Delve Health and UW Medicine are set to partner to improve research into Type 2 Diabetes by using artificial intelligence and machine learning (AI/ML) and remote data capture through the former’s digital healthcare platform.
Delve Health’s mobile and web-based Clinical StudyPal platform enables decentralised clinical trial research and remote patient monitoring.
It has been configured for capturing patient information through a wearable device (smart watch), 24-hours a day, procured and delivered through the company’s services network.
The app will simultaneously gather quality data such as the heart rate of a patient at 15 second intervals, activity levels, and SpO₂ levels.
By monitoring, tracking, and analysing, the digital health solution will report the patient’s data analytics to the clinical study staff team.
Delve Health CEO and founder Wessam Sonbol said: “This endeavor with UW Medicine, focused on AI/ML, will provide clinical insights captured through remote patient monitoring and, therefore, will advance diabetes research.
“Having real-world evidence (RWE) in near real-time will not only assist our collective efforts to ultimately improve diabetes clinical research trials and the overall patient experience, but we will also retain actionable data that additional diabetes studies can learn from and build upon.”
In this new clinical trial, UW Medicine aims to gather a cross-sectional dataset of over 4,000 individuals in the US, with dual-balancing for self-reported race/ethnicity and four stages of diabetes severity.
The Clinical StudyPal platform from Delve Health will help decentralise the trial and allow UW Medicine to recruit patients from all over the country, regardless of the geographical location of a patient.
University of Washington School of Medicine Ophthalmology associate professor Dr Aaron Lee said: “Our NIH-funded Bridge2AI project called Artificial Intelligence Ready Equitable Atlas for Diabetes Insight (AI-READI; award project number 1OT2OD032644-01) will collect and release a flagship medical dataset for salutogenesis that will hopefully accelerate machine learning applications and generate novel hypotheses about Type 2 Diabetes Mellitus.
“As part of this dataset, we are collecting wearable fitness tracking data, along with continuous glucose monitoring, to build a biophysical profile of each participant.”
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