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Big data is revolutionizing health care

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“The cure for cancer is big data.”

This bold statement was made earlier this year by Norman Sharpless, the director of the National Cancer Institute (NCI). He shared this idea as part of a story he told regarding a particular cancer treatment approach. A gene study, which used lots of data, dictated the new approach and saved lives.

While his words were meant to get people’s attention, the underlying point is really important. Healthcare, like most industries, is undergoing a significant digital transformation and using the power of information to drive results.

Digital transformation challenges in healthcare

We all know that digital transformations are complex. However, I believe the highly sensitive and critical nature of medical data makes these initiatives in healthcare unique. The Health Insurance and Portability and Accountability Act’s (HIPAA) most critical requirement is that protected health information (PHI) be secured constantly and in all forms.

Working within these requirements, first responders and medical providers need to access highly secure information within moments to properly handle emergency situations while also protecting data from unauthorized use. Healthcare organizations have to share information with each other, with the patient and with insurers for reimbursement. The data management imperative in healthcare is clear.

Big data, bigger payoffs

Medical insights based on data analysis, such as the relative efficacy of different treatments, can improve reimbursements, drive new treatment protocols and reduce costs.

One example where big data is making a mark is in the fight against opioid abuse. The Health and Human Services (HHS) Office of Inspector General (OIG) uses data analytics to search for fraud, waste and abuse in Medicare and Medicaid. The OIG recently created an Integrated Data Repository containing petabytes of data on patient claims, providers, risk scores and other topics which investigators continuously analyze to spot potential wrongdoers. They can look for patients that receive prescriptions from multiple doctors, anomalous dosing information and many other risk factors. The office is now building predictive capabilities into the system and sharing this data with state and local law enforcement.

Big data’s potential extends beyond fraud detection to medical Artificial Intelligence (AI). Recently, researchers at the University of Central Florida (UCF) created an AI capable of spotting small tumors in CT scans. The new system identifies tumors with 95 percent accuracy, compared to 65 percent for a human radiologist. To achieve this huge increase, the team scanned thousands of CT scans (many terabytes of unstructured data) into their system to train the AI how to look for a tumor.

“Paging Dr. Data Governance”

Federal law typically requires data to be contained within the “official medical record” for 21 years, with longer retention periods in some states. Further, new technologies like home medical devices, IoT and even Fitbits create mountains of data and new privacy risks.

With all of this data, it is critical that organizations deploy enterprise data governance and data management strategies.

This is especially true when it comes to electronic health records (EHRs). Clinicians primarily rely on EHRs to manage the medical information from lab tests, medical devices, observations and more. The volume of data in EHRs creates incredible complexity and patient risk in intensive care units and emergency rooms across the world.

Tens of thousand of data points often obscure key information. However, the Mayo Clinic found that only about 60 pieces of data in a EHR are crucial for a clinician to be able to access quickly. As a result, they built a new EHR user interface to deliver this critical information. Algorithms constantly analyze and improve this data to drive timely and accurate decision-making. The system saves time, lowers costs and improves clinician performance, leading to improved patient outcomes.

Final thoughts

More than ever, healthcare demands data governance.

The majority of healthcare data, like CT scans, is unstructured data. As the HHS OIG and UCF examples show, AI and big data analytics require more than just EHRs. To derive new medical insights, a healthcare data governance strategy must extend beyond the data contained in the EHR to the volumes of data surrounding patients.

Managing all of this data is no small task. Fortunately, proper governance today leads to cost savings, a more effective workforce and organizational efficiencies. One day, it might also lead to a cure for cancer.

This content was originally published by GovLoop on September 4th, 2018, and the original article can be found here via