Personalized surgical risk assessment using population-based data analysis

Abstract

The volume of information generated by healthcare providers is growing at a relatively high speed. This tremendous growth has created a gap between knowledge and clinical practice that experts say could be narrowed with the proper use of healthcare data to guide clinical decisions and tools that support rapid information availability at the clinical setting. In this thesis, we utilized population surgical procedure data from the Nationwide Inpatient Sample database, a nationally representative surgical outcome database, to answer the question of how can we use population data to guide the personalized surgical risk assessment process. Specifically, we provided a risk model development approach to construct a model-driven clinical decision support system utilizing outcome predictive modeling techniques and applied the approach on a spinal fusion surgery which was selected as a use case. We have also created The Procedure Outcome Evaluation Tool (POET); which is a data-driven system that provides clinicians with a method to access NIS population data and submit ad hoc multi-attribute queries to generate average and personalized data-driven surgical risks. Both systems use patient demographics and comorbidities, hospital characteristics, and admission information data elements provided by NIS data to inform clinicians about inpatient mortality, length of stay, and discharge disposition status.