The tidal wave of big data is making its way into healthcare. Medical practitioners, researchers and hospital executives are tantalized by the prospect of integrating huge amounts of patient information from disparate sources to develop diagnostic profiles, direct treatment and proactive therapies, and improve follow-up care. Healthcare administrators are beginning to realize big data’s potential for cost reduction and improved efficiency.

Although challenges regarding patient privacy, the exchange of health information and funding are slowing progress, big data is already making a significant impact in the healthcare industry. Some areas include:[1]

  • Prevention – Wearables and smartphones not only can help people track their progress toward fitness goals, but they also can help monitor health conditions like diabetes, heart disease and Parkinson’s disease. Researchers are beginning to compile databases that will provide a better understanding of the interaction between lifestyle and disease.
  • Diagnosis – Although the medical industry already collects massive amounts of patient data, it’s often limited to individual physician practices, hospitals or health systems. Electronic health records (EHR) were the first move to digitizing and sharing patient records across a healthcare system. The next step is to draw more information from multiple areas including wearable devices, medical and insurance records, genetic data and other sources. Predictive analytics algorithms are being used with machine learning to improve diagnostic practices for diseases such as cancer and eventually will help catch disease progression earlier for better outcomes.
  • Treatment – Machine learning algorithms are being used to review large amounts of research data and synthesize findings into information that a doctor may need to treat a specific case. Similarly, data on clinical drug trials can be reviewed and may reveal new uses for medications. Big data also is being used worldwide to track, analyze and address epidemics such as Zika and Ebola.
  • Follow-up care – Relapses and hospital re-admissions can be predicted and reduced with big data analytics. With the population aging and more people living alone, apps are being developed to monitor whether a patient takes scheduled medication and if behavior seems unusual and warrants a call from family members or medical practitioners.
  • Healthcare operations – Big data can improve operational efficiency and reduce costs in healthcare systems. Machine learning applications are being used to find patterns in claims resolution to improve success rates. Machine learning can predict payer activity and automate much of the collections process.

[1] “Big Data: A Game Changer in Healthcare,” by Bernard Marr, Forbes, May 24, 2016. Available at:

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