Honors Gallery

New Enterprise Technology Transfer System Revolutionizes Tech Transfer at NIH

Award: Technology Transfer Innovation Award

Year: 2024

Award Type: National

Laboratory:
Department of Health and Human Services,​ National Institutes of Health, Office of the Director​

 

THE PROBLEM: At the National Institutes of Health (NIH), an inefficient system for documenting and storing data related to technology transfer (T2) activities made sharing information cumbersome, resulting in information silos and long wait times for information. Tech Transfer Offices (TTOs) in the NIH’s 27 Institutes and Centers work to manage the T2 activities of more than 6,000 researchers; the TTOs decide if patenting is appropriate, seek partners and licensees and negotiate agreements. However, TTOs had to access nine separate databases to enter this information, track the lifecycle of a technology and pull data to create reports. The NIH needed a centralized source of information that would allow stakeholders to view all data and relationships between patents, licenses, inventions and expenses. 

THE SOLUTION: A new database, the Enterprise Technology Transfer (ETT) system, was created to serve as the system of record for all activities performed by NIH’s TTOs. Technology Transfer activities – including inventions, patents, licenses and agreements – across all 27 NIH Institutes and Centers are now accessible from a single database. Over five years, over 50 people worked to bring the new database online – a process that entailed cleaning, consolidating and migrating 7,857 data fields; 591 data tables; and 13,337,463 records. 

ETT was built to bring automation to processes and workflows, improve efficiency by eliminating the need to duplicate work, help support full compliance with security and policy guidelines, provide increased transparency into NIH-wide approaches for negotiating agreements, and provide flexibility and support to users. When creating ETT, the team strived to make T2 tasks easier with features like dashboards and options to show a user a summary of the records (such as agreements or patents) they manage. Custom query grids, a popular ETT feature, allow users to review the results of a set search criteria with one click; this greatly speeds up the search process and ensures that the right results are returned by removing the possibility of missing a filter through user error. 

THE IMPACT: ETT makes T2 at NIH more efficient, which helps move more inventions from the lab to market to benefit public health. The data, processes and reports housed in ETT have an immense impact on public health. The 200+ staff who rely on this system to complete and track their work manage 39,324 active transactional agreements, 2,020 active licenses, 9,285 patents, 586 CRADAs, 1,638 technologies available for licensing or collaboration, and collected more than $704.4 million in royalties in fiscal year 2022. They also executed 440 research collaboration agreements and 80 clinical trial agreements. 

ETT has become the backbone of T2 at NIH, automating processes, data validation and approval workflows across the agency’s entire T2 community. The system enables anyone to gather information they need without relying on another person while also improving transparency by providing the real-time status of objects such as patent submissions and license applications. ETT eliminates the need to replicate data across multiple systems or places in the system, supports full compliance with all NIH security guidelines and provides flexibility for TTOs by enabling customized solutions as needed. 

Team Members:

Bill Bigelow, Akshay Bhardwaj, Adam Dahl, Kyle Doss, Brian Gallagher, Catherine Goldsborough, Terry Goodell, Mitchell Ha, Richelle Holnick, Timothy Leahy, Jarod Raedels, Prasanna Raja, Nicholas Ratliff, Sougata Roy, Falguni Sanghani, Tarunikha Sriram, Amanda Wingo, Antony Zacharias, Susan Ano, Krishna Balakrishnan, Carolyn Buller, Michael Davis, Christopher Dillon, Suzanne Frisbie, Bruce Goldstein, Tara Kirby, Haiqing Li, Mike Mowatt, Charles Niebylski, Thomas Stackhouse, Anna Solowiej, Surekha Vathyam, Jenny Wong, Mayra Alvarez, Andy Burke, David Bradley, Eggerton Campbell, Anton Dawson, John Devany, Terry Diaz, Kevin Doran, Lisa Finkelstein, Kathy Higinbotham, Bejamin Hurley, Laura Bailey Joell, Vincent Kolensnitchenko, Laura Lana-Unsworth, Charlene Maddox, Yogikala Prahbu, Richard Rodriguez, Jill Roering, Karen Rogers, Svetlana Smith, Betty Tong, Rosemary Walsh.

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