Taking "remote monitoring" to a new level: Deploying continuous physiology monitoring to manage suspected Ebola patients in Sierra Leone
In the Fall of 2014, Ebola was raging throughout western Africa. Tens of thousands of cases of infection and over eleven thousand deaths on the African continent were the result of the most insidious epidemic in the past 50 years. The news cycle was dominated by cases spreading to the U.S. and global health officials were desperate to find solutions that could scale in some of the poorest areas in the world. In an effort to stem the transmission of disease, support healthcare workers on the ground, and prepare for future outbreaks, U.S. Agency for International Development (USAID) partnered with the White House Office of Science and Technology Policy, the Centers for Disease Control and Prevention and the U.S. Department of Defense to launch Fighting Ebola: A Grand Challenge for Development.
PhysIQ was one of the companies selected in the process, as part of a coalition formed and managed by the Scripps Translational Science Institute.
The clinical publication can be accessed by this link or downloaded from this page.
While this was obviously an extreme use case, in all senses of the word, several lessons learned resonate and apply to more traditional ambulatory acute or post-acute scenarios:
- Mobile wireless technology is ready for prime-time, clinically speaking: End-to-end mobile wireless infrastructure was successfully deployed in Makeni, Sierra Leone. This is proof that ICU-quality clinical data can be a reality anywhere.
- There is a wealth of information within continuous physiological signals, such as ECG and accelerometry data: From ECG and accelerometry alone, physIQ is able to calculate over 60 features that help characterize physiology.
- “Band-aid” wearable sensors are well tolerated: For very sick patients in an extreme (read: hot) environment, the biosensors proved wearable and comfortable.
- Personalized, multivariate analytics are able to distill large amounts of physiological data into a simple index to indicate physiological change at the individual level: The wearable sensors generate a tremendous amount of raw data. Without advanced analytics, filtering the signal from the noise is extremely difficult.
AUDIENCE: Post Acute Care