Finalist: Development of an Automated Multimodal Sensor to Improve Patient Outcomes in Hemodialysis
The proposed innovation provides for an integrated, automated means of monitoring of important physiological parameters during hemodialysis, including blood volume status (absolute and relative), vascular access function (flow rate and circulation) and ultrafiltration rate. In the current ESRD care environment in the United States, minimizing workflow and usability burden is of paramount importance in driving technology adoption. Specially designed flow probe sensors are integrally mounted to a hemodialysis machine, and seamlessly couple with the patient's blood tubing set when it is attached for treatment. All actions needed to perform measurements are timed and fully automated by the hemodialysis machine, eliminating the need for user intervention and associated user-based error.
By taking this approach, measurements which previously require trained users, standalone equipment and/or significant workflow disruption can be performed easily and more frequently. This is especially impactful in care settings such as in-home or in-center self-care hemodialysis, where patients takes on greater ownership of their therapy. By eliminating dependence on external factors, the technology is highly applicable in these settings, giving the patient expanded empowerment and increased participation in their care.
Data obtained is not only valuable to guide patient care (e.g. guiding ultrafiltration rate or goal, referrals for preventative vascular access procedures) but can also be a powerful tool to drive patient engagement and autonomy. Curating and delivering this data via smartphone app to a patient can be used to provide direct feedback and encourage 'nudges' in lifestyle activities (e.g. fluid intake regulation, care of their vascular access). Application of data science and advanced analytics to large pools of such data, anonymized and coupled with symptoms/outcome information can be used to develop smart algorithms for real-time, closed loop treatment control.
Submitted by Dean Hu, Michael Aragon, and Nikolai Krivitski on behalf of Outset Medical, Inc. and Transonic Systems.
To learn more, please visit www.outsetmedical.com.