health care decision making by emergency department nurses
POST 1
KELLY
Utilizing conjoint analysis to explicate health care decision making by emergency department nurses: a feasibility study.
The goal of this study was to study the feasibility of using clinical simulation to understand proxy decision making by emergency department (ED) nurses for individuals with intellectual disability (ID) (Fishner, Orkin, & Frazer, 2008). The purpose was to enhance the comprehension of the complexities of services and supports that nurses are expected to provide (Fishner, Orkin, & Frazer, 2008). Conjoint analysis was used. Conjoin analysis is a measurement tool that uses simulation coupled with experimental design to mathematical model decision processes at the baseline of the individual decision maker (Fishner, Orkin, & Frazer, 2008). Most of the nurses were women, with an average of 7 years with ED experience (Fishner, Orkin, & Frazer, 2008). The results indicated that the nurses work site, age, education, and years of experience did not discriminate or alter these decision-making patters in the sample (Fishner, Orkin, & Frazer, 2008). The limitations of this study where the simulation only relies on an additive utility model of decision making that may not capture the complexity of a specific decision (Fishner, Orkinn, & Frazer, 2008). The conjoint analysis was a strength as it was proven to be robust. In my nursing practice complexity models and simulation tools have been used. Both tools benefited the selected facilities. The nurses were not sound in making optimal decisions regarding scheduling and patient care. The simulation would and complexity model would assist the nurse of how and when to schedule the patient.
Development and Pilot Testing of Guidelines to Monitor High-Risk Medications in the
Ambulatory Setting
The goal of this study was to develop guidelines to monitor high-risk medications and to assess the prevalence of lab testing for medications among a multispecialty group practice (Tija et al., 2010). The study design selected was a safety intervention trial (Tija et al., 2010). Guidelines were developed for the laboratory monitoring of high-risk medications as part of a patient safety interventional trial (Tija et al, 2010). The experts selected a 2-round internet-based Delphi process to assist with the guideline medications based on the importance of monitoring for efficacy, safety, and drug to drug interactions (Tija et al., 2010). The results were achieved in 2 rounds. The results concluded that laboratory monitoring is vital, the prevalence of monitoring is highly variable (Tija et al., 2010). The limitations of the study were based off a single group practice. An important finding of the study indicated that patients using infrequently prescribe drugs were less likely to complete a recommended laboratory test (Tija et al., 2010). This tool would contribute to nurse practice. Being able to identify high-risk medications could prevent hospitalization and improve the overall quality of life.
The statistical method that has been most frequently used are cross-sectional surveys. Additionally, I have discovered that some studies rely on data from a subset of journal and articles that have been previously written. It is my opinion that these methods are used opposed to others as it requires less time to find a conclusion. Parametric methods are inappropriate to use for statistical analysis as they do not provide or offer accuracy of other statistical models. Nonparametric analysis is best suited when considering the order of something, meaning even if the numerical data changes, the results will likely not change (Grant & James, 2020).
References:
Fishner, K., Orkin, F., & Frazer, C. (2008). Utilizing conjoining analysis to explicate health care
decision making by emergency department nurses: a feasibility study. Applied
Nursing Research, 23(1), 30-35. doi:10.1016/j/apnr.2008.03.004
Grant, M., & James, M. (2020). Nonparametric Statistics. Retrieved from:
https://investopedica.com/terms/n/nonparametric-statistics.asp
Tija, J., Field, T., Garber, L., Donovan, J., Kanaan, A., Raebel, M.,…Gurwitz, J. (2010).
Development and pilot-testing of guidelines to monitor high-risk medications in the
ambulatory setting. American Journal of Managed Care, 16(7), 489-496.
POST 2
Jacqueline
Utilizing conjoint analysis to explicate health care decision making by emergency department nurses: a feasibility study.
This study aimed to test the feasibility of conjoint analysis in studying the proxy decision-making process among emergency department (ED) nurses and ascertain their experiences with and perceptions of caring for individuals with Intellectual Disabilities ( Fisher et al., 2010).
The contingency tables with nonparametric tests (chi-square and Fisher’s exact tests) are used to explain the decision-making patterns associated with the nurses’ characteristics. Fisher’s exact test is a statistical significance test used in the analysis of contingency tables. In practice, it is usually employed when sample sizes are small ( Fisher et al., 2010).
Alternately, there are Parametric tests such as the Anova test when testing more than two groups to find out if there is a difference between them, and the t-test, another parametric test, is a method that determines whether two populations are statistically different from each other. Hence, the parametric test is notappropriate for this study.
This multivariate statistical method -Conjoint analysis is a measurement technique that uses simulation coupled with a rigorous experimental design to mathematically model decision processes at individual decision-making level ( Fisher et al., 2010). Because of this design choice, the analysis is limited to the role of each factor at each factor level in decisions (“main effects”) and specifically cannot explore potential influences (“interactions”) of factors at given factor levels on one another ( Fisher et al., 2010).
The sample size was insufficient to undertake a meaningful explanation of the observed decision-making patterns consisting of only twenty-three ED nurses. There are many disadvantages to having a small sample in the study; small samples lead to biases and create limited statistical power.
A noted weakness in this study is that although conjoint analysis appears to be valid, it is not known if the nurses responded as they might have to an actual ED patient or if there would be a difference in their decision-making responses if they were providing care for the patient versus completing a simulation exercise. The conjoint analysis relies on an additive utility model of decision making that may not capture the complexity of a particular decision ( Fisher et al., 2010).
One notably strength in the use of conjoint analysis in this study allows the researchers to ask questions that mimic real life. With conjoint analysis, the researcher can mimic the decision process of the participant. As we know, the nurse’s role is ever-changing, with increasing demands on decision-making.
Results from this study can not only provide information to the healthcare educators on what information the nurses as proxy decision-makers value, but it can also additionally allow the nurse herself to reflect on her thoughts and possible biases regarding decision making for this vulnerable population.
The purpose of this study is to develop guidelines to monitor high-risk medications and to assess the prevalence of laboratory testing for these medications among a multispecialty group practice (Tija et al. l, 2010).
