Between 370,000 and 750,000 cardiopulmonary resuscitations are attempted each year in US hospitals. Approximately 80% of these patients do not survive to discharge. However, for many, pulseless ventricular tachycardia or ventricular fibrillation (VT/VF) is the first monitored arrhythmia, which may be treated successfully with prompt defibrillation. The American Heart Association recommends defibrillation therapy within two minutes of cardiac arrest onset. Yet, for 30% of patients, defibrillation is delayed more than two minutes, reducing their chance of survival to hospital discharge by half. There are few more important objectives in hospitalizing a patient at risk than being able to effectively detect potentially fatal arrhythmias, yet there is little reliable evidence to guide how this should be done. To increase the potential for timely detection of cardiac events, more and more at-risk patients are now monitored remotely by cardiac telemetry technicians. However, decisions regarding the appropriate number of patients that a single technician may safely and effectively monitor appear to be primarily based on technological capabilities and not on our understanding of human information processing limitations. Simulation provides an opportunity to observe and measure responses to life-threatening cardio-respiratory events in a time frame and with a degree of accuracy not feasible through assessment of response to true events. We propose to use simulation to assess human- system performance in the context of remote monitoring. Our objective is to determine the impact of increasing the number of patients monitored on response time to cardio-respiratory events. To achieve this objective, we will first design and test high-fidelity simulation of cardiac telemetry as a method for measuring patient load effects on technician performance. We will design a realistic simulation that replicates the work of cardiac telemetry technicians using a combination of real patient data and a simulated patient experiencing a VT/VF event. We will pilot test the experimental procedure with different patient loads. Second, we will carry out a randomized controlled trial to determine the impact of increasing number of patients monitored on response time to life-threatening arrhythmias. We will compare response times across five number-of-patient conditions. Through statistical analyses and plots of number of patients vs. response times, we will identify ratios at which performance declines. The expected outcome of this study is a scientifically established technician-to-patient ratio that will balance the critical importance of timely arrhythmia detectio with cost and efficiency requirements. The knowledge to be gained will inform efforts to study this problem in real-world cardiac telemetry and, ultimately, help to develop evidence-based standards for remote monitoring. The application of such standards is expected to improve survival after in-hospital cardiac arrest. PUBLIC HEALTH RELEVANCE: To increase the potential for timely detection and treatment of in-hospital cardiac events, more and more at-risk patients are now monitored remotely by cardiac telemetry technicians. However, decisions regarding the appropriate number of patients that a single technician may safely and effectively monitor are largely based on technological capabilities and not on our understanding of human information processing limitations. We propose to use high-fidelity simulation of cardiac telemetry to determine the impact of increasing the number of patients monitored on response time to cardio-respiratory events. The knowledge to be gained will inform efforts to study this problem in real-world cardiac telemetry and, ultimately, help to develop evidence-based standards for remote monitoring.