DNP 825 TOPIC 3 DQ 1

DNP 825 TOPIC 3 DQ 1

DNP 825 TOPIC 3 DQ 1

How can large aggregated databases be used to improve population health? Provide an example of a current disease affecting your population of interest and explain what health promotion or disease prevention evidence-based strategies you would recommend and why. Explain how related data could improve your strategies to promote health and prevent disease. Support your response with relevant literature.

How can large aggregated databases be used to improve population health? Provide an example of a current disease affecting your population of interest and explain what health promotion or disease prevention evidence-based strategies you would recommend and why. Explain how related data could improve your strategies to promote health and prevent disease.   

Data aggregation refers to the process of gathering raw data and presenting it in a summarized format for statistical analysis. The data can be gathered from multiple sources, brought together with the intention of combining the sources to produce a data summary that can then be subjected to analysis. Despite the tight burdens of legislative and policy compliance, such as patient health information privacy and confidentiality, population health benefits from data aggregation. Much of the benefit is from case analysis. Through aggregating data from numerous similar cases within the same population, medical personnel can identify the most effective treatment methods to accelerate overall health care treatment within the population (Suresh, 2018).

Examples of this can be seen in the case of vaccinations and treatments targeting Covid-19. Currently, Covid-19 is considered a pandemic that constitutes a real and present danger to population health. It was through epidemiology data aggregation that the source of the virus was first identified as Wuhan Province in China. Travel data aggregation revealed that the earliest persons to contract Covid-19 had either been to Wuhan Province, and had been in contract with persons who had travelled to Wuhan Province within the last one month (Zhu, Wei & Niu, 2020). In addition, large data aggregates have been used to determine the effectiveness of public health measures in preventing the spread of the virus. Analysis of the data aggregates have presented statistical evidence showing that hand hygiene and personal protective equipment are effective protections for medical personnel against contracting the virus from patients to whom they provide care (Lio et al., 2021). Besides that, large data aggregates have helped in determining the efficacy of different Covid-19 vaccines and medication. Statistical analysis of the data aggregates revealed the effectiveness of the vaccines and medication, how long they offer protection, effective doses, and their long-term effects (Pormohammad et al., 2021). Overall, large aggregate databases present data that can be subjected to statistical analysis to determine the most effective strategies for promoting health and preventing diseases within a population.

Online Nursing Essays

Struggling to Meet Your Deadline?

Get your assignment on DNP 825 TOPIC 3 DQ 1 done on time by medical experts. Don’t wait – ORDER NOW!

References

Lio, C. F., Cheong, H. H., Lei, C. I., Lo, I. L., Yao, L., Lam, C., & Leong, I. H. (2021). Effectiveness of personal protective health behaviour against COVID-19. BMC Public Health, 21, Article number 827. https://doi.org/10.1186/s12889-021-10680-5

Pormohammad, A., Zarei, M., Ghorbani, S., Mohammadi, M., Razizadeh, M. H., Turner, D. L., & Turner, R. J. (2021). Efficacy and Safety of COVID-19 Vaccines: A Systematic Review and Meta-Analysis of Randomized Clinical Trials. Vaccines9(5), 467. https://doi.org/10.3390/vaccines9050467

Suresh, S. (2018). Nursing Research and Statistics (3rd ed.). Elsevier Health Sciences.

Zhu, H., Wei, L. & Niu, P. (2020). The novel coronavirus outbreak in Wuhan, China. Global Health Research and Policy, 5, Article number 6. https://doi.org/10.1186/s41256-020-00135-6

REPLY

Heathcare databases, also known as “big data”, aid in maximizing medical data sets to enhance research for clinical related and policy driven aspects. In order to improve research for population health, databases must be aggregated, interpreted and integrated into a population level and on an individual level (Machluf, Tal, Navon, & Chaiter, 2017). Currently, a mix of multiple respiratory illnesses are inundating the emergency department such as Influenza, rhinovirus, Respiratory Syncytial virus (RSV), and Coronavirus (all forms, including COVID-19). These respiratory illnesses are causing an overwhelming number of pediatric admissions amongst all the healthcare organizations within this count and neighboring counties. Recently, the California Department of Public Health (CDPH), approved healthcare institutes to admit pediatric patients to adult departments to aid in offloading emergency room admission boarders. Although seasonal, influenza is a respiratory illness that is commonly received from the community, especially within family households. According to the Centers for Disease and Prevention (CDC, 2021), influenza prevention strategies include vaccination, respiratory hygiene, cough etiquette, infection control precautions, and steps to minimize exposure. The data of the community population, healthcare workers, students, and parents of children distinguish vaccine compliance and vaccine hesitancy as it relates to respiratory illnesses and the need for admission. COVId-19 vaccine compliance is said to directly correlate to influenza vaccine compliance. Dror et.al., (2020) reports that medial doctors within internal medicine have the highest rate of vaccination. Furthermore, the acceptance rates for the seasonal influenza vaccine were discovered during a survey of doctors (92% vaccination rate), nurses (69% vaccination rate), and the general population (66% vaccination rate). The first step to integrating strategies to improve compliance in vaccination, is to collect data regarding the reasons for non-vaccination. The integration of vaccine strategies within an emergency department includes encouraging healthcare workers to be vaccinated, screening all patients for vaccination status and encouraging administration of the vaccine during their visit (if applicable based on providers medical screen). Within the community, the goal is to educate and encourage vaccines to those who are pregnant, have chronic illnesses, and children. A continued campaign on influenza and vaccines are needed to achieve higher rates (Harding, 2018).  

