POST 1 KELLY Utilizing conjoint analysis to explicate health care decision making by emergency department nurses: a feasibility study

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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).