Dynamic Optimization of Caregiver Schedules Based on Vital Sign Streams

Abstract

Hospital facilities use a collection of heterogeneous devices, produced by many different vendors, to monitor the state of patient vital signs. The limited interoperability of current devices makes it difficult to synthesize multivariate monitoring data into a unified array of real-time information regarding the patients state. Without an infrastructure for the integrated evaluation, display, and storage of vital sign data, one cannot adequately ensure that the assignment of caregivers to patients reflects the relative urgency of patient needs. This is an especially serious issue in critical care units (CCUs). We present a formal mathematical model of an operational critical care unit, together with metrics for evaluating the systematic impact of caregiver scheduling decisions on patient care. The model is rich enough to capture the essential features of device and patient diversity, and so enables us to test the hypothesis that integration of vital sign data could realistically yield a significant positive impact on the efficacy of critical care delivery outcome. To test the hypothesis, we employ the model within a computer simulation. The simulation enables us to compare the current scheduling processes in widespread use within CCUs, against a new scheduling algorithm that makes use of an integrated array of patient information collected by an (anticipated) vital sign data integration infrastructure. The simulation study provides clear evidence that such an infrastructure reduces risk to patients and lowers operational costs, and in so doing reveals the inherent costs of medical device non-interoperability.