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Edward C. Benzel and Zoher Ghogawala

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Anthony L. Asher, Matthew J. McGirt, and Zoher Ghogawala

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Zoher Ghogawala, Melissa R. Dunbar, and Irfan Essa


There are a wide variety of comparative treatment options in neurosurgery that do not lend themselves to traditional randomized controlled trials. The object of this article was to examine how clinical registries might be used to generate new evidence to support a particular treatment option when comparable options exist. Lumbar spondylolisthesis is used as an example.


The authors reviewed the literature examining the comparative effectiveness of decompression alone versus decompression with fusion for lumbar stenosis with degenerative spondylolisthesis. Modern data acquisition for the creation of registries was also reviewed with an eye toward how artificial intelligence for the treatment of lumbar spondylolisthesis might be explored.


Current randomized controlled trials differ on the importance of adding fusion when performing decompression for lumbar spondylolisthesis. Standardized approaches to extracting data from the electronic medical record as well as the ability to capture radiographic imaging and incorporate patient-reported outcomes (PROs) will ultimately lead to the development of modern, structured, data-filled registries that will lay the foundation for machine learning.


There is a growing realization that patient experience, satisfaction, and outcomes are essential to improving the overall quality of spine care. There is a need to use practical, validated PRO tools in the quest to optimize outcomes within spine care. Registries will be designed to contain robust clinical data in which predictive analytics can be generated to develop and guide data-driven personalized spine care.

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John Paul G. Kolcun, Gregory W. Basil, Zoher Ghogawala, and Michael Y. Wang

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Paul M. Arnold, Zoher Ghogawala, and Candan Tamerler

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Zoher Ghogawala, Daniel K. Resnick, Steven D. Glassman, James Dziura, Christopher I. Shaffrey, and Praveen V. Mummaneni

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Gregory W. Basil, Annelise C. Sprau, Zoher Ghogawala, Jang W. Yoon, and Michael Y. Wang

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Anthony L. Asher, Paul C. McCormick, Nathan R. Selden, Zoher Ghogawala, and Matthew J. McGirt

Patient care data will soon inform all areas of health care decision making and will define clinical performance. Organized neurosurgery believes that prospective, systematic tracking of practice patterns and patient outcomes will allow neurosurgeons to improve the quality and efficiency and, ultimately, the value of care. In support of this mission, the American Association of Neurological Surgeons, in cooperation with a broad coalition of other neurosurgical societies including the Congress of Neurological Surgeons, Society of Neurological Surgeons, and American Board of Neurological Surgery, created the NeuroPoint Alliance (NPA), a not-for-profit corporation, in 2008. The NPA coordinates a variety of national projects involving the acquisition, analysis, and reporting of clinical data from neurosurgical practice using online technologies. It was designed to meet the health care quality and related research needs of individual neurosurgeons and neurosurgical practices, national organizations, health care plans, biomedical industry, and government agencies. To meet the growing need for tools to measure and promote high-quality care, NPA collaborated with several national stakeholders to create an unprecedented program: the National Neurosurgery Quality and Outcomes Database (N2QOD). This resource will allow any US neurosurgeon, practice group, or hospital system to contribute to and access aggregate quality and outcomes data through a centralized, nationally coordinated clinical registry. This paper describes the practical and scientific justifications for a national neurosurgical registry; the conceptualization, design, development, and implementation of the N2QOD; and the likely role of prospective, cooperative clinical data collection systems in evolving systems of neurosurgical training, continuing education, research, public reporting, and maintenance of certification.