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
The uploading of aspects of a discharge record into a database gives useful information. Providers who can assess a patient’s discharge plans, medications, and history can guide care decisions for the best potential outcomes. A shared database could make specific elements like discharge instructions, medications or prescriptions, and diagnoses available to all medical providers. “Health Current is a health information exchange (HIE) that connects communities and information across Arizona to assist partners transform care. The HIE secures access to patient health information and the exchange of patient health information between the HIE and its partner organizations and providers. More comprehensive information is more meaningful, resulting in better care and outcomes. It enables healthcare change ” (HIE, 2022).
HIE 2022, Healthcurrent, https://healthcurrent.org/hie/
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).
References:
Alexander, S., Hoy, H., & Frith, K. (2019). Applied clinical informatics for nurses (2nd ed.). Jones & Bartlett Learning.
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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.
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.