Aditya V. Karhade
This is really great Alex, thanks for sharing. I especially value your point that the total sum of time spent in healthcare interactions for most patients is tiny compared to their experience outside of interactions with providers and payors. This is an unmet need that will be addressed, as you said, by using existing technologies, such as smartphones and wearables, to understand what types of data can be gather about patients continuously and how we can identify meaningful patterns in this massive stream of data that can then inform the care that we provide to patients. I’m hopeful that we will see this come to fruition in our lifetime.
This is amazing work and I’m very excited for the future of medicine reading about the advancements you’ve described. In some ways I hope that we can generalize the principles of AUTOMAP, effectively using AI to extend our human capabilities and achieving better patient outcomes by combining unique insights provided by physicians and algorithms. This relates to your questions about disagreements between physicians and AI and here I think advancements in AI that can provide more explanation will be crucial. One solution may be systems that will provide a diagnosis but simultaneously highlight in green the particular parts of the image that the algorithm used to support one diagnosis versus highlighting in red the parts of the image that the algorithm used to support a different diagnosis.
Thanks for sharing, this is really great work. Your question, “How can we provide transparency into why a machine learning algorithm is making a recommendation?” is particularly noteworthy because machine learning does offer the opportunity for more flexible modeling that may better approximate the true function that maps complex clinical inputs into meaningful insights. However, in order for machine learning to be deployed widely, there need to be real-time checks and balances on the algorithms developed in healthcare and as such providing both predictions and explanations for providers and patients will be crucial.
Thank you very much for sharing this important topic. You mentioned that 409.4mm glucose measurements, 86.4mm monitoring hours and 63.8mm scans by 50,000 people have been collected to date. This volume of data poses challenges for providers and patients and I’m particularly interested in the training and education that Abbott hopes to provide so that patients from all backgrounds can access and utilize this technology to the fullest extent.
This is very interesting; thank you for sharing. I’m wondering what types of data (“variety”) OneGuide currently collects and to what extent the data collection process is passive versus active. In addition to the clinical and physiologic data that Cigna has, I wonder if they’re making similar investments into patient-reported outcomes and how they see the role of technologies such as digital phenotyping (moment-to-moment quantification of patient health states and well-being) informing critical decisions such as early intervention, prevention and population health.
This is amazing work Casilda. Thank you for sharing. I’m wondering, based on your projected rates of fraud ($4 for every $1000), whether having imbalanced data affects the overall challenge of developing algorithms that can detect these problems.