Apps, International

MIT launches app in USA using crowdsourced data to highlight hospital resources

Massachusetts Institute of Technology has launched a tool in the United States, based on crowdsourced data, to help patients and professionals see real-time availability of hospital resources.

The tool aims to highlight and support capacity and resource capability as cases for COVID-19 continue to climb in parts of the United States.

A group of researchers in MIT’s Computer Science and Intelligence Laboratory (CSAIL), working with the MIT spinoff Mobi Systems, are aiming to help level demand across the health care network by providing real-time updates of hospital resources, which they hope will help patients and professionals quickly decide which facility is best equipped to handle a new patient at any given time.

The interface allows users to report a hospital’s current status, from the average wait time, to the number of ventilators and ICU beds, which doctors and nurses may be able to approximate.

MIT graduate Anna Jaffe ’07, CEO of Mobi Systems said “we want to flatten the Covid curve by physical distancing over the course of months. But there’s another curve to flatten, which is this real-time challenge of getting the right patient to the right hospital, in the right moment, to level the load on hospitals and health care workers.”

The app is reliant on crowdsourced data, Anna said “our question was, how can the resources statewide or nationwide be used most effectively, in order to keep the most people healthy. The reporting options right now are very specific. But what we really want to know is, can your hospital accept a patient right now?”

The team’s app is heavily dependent on crowdsourced data, and the willingness of patients and medical professionals to report on various metrics, from a hospital’s current wait time to the approximate number of ICU beds and ventilators available.

To address the potential of false reporting, the app assumes that one user’s reporting of a hospital’s status is one of low confidence, which is initially not weighed heavily in the overall estimation for that metric. They can then incorporate this one data point into all the other reports they’ve received for that metric. If most of those reports have also been rated with low confidence, but report the same result, that estimate, such as of wait time, is automatically weighed more heavily, and therefore rated at a higher confidence overall.

The team is now reaching out to thousands of medical professionals to test-drive the reporting tool.

Anna said “Even in a recovery period, hospitals will have to resume normal care, concurrently with treating Covid-19 over time. Our app may help load balance in that way as well, so hospitals can more effectively predict how many floors they need to quarantine for Covid-19, so that the rest of the hospital can go back to things like having families around a mother giving birth. We aim to really understand how to bring things back to a more normal operational status, while still handling the crisis.”