The Role of Epidemiological Data in Healthcare

Epidemiological data are defined as all types of non-experimental information, data, and observations made in the field related to the determinants (risk factors and causes) and distribution (patterns and frequency) of diseases and other health-related states in specific settings or populations, such as global setting, country, state, city, school, or neighborhood (Yang, 2017). Epidemiological data plays a fundamental role in helping healthcare financiers and providers (including healthcare facilities and staff members) make informed decisions in practice, such as whether to increase funding, repeal particular policies, increase staff members, commission new training, change care strategies, and so on. Epidemiologic data is essentially used to plan for a more robust future (Fairchild et al., 2018).

Therefore, what can necessitate a change in practice is when epidemiological data reflects negative or poor health outcomes, such as an increase in medication errors in a particular hospital or a surge in the rates of nosocomial diseases (also called health-acquired infections) in a particular state or countrywide. For example, suppose the daily incidence rates of COVID-19 infections increase abnormally in, for example. In that case, Texas within two weeks, the Department of Health in the state of Texas or the Department of Health and Human Services (countrywide) will likely be forced to make new healthcare practices and policies in the region. Since COVID-19 is a highly contagious and airborne disease that spreads quickly in crowded regions, the most likely healthcare practice measure is to isolate patients in the hospital, separating infected patients from healthy ones. Bedoya et al. (2016) note that healthcare workers will also need to change their routine practices by wearing masks, sanitizing their hands, and wearing disposable gloves every time they visit isolated patients.

References

Bedoya, G., et al. (2016). Observations of infection prevention and control practices in primary healthcare, Kenya. Bulletin of the World Health Organization, 95, 501-516. doi: http://dx.doi.org/10.2471/BLT.16.179499

Fairchild, G., et al. (2018). Epidemiological data challenges: Planning for a more robust future through data standards. Frontiers in Public Health, 6, 336. doi: 10.3389/fpubh.2018.00336

Yang, W. (2017). Early warning for infectious disease outbreak: Theory and practice. Elsevier Inc.

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