DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining
Grand Canyon University DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining-Step-By-Step Guide
This guide will demonstrate how to complete the DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining assignment based on general principles of academic writing. Here, we will show you the A, B, Cs of completing an academic paper, irrespective of the instructions. After guiding you through what to do, the guide will leave one or two sample essays at the end to highlight the various sections discussed below.
How to Research and Prepare for DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining
Whether one passes or fails an academic assignment such as the Grand Canyon University DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining depends on the preparation done beforehand. The first thing to do once you receive an assignment is to quickly skim through the requirements. Once that is done, start going through the instructions one by one to clearly understand what the instructor wants. The most important thing here is to understand the required format—whether it is APA, MLA, Chicago, etc.
After understanding the requirements of the paper, the next phase is to gather relevant materials. The first place to start the research process is the weekly resources. Go through the resources provided in the instructions to determine which ones fit the assignment. After reviewing the provided resources, use the university library to search for additional resources. After gathering sufficient and necessary resources, you are now ready to start drafting your paper.
How to Write the Introduction for DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining
The introduction for the Grand Canyon University DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining is where you tell the instructor what your paper will encompass. In three to four statements, highlight the important points that will form the basis of your paper. Here, you can include statistics to show the importance of the topic you will be discussing. At the end of the introduction, write a clear purpose statement outlining what exactly will be contained in the paper. This statement will start with “The purpose of this paper…” and then proceed to outline the various sections of the instructions.
How to Write the Body for DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining
After the introduction, move into the main part of the DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining assignment, which is the body. Given that the paper you will be writing is not experimental, the way you organize the headings and subheadings of your paper is critically important. In some cases, you might have to use more subheadings to properly organize the assignment. The organization will depend on the rubric provided. Carefully examine the rubric, as it will contain all the detailed requirements of the assignment. Sometimes, the rubric will have information that the normal instructions lack.
Another important factor to consider at this point is how to do citations. In-text citations are fundamental as they support the arguments and points you make in the paper. At this point, the resources gathered at the beginning will come in handy. Integrating the ideas of the authors with your own will ensure that you produce a comprehensive paper. Also, follow the given citation format. In most cases, APA 7 is the preferred format for nursing assignments.
How to Write the Conclusion for DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining
After completing the main sections, write the conclusion of your paper. The conclusion is a summary of the main points you made in your paper. However, you need to rewrite the points and not simply copy and paste them. By restating the points from each subheading, you will provide a nuanced overview of the assignment to the reader.
How to Format the References List for DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining
The very last part of your paper involves listing the sources used in your paper. These sources should be listed in alphabetical order and double-spaced. Additionally, use a hanging indent for each source that appears in this list. Lastly, only the sources cited within the body of the paper should appear here.
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DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining
Topic 5 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.DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining
REPLY TO DISCUSSION DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining
Data mining, according to Alexander et al. (2019), is the process of analyzing vast amounts of data to uncover important and intelligible patterns. Such patterns have been shown to be helpful in medicine and the healthcare business for projecting trends, enhancing product safety and usability, and improving patient experience. Electronic health records have greatly enhanced data collecting and have aided data mining in the prevention and reduction of medical errors.
Data mining can provide an answer to the following question: Is telemedicine useful in reducing hospital readmissions for patients with congestive heart failure (CHF)? According to Reddy and Borlaug (2019), CHF is a common cause of hospitalization that accounts for almost $30 billion in US spending. Over five million people are affected by CHF, and studies suggest that readmission rates for those who were hospitalized due to the disease have increased (Garcia, 2017).
Tracking patterns, association, and prediction are some data mining approaches that can be applied. Clustering analysis is a technique that I would not use.
References:
Alexander, S., Frith, K., & Hoy, H. (2019). Applied clinical informatics for nurses (2nd ed.). Jones & Bartlett Learning.
Reddy, Y. N. V., & Borlaug, B. A. (2019). Readmissions in heart failure: It’s more than just the medicine. Mayo
Clinic Proceedings, 94(10),
- https://doi-org.lopes.idm.oclc.org/10.1016/j.mayocp.2019.08.015
REPLY
That is an excellent question and a great potential use of data mining. I do agree with you that clustering analysis would not be the best technique to use. What is you hypothesis associated with this? Do you believe that it could reduce this due to the ability of visits? I can say from my experience it is challenging to get the generation that is most commonly suffering with heart failure comfortable with using telemedicine.
