I need discussion question DQ 2 answered. I have provided the clinical issue and DQ 1 in the attachment

Module 5 DQ 1 2

DQ 1

Select a specific clinical problem and post a clinical question that could potentially be answered using data mining. Identify data mining techniques you would apply to this challenge and provide your rationale. Are there any specific data mining techniques you would not use? Support your decision.

A clinical challenge faced by many nursing professionals is patient compliance in

self-care and measures of disease control. This is particularly true for nursing staff involved in training

and educating patients with Type II Diabetes, in actions they can take to monitor and maintain proper

levels of blood glucose. The clinical question to be answered is “How many patients (percentage) can maintain satisfactory blood glucose levels and control after patient education?” By answering this question, the clinical nursing professional can evaluate any ongoing education programs, literature, and techniques used in the patient teaching process, for effectiveness.

The many data mining techniques available provide an opportunity to look at data in many ways, including comparison, analysis of multiple variables, and to determine the effectiveness of treatments applied. For the nurse educator who wants to know the efficacy of a specific patient education program, univariate and multivariate statistical analyses are suitable methods, as they can help the nursing professional compare the effects of the program by age group, number of sessions (if more than one education opportunity has been offered, by patient level of education, by geographic region, and other factors that may influence understanding, and therefore compliance. The nursing professional can determine compliance through health indicators, such as fasting blood sugar levels or hemoglobin A1C levels, when such values are provided in the patient record. The univariate method is helpful in determining overall compliance and effectiveness of education. The multivariate analysis may provide further insights into whether education challenges exist among patients in specific groups, such as older, or less educated patients. Such data can help the professional nursing tailor the education program to address those influences, by taking more time with older patients or by using plain language for those in lower education brackets. Clustering is a means of data mining that may provide insight into the geographic regions of patients with less compliance or the age groups in which such patients fall into. However, it is less helpful when a k-nearest neighbor or decision trees are involved, unless the clinician is seeking to gain an understanding of which patients may be at future risk for Type II Diabetes (Chaurasia & Pal, 2014).

Reference

Chaurasia, V. & Pal, S. (2014). Data mining techniques: To predict and resolve breast cancer survivability. International Journal of Computer Science and Mobile Computing, 3(1), 10-22.

DQ 2

Using the clinical question you identified from above, determine the individual components to that question and pinpoint the location in the hypothetical database where the information you require will be extracted

Response 1

Response 2

Response 3