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

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.

REPLY TO DISCUSSION

Having elements of a discharge record being uploaded into a database provides useful information. Providers being able to review discharge plans, medications and history of a patient can guide clinical decisions for the best outcomes possible. Specific items such as discharge instructions, medications or prescriptions, and diagnosis could be available to every medical provider via a communal database. “Health Current is the health information exchange (HIE) that helps partners transform care by bringing together communities and information across Arizona. The HIE, provides secure access to patient health information as well as the secure exchange of patient health information between the HIE and its participating organizations and providers. More complete information is more meaningful and leads to better care and better outcomes. It makes healthcare transformation possible” (HIE, 2022).

 

Healthcurrent. HIE. 2022. https://healthcurrent.org/hie/

The clinical problem from the previous discussion was chronic heart failure and the clinical question is if they will continue to use standardized medication treatments that may not be working with the complexity of chronic heart failure (Bai, Yao, Jiang, Bian, Zhou, Sun, Hu, Sun, Xie, & He, 2022). The Individual components to this question would be located in the electronic health records (EHR) of the patient within the database of the healthcare system. The individual components would be the patient demographics which includes the name of the patient, account number, sex, date of birth, race, religion, address, and insurance information, admission history and physical, the medication list, the laboratory results, the nursing records and the physician records. EHRs are designed to hold many types and ranges of patient data such as listed above and it has an endless capability for being customized to the particular needs of the patient and the HCP as well as the organization (Alexander, Hoy, & Frith, 2019).

References:

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 Pharmacology, 12. https://doi.org/10.3389/fphar.2021.758573

Read Also: DNP 805 Assignment EHR Database and Data Management Essay

REPLY

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.

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The clinical question proposed was, what interventions are impactful in improving decreasing nursing turnover among nurses? To do this you look at the nursing turnover rate and comparing it to leapfrog rating, CMS Stars, Magnet status, mandated patient ratios,

workplace violence incidents, employee injuries, and union hospitals. This would allow for correlations of what makes facilities more appealing to nurses. This data could also be regionalized, because what is more important to nurses in California may be very different for those in Mississippi. They allows for targeted recruiting and retaining techniques. Some of these have already been studied on a smaller scale. For example, one study determined a correlation with workplace violence and turnover in two large teaching hospitals (Yeh et al., 2020). Another example is Magnet units have lower turnover than units a non-Magnet facilities (Park et al., 2016). What we don’t know is how widespread this is and if it varies across regions. This will add to that ability. Also, how has this changed post pandemic. Have the priories on what is keeping nursing from turning over the same? All are things that can be answered by this database.

Reference

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 Administration, 46(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 Financing, 57, 46958020969313. https://doi-org.lopes.idm.oclc.org/10.1177/0046958020969313

REPLY

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.