Clinical decision making (not to take blood cultures from a febrile Essay

Clinical decision making (not to take blood cultures from a febrile patient) - Essay Example To diagnose the cause, blood culture is mostly required for identification of causative bacteria or fungus. A nurse caring for a patient with acute leukaemia is many a time confronted with the clinical scenarios where he or she is required to make clinical decision to take blood culture from a febrile patient. The nurse may herself make the decision to obtain a blood sample for culture on suspicion of an infection or may act on the orders of a physician. In an autonomous decision, the complexity and the nature of the decision task affect the approach taken towards problem solving (Thompson, Kirkness & Mitchell 2007). The decision taken by the nurse can be analysed for the application of evidence based medicine in routine clinical situations. Decision analysis allows to share a decision with seniors and colleagues and to evaluate its advantages and disadvantages (Bucknall 2003). Nurse uses the domains of prior knowledge about the patient and his circumstances, ethical knowledge and specific knowledge. This knowledge is accessed and applied by the means of pattern recognition and heuristics (Bohinc & Gradisar 2003). First of all, the component of problem recognition requires the nurse to identify the ‘cues’ or clinical symptoms such as fever in this case. The recall of these cues leads to formulation of a hypothesis of a problem (Jenks 1993). Once the problem has been recognised, the decision maker proceeds on to the next step of assessment in which the data is gathered, assimilated and analysed (Klein 2005). The nurse records the temperature, maintains a temperature chart and records associated symptoms such as chills, sweating, cough and pattern of fever etc. as a part of data collection. To be able to form a judgement, it is imperative to evaluate and make a choice (Higgs et al 2008; Connolly, Arkes & Hammond 2000). The nurse evaluates the data and infers about what should be done (Thompson &

Descriptive statistics Essay Example | Topics and Well Written Essays - 750 words

Descriptive statistics - Essay Example Differences in the mean pretest and posttest scores were computed to find out the extent in the change of confidence level brought about by the CRRP course. A higher mean difference value would indicate a higher degree of change in confidence level brought about by the CRRP course. Range and standard deviation measured the variability of the computed values in the study (Agresti & Finlay, 2009). A nurse leader may use descriptive statistics in cases when the â€Å"average† result is helpful in determining a course of action. In such cases, descriptive statistics are persuasive enough because it is able to give an overall picture of the data set in discussion. However, descriptive statistics, as the name implies simply provides a description of the data set and does not allow the nurse leader, to make inferences regarding the data (Malone, 2001). Based on my personal experience, we use descriptive statistics (particularly mean values) to find the prevalent cases in the nursing unit. Our department also routinely conducts a nurses’ evaluation assessment and our mean performance scores are usually given to us. Usefulness of Confidence Intervals in Determining Clinical Significance Confidence intervals indicate how variable the study data are, that is, the average distance of the data set values from the mean (Lee & Zelen, 2000). It should be noted that the true condition of a given population would be almost impossible to determine. Thus, researchers rely on the condition of a sample to provide a picture of the population. Confidence intervals aid researchers, analysts and practitioners in making decisions with regards to the clinical relevance of the data at hand. For example, if a study indicates a confidence interval of 95%, then the reader is able to determine that the values or the assessment given in the study is true for the population 95% of the time. The shorter the confidence interval, the more accurate is the assessment (Maki, 2006). For e xample, suppose a trial was conducted on the effectiveness of a weight loss pill against a placebo. Results of the study indicate that at a 95% confidence level, the weight loss was given to be 9 lbs. This means that the weight loss range would be between 4 to 14 lbs. Another interpretation of this information would be that it is highly likely for the pill to reduce one’s weight by at least 4 lbs, but highly unlikely for it to reduce one’s weight by more than 12 lbs. In this case, although the 9 lb weight loss arrived at was essentially just an estimate, the confidence interval that was set for the trial was able to quantify the uncertainty that was associated with that estimate (Malone, 2001). Clinical Significance vs. Statistical Significance Statistical significance measures the likelihood that the differences in the results of a particular test is due to the intervention applied on the treatment group and not simply due to chance (Malone, 2001). The most common mea sures of statistical significance, or hypothesis testing, are confidence intervals and p-values. On the other hand, clinical significance measures the magnitude of the differences created by the intervention on the daily lives of the participants (Agresti & Finlay, 2009). One controversy surrounding the issue between clinical and statistical significance is that statistical significance does not provide a clear picture of how large is the