Role of Large Clinical Datasets From Physiologic Monitors in Improving the Safety of Clinical Alarm Systems and Methodological Considerations: A Case From Philips Monitors.
BACKGROUND
Large datasets of the audit log of modern physiologic monitoring devices have rarely been used for predictive modeling, capturing unsafe practices, or guiding initiatives on alarm systems safety.


OBJECTIVE
This paper (1) describes a large clinical dataset using the audit log of the physiologic monitors, (2) discusses benefits and challenges of using the audit log in identifying the most important alarm signals and improving the safety of clinical alarm systems, and (3) provides suggestions for presenting alarm data and improving the audit log of the physiologic monitors.


METHODS
At a 20-bed transplant cardiac intensive care unit, alarm data recorded via the audit log of bedside monitors were retrieved from the server of the central station monitor.


RESULTS
Benefits of the audit log are many. They include easily retrievable data at no cost, complete alarm records, easy capture of inconsistent and unsafe practices, and easy identification of bedside monitors missed from a unit change of alarm settings adjustments. Challenges in analyzing the audit log are related to the time-consuming processes of data cleaning and analysis, and limited storage and retrieval capabilities of the monitors.


CONCLUSIONS
The audit log is a function of current capabilities of the physiologic monitoring systems, monitor's configuration, and alarm management practices by clinicians. Despite current challenges in data retrieval and analysis, large digitalized clinical datasets hold great promise in performance, safety, and quality improvement. Vendors, clinicians, researchers, and professional organizations should work closely to identify the most useful format and type of clinical data to expand medical devices' log capacity.
School of Nursing, Department of Health Restoration & Care Systems Management, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States. sowan@uthscsa.edu.
Azizeh Khaled
http://orcid.org/0000-0001-7719-2037
AK
Sowan
Charles Calhoun
http://orcid.org/0000-0001-6037-538X
CC
Reed
Nancy
http://orcid.org/0000-0002-0960-341X
N
Staggers
alarm fatigue; audit log; clinical alarms; intensive care unit; large clinical data; nursing; physiologic monitors
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