Within the healthcare system, nurses, are involved in many critical steps of the patient care process such as surgery triaging, post-procedure recovery monitoring and handoff release to a caregiver. A significant portion of their time is spent on the hospital floors where patients recover from their medical procedures. In today’s healthcare environments, multiple devices – typically monitors, ventilators, and infusion pumps – are used during said patient recovery process. Health equipment manufacturers often add alarms to medical devices, which serve a variety of purposes, ranging from simple notifications to warnings and alerts about potential hazards that require rapid action. In typical hospital units, several types of medical devices that monitor a variety of parameters based on patient and nurses/assistants needs. Many devices have similar alarm tones, regardless of risk levels. A typical nurse will attend to multiple patients, and the number of alarms that require attention place tremendous demands on nurses’ cognition, which causes enormous alarm fatigue. Alarm fatigue is not a new phenomenon and is very common in other industries, such as chemical processing, and nuclear power. The additional stress and burden of false alarms and non-actionable alarms is also troublesome. Many for-profit companies have developed commercial alarm management tools and aids to combat these problems and the rapid adoption of smart phones and tablets in healthcare has made alarm management more mobile and visual. However, even after these advances, the number of deaths and adverse events are still at an unacceptable level. The purpose of this study to establish that the current training methods used by various hospitals are inadequate and to explore the effects of rigorous one-on-one training and metacognitive intervention in managing alarm related adverse events. This study also identifies deficiencies in the current training methods and assesses the impact of individualizing alarm threshold settings on alarm workload, response and error rates.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-4165 |
| Date | 14 December 2018 |
| Creators | Shanmugham, Manikantan |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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