NUR 705 Discussion 11.1 Statistical Tests That Predict Outcomes
University:
St. Thomas University
NUR 705 Discussion 11.1 Statistical Tests That Predict Outcomes
Paper Instructions
Introduction
In this discussion, you will discuss statistical tests that predict outcomes or group membership.
Discussion Guidelines
Initial Post
For this discussion, you will participate in a group discussion. Choose a group and think about your current practice environment. What are some of the current issues there? Discuss how you could use regression to identify a predictive statistical model that could be studied. Please avoid the topic of falls, as this was used in the lecture this week. Remember to identify the type of regression (linear or logistic) you would use based on how you would measure the variables. Be sure to identify the independent and dependent variables in your example and identify how you would measure the variables.
Response Posts
Respond to at least two classmates’ posts.
Follow the RISE Model for Meaningful Feedback (PDF)Links to an external site. when writing your response posts.
Submission
Post your responses and review full grading criteria on the Discussion 11.1 Statistical Tests That Predict Outcomes page.
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Sample Answer
There are a number of current issues in my practice environment as a clinical nurse. One issue is the increasing use of electronic health records (EHRs). This has led to nurses spending more time on computers and less time interacting with patients (Baumann et al., 2018). This can interfere with providing high-quality patient care.
Another issue is the ever-increasing acuity of patients. This means that patients are sicker when they come into the hospital and need more complicated care. This puts a strain on nurses, who must constantly adapt to new challenges. Finally, there is a shortage of nurses across the country. This means that nurses are often overworked and understaffed. This can lead to burnout, which can have a negative consequence on the patient outcome.
A predictive statistical model is a model that can be used to make predictions about future events. In order to identify a predictive statistical model, one would need to collect data on past events and then use regression analysis to identify the best-fitting linear or nonlinear model. Once the model has been identified, researcher can use it to make predictions about future events. From the clinical issues given above, a regression model can be used to identify a predictive statistical model that can be used to predict the use of electronic health record system in the healthcare system (Mohammad & Goswami, 2021).
The model can also be applied to predict the percentage increase in the acuity of patients and possible shortage or number of nurses versus the number of patients in future i.e., nurse to patient ratio. Based on the measurement of the variables (issues in healthcare practice), linear regression model can be applied to predict the future of the variables.
To develop an affective linear regression model, it is necessary to identify both the dependent and independent variable. In this case the study will involve determination of the impacts of electronic health record on shortage of nurses and the ever-increasing acuity of patients. Number of departments involved in the use of electronic health records will be the independent variable while shortage of nurses and the ever-increasing acuity rate will be dependent variable.
The independent variable will be measured in terms of a continuous variable. Also, the dependent variable will be measured as continuous variables.
References
- Baumann, L. A., Baker, J., & Elshaug, A. G. (2018). The impact of electronic health record systems on clinical documentation times A systematic review. Health policy, 122(8), 827-836. https //doi.org/10.1016/j.healthpol.2018.05.014
- Mohammad, P., & Goswami, A. (2021). A spatio-temporal assessment and prediction of surface urban heat island intensity using multiple linear regression techniques over Ahmedabad City, Gujarat. Journal of the Indian Society of Remote Sensing, 49(5), 1091-1108. https //link.springer.com/article/10.1007/s12524-020-01299-x
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