Optimizing organ donation: When big data and analytics helps save lives

Organ sharing network relies on technology platform to speed up vital report generation
Written by Bob Violino, Contributor

You might hear business executives talk about how critical big data and analytics initiatives are for their companies. But for some organizations, leveraging these technologies can actually be life-saving for individuals.

That's certain the case at the United Network for Organ Sharing (UNOS), the private, non-profit organization that manages the nation's organ transplant system. UNOS matches donated organs to transplant candidates who are waiting for them.

Also: IoT, big data, and wearables combine to boost safety

As part of its post-transplant research, UNOS generates Organ Offer Reports to supply data to transplant centers, but we wanted to make more information available more quickly via a self-service model. The reports give transplant centers a listing of all transplantation activity at their hospital for a given month.

To help accomplish this goal, UNOS has deployed technology from Talend, a software company that specializes in big data integration and management.

UNOS uses Talend's big data platform to generate Spark code to accelerate the integration of data. Talend data pipelines are feeding three separate Hadoop clusters, and the company's software generates results to a source system where Tableau data visualization software reads them and then serves up the Organ Offer Reports.

In general, transplant centers' success is measured by the number of successful outcomes, said Alex Tulchinsky, chief technology officer at UNOS.

Since using the Talend platform, UNOS has reduced data processing times from 18 hours to three or four, an 84 percent reduction in the amount of time needed to generate the reports.

"Now, more than 85 people are given a second chance at life each day," Tulchinsky said. With the help of big data and analytics, transplant surgeons can now evaluate their decisions and other surgeons' decisions, helping them be more informed and successful with their next transplantation, he says.

Getty Images/iStockphoto

The technology "has helped us create a much richer research environment for the community, with a self-service model that enables easier, faster access to more information than ever before," Tulchinsky said. "Now, the data's there, and we can refresh it weekly. Previously, it would have taken several weeks before we would get the information we needed. We are looking forward to seeing how this continues and how many more lives we can help save in the future."

There are nearly 120,000 people on the organ donation waiting list, making organ optimization essential and an ongoing challenge, Tulchinsky said. "When a transplant hospital accepts a transplant candidate and an organ procurement organization gets consent from an organ donor, both enter medical data into UNOS' computerized network," he said. Using the combination of candidate and donor information, the UNOS system generates a "match run," or a rank-order list of candidates for each organ.

"There is a tremendous sense of urgency, as a doctor has just one hour to decide whether or not to accept an organ for their patient on the list," Tulchinsky said. "This timeframe is critical, as organs have a limited timeframe in which they must be transplanted. We needed a way to give everyone access to the data and analytics to help them learn from others' decisions to make theirs more quickly and safely."


TechRepublic: How a software company is using AI to combat the opioid epidemic

Hc1 is applying analytics to drug usage and addiction datasets to provide national insights.

CVS Health and Aetna bet $69 billion merger on analytics, data, digital transformation

CVS Health and Aetna have merged in a bid to reinvent health care with a vertically integrated stack of services and touch points. And now comes the hard part: Using data and analytics to really make it all work.

Tech Pro Research: Culture, automation and self-service: The keys to big data success

Sumit Nijhawan, CEO and president at Infogix, outlines why many big data projects fail, and how companies can make the most of their investments in this area.

Editorial standards