AI is transforming many industries. But addressing the full cycle, from development through deployment, requires key system engineering building blocks. Without these frameworks, efforts can be costly and unsuccessful. Learn how an AI systems engineering approach can avoid implementation pitfalls in this live webinar—a preview of the upcoming live virtual course AI Strategies and Roadmap: Systems Engineering Approach to AI Development and Deployment.
Principal Investigator Arthur Baggeroer
Principal Investigator Richard Cohen
We present an example of ongoing research in the space of analytics-driven personalized healthcare and showcase an example of a healthcare technology startup spun off of our research endeavor.
The first part of the talk discusses an ongoing research work on personalized diabetes management. Current clinical guidelines for managing type 2 diabetes do not differentiate based on patient-specific factors. We present a data-driven approach for personalized diabetes management that improves health outcomes relative to the standard of care. We modeled outcomes under thirteen pharmacological therapies based on electronic medical records from 1999 to 2014 for 10,806 type 2 diabetes patients from Boston Medical Center. We developed a recommendation algorithm that prescribes a regimen if the expected improvement from switching regimens exceeds a threshold. For patient visits in which the algorithmic recommendation differed from the standard of care, the mean post-treatment glycated hemoglobin (HbA1c) under the algorithm was lower than standard of care by 0.44% +/- 0.03% (p << 001), from 8.37% under the standard of care to 7.93% under our algorithm. A personalized approach to diabetes management yielded substantial improvements in HbA1c outcomes relative to the standard of care. Our prototyped dashboard visualizing the recommendation algorithm can be used by providers to inform diabetes care and improve outcomes.
The second part of the talk presents an overview of MyA Health, a spinoff based on similar research efforts aimed at personalizing health care down to the individual. MyA is powered by a wealth of data sources encompassing historical claims, electronic medical records, wellness and biometric data, wearable device records, and consumer lifestyle data. The backend of MyA is empowered by a high-dimensional analytics engine with: (1) a suite of predictive machine learning algorithms to predict future healthcare costs, disease progression and outcome variability; and (2) robust optimization algorithms to optimize and personalize healthcare decisions that will best mitigate an individual’s financial burden and maximize their healthcare outcomes. To the consumer, MyA is an individual’s healthcare advisor that personalizes decisions ranging from what health plan is best to cover their risk to what drug/treatment is likely to benefit them the most. MyA is unique in that it takes the totality of data sources available to make personalized recommendations, a concept that is made possible given the healthcare data digitization revolution and the increasing adoption of wearable wellness and health monitoring devices.
2016 MIT Digital Health Conference
Principal Investigator George Westerman