive analyses were applied, and cluster-analytic techniques were used to assess variation in call communication and factors associated with that variation. Results. Cases were described, and four communication styles were identified. The informational cluster represents calls with relatively high levels of SPI clinical information and caller questions. The Facilitative cluster represents calls with a pattern of relatively high SPI questions and caller information provision. The Planning cluster represents calls with relatively high levels of SPI relationship talk. The Emotional cluster represents calls with relatively high caller and SPI emotion. Further analyses revealed relationships between call characteristics, SPI identity, and cluster membership. Conclusion. This study provides a beginning step to understanding SPI communication behaviors. Our results suggest that SPIs are able to use a range of communication strategies that often involve not only information but also emotional responsiveness and rapport building. Findings also point to the opportunity for future communication training for SPIs to meet the needs of the heterogeneous caller population.

Requirements analysis for HL7 message development in poison control center.

HL7 is the commonly accepted messaging standard for achieving interoperability among information systems. Until now, no analysis has been done on how poison control data can be matched in HL7 messages. The purpose of this study was to create a preliminary domain analysis model which can be used to identify the data required to message poison control data in HL7 messages.

Predicting Self-Reported Health Status Using Artificial Neural Networks

Self-reported health status is shown to be an excellent predictor of mortality, health care utilization, and disability. While the strength of effect varies, the predictive power of self-reported health status is found in different countries, racial/ethnic groups, age groups, and patient populations. There appears to be an intrinsic prediction power of self-reported health status on health outcome above that explained by other factors including gender, age, social and economic resources, and medical condition. All of these studies have used standard statistical methods (e.g., Cox-regression and logistic regression) for determining association with the factors and health status. This chapter uses neural networks to derive a model that will accurately predict self-reported health status. The National Health and Nutrition Examination Survey (NHANES) 2003–2004 data are also used. These data encompass a wide range of factors that span many aspects of health, providing for a more comprehensive prediction model. Neural networks allow to detect complex interactions and patterns in the data, and increase the classification accuracy. A classification algorithm for self-reported health status should give health care providers a better understanding of the underlying factors associated with self-reported health status and in turn provides better knowledge of factors associated with mortality and morbidity.