Advancing Value-Based Surgical Care: A Real-Time Deep Learning Approach for Postoperative Complication Risk Stratification in Robot-Assisted Nephrectomy

This project applies deep learning and healthcare informatics to enhance robot-assisted nephrectomy (RPN) outcomes, focusing on implementing a sophisticated risk prediction system for RPN procedures. The objective is to accurately predict patient risk before, during, and after surgery using preoperative and real-time intraoperative data. Although RPN is our initial focus due to its high volume at HMC, the project's principles have broad applicability to potentially revolutionize perioperative care across surgical disciplines. Our system aims to improve patient outcomes and reduce healthcare costs by enabling early interventions, minimizing severe postoperative complications, and reducing extended hospital stays, readmissions, and unnecessary additional interventions. Through advanced deep learning models like recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, we aim to personalize surgical care and enhance preoperative planning, intraoperative decision-making, and postoperative management. The project is structured into stages, or 'Work Packages,' to systematically collect necessary data, analyze it using deep learning techniques, and create, test, refine, and validate initial risk models. The project will directly contribute to the national health objectives outlined in the QRDI 2030 Strategy, demonstrate the value of deep learning and real-time data integration in clinical settings, and pave the way for more medical applications.

Hamad Medical Corporation