{"id":251058,"date":"2022-09-26T13:21:19","date_gmt":"2022-09-26T13:21:19","guid":{"rendered":"https:\/\/nursingstudy.org\/?p=251058"},"modified":"2025-06-18T04:49:55","modified_gmt":"2025-06-18T04:49:55","slug":"how-can-large-aggregated-databases-be-used-to-improve-population-health","status":"publish","type":"post","link":"https:\/\/nursingstudy.org\/examples\/how-can-large-aggregated-databases-be-used-to-improve-population-health\/","title":{"rendered":"How can large aggregated databases be used to improve population health – DNP-825 Questions Solution"},"content":{"rendered":"\n

How can large aggregated databases be used to improve population health – DNP-825 Questions <\/u><\/strong><\/h2>\n\n\n\n

QUESTION<\/u><\/strong><\/p>\n\n\n\n

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.<\/strong><\/p>\n\n\n\n

SOLUTION<\/u><\/strong><\/p>\n\n\n\n

Aggregated databases are the type of databases that combine or accumulate full texts of electronic publications into an uncommon, identifiable, and searchable databases. These databases vary significantly in size, origin, scope, and price. Data is aggregated when data is gathered and expressed in a summary form. In the present-era of big data<\/a>, most medical communities want to maximize the utilization and processing of their rapidly expanding medical datasets for clinical-related and policy-driven research. <\/p>\n\n\n\n

Hence, they need a medical database that can be aggregated, interpreted, and integrated at both the individual and population levels (Machluf et al., 2017). For instance, for many years PubMed suffered from author ambiguity problem. It only used internal metadata for development. <\/p>\n\n\n\n

However, some of them are incomplete (e.g., a large number of names are only abbreviated, and their full names are not available) or less discriminative. However, today it has presented a new disambiguation method by aggregating information from external databases (Zhang et ai., 2022).<\/p>\n\n\n\n

Groups of patients can be characterized by analyzing aggregate data. This facilitates the ability of healthcare providers to recognize shared features that may indicate prognosis or reveal insights into optimal treatment. It sees widespread application in the field of disease prevention<\/a>. Patient interviews, scientific studies, and statistical analysis<\/a> can help with this. Using pooled information, scientists could discover causal relationships between smoking and lung cancer or between diabetes and physical activity. It’s a terrific tool for informing patients and doctors about the correlations between patients’ lifestyle choices and health outcomes (Hinds, 2017). <\/p>\n\n\n\n

The disease affecting my population is diabetes, and it has become a huge challenge where it burdens the healthcare system and affects people from engaging in their day-to-day operations. I recommend the evidence-based practice<\/a> toward diabetes to be dietary interventions and physical activities for the population to avoid the disease. <\/p>\n\n\n\n

Are you looking for answers to a similar assignment?Nursingstudy.org has the top and most qualified writers to help with any of your assignments. All you need to do is place an order with us.<\/p>\n\n\n\n

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How can large aggregated databases be used to improve population health<\/figcaption><\/figure>\n\n\n\n

The change of lifestyle for the population is critical, where a change of diet based on the research on the best foods to take and the ones to avoid and recommending regular exercise that minimizes the risk factors<\/a> like obesity (Hinds, 2017). Exercise and diet help reduce the disease’s risk factors, and prevention is a better intervention. Related data is critical for informed decision-making in the strategies adopted in managing diseases<\/a>.<\/p>\n\n\n\n

References<\/p>\n\n\n\n

Hinds, P. (2017). Withholding, withdrawing or not analyzing data:<\/a> Can researchers allow data to be silent?  Cancer Nursing, 40(3), 173.  https:\/\/doi.org\/10.1097\/NCC.0000000000000489<\/u><\/p>\n\n\n\n

Machluf, Y., Tal, O., Navon, A., & Chaiter, Y. (2017). From population databases to research and informed health decisions and policy.  Frontiers in Public Health, 5, 230.  https:\/\/doi.org\/10.3389\/fpubh.2017.00230<\/u><\/a><\/p>\n\n\n\n

Zhang, L., Huang, Y., Yang, J., & Lu, W. (2021). Aggregating large-scale databases for PubMed author name disambiguation.  Journal of the American Medical Informatics Association : JAMIA, 28<\/em>(9), 1919-1927.  https:\/\/doi.org\/10.1093\/jamia\/ocab095<\/u><\/p>\n\n\n\n

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