EHR/EMR generate tons of data. Data is being generated at a faster rate than people can analyze, profit from, and make use of. Questions about who owns the data being collected have raised privacy concerns while people continue to share their data online for the world to see. However, not all data is equally valuable. In healthcare, different types of data can provide different insight. Some can help make better analysis than others.
Survey data is more difficult to work with. Surveys use questionnaires and interviews to probe for information. The difficulty with survey data is its subjectivity and that respondent’s don’t always want to disclose information. For example, in Africa, culturally sensitive topics such as sexual activity can be embarrassing for individuals to talk about (Rerimoi et al., 2019). Questions about HIV status can greatly harm an individual. Questions such as income can also be culturally sensitive. Even the layout of the questions such as wording, categories, and ordering can greatly affect answers (Sirken, 1986). Furthermore, the information given in the surveys maybe difficult to verify.
One data type that is easy to work with and has shown great promise in affecting health are zip codes. For one thing, zip code data is consistent (Jamsa, 2020, p. 42). Zip code data is easy to confirm because it ties in with a person’s address and ID cards such as a driver’s license. Zip codes rarely change because people rarely move. Perhaps, the most interesting part of zip code data is how it can have a strong influence on health (Ducharme, 2019). Where a person lives exposes them to different environmental toxins, accessibility to different medical facilities and healthy food products. Zip code data has correlated with life expectancy.
Axioms such as “garbage in, garbage out” or “what gets measured gets managed” are valid. The quality and type of data are important. However, the analysis of the data are equally important. Knowing what to look for and thinking outside the box can go a long way.
References
Ducharme, J. (2019). Does ZIP Code Equal Life Expectancy?. Time, 194(2), 8-8.
Jamsa, K. (2020). Data Mining and Analytics. S.I.: Jones & Bartlett Learning.
Rerimoi, A. J., Niemann, J., Lange, I., Timæus, I. M., & Navaneetham, K. (2019). Gambian cultural beliefs, attitudes and discourse on reproductive health and mortality: Implications for data collection in surveys from the interviewer’s perspective. PLoS ONE, 14(5), e0216924-17.
Sirken, M. G. (1986). Error effects of survey questionnaires on the public. American Journal of Public Health, 76(4), 367-368.