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MIT Better World

Young companies with MIT origins are leveraging data to improve human health in bold, creative ways—spanning such areas as insurance plans, neurological research, and drug development, as these three examples illustrate.

Benefits Science Technologies

Spun out of MIT in 2012, Benefits Science Technologies (BST) takes a three-pronged approach—descriptive, predictive, and prescriptive analytics—to helping companies optimize their health plans in a way that benefits both employer and employee. BST analyzes complex data on companies’ current health care spending and provides insights on the efficacy of the health care plan. Then, it predicts employees’ future health care costs, and based on these predictions optimizes the design of the health plan. The company’s science team is led by longtime MIT faculty member Dimitris Bertsimas SM ’87, PhD ’88, the Boeing Leaders for Global Operations Professor of Management at MIT Sloan, who co-directs MIT’s Operations Research Center.


Among the ambitious computing challenges being tackled at LeafLabs, founded by MIT alumni in 2009, is how to wrangle one of the biggest data producers in the world: the brain. In collaboration with the MIT Media Lab’s Synthetic Neurobiology group—which is led by professor of biological engineering and brain and cognitive sciences Ed Boyden ’99, MNG ’99 — LeafLabs has created Willow, a system for collecting and storing massive amounts of neural data. The system can process and store thousands of channels of electrophysiological data. This enables researchers to observe the activity of entire populations of neurons as they study crucial topics such as development and cognition, as well as brain diseases such as Alzheimer’s, epilepsy, and depression.


The path from clinical trial to FDA approval of new drugs and devices is lengthy and complicated. Spun out of the work of MIT CSAIL’s Turing Award-winning faculty member Michael Stonebraker in 2013, Tamr is shortening the path of clinical data conversion to standardized (CDISC) format through a combination of machine learning and human guidance. Tamr’s solution aggregates, cleans, validates, and converts study data into the submission standards mandated by the FDA, reducing data prep time by 85% to 90%. When CDISC standards are changed, Tamr updates older submissions to match new requirements. Tamr recently made headlines by offering to lend its technology and expertise to the White House Cancer Moonshot Task Force, free of charge.