Integrating data, algorithms and clinical reasoning for surgical risk assessment

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 DESCRIPTION (provided by applicant): Major postoperative complications (PC) are common and lead to increase in mortality and healthcare cost. Some cost-effective strategies, implemented in a timely fashion, can ameliorate the risk for PC but the ability to use them depends on the timely and accurate identification of those patients at greatest risk. Assessment of that risk requires timely, accurate and dynamic synthesis of the large amount of clinical information obtained throughout the perioperative period. Today it is not possible to predict and quantify, for a given patient, a personal and real-time risk for PC that integrates preoperative risk with the risk incurred by the physiologic response to events during surgery. The interventions that could prevent PC are applied without consideration of a patient's personal risk profile or often not applied at all because the risk is underestimated. There is an abundance of physiologic, laboratory and other clinical data in the perioperative electronic health records (EHR), but their magnitude and complexity often overwhelms a physicians' ability to comprehend and use the information in an optimal and timely way. The objective is to develop an intelligent system, composed of high- performance computers, algorithms and physicians interacting in real time, which can generate usable medical knowledge with both increased speed and accuracy using complex clinical data. Our multidisciplinary team of scientific experts in medicine and engineering will address methodological challenges related to implementation of real-time data integration and processing, data analytics and knowledge exchange between computers and physicians in the clinical environment. There are three specific aims: 1. Refine and validate predictive risk models for major complications using EHR integrated with intraoperative physiologic time series using a temporal database for 10,000 surgical patients. 2. Implement and validate two-way knowledge exchange between predictive risk models and physicians. We will design an interactive knowledge exchange application that presents the knowledge behind predictive models to physicians, while allowing them to input their own assessment into the models. 3. Implement and evaluate an intelligent perioperative system for automated risk analysis using real-time EHR data. In a prospective clinical study of 60 physicians we will validate the diagnostic performance of predictive risk models, compare them with the physicians' risk assessment and measure change in physicians' risk perception after knowledge exchange with the system. This methodology will provide a significant step towards personalized perioperative medicine by modeling and quantifying the body's responses to surgery while using clinical data acquired during routine medical care.