Select a defined patient population for example; diabetic patients over 65 years of age.

4:1

The defined patient population is elderly patients with chronic obstructive pulmonary disease (COPD). Elements that can be valuable in a database include patient demographics such as name, gender, age, and address, medication history, laboratory test results, spirometry readings, smoking history, and pulmonary function test results. The demographics should be text data. Medication history should include text data on medication name, strength, dosage, route of administration, and frequency of use. Laboratory test results should include numerical data, such as the date of the test, the value of the test, and the units of measurement. Spirometry readings should be numerical data. Smoking history should be binary data. Pulmonary function test results should include numerical data, such as the date of the test, the value of the test, and the units of measurement. An element that can be more than one type of data is the date of birth, which can be both text and date data.

Reference: Wang, J., Hu, H., Xu, J., Li, R., & Li, Y. (2019). Building a COPD patient cohort based on electronic health records: a comprehensive procedure. BMC medical informatics and decision making, 19(6), 1-10.

4:2

The integration of data from the defined patient population in Topic 4:1 would require clinical and administrative data integration. The electronic health record (EHR) database can facilitate this type of integration by allowing different healthcare providers to access patient records, identify problems, and track the patient’s progress over time. It can also facilitate the integration of data from laboratory results, medication use, and spirometry readings, among other things. By integrating data, healthcare providers can make more informed decisions about treatment options and ensure that patients receive the best possible care. To facilitate data integration, the EHR database should allow for secure sharing of patient data, have a standard data format, and provide the ability to perform data analytics.

Reference: Alberdi, A., Aztiria, A., Basarab, A., & El-Fakdi, A. (2016). Towards an integrated and secure ehealth ecosystem in the Basque Country. Journal of medical systems, 40(9), 1-8.

5:1

The clinical problem is medication adherence in patients with COPD. The clinical question is, “What is the correlation between medication adherence and COPD exacerbation rates?” The data mining techniques that can be applied to this challenge are classification and regression trees, decision trees, and logistic regression. The rationale for using these techniques is that they can identify patterns in data that may not be immediately obvious, and these patterns can be used to develop predictive models for medication adherence and COPD exacerbation rates. However, neural networks may not be suitable for this challenge because the sample size may not be large enough to train the model effectively.

Reference: McBurney, R. N. (2017). Health informatics: the transformation of healthcare with information technology. John Wiley & Sons.

5:2

To answer the clinical question, “What is the correlation between medication adherence and COPD exacerbation rates?” the data can be extracted from the patient’s electronic health record (EHR) database. The specific components of the question that need to be identified include patient medication adherence data, COPD exacerbation rate data, and patient demographic data. The medication adherence data can be extracted from the medication history, and the COPD exacerbation rate data can be extracted from the spirometry readings and pulmonary function test results. The patient demographic data can be extracted from the patient demographics element.

Reference: Raza, S., & Hussain, M. (201