A Conceptual Framework for Integrating Electronic Health Records and Data Analytics in Healthcare Systems
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Presently, onsite observation and interaction with some healthcare facilities in Nigeria indicate that there is a gap between Electronic Health Records (EHRs) stored in the department of Health Information Management (HIM) and Data Analytics. Therefore, it is important to combine the historical data that is stored electronically in the Health Information Management department with data analytics through the use of machine learning models like Support Vector Machine (SVM), Regression model, Artificial Neural Network (ANN), Random Forest (RF), among others. This method allows simple access to health data and records, which can be retrieved and saved in various format from the health information management database. The results from these data analytics can help find diseases early and predict their occurrences, which allows healthcare workers to take action earlier. This proactive approach helps in controlling, managing, and preventing diseases before they actually happen or start to spread. This paper proposes a conceptual framework that combines electronic health records with data analytics, which can help in predicting and managing diseases in Nigeria's healthcare system by supporting better decision-making in clinical settings.
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