Brigham Research Institute Poster Session Site logo-1
Close this search box.

Nicole Rosario Nieves, M.Eng.







Research Assistant II


Internal Medicine


*Nicole Rosario, MS, Madison Kimball, MS, Henry Mitchell, Carme Hernandez, PhD, MSc, RN, FERS, David M Levine, MD MPH MA1,5

Clinical outcomes during continuous monitoring of acutely ill adults

From a young age, I have always been interested in science. This led me down the route of medicine for most of my academic career. However, I quickly found my niche data analytics and the use of programming languages for biology-based research. Through the examples of women like my university professors and other women in STEM, I found my passion in academia and my professional career at Brigham and Women’s Hospital. Here I’ve had the ability and versatility to engage with patients at a personal level as well as further my expertise in machine learning and analytical perspectives.


Alarm oversaturation and subsequent alarm fatigue experienced by clinicians are patient safety hazards. Clinicians desensitized to alarms are more likely to miss or ignore patients with actionable alarms. This effect is proportional to the perceived reliability of the alarm system. This study sought to quantify alarm burden and clinical utility. 


A VitalConnect patch and monitor system was attached to the study patients to collect vitals data and produce and record fall, respiratory rate, and heart rate. Clinicians prospectively annotated any actions taken in response to the triggered alarms. 


In a study of 593 patients, a total of 6324 alarms were triggered: 2184 HR, 4047 RR, and 93 fall alarms. Only 22 HR, 8 RR, and 3 fall alarms showed clinical significance. Patients had an average of 10.7 alarms per stay only 0.06 of which were actionable on average. Most commonly, patients had no alarms (19%) and only 10% of patients had 28 or more throughout their stay.  


The alarm systems studied produced large alarm burdens, with high false-positive rates, and few actionable alerts. This shows the need for new systems built via AI and machine learning, that provide fewer, more clinically useful alerts, better informing hospital staff of patient needs.