Variance Reduction in Neurosurgical Practice: The Case for Analytics-Driven Decision Support in the Era of Big Data

Abstract

Variance between providers in neurosurgery can lead to inefficiencies and poor patient outcomes. Evidence-based guidelines (EBGs) have been developed; however, they have not been well implemented into the clinician workflow. Therefore, clinicians have been left to make decisions with incomplete information. Equally underused are the electronic health records (EHRs), which house enormous amounts of health data, but the power of that "big data" has failed to be capitalized on. Early attempts at EBGs were rigid and nonadaptive; however, with the current advances in data informatics and machine learning algorithms, it is now possible to integrate "big data" and rapid data processing into clinical decision support tools. We have presented an overview of the background of EHRs and EBGs in neurosurgery and explored the possibility of integrating them to reduce unwanted variance. As we strive toward variance reduction in healthcare, the integration of "big data" and EBGs for decision-making will be key. We have proposed that EHRs are an ideal platform for integrating EBGs into the clinician workflow and have presented as an example of a successful early generation model, Neurocore. With this approach, it will be possible to build EBGs into the EHR software, to continuously update and optimize EBGs according to the flow of patient data into the EHR, and to present data-driven clinical decision support at the point of care. Variance reduction in neurosurgery through the integration of evidence-based decision support in EHRs will lead to improved patient safety, a reduction in medical errors, maximization of the use of the available data, and enhanced decision-making power for clinicians.