Defeating information overload in health surveillance using a metacognitive aid innovation from military combat systems

Abstract

Modern sensors for health surveillance generate high volumes and rates of data that currently overwhelm operational decision-makers. These data are collected with the intention of enabling front-line clinicians to make effective clinical judgments. Ironically, prior human–systems integration (HSI) studies show that the flood of data degrades rather than aids decision-making performance. Health surveillance operations can focus on aggregate changes to population health or on the status of individual people. In the case of clinical monitoring, medical device alarms currently create an information overload situation for front-line clinical workers, such as hospital nurses. Consequently, alarms are often missed or ignored, and an impending patient adverse event may not be recognized in time to prevent crisis. One innovation used to improve decision making in areas of data-rich environments is the Human Alerting and Interruption Logistics (HAIL) technology, which was originally sponsored by the US Office of Naval Research. HAIL delivers metacognitive HSI services that empower end-users to quickly triage interruptions and dynamically manage their multitasking. HAIL informed our development of an experimental prototype that provides a set of context-enabled alarm notification services (without automated alarm filtering) to support users’ metacognition for information triage. This application is called HAIL Clinical Alarm Triage (HAIL-CAT) and was designed and implemented on a smartwatch to support the mobile multitasking of hospital nurses. An empirical study was conducted in a 20-bed virtual hospital with high-fidelity patient simulators. Four teams of four registered nurses (16 in total) participated in a 180-minute simulated patient care scenario. Each nurse was assigned responsibility to care for five simulated patients and high rates of simulated health surveillance data were available from patient monitors, infusion pumps, and a call light system. Thirty alarms per nurse were generated in each 90-minute segment of the data collection sessions, only three of which were clinically important alarms. The within-subjects experimental design included a treatment condition where the nurses used HAIL-CAT on a smartwatch to triage and manage alarms and a control condition without the smartwatch. The results show that, when using the smartwatch, nurses responded three times faster to clinically important and actionable alarms. An analysis of nurse performance also shows no negative effects on their other duties. Subjective results show favorable opinions about utility, usability, training requirement, and adoptability. These positive findings suggest the potential for the HAIL HSI system to be transferrable to the domain of health surveillance to achieve the currently unrealized potential utility of high-volume data.

It’s not you, it’s the design - common problems with patient monitoring reported by anesthesiologists: a mixed qualitative and quantitative study.

“…All six statements were significantly agreed on, which by quantitative confirmation of the agreement with the central statements, increases the validity of the results. Although alarm fatigue, [19][20][21][22] information overload, 23 and measurement artifacts, 24 had already been identified as problematic, this study confirms that these issues are still regarded as problematic by anesthesiologists in 2018 and, additionally, there still seems to be considerable need for improvement also in previously less-known areas of ubiquitously used patient monitors. As a descriptive example, in Figure 4, we provide a photo, which illustrates the problem of "too many alarms" and the users' response to it.…”

“…They are often false positive, e.g., as a result of measurement artefacts, leading to alarm fatigue and potentially causing true positive alarms to go unnoticed because of induced insensitivity. [19][20][21][22][23][24] The rationale for this study was to learn more about the problems anesthesiologists consider common in their daily work with patient monitors. We hoped that these results would allow us to identify critical aspects for further development in future patient monitors.…”

Faster clinical response to the onset of adverse events: A wearable metacognitive attention aid for nurse triage of clinical alarms

“…Triaging an alarm/alert requires the clinician to understand its meaning and relevance to an individual patient’s care [ 36 ]. This usually requires substantial effort when the alarm signal is only an audible alarm sounding in a patient’s room, and includes: determining which patient’s alarm/alert is sounding and which RN is responsible; interrupting other tasks; walking to the room; and introduction of other task pressures that interfere with resumption of pre-interruption work [ 94 ]; accessing the alarm/alert information; and accessing relevant contextual information (ex., the patient’s vital signs) [ 107 ].…”

“…At best, relevant clinicians are notified at their mobile location about the types of alarms/alerts that have occurred. However, current solutions do not provide sufficient integrated context information to triage the alarm/alert occurrences without physically visiting the patient [ 94 ]. At worst, alarms/alerts noise sounds only at the bedside, and demand triaging from everyone within earshot, including patients themselves and their families [ 111 – 113 ].…”

“…It can force clinicians to choose to either perform their planned care delivery or check alarms. The core alarm safety problem, therefore, is not the high alarm rate (an acknowledged annoyance), but rather the high cost in time and mental resources required to determine whether any single alarm is clinically relevant or not [ 94 ]. This also clarifies why three decades of advancements in alarm generation technologies have not yet produced a practical solution for care delivery in practice [ 78 , 114 ].…”