Great post Audimar—The work to reduce hospital readmissions is going to be ongoing for a long time because of the complexity of CHF, this is an issue with all hospitals. To reduce the number of preventable readmissions, the Centers for Medicare & Medicaid
Services (CMS) initiated the Hospital Readmissions Reduction Program (HRRP) in 2012. Also, they realized that only 30% of all the patients with CHF had a scheduled follow-up appointment with the PCP or cardiologist on discharge and that of all those who were discharged, only 37% kept their follow-up appointments and, about 41% were lost to follow-up visits.
The hospitals started an intervention program to reduce the readmission rate by making sure that all the CHF patients had follow up appointments and they started a weekly or biweekly phone call to the patients, telemonitoring, and home visits.
At the end, about 60% of the CHF patients when discharged had a scheduled follow-up appointment with a PCP or cardiologist within two weeks. At the end of the intervention about 56% of all discharged patients kept their follow-up appointments and they were able to reduce the 30-day readmission rates for CHF patients to 14%, which was a 50% reduction from the previous rates.
These interventions have proven to help reduce the readmission rates of CHF patients as well as by having an adequate number of nursing staff to help with the education, optimizing of medical therapy and carrying out the interventions (Nair, Lak, Hasan, Gunasekaran, Babar, & Gopalakrishna, 2020).
References:
Nair, R., Lak, H., Hasan, S., Gunasekaran, D., Babar, A., & Gopalakrishna, K. V. (2020). Reducing all-cause 30-day hospital readmissions for patients presenting with acute heart failure exacerbations: A quality improvement initiative. Cureus. https://doi.org/10.7759/cureus.7420
REPLY
If the patient or family is unable to provide these details, obtaining a medical and psychiatric history can be extremely difficult. Having a database of patient information that includes any Emergency Department visits, Psychiatric services, any outpatient clinic or primary care visit, and current medications, discharge plans, or appointments that the patient was scheduled to attend.
This is especially difficult when dealing with patients who have serious mental illnesses and require emergency services due to dangerous behavior or thoughts. The individual may be too ill to provide a medication list or historical data.
Having a database of medical and psychiatric history at the fingertips of every provider allows for the most efficient decisions for the patient. This removes barriers to treatment and can hasten the patient’s recovery. “HIE, or health information exchange, connects providers’ and clinicians’ electronic health record (EHR) systems, allowing them to securely share patient information and better coordinate care.
Health Current is Arizona’s health information exchange, connecting over 900 Arizona organizations ranging from first responders to hospitals, labs, community behavioral health and physical health providers, as well as post-acute care and hospice providers.” (2022, Healthcurrent).
Healthcurrent. What is HIE?. https://healthcurrent.org/hie/what-is-hie/.2022
REPLY
The Covid 19 pandemic has left nursing across all disciplines forever changed. For some this has resulted in burn out and nursing leaving the profession. This in parallel to the nursing shortage has a potential for a negative impact in being able to provide care for those in need. The demand will continue to rise as the baby boomers continue to age in greater numbers than historically seen. What interventions are impactful in improving decreasing nursing turnover among nurses?
Data mining is looking at relationships and correlations to aims to predict outcomes. This is not a typical problem that you would think as being something that can be work on with data mining, but there are some opportunities using a principle component analysis. Data mining can reveal if there is a relationship between preventable nursing turnover and nurse salaries. DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining
Examples of unpreventable nursing turnover is retirement, relocation, death, and involuntary termination. Other variables to look at related to preventable nursing turn over would be leapfrog rating, CMS Stars, Magnet status, mandated patient ratios, workplace violence incidents, employee injuries, and union hospitals. DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining
The ability to data mine these items in comparison preventable nursing turnover will help guide what is most important to nursing to then have targeted interventions to decrease this turnover and keep nurses in the profession. One study did find a correlation with workplace violence and turnover in two large teaching hospitals (Yeh et al., 2020).
Once it is identified what seems to be the most important components that keep nurses in their roles will allow for focusing on those things to improve and then market that when recruiting nurses into the organization. With the shortage it is important to retain the nurses that you have and creatively market new ones in. This includes taking more new graduate nurses than historically taken.
