DNP 805 Topic 5 DQ 2 Using the clinical question you identified in the previous discussion question, determine the individual components to that question

DNP 805 Topic 5 DQ 2 Using the clinical question you identified in the previous discussion question, determine the individual components to that question

Topic 5 DQ 2

May 12-16, 2022

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


Uploading aspects of a discharge record into a database yields useful data. Care decisions can be guided by providers who can assess a patient’s discharge plans, medications, and history. Specific elements such as discharge instructions, medications or prescriptions, and diagnoses could be made available to all medical providers through a shared database. “Health Current is a health information exchange (HIE) in Arizona that connects communities and information to help partners transform care. The HIE protects patient health information and facilitates patient health information exchange between the HIE and its partner organizations and providers. More detailed information is more meaningful, which leads to better care and outcomes. It enables healthcare transformation ” (HIE, 2022).

The preceding discussion’s clinical problem was chronic heart failure, and the clinical concern is whether they will continue to employ standardized drug therapies that may not be working with the complexity of chronic heart failure (Bai, Yao, Jiang, Bian, Zhou, Sun, Hu, Sun, Xie, & He, 2022). Individual components to this question would be found in the patient’s electronic health records (EHR) within the healthcare system’s database. Individual components include the patient’s name, account number, gender, date of birth, race, religion, residence, and insurance information, admission history and physical, medication list, laboratory findings, nursing records, and physician records. EHRs are designed to hold numerous types and ranges of patient data, such as those stated above, and they have an infinite ability to be customized to the specific needs of the patient, the HCP, and the organization (Alexander, Hoy, & Frith, 2019).

Read Also:


Alexander, S., Hoy, H., & Frith, K. (2019). Applied clinical informatics for nurses (2nd ed.). Jones & Bartlett Learning.

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 Pharmacology12


This is another great example of how data mining can help to improve the care for heart failure patients. I agree that most of this data is easily abstracted from the electronic medical record. What is your hypothesis for your clinical question. Looking for correlations can help greatly with this dat mining.

The clinical question posed was, “What interventions are effective in reducing nursing turnover among nurses?” This is accomplished by comparing the nursing turnover rate to the leapfrog rating, CMS Stars, Magnet status, mandated patient ratios, workplace violence incidents, employee injuries, and union hospitals. This would enable correlations to be drawn between what makes facilities more appealing to nurses. This information could also be regionalized, because what is important to nurses in California may be very different from what is important to nurses in Mississippi. They enable targeted recruiting and retention techniques. On a smaller scale, some of these have already been investigated. One study, for example, discovered a link between workplace violence and turnover in two large teaching hospitals (Yeh et al., 2020).


Park, S. H., Gass, S., & Boyle, D. K. (2016). Comparison of Reasons for Nurse Turnover in Magnet ® and Non-Magnet Hospitals. The Journal of Nursing Administration46(5), 284–290.

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 Financing57, 46958020969313. 


There are many valid points for consideration in data mining related to nursing turnover. Considering the nursing shortage we are in, it is a hot topic and one worth investigating. There are two vital sides to this topic one concerns the nurse and the other concerns care for the patient. I appreciate your data mining considerations have variables that relate to both sides of this issue. In addition to considering the impact on the patient and nurse, the locality is another important factor that you addressed. Because this information can vary from location to location, it would be interesting to compare one region to another. Different factors help to keep staff at the bedside, and compensation is one of those factors (Halim, et al., 2020). With proper data mining techniques, this can be analyzed to entice nurses to stay. With the cost to replace a nurse, the current shortage, and patients who depend on nursing care, this data mining would be fruitful to the nursing profession.

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