Given the importance of the overnight period in inpatient care, it remains challenging to get objective, quantitative data on whether or not a patient had a good night. And, to be provocative: how do we even define what is meant by a “good night?” While there is growing awareness that patients very often have sleepless […]
This work builds on our initial research in AI computer vision in the Duke medical intensive care unit. This research demonstrated several compelling data-driven readouts of patient activity and room environment, highlighting its potential utility in sleep, sedation, agitation, and delirium
In this blog post, I review LookDeep Health’s IRB-approved collaboration with Oregon Health Sciences University (OHSU) on AI-enabled computer vision monitoring of trauma patients at-risk for delirium.
Inpatient video has massive potential as a new form of patient monitoring and as a new kind of medical data type. One obstacle to overcome to realize this potential is the fact that video generates massive amounts of data, way more than any human can interpret (much less clinicians who are pressed for time taking care of sick patients).
Monitoring patient activity in the ICU is essential for sedation management, identifying agitation, and diagnosing delirium. Despite its importance in ICU care, activity monitoring is intermittent, subjective, and qualitative.
