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
University of Minnesota Ph.D. dissertation. February 2013. Major: Health Informatics. Advisor: Dr. Terrence J. Adam. 1 computer file (PDF); viii, 175 pages, appendix A.
AbuSalah, Ahmad Mohammad.
Personalized surgical risk assessment using population-based data analysis.
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