Earlier this year (and quietly, we might add) a group of Northwestern University engineers published a study that focused on how to leverage data hiding in plain sight for pain care. The scientists developed and applied artificial intelligence (AI), or machine-learning, algorithms to physiological data. They used respiratory rate, blood pressure, heart rate, body temperature and oxygen levels from patients with chronic pain from sickle cell disease. Their approach outperformed baseline models to estimate subjective pain levels. It also detected changes in pain – atypical pain fluctuations.
Pain is subjective, so it’s tricky to assess when trying to treat patients,” said Northwestern’s Daniel Abrams, senior author of the study. “Doctors don’t want to undermedicate patients and not provide enough pain relief. But they also don’t want to overmedicate their patients because there is a risk of side effects and addiction.
The study was published March 11 in the journal PLOS Computational Biology. Find it here. Boom! This is the first paper to demonstrate that machine learning can be used to find clues to pain hidden within data from patients’ own vital signs. Watch for more blog posts on pain RWD.
Our most innovative Celéri client providers use RWD from our Real World Outcomes Engine™ in their everyday practice to support care planning and population health. Here
Real world data (RWD) involves data collected outside of clinical studies and their scientific constraints. It often comes from EHR, EMR, registries, and claims data.
Pain severity and poverty level. Adverse pain outcomes and their association with unemployment and lower education. These ‘social determinants of health’ (SDoH) refer to
