Leveraging the Metadata of Physiologic Clinical Alarms to Impact Alarm Management, Patient Safety, Utilization, and Staffing

Reducing patient harm associated with clinical alarm systems is a national patient safety goal that is a multifaceted problem, requiring systematic and interdisciplinary coordinated solutions. A means to developing a coordinated solution is to leverage physiologic clinical alarm data queries that provide actionable insights. The rich metadata associated with physiologic clinical alarms can be used to develop queries with vast operational use models. These use models should be accessible to a wide range of professionals, including the bedside caregiver.

A foundational alarm query with numerous use models is alarm load. As opposed to aggregated counts of alarms, alarm load rationalizes alarm volume to alarms per monitored bed day. Simply the number of alarms divided by monitored bed days. Filters such as time, unit, bed, and institution can be applied to this query. This query allows an end user to benchmark alarm data, or alarm management methodology, between filter criteria elements. Often unit leadership will benchmark their alarm load queries between like units to gain insights on how unit practices and monitoring utilization may impact clinical alarm management between units.
The high-risk nature of clinical alarm management provides an opportunity for informatics professionals to evaluate alarm queries used in other high-risk fields. Alarm floods defined by the American National Standard Institute (ANSI) as 10 or more annunciated alarms in a 10-minute period per operator is a query that has utility in health care. Alarm floods have the potential to exhaust a caregiver's situational awareness and negatively impact decision-making. The metadata of alarm annunciated time, end time, and caregiver assignment can be used to generate this query. A potential use model for this query is within the care environment level. A charge nurse can utilize this query to adjust staffing levels to better care for high-acuity patients with multiple alarm floods and support staff cognitive demands while caring for these patients.

Caregiver response to physiologic clinical alarms is centric to an institution's alarm management methodology. Measuring the time a caregiver takes to respond to an alarm requires the metadata of alarm type, alarm start time, alarm stop time (from an intentional action), and data points for contextual filtering, such as unit, bed, and alarm severity. The response time can be calculated as an average with a contextual filter applied, such as unit, bed, and severity, to provide additional meaning. Various professionals at an organization can use response times to evaluate if alarms are meaningful, staffing is appropriate, or the policy supports response. Evaluating alarm queries related to nuisance alarms and how alarms terminate or end may complement alarm response queries.

Viewing physiologic clinical alarms as data rich with metadata provides an informatics professional with a sense of creativity in developing alarm data queries. The informatics professional should strive to develop use models for these queries that all levels of an organization can operationalize.

Kyle Karajankovich discloses that he is an employee of Philips Healthcare.

Jennifer Rist discloses that she is an employee of Philips Healthcare.