Identifying Risk Factors using Electronic Health Records

Identifying Risk Factors using Electronic Health Records

Identifying Risk Factors using Electronic Health Records

The article “Combining Knowledge and Data Driven Insights for Identifying Risk Factors using Electronic Health Records” by Sun et al. (2012) combines information obtained from a knowledge base with data from electronic health records (EHRs) in order to produce predictive risk factors for heart failure. They explain that heart failure is an increasingly common condition that results in drastic costs for Medicare. For many people, heart failure decreases their quality of life and increases their time spent in hospitals. Consequently, improving the ability to identify heart failure risk factors benefits healthcare organizations, insurance companies, and most importantly, patients. They explain that a knowledge-driven approach incorporates guidelines and expert opinions. Additionally, they state that this approach fails to sufficiently represent the complicated disease process that is seen in patients with heart failure. On the other hand, the data-driven approach is a result of observational data. They utilized diagnosis, medication, symptoms, and lab results from Geisinger’s Enterprise data warehouse which contains data on primary care patients. The addition of this data assists with the complicated disease process. Combining the information from both sources provides more accurate information on the risk factors that can cause a patient to develop heart failure. They confirmed that combining these two sources of information improves prediction.

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A similar study conducted by Sun et al. in 2014 utilized EHR’s in relation to blood pressure control. They utilized the ability of EHR’s to include at-home blood pressure measurements provided from patients. Through their study, they were able to show that data from EHR’s can be utilized to detect changes in blood pressure control. The researchers explain their study is the start of personalized recommendations for blood pressure medication.

Significance of Combining Knowledge

As shown in the above examples, the data from EHR’s can make a huge difference in the world of healthcare. The overall adoption of health informatics produces vast amounts of data that can be utilized to produce more effective healthcare solutions (Braunstein, 2010). Furthermore, combining all of this data can be used to improve disease management and prevention. As technology in healthcare continues to improve, we will accumulate even more data and information on each patient. The real challenge is accurately combining this information for the benefit of the patients. Nelson and Staggers (2018) even suggest that the information gained from personal health devices may soon be combined with EHR’s. The combination of this data is not a simple process, but by combining the extra knowledge we can produce more accurate predictions. Trifirò et al. (2018) states, “Indeed, it is important to remember that findings generated using big data require robust clinical interpretation and critical judgment” (p. 148). It is especially important that this clinical interpretation be incorporated at the beginning of the process of data combination to ensure accuracy. Obtaining all of the knowledge from the data is simply the first step, but it opens the door to an incredible amount of possibilities within healthcare. Knowledge truly is power. The Bible teaches us the importance of knowledge in Proverbs 18:15 which states, “An intelligent heart acquires knowledge, and the ear of the wise seeks knowledge” (English Standard Version, 2001). Further research on the data and knowledge obtaining from EHR’s is crucial for the future of patient care.

In conclusion, the integration of technology and health informatics into healthcare has created a plethora of health data. Taking this data and combining it with the knowledge that we have from experts can provide an even more accurate way to predict and treat disease and illness. The more we study the combination of this data, the closer we will get to a more personalized healthcare experience. Identifying Risk Factors using Electronic Health Records


Braunstein, M. L. (2014). Contemporary Health Informatics. AHIMA Press.

English Standard Version Bible. (2001). Crossway.

Nelson, R., & Staggers, N. (2018). Health Informatics: An Interprofessional Approach. Elsevier.

Sun, J., Hu, J., Luo, D., Markatou, M., Wang, F., Edabollahi, S., Steinhubl, S. E., Daar, Z., & Stewart, W. F. (2012). Combining knowledge and data driven insights for identifying risk factors using electronic health records. AMIA Annual Symposium proceedings. AMIA Symposium, 2012, 901–910.

Sun, J., McNaughton, C. D., Zhang, P., Perer, A., Gkoulalas-Divanis, A., Denny, J. C., . . . Malin, B. A. (2014). Predicting changes in hypertension control using electronic health records from a chronic disease management program. Journal of the American Medical Informatics Association: JAMIA, 21(2), 337-344.

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.



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