Reference DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining
Yeh, T.-F., Chang, Y.-C., Feng, W.-H., Sclerosis, M., & Yang, C.-C. (2020). Effect of Workplace Violence on Turnover Intention: The Mediating Roles of Job Control, Psychological Demands, and Social Support. Inquiry : A Journal of Medical Care Organization, Provision and Financing, 57, 46958020969313. https://doi-org.lopes.idm.oclc.org/10.1177/0046958020969313
REPLY DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining
Thank you for your post. I agree with you that data mining is looking at relationships and correlations to aims to predict outcomes. Prediction is a very powerful aspect of data mining that represents one of four branches of analytics. Predictive analytics use patterns found in current or historical data to extend them into the future.
Thus, it gives organizations insight into what trends will happen next in their data. There are several different approaches to using predictive analytics. Some of the more advanced involve aspects of machine learning and artificial intelligence. However, predictive analytics does not necessarily depend on these techniques, it can also be facilitated with more straightforward algorithms. (Zentut,2018).
In evidence-based practice (EBP), the clinical question is generated before the literature search. The question serves as a guide for the search to find a targeted, empirically based answer, followed by integration of the external and internal evidence (clinical expertise with patient values and preferences) The clinical question subsequently guides the research, planning, implementation, and evaluation of a practice (Bermudez, 2021).
The clinical question helps to narrow down the literature search results; therefore, the results of the search depend on a well-written question (Melnyk & Fineout-Overholt, 2019). DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining
Data mining is a process that identifies valid and useful patterns in data as defined in research by Fayyad. Data mining is also sometimes used interchangeably with data analytics to describe also a process of discovering knowledge by using large databases though there are some small differences between the two (Alexander, Hoy, & Frith, 2019). DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining
The algorithms used in analyzing data in data mining are a set of mathematical instructions that is used in combinations for constructing predictive models which makes predictions about data with known results while descriptive models are used to explore data and identify the patterns or their relationships. An algorithm is a set of instructions that is used to perform a task (Alexander, Hoy, & Frith, 2019). DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining
Examples of predictive algorithms are decision tree, artificial neural networks, instance-based learning classifiers, support vector machine modeling, Bayesian modeling, classification, regression, time series analysis, and prediction and the forms of descriptive algorithms was summarization, clustering rules and association rules, sequence discovery (Alexander, Hoy, & Frith, 2019), (Anwar Lashari, Ibrahim, Senan, & Taujuddin, 2018).
The clinical problem of chronic heart failure within healthcare and the clinical question of whether to continue to use standardized medication treatments that may not be working is a question that needs to be answered. The guidelines have recommended standardized drug treatments for heart failure but there are still challenges with making the right clinical decision for the medications because of the complicated clinical presentations of heart failure. The decision trees used will be non-mutually exclusive and will have several leaf nodes and recommendations which are constructed via knowledge rules summarized from the HF clinical guidelines using the Apriori algorithm tool to mine the frequent patterns for transaction data (Bai, Yao, Jiang, Bian, Zhou, Sun, Hu, Sun, Xie, & He, 2022).
The other predictive algorithms that I will not use would be the Bayesian modelling which is used to estimate the conditional probability of given data points which belongs to a particular class. Decisions of classifications are made when collective probabilities are used with prior knowledge. This system is great with disease outbreaks and pandemics (Alexander, Hoy, & Frith, 2019).
References: DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining
Anwar Lashari, S., Ibrahim, R., Senan, N., & Taujuddin, N. S. (2018). Application of data mining techniques for medical data classification: A review. MATEC Web of Conferences, 150, 06003. https://doi.org/10.1051/matecconf/201815006003
Alexander, S., Hoy, H., & Frith, K. (2019). Applied clinical informatics for nurses (2nd ed.). Jones & Bartlett Learning. DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining
Bai, Y., Yao, H., Jiang, X., Bian, S., Zhou, J., Sun, X., Hu, G., Sun, L., Xie, G., & He, K. (2022). Construction of a non-mutually exclusive decision tree for medication recommendation of chronic heart failure. Frontiers in Pharmacology, 12. https://doi.org/10.3389/fphar.2021.758573
Bermudez, N. (2021). Formulating Well-Written Clinical Practice Questions and Research Questions. Nursing & Health Sciences Research Journal, 4(1), 70-82. https://doi.org/10.55481/2578-3750.1113
Melnyk, B. M., & Fineout-Overholt, E. (2019). Evidence-based practice in nursing & healthcare: A guide to best practice. Fourth edition. Lippincott Williams & Wilkins. DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining