There is also a concern that the implementation of new devices with alarm capabilities will add to the growing problem of alarm fatigue. As these devices are essential in capturing and using real-time patient data for timely interventions, a concerted effort should be made to mitigate this issue.
This article focuses on the unique challenges of alarm management and real-time data delivery in the care and management of patients at risk for respiratory depression. It also includes strategies that promote safer, efficient monitoring of these patient populations, and better use of real-time patient data.
Common monitoring strategies
Monitoring typically falls into three broad categories, each varying in complexity and comprehensiveness of surveillance. The first involves transmitting physiologic sensor measurement data and alarm signals sent from multi-parameter monitoring devices to a central station. Patients may not necessarily be continuously monitored in such cases. The second is sending the preceding data and alarm signals to a telemetry room, which can be quickly and easily overwhelmed by the hundreds of alarm signals that could potentially be generated by a single patient—of which 85% to 99% require no intervention (Bernoulli, 2016; Zaleski, 2014).
The third and arguably most sophisticated category is the use of smart alarms—tailored to identify clinically actionable notifications—that are transmitted to a device held by direct-care clinical staff. This approach provides an accurate and real-time picture of a patient’s condition, enabling direct-care patient staff and physicians to intervene before the patient begins to deteriorate. Moreover, attenuating alarm data balances the communication of contextual patient safety information with the minimization of false alarms or events that do not indicate a threat to patient safety (Bernoulli, 2016; Zaleski, 2014).
Finally, smart alarm strategies allow for the analysis of not just the alarm signals, but also the high-fidelity physiological data associated with them, including time trends, in-depth alarm sensitivity, and statistical and predictive analysis.
Alarm reduction and continuous monitoring
Nine in 10 hospitals indicated they would increase their use of patient monitoring, particularly capnography and pulse oximetry, if false alarms could be reduced (Wong, Mabuyi, & Gonzalez, 2013). However, the number of alarm-enabled medical devices on the market today, narrow alarm limits, and inaccurate default settings can make alarm management a complex endeavor.
Several techniques and strategies exist for reducing alarms, including trending alarms, which expand or contract patient alarm limits on individual devices; consecutive alarms, in which patterns of a consistent alarm are detected, occurring over a clinician-defined period of time; sustained alarms, which set a minimum time threshold that an alarm limit must be violated prior to the alarm sounding; and combination alarms, in which multiple simultaneous parameters from different devices may together indicate patient degradation.
Leveraging these techniques requires both middleware technology (more on this follows) and a holistic perspective of the care unit’s current environment. The latter can be enabled with a baseline alarm study that includes time trends, as well as in-depth alarm sensitivity and statistical and predictive analysis. The end result is a critical tool for standardizing alarm management and developing evidence-based best practices to safeguard patient safety, increase efficiency, and identify critical areas for improvement.
Data delivery, communication, and integrity
Certainly, physiologic devices are critical components to continuous patient monitoring and capture a more complete and real-time picture of a patient’s condition. Capnography, along with continuous pulse oximetry monitoring, could provide a sensitive and early predictor of opioid-induced respiratory depression. Capnography is used to measure exhaled end-tidal carbon dioxide (ETCO2) and fraction of inspired carbon dioxide (FiCO2) to determine a patient’s respiratory rate and generate waveforms (i.e., capnograms) of exhaled carbon dioxide over time (see sidebar).
Just as critical is implementing a device-agnostic middleware platform for interfacing with bedside devices. Middleware can be leveraged to combine data from medical devices with other data in a patient’s record to create a more holistic and complete picture of the patient’s current state. Combining analysis with real-time data at the point of collection creates a powerful tool for prediction and decision support. The ability to track patients throughout the hospital, continuously add new devices, and distribute real-time patient monitoring to centralized dashboards and mobile devices should be a major consideration in technology selection.