Design Application of EHR in Population-Based Analytics

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EHR

The term EHR has the definition of “a repository of electronically maintained information about an individual’s health status and health care, stored such that it can serve the multiple legitimate uses and users of the record” (McDonald, Tang, & Hripcsak, 2014, p. 391). According to Shillingstad, EHRs have done a number of “big things,” including the automation and integration of health records, which made them more accessible. Apart from that, EHRs contributed to the promotion of standardization and networking in healthcare. EHRs also offer more opportunities for data protection and durability (Johnson et al., 2014). These “big deeds” of EHR eventually resulted in improved healthcare outcomes for patients and professionals. Indeed, the former enjoy better care and safety while for the latter, EHRs simplify some of the processes. For instance, as mentioned by Shillingstad, that of obtaining HR or managing patients’ information and filling out some information automatically, which are the features that I have experienced myself.

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EHR and Public Health

According to Shillingstad, from the point of view of public informatics and health, EHRs have become a form of a “program factory,” which allows their use for large-scale monitoring and prevention of diseases and their negative outcomes. An example of such use is the monitoring of health-related behaviors like immunization, which allows determining risk groups and designing interventions to prevent the possibility of disease or negative outcomes.

Apart from that, EHRs can be used for public health research. Bower et al. (2017) demonstrate that while there are certain limitations to EHR representativeness, EHRs are a rather effective sampling and recruitment method. Similarly, Jensen, Jensen, and Brunak (2012) point out that ethical and legal privacy, consent, and autonomy issues, as well as technical constraints, can be limiting.

Public Health Study Design

(Casey, Schwartz, Stewart, & Adler, 2016)

One of the most common applications of EHRs in public health research is epidemiology, including social and environmental epidemiology (Casey, Schwartz, Stewart, & Adler, 2016). As a result, a study on environmental epidemiology on the correlation between the proximity of environments that promote physical activity and body mass index can be proposed. According to Casey et al. (2016), information on BMI and addresses is available from EHRs; the necessity of health information exchange would be defined by the access to EHRs and the sample size. The study would be quantitative in design with EHR being used for sampling and data collection.

EHR-related legislation is predominantly helpful due to its contribution to the standardization and promotion of the use of EHRs. However, it would also pose certain limitations to the study, especially with respect to privacy (Krager, & Krager, 2016; Sheikh, Sood, & Bates, 2015).

The potential outcomes for community health in the case of this study could include either the determination of correlations that could help in the detection of risk groups or a contribution to the literature on the topic that would not establish meaningful correlations. Thus, the study is feasible regardless of the outcome.

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The described study is only one example of the numerous possibilities related to the use of EHR for public health research, and as demonstrated by Shillingstad and Casey et al. (2016), the benefits that EHRs promise to public healthcare are immense.

References

Bower, J., Bollinger, C., Foraker, R., Hood, D., Shoben, A., & Lai, A. (2017). Active use of Electronic Health Records (EHRs) and Personal Health Records (PHRs) for epidemiologic research: Sample representativeness and nonresponse bias in a study of women during pregnancy. Egems (Generating Evidence & Methods to Improve Patient Outcomes), 5(1), 1-11.

Casey, J., Schwartz, B., Stewart, W., & Adler, N. (2016). Using Electronic Health Records for population health research: A review of methods and applications. Annual Review of Public Health, 37(1), 61-81.

Jensen, P., Jensen, L., & Brunak, S. (2012). Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics, 13(6), 395-405.

Krager, C., & Krager, D. (2016). HIPAA for health care professionals. New York, NY: Cengage Learning.

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McDonald, C. J., Tang, P.C., & Hripcsak, G. (2014). Electronic health record systems. In E. Shortliffe & J. Cimino (Eds.), Biomedical Informatics (pp. 391-422). London, UK: Springer.

Sheikh, A., Sood, H., & Bates, D. (2015). Leveraging health information technology to achieve the “triple aim” of healthcare reform. Journal of the American Medical Informatics Association, 22(4), 849-856.

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NursingBird. (2022, August 24). Design Application of EHR in Population-Based Analytics. Retrieved from https://nursingbird.com/design-application-of-ehr-in-population-based-analytics/

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NursingBird. (2022, August 24). Design Application of EHR in Population-Based Analytics. https://nursingbird.com/design-application-of-ehr-in-population-based-analytics/

Work Cited

"Design Application of EHR in Population-Based Analytics." NursingBird, 24 Aug. 2022, nursingbird.com/design-application-of-ehr-in-population-based-analytics/.

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NursingBird. (2022) 'Design Application of EHR in Population-Based Analytics'. 24 August.

References

NursingBird. 2022. "Design Application of EHR in Population-Based Analytics." August 24, 2022. https://nursingbird.com/design-application-of-ehr-in-population-based-analytics/.

1. NursingBird. "Design Application of EHR in Population-Based Analytics." August 24, 2022. https://nursingbird.com/design-application-of-ehr-in-population-based-analytics/.


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NursingBird. "Design Application of EHR in Population-Based Analytics." August 24, 2022. https://nursingbird.com/design-application-of-ehr-in-population-based-analytics/.