Utilizing Conjoint Analysis to Explicate Health Care - Essay Example

?Reference of the Study on which the evaluation is based Fisher,K, Orkin,F, and Frazer,C. Utilizing conjoint analysis to explicate health care decision making by emergency department nurses: a feasibility study. Applied Nursing Research 23 :30–35 1.Objectives and goals of the study The purpose and goal of this study was to test the acceptance of conjoint analysis in studying and evaluating the proxy-decision making process that occurs in the nurses serving the Emergency Department(ED) and in ascertaining their experiences with and the perceptions of caring for people with Intellectual disability (ID). Conjoint analysis is a multivariate statistical method which analyzes the relative importance of various factors (variables) involved in decision making process which was used in this study to find out the factors involved in the decision making process for the care giving to a patient with dental abscess and ID. 2.Non parametric tests like Chi square tests and Fischer’s test (as used in this study) are used when the sample size is small and does not represent the population in totality and also when the variables are ordinal, nominal and discrete variables( variables which cannot be measured and even if measured cannot be extrapolated to decimal places). Chi square value evaluates the association or independence between the two variables. If the probability value (p value) for null hypothesis for a particular value of chi square exceeds the critical chi square value then it is inferred that the two variables are not independent and the two variables are significantly associated with each other. In this case after that was found out and the degree of correlation found by Pearson r which means the direction in which the two variables are related9whether increase of one variable increases or decreases the value of another variable with which association is found by chi square and gives the component of that correlation, a regression equation was drawn as a multivariate predictor of the decision making capability( called a criterion or dependent variable) with the other variables( known as predictors or independent variables, which in our case are all the factors that influence the decision making process) The results implicated the mean importance values for each factor for the group of 21 nurses studied which were likely to influence decision making patterns were- future health status, 39%; family input, 19%; person's age, 13%; extra cost to agency, 12%; functional status, 10%; and mental competence, 6%. There were three other decision-making patterns, each exhibited by one nurse: One nurse relied heavily on mental competence (43%) and person's age (52%), another emphasized mental competence (43%) and functional status (29%), and the third used extra cost to agency (66%) supplemented by person's age (18%) for treatment of ID. Nurse's work site, age, education, and years of experience did not discriminate among these decision making patterns in this small pilot study sample.(These factors were not associated or correlated with decision making ) 3. Parametric tests like Student’s t test and ANOVA wee not suitable for this study as because the variables in question were not quantitative variables(measurement variables) and also because the sample size was too small. 4. The strengths of the study was rather than a prescriptive or normative perspective on decision making the method revealed how actually a decision making happens in a real life simulated situation. The measurements were appropriate in relation to chi square, Pearson’s r and Fischer’s test considering small and non-representative sample of the total population. The study design included all the appropriate variables that could have affected decision making process. The limitations were the sample size which needed to be more to have a correct extrapolation to the ID population treated at the ED on totality. Real-world decision making may depart from what was found in this study because simulation provides only an approximation of reality, and conjoint analysis relies on an additive utility model of decision making that may not capture the complexity of a particular decision which takes place in absolutely real situations. Moreover ordinary least squares regression could have been implemented to provide a more weighted prediction of various factors to decision making. 5. Most of the nurses based their decision making process with the factors related to ID and less on the external factors. Nurses as health educators and advocates of care giving must know what information proxy decision makers value to bring out positive health outcome in a patient. References Bagdonavicius, V., Kruopis, J., Nikulin, M.S. (2011). "Non-parametric tests for complete data", ISTE&WILEY: London&Hoboken Corder, G.W. & Foreman, D.I. (2009) Nonparametric Statistics for Non-Statisticians: A Step- by-Step Approach, Wiley David A. Freedman(2005). Statistical Models: Theory and Practice, Cambridge University Press Reference of the Study(2) on which the evaluation is based Tjia,J,.Field,S, Garber,L, Donovan,J, Kanaan, A,. Raebel,M, Zhao,Y ,Fuller,J, Gagne,S, Fischer,S, and Gurwitz,J.(2010) MD Development and Pilot Testing of Guidelines to Monitor. High-Risk Medications in the Ambulatory Setting .The American Journal Of Managed Care Vol. 16, No. 7:489-496 1.Objectives and goals of the study It is known that most of the drug related adverse effects occurs due to inadequate monitoring So the specific objectives of this study were (1) to develop recommendations to guide the monitoring of high-risk medications in the ambulatory setting, (2) to assess the use prevalence drug–drug interactions; and (3) to determine the need to include infrequently prescribed medications in the guidelines. The initial list of high-risk drugs included those commonly implicated in adverse events among patients in the ambulatory setting1 and those associated with adverse events leading to emergency department visits, as well as drugs with low monitoring rates, drugs included in national quality guidelines, and drugs with black box warnings. 2. Non Parametric tests were used for estimations. For new medication dispensings, less than 20.0% of the recommended 61 drug–laboratory test pairs were monitored in the majority (>75.0%) of patients. In contrast, close to 40.0% of the recommended drug–laboratory test pairs for chronic dispensing were monitored in greater than 75.0% of patients They also found that the frequency of medication use was associated with the frequency of recommended test completion Infrequently dispensed medications had a lower prevalence of test completion. Nonparametric test for trend showed a significant trend toward more frequently dispensed medications having a higher prevalence of appropriate testing (P Read More