AANS Neurosurgeon | Volume 28, Number 1, 2019


The Vs of Big Data Outcomes Research in Neurosurgery

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Big Data is an increasingly popular concept in medical research, and the field of neurosurgery is no exception. Littered across the most impactful journals are articles centered on this hot topic. The term Big Data itself refers to the use of massive samples of patient data, which are pooled from many institutions across the country into one database. Typically, Big Data research focuses on analyzing patient comorbidities and outcomes; however, its utility extends to other areas of research as well. The benefits of Big Data analyses, like the databases themselves, are enormous. It allows for a larger, multi-center sample size, which equates to a more accurate and translatable population sample compared to a single center dataset. Big Data also allows for more accurate analyses of rare diseases or situations. One of the biggest benefits of Big Data research is increased efficiency. Data acquisition is more streamlined, especially when considering the vastly increased sample size, and analyzing these datasets allows a researcher to analyze much more data than could be obtained individually.

In the data sciences literature, Big Data is defined using four characteristics:1

  • Volume: The number of patients in a database
  • Variety: The range of scope of information captured for each patient
  • Velocity: The accumulation of data points for a patient
  • Veracity: The quality of the data being recorded

Volume is generally self-explanatory. Good variety is multifactorial and could include presenting symptoms, laboratory values, imaging measurements, vital sign measurements, pathology reports, follow up reports and complication rates, while poor variety includes only one or few of those attributes. An example of velocity is that a good medical student would never present only one overnight heart rate recording to their attending physician during morning rounds; an overnight range would be more appropriate. Similarly, a dataset with good velocity includes many recordings from the care episode. Veracity is perhaps the most controversial aspect of Big Data research. Medicine is an art, and many aspects of medicine do not comfortably fit into the all-or-none, binary definitiveness of most databases.

Many of the largest surgical databases were created for a wide variety of specialties and conditions by institutions like Medicare, insurance companies, or professional medical groups (e.g., the American College of Surgeons). These databases generally focus on quality improvement and healthcare cost, which go hand-in-hand. Each individual database has its advantages and disadvantages with a wide range:

  • From the timeframe of recording (e.g., purely inpatient stay or 30-post-operative),
  • The data points that are collected, and
  • Goals of collection.2

One of the biggest complaints of the use of Big Data for neurosurgery research is that these datasets are not specifically created for neurosurgeons or for neurosurgical issues, and they must be retrofit for this purpose. Post-creation manipulation is fraught with challenges to the quality of the analysis.

The future of neurosurgical Big Data lies in specialty-tailored, neurosurgery-focused datasets. When these datasets are created, it will be important to think critically about what can be learned from the successes and shortcomings of previous Big Data projects. One such example of a neurosurgery-focused Big Data initiative is the Quality and Outcomes Database (QOD), which was created by the American Association of Neurological Surgeons (AANS) in 2012.3 We are fortunate to be in a particularly academic and collegial field, where a large group of clinicians and researchers working across institutions is common. Because of these factors, the future of neurosurgical Big Data is bright. With clever Big Data creation and study design, these massive datasets could soon be leveraged to optimize patient safety, reduce patient risks, and answer age-old questions about best practices.


1. Harary, M., Smith, T. R., Gormley, W. B., & Arnaout, O. (2018). Letter: Big Data Research in Neurosurgery: A Critical Look at This Popular New Study Design. Neurosurgery, 82(6).

2. Karhade, A. V., Larsen, A. M., Cote, D. J., Dubois, H. M., & Smith, T. R. (2017). National Databases for Neurosurgical Outcomes Research: Options, Strengths, and Limitations. Neurosurgery, 83(3), 333-344.

3. Mcgirt, M. J., Speroff, T., Dittus, R. S., Harrell, F. E., & Asher, A. L. (2013). The National Neurosurgery Quality and Outcomes Database (N2QOD): General overview and pilot-year project description. Neurosurgical Focus.

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