Creating a Data Set for Elderly Diabetic Patients Term Paper

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Diabetes Database for Older Patients

A diabetes database must capture information that is directly related to the disease and some of the valuable elements include age, A1C results, skin fold, blood pressure, weight, date of first symptoms, and blood glucose level. Age of the patient is valuable because it assists in determining how old the patient is and it can also be used to determine the best cause of treatment to be used especially for diabetes. The age element will have a data type of number since age is recorded in figures. A1C results are obtained after the patient has undergone testing for diabetes. Theses element is valuable because the results will help establish if the patient is diabetic or prediabetic (Balas & Boren, 2000). A1C results are recorded in number format because the results are normally obtained in percentage. Skinfold element is used to determine if the patient has any skin disease that is related to diabetes. Skinfold will be recorded as a binary because it requires a yes or no. Blood pressure is closely monitored for diabetic patients because a high blood pressure can be lethal especially for diabetic patients. Blood pressure will be a text value because it is measured in terms of systolic pressure over diastolic pressure. Weight element offers information regarding the BMI of the patient, which is vital for checking if the patient is overweight or underweight. Weight will be a number value.

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Date of first symptoms assists in establishing the onset of the disease and this can be beneficial for treatment management. The value will be a date. Blood glucose level will establish the patient's blood glucose level at first visit and assist in determining the appropriate treatment. This will be a number value in the database.

Type of Data Integration

This patient population would require a virtual integration. This is because the information stored in the database should only be viewed by others and not modified. A virtual integration allows for the unified view of data from a single logical source, which reduces the complexity of accessing data across distributed systems or data sources that have different models (Weaver, Ball, Kim, & Kiel, 2016). A majority of the other systems would only need to have an overview of the information and since they might be using different models, the best way is to allow them to access only the information they require. This would also increase the privacy of the patient and ensure that the data is not modified by unauthorized personnel. Virtual integration has zero latency of data updates and there is no need to have separate storage for the consolidated data. This is because the data is only provided as stored in the other dataset and no modification is allowed to the data.
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