Created by students at Rensselaer Polytechnic Institute (RPI), an app 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 US Department of Health and Human Services (HHS).
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.
Kristin P. Bennett, PhD, Rensselaer Polytechnic Institute.
“We are designing MortalityMinder for decisionmakers at all levels,” says data scientist Kristin P. Bennett, PhD, professor of mathematical and computer sciences at RPI 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 that we can actually develop better policies and programs to help people and make them have healthier, longer lives.”
The HHS Agency for Healthcare Research and Quality awarded a $10,000 prize to Phase 1 winners of its 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,” says Curt Breneman, dean of the RPI 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, who also serves as associate director of the RPI Institute for Data Exploration and Applications, is attracting the attention of hospitals and health insurers for her work in ‘precision healthcare’ research and pedagogy. By contrast with precision medicine, which uses data to tailor treatments to a specific patient, precision healthcare uses data for a broader and more complex purpose: improving healthcare delivery and outcomes for groups of patients with distinct needs.
An example of Bennett’s work analyzes Medicare patient records from a local hospital to find out why some patients are likely to land back in the emergency room within 3 days of a hospital visit.1 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.
RPI’s team of students will be finalizing their Phase 2 entry during the fall semester. The MortalityMinder app will be competing against 11 other finalists from industry, academia, healthcare institutions, and private individuals for the first prize of $50,000.
Reference
- Ryan J, Hendler J, Bennet K. Understanding emergency department 72-hour revisits among Medicaid patients using electronic healthcare records. Big Data. 2016;3(4):238–248; doi: 10.1089/big.2015.0038.
Featured image:
Students in the health analytics challenge lab course at Rensselaer Polytechnic Institute gather around the ‘Campfire,’ a multiuser, collaborative, immersive computing interface. Photo courtesy Rensselaer Polytechnic Institute.
