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It Takes a Network: A Network Science- and Command Center-Based Approach to Hospital Error Detection, Prevention, and Mitigation

​This project aims to develop a set of algorithms for generic error detection, prevention, and mitigation in the hospital setting. The goal is to develop algorithms that can operate in real time so as to support an existing infrastructure (the eHospital Safety Net, a Kaiser Permanente Northern California system that involves trained registered nurses who monitor the inpatient hospital record in real time, remotely). To develop these algorithms, network science, neural networks, and machine learning will be employed to identify discrete, transient (temporal frame of one to two hours) patient clusters at high risk for errors and process failures. This project builds on work conducted by the same team (assignment of severity of illness scores in real time, and quantification of in-hospital ecological markers such as occupancy and hourly change in census), as well as existing systems such as the Hospital Throughput Monitor. It expands on this work by developing an approach to quantify cognitive load on networks of caregivers and patients as well as hospital units. The project involves a collaboration with Stanford University.

Investigator: Escobar, Gabriel

Funder: Kaiser Foundation Hospitals

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