Big data analysis of patient information and medical literature can facilitate data-driven diagnoses, treatment options, customized care, and improvement in patient outcomes


The third installment in our series on big data in healthcare.


One of the first steps in adapting clinical care to big data analytics (BDA) is to digitize patient health records and leverage their content. Electronic health records (EHRs) in Canada have already had a positive impact on patient care because it has allowed for the sharing of more accurate and complete information between health care providers, improved the security of health information, and reduced wait times for patient appointments. EHRs have helped physicians improve care by facilitating their immediate access to patient information, reduce adverse drug reactions through electronic prescribing and record-keeping, and given them more time to focus on patients.

The addition of BDA to existing EHR structures, while costly in implementation, will undoubtedly have far-reaching effects. BDA will allow for the real-time analysis of patient information by health care providers to mine and access large volumes of relevant medical literature as well as their patient’s health records. With analyzed health data, clinicians at the point-of-care will have additional support to form diagnoses, suggest alternative treatment options, and improve patient outcomes, especially those with highly complex illnesses.

As in research and development, BDA techniques such as predictive analysis have the potential to advance personalized medicine. For example, the adoption of BDA tools combining EHR data with genomics and the latest medical research can support early disease detection before the patient develops the disease, and help their physician to suggest customized intervention strategies or treatment.

Last, remote patient monitoring and clinical operations also benefit from BDA. The ability to access and analyze data remotely is very cost-effective for both doctors and patients to track treatment adherence, reduce patient in-hospital bed days, cut down emergency department visits, and reduce long-term health complications. For clinical operations, data mining of hospital wait-times has helped to better understand of wait-time patterns and improve the ability to predict events that affect them.




Murdoch, TB and Detsky, AS. The inevitable application of big data to healthcare. Journal of the American Medical Association, 309(13): 1351-1352, 2013.

Big Data Analytics in Health White Paper by Canada Health Infoway:

Electronic Health Records, eHealth Ontario:






Written by Fiona Wong, PhD


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