An app created by Rensselaer Polytechnic Institute students that identifies social conditions contributing to declining life expectancy at a community level is a Phase 1 winner in a data visualization competition sponsored by the U.S. Department of Health and Human Services.
The Phase 1 prototype of “MortalityMinder” identifies social determinants – including measures of health behavior, clinical care, the physical environment, and social and economic factors – that contribute to “deaths of despair” due to suicide and substance abuse in New York state. As they advance to Phase 2, the student developers will expand the app to identify social determinants that contribute to the leading causes of death nationwide.
“We are designing ‘MortalityMinder’ for decision-makers at all levels,” said data scientist Kristin Bennett, a professor of mathematical sciences and leader of the Health Analytics Challenge Lab course, whose students are building the app. “We hope that by finding the community-level factors, we will provide insights into potential causes that are actionable, so we can actually develop better policies and programs to help people and make them have healthier, longer lives.”
The Health and Human Services Agency for Healthcare Research and Quality awarded a $10,000 prize to Phase 1 winners of the Visualization Resources of Community-Level Social Determinants of Health Challenge.
“Congratulations are in order for the Rensselaer students in the Health Analytics Challenge Lab who are developing this fascinating tool,” said Curt Breneman, dean of the School of Science. “The interplay between our community and our lifespan is a perspective that has been neglected, but I suspect the app will reveal just how central our community is to our well-being. It’s an excellent example of the power of data science and the fusion of disciplines at the core of The New Polytechnic.”
Bennett, associate director of the Rensselaer Institute for Data Exploration and Applications, is attracting the attention of hospitals and health insurers for her work in “precision health care” research and pedagogy. In contrast with precision medicine, which uses data to tailor treatments to a specific patient, precision health care uses data for a broader and more complex purpose – improving health-care delivery and outcomes for groups of patients with distinct needs.
An example of her work, published in Big Data, analyzes Medicare patient records from a local hospital, to find out why some patients are likely to land back in the ER within three days of a visit. In a new project funded by insurer Capital District Physicians Health Plan, Bennett is using machine learning and data analytics to understand why costly interventions succeed for some patients but not others.
Bennett’s work has also propelled a number of pedagogical innovations at Rensselaer, including the first-in-the-nation “data dexterity” requirement for all undergraduates at the institute. She is also preparing the next generation of professionals for careers in health-care data through a $1.1 million project funded by United Health Foundation. A critical component of the project addresses the barriers posed by patient privacy restrictions through a “synthetic data generator” under development in partnership with Optum labs. The tool, analogous to a flight simulator, trains students for health-care data careers on realistic simulated data, allowing them to tackle real-world health data challenges, and test solutions.
Rensselaer’s team of students will be finalizing their Phase II entry during the fall semester. The MortalityMinder App will be competing against 11 other finalists from industry, academia, health-care institutions, and private individuals for the first prize of $50,000.