Improving Outcomes: Big Data and Predictive Analytics

Computational Neurosurgery Outcomes Center

Dr. Timothy R. Smith and Dr. William B. Gormley founded Computational Neurosurgery Outcomes Center (CNOC) at Brigham and Women's Hospital and Harvard Medical School in 2015. The purpose of this center is to centralize disparate administrative, clinical, and financial data sources and detailed outcomes tracking and reporting in order to both internally increase quality improvement guidelines for high-value care delivery at Partners Healthcare and to externally synthesize big data to inform best practices and protocols for the international neurosurgical community.

Four major categories of new knowledge creation and value-based innovation at CNOC are

• 1.

  Hospital-based process improvements.

• 2.

  Implantable and wearable technology outcomes-data acquisition and sharing.

• 3.

  Outcome prediction based on artificial intelligence.

• 4.

  Improved access to specialty care via telemedicine.

Four examples of these potential value-based techniques include patient discharge optimization with automated medication reconciliation to help prevent readmission, the use of implantable and wearable technology to capture objective measures of patient function, the application of machine learning via natural language processing (NLP) and predictive model development to predict outcomes, and the expansion of telemedicine to increase access to care.

This chapter will focus on the role of CNOC in applying big data and predictive analytics to improve outcomes by reducing complications.