Workload Effects on Response Time to Life-Threatening Arrhythmias

A growing number of hospitalized at-risk patients are being monitored remotely by cardiac telemetry technicians to increase the potential for timely detection of cardiac events. However, decisions regarding the number of patients that a single technician can safely and effectively monitor appear to be primarily based on technological capabilities and not on our understanding of human information processing limitations. Limitations of human visual memory and eye scan rates may place upper limits on the number of patients that a technician can safely monitor. In addition, we know that humans are relatively poor at maintaining attention over long periods of time, and quickly succumb to a vigilance decrement, a reduction in detection performance over time. At Duke University Hospital, we estimate that our technicians see a life-threatening pulseless ventricular tachycardia or ventricular fibrillation (VT/VF) rhythm about once or twice a month. Because these are rare events, they are difficult to study in clinical practice, and it is difficult to assess the effect of different patient loads on performance with respect to prompt response to these life-threatening events. We are completing a grant funded by the Agency for Healthcare Research and Quality, “Workload effects on response time to life-threatening arrhythmias,” in which we used laboratory-based simulation to determine the impact on detection time of increasing the number of patients monitored.

Figure 1. Simulated VF patient (number 15) embedded with real patients.

Figure 1. Simulated VF patient (number 15) embedded with real patients.

We designed a realistic simulation that replicated the actual tasks performed by remote telemetry technicians at Duke University Hospital. We video- and audio-recorded true patient data with a single simulated patient embedded in the patient set. The technical implementation involved connecting an ECG rhythm simulator into the hospital’s network that transmits physiological signals to remote telemetry monitors. The signal appeared in exactly the same way as it would appear for a real patient. Since multiple patients are monitored simultaneously, the simulated signal appeared on the monitor as one of many signals (Figure 1).

We set up one display with 15 true patients and one simulated patient (Figure 1), another display with 8 patients, and 2 displays with 16 patients each, to simulate 16, 24, 32, 40, and 48 patients (see Figure 2 for the 48-patient setup). We recorded both audio (alarm) and video data for these screen setups for 4 hours. During the audio and video recording period, we simulated one VF arrhythmia. We timed the event to be well into the data collection period (after over 3 hours), to allow participants to become comfortable with the task environment and to possibly experience a vigilance decrement due to a long time on the task, similar to their daily work environment.

Figure 2. Experiment setup for 48 patients.

Figure 2. Experiment setup for 48 patients.

For the experiment, study participants monitored patients in a private room (Figure 2). They were randomly assigned to 1 of the 5 patient loads, and completed a 4-hour monitoring session. The study coordinator received and responded to calls made by participants to “the nurse” or “the unit coordinator.” Responses to calls were scripted. Participants were given instructions to call one number for routine calls and a different number for urgent calls (a button press to choose a line). Performance data were recorded manually in real time. During this session, the simulated patient sustained VF, and the time required for the participants to identify this arrhythmia was recorded.

The primary dependent variable is time lapse from the point at which the arrhythmia began to the time of the urgent call. Two secondary measures include (1) an overall percent correct score on the required actions defined for real patients, e.g., documentation and phone calls, and (2) an overall score for interpreting the patients’ ECG strips. Forty-two participants – remote telemetry technicians and nurses from cardiac units (e.g., cardiothoracic intensive care units) at Duke University Hospital and 3 surrounding hospitals – completed the study. Data analysis is ongoing.

The knowledge to be gained from our study 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.

Reference

Segall N, Hobbs G, Bonifacio AS, Anderson A, Taekman JM, Granger CB, Wright MC. Simulating remote cardiac telemetry monitoring. 14th International Meeting on Simulation in Healthcare. 2014.

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