Leveraging Computer Vision in the Intensive Care Unit (ICU) for
  Examining Visitation and Mobility
Scott Siegel Jiaqing Zhang Sabyasachi Bandyopadhyay Subhash Nerella Brandon Silva Tezcan Baslanti Azra Bihorac Parisa Rashidi
Despite the importance of closely monitoring patients in the Intensive Care
Unit (ICU), many aspects are still assessed in a limited manner due to the time
constraints imposed on healthcare providers. For example, although excessive
visitations during rest hours can potentially exacerbate the risk of circadian
rhythm disruption and delirium, it is not captured in the ICU. Likewise, while
mobility can be an important indicator of recovery or deterioration in ICU
patients, it is only captured sporadically or not captured at all. In the past
few years, the computer vision field has found application in many domains by
reducing the human burden. Using computer vision systems in the ICU can also
potentially enable non-existing assessments or enhance the frequency and
accuracy of existing assessments while reducing the staff workload. In this
study, we leverage a state-of-the-art noninvasive computer vision system based
on depth imaging to characterize ICU visitations and patients' mobility. We
then examine the relationship between visitation and several patient outcomes,
such as pain, acuity, and delirium. We found an association between
deteriorating patient acuity and the incidence of delirium with increased
visitations. In contrast, self-reported pain, reported using the Defense and
Veteran Pain Rating Scale (DVPRS), was correlated with decreased visitations.
Our findings highlight the feasibility and potential of using noninvasive
autonomous systems to monitor ICU patients.