Centers for Disease Control and Prevention. (2021, May 13). Prevention strategies for seasonal influenza in healthcare settings. Centers for Disease Control and Prevention. Retrieved December 10, 2022, from https://www.cdc.gov/flu/professionals/infectioncontrol/healthcaresettings.htm 

Dror, A. A., Eisenbach, N., Taiber, S., Morozov, N. G., Mizrachi, M., Zigron, A., Srouji, S., & Sela, E. (2020). Vaccine hesitancy: The next challenge in the fight against COVID-19. European Journal of Epidemiology, 35(8), 775–779. https://doi.org/10.1007/s10654-020-00671-y 

Harding, A., & Heaton, N. (2018). Efforts to improve the seasonal influenza vaccine. Vaccines, 6(2), 19. https://doi.org/10.3390/vaccines6020019 

Machluf, Y., Tal, O., Navon, A., & Chaiter, Y. (2017). From population databases to research and informed health decisions and policy. Frontiers in Public Health5. https://doi.org/10.3389/fpubh.2017.00230 

How can large aggregated databases be used to improve population health?

Aggregate data is any data that is not limited to one person, but data that is tracked across time, organization, and patient population which is used to develop information about groups of patients. Big data helps health care professionals to assess the needs of their patients and provide care to close any health risk gaps and for disease prevention. Interdisciplinary data Aggregation provides a holistic view of the patient’s risk factors and assist nurses to modify disease prevention strategies. Using the EHR to gather data from providers and nurses notes allows learners to determine the diabetic Mellitus risk through laboratory investigation or medical history of a patient to be able to provide high quality care (Sumit, et al., 2019).

Provide an example of a current disease affecting your population of interest

The current disease affecting my population of interest is diabetes mellitus and its complications which includes heart attacks, heart failure, stroke, and kidney failure. Cardiovascular disease is the leading cause of death in patients with diabetes mellitus (Sumit, et al., 2019).

Explain what health promotion or disease prevention evidence-based strategies you would recommend and why.

The health promotion and disease prevention evidence- based strategies that I would recommend for diabetes mellitus patients are regular exercise program, maintaining healthy eating habits using Dietary Approaches to stop Hypertension (DASH) diet regimen, regular follow-up with primary health care provider for prescription of anti-diabetic medications, to maintain good glycemic index to control and prevent complications such as Nephropathy, regular eye checks to prevent Retinopathy, foot checks to control Neuropathy, weight loss program, and regular blood sugar monitoring (Sumit, et al., 2019)

Explain how related data could improve your strategies to promote health and prevent disease.

Accordingto Stoto, et al., (2022), databases in health care improves,

-interactions between patients and their providers.

-Aggregate data allows researchers to identify common characteristics of a disease condition that which may predict or provide information about the most effective way to treat them

-Health care databases assist with diagnosis and treatment, manage documentation and biling, and help reduce errors in medical operations and management.

– Big data enables widespread and specific research trails of stratified and segmenting populations at risk for a variety of health problems such as diabetes mellitus.

– Big data assist in health risk surveillance, predicting future risk, targeted intervention, and understanding disease (Stoto, et al., 2022).

Reference

Stoto, M. A.,  Davis, M. V., & Atkins A. (2022). Making better use of population health data for community.  Retrieved from http://www.researchgate.net

Sumit, P. S. C., Sarabjot, K., Aman, B., Ravinder, G., Manjeet, K., Divya, S., Amrita, G., Ranabir, P. (2019). Impact of health education on knowledge, attitude, practices and glycemic control in type 2 diabetes mellitus. Journal of Family Medicine & Primary Care. 8 (1), 261-268. Retrieved from DOI: 10.4103/jfmpc.jfmpc_228_18, https://eds-s-ebscohost-com.lopes.idm.oclc.org/eds/pdfviewer/pdfviewer?vid=5&sid=edaf9306-cf47-4447-bb81-01f57bbefc28%40redis

Don’t wait until the last minute

Fill in your requirements and let our experts deliver your work asap.