Big data in healthcare: 3 spine surgeons weigh in

Practice Management

The use of big data is growing across several industries, and healthcare is no exception.

Tech companies are investing millions in artificial intelligence, machine learning and advanced algorithms, which will be increasingly used to predict patients' healthcare needs based on their habits and prior medical history. Benefits of big data can include early intervention for at-risk patients and higher-quality care, but the industry must also weigh the risks of replacing physicians and sacrificing the patient's right to privacy.

Three surgeons consider the pros and cons of big data and discuss how it will affect spine care and the wider healthcare field.

Ask Spine Surgeons is a weekly series of questions posed to spine surgeons around the country about clinical, business and policy issues affecting spine care. Becker's invites all spine surgeon and specialist responses.

Next week's question: In five years' time, what will outpatient spine surgery look like?

Please send responses to Alan Condon at by 5 p.m. CST Wednesday, Dec. 8.

Editor's note: The following responses were lightly edited for style and clarity.

Question: How do you anticipate the selling and mining of data evolving in healthcare over the next decade?

Brian Gantwerker, MD. The Craniospinal Center of Los Angeles: There are at least two efforts being directed at data mining currently in the space. One in San Francisco, the other in Cleveland. These are prime examples of how machine learning is being used by looking at the results of surgeries in large patient databases. Basically, they are trying to discover the "secret sauce" to good outcomes in spinal surgery. The results from these analyses, and the potential guidance they can provide could become invaluable to surgeons.

Unfortunately, what will likely happen is that the results from these will likely be weaponized and used by insurance companies who will cherry pick or ignore the information and use it to reinforce their randomly generated sets of criteria used to authorize surgery. Worse yet, the algorithms may be sold to insurers, who will then decide openly who gets an operation or who doesn't. This would be our singularity moment in spine surgery, and I hope we can have the foresight and self-control to stop that from happening.

Vijay Yanamadala, MD. Hartford (Conn.) HealthCare: Electronic medical records are quickly consolidating with a few dominating players in the market. Epic and Cerner comprise more than 50 percent of the market. Mining of healthcare data on a large scale is undoubtedly facilitated by this consolidation. There are very powerful applications of data mining in healthcare, from the evaluation of treatment effectiveness and safety, quality assessment to detecting fraud and unnecessary procedures. Big data will shape the ways we practice medicine in ways that randomized controlled trials and existing evaluation tools are simply inadequate to address.

However, there are real risks to patient confidentiality that need to be addressed as we continue to expand the scope and pace of data mining in healthcare. Data protection is essential. This will likely come from government regulation on a state and federal level. However, hospitals, health systems and electronic medical record companies will all have a stake in ensuring patient data is used in a manner that ultimately values the patient's right to privacy and protection as paramount. 

Christian Zimmerman, MD. St. Alphonsus Medical Group and SAHS Neuroscience Institute (Boise, Idaho): Data mining in healthcare is defined as the process of finding unknown patterns and trends in databases and using information to build predictive models. The analytic process is designed to explore large amounts of data in search of consistent patterns and/or systematic relationships between variables, and then validate the findings by applying that information to certain other subsets of data. 

Data mining can improve decision-making by discovering patterns and trends in large amounts of complex data. This analysis has become useful as financial pressures have imposed the need for healthcare organizations to make decisions based on the analysis of clinical and financial data. Conclusions gained from data mining can influence cost, revenue and operating efficiency while maintaining a higher level of care.

Healthcare organizations that perform data mining are better positioned to meet their long-term needs; data can be a great asset to healthcare organizations, subsequently augmenting the value of this service and objectifying its need for expansion. Merely utilizing data mining as a comparative tool to one's competition places vast interdependence on its use. This field can only grow.

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