Select a defined patient population; for example, diabetic patients over 65 years of age. List elements that you think will be valuable in a database. Describe each element and explain each term of its type of data (text, numbers, date, time, binary, etc.). If an element can be more than one type of data, explain how that is possible.
Discussion Question
Technological advancements that have led to the creation of essential aspects like patient databases have improved patient services and better health outcomes. Formulating such databases requires the inclusion of various data elements and their integration for better functionality (Trifirò et al., 2018). The following elements can be valuable in a database for stroke patients of age 65 years and above.
- Gender-gender is a binary variable since it can be assigned a numerical value. This element can be described as male, female or other.
- Age: Age is a number variable. It describes the specific age of the stroke patients of at least 65 years of age.
- Ethnicity: This is a categorical variable, and numbers can be used for various ethnicities. It describes the ethnic group where an individual comes from since stroke has been shown to vary from one ethnic group to another.
- BMI: This is a number data and describes whether an individual has a healthy weight or not. The import is that obesity is a stroke risk factor.
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Data integration is key in bringing together data from various sources to create a cohesive dataset that offers consistent data and information access (Shafqat et al., 2020). Depending on the data type, various integration types can be applied. Therefore, in integrating the data described in the previous section, application-based integration can be used (Moreira et al., 2018). In this method, a software application is used to do the whole process of integration. The EHR database can facilitate this type of integration between clinical and administrative systems. EHR data integration achieves this by increasing the amount of information available to the clinical and administrative systems and ensuring that data integrity levels are high and maintained.
References
Moreira, J., Carvalho, A., & Horvath, T. (2018). A general introduction to data analytics. John Wiley & Sons.
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Select a defined patient population; for example, diabetic patients over 65 years of age. List elements that you think will be valuable in a database. Describe each element and explain each term of its type of data (text, numbers, date, time, binary, etc.). If an element can be more than one type of data, explain how that is possible.
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Shafqat, S., Kishwer, S., Rasool, R. U., Qadir, J., Amjad, T., &Ahmad, H. F. (2020). Big data analytics enhanced healthcare systems: a review. The Journal of Supercomputing, 76(3), 1754-1799. https://doi.org/10.1007/s11227-017-2222-4
Trifirò, G., Sultana, J., & Bate, A. (2018). From big data to smart data for pharmacovigilance: the role of healthcare databases and other emerging sources. Drug safety, 41(2), 143-149. https://doi.org/10.1007/s40264-017-0592-4
Select a defined patient population; for example, diabetic patients over 65 years of age. List elements that you think will be valuable in a database. Describe each element and explain each term of its type of data (text, numbers, date, time, binary, etc.). If an element can be more than one type of data, explain how that is possible.
In a separate paragraph discuss the type of integration data from your defined patient population in the first paragraph would require. How could the EHR database facilitate this type of integration between clinical and administration systems.
Use primary sources published after 2015. Provide citations and references for all sources used.