Our long-term goal is to develop a system to monitor provider stress in real time, allowing healthcare organizations to reduce the risk of burnout and medical error. The overall objectives in this proposal are to develop a scalable data stream of physiological data and validate knowledge extracted from the data stream. Our rationale is that physiological data (e.g., heart rate and movement) are related to stress but can be monitored unobtrusively. We will combine knowledge from HFE with machine learning with three specific aims. We will first pilot and evaluate the use of wearable sensors to monitor heart rate and movement of nurses. Second, we will monitor stress and workload using accepted HFE techniques. Third, we will use machine learning to extract knowledge from physiological data and validate it with data about stress and workload and nurse-sensitive process and outcome indicators.
Funding: Jump ARCHES endowment through the Health Care Engineering Systems Center