Excerpt featuring Jeff Timrbook, CEO of Thynk Health
In 2015, a team of radiologists acted on their frustrations with the inefficiencies surrounding lung cancer screening (LCS) . Practicing in a geographical area experiencing some of the highest rates of lung cancer in the nation, they saw lives cut short too often due to a lack of data management. The manual, human entry of patient data took too long and was prone to error resulting in inaccurate and incomplete information. This caused, among other things, at-risk, eligible patients to go unscreened simply because there was not enough capacity, or they had fallen through the cracks of a complex and overburdened system.
One major issue the team focused on was correcting the process of lung cancer screening programs’ data quality and reporting, which often required staffing several people to manually read patient records from disparate sources and re-enter them into data registries. This process was so time-consuming, that patient follow-ups and addressing incidental findings were being neglected. They knew they would need to completely automate the data abstraction process so that healthcare professionals could focus on patient care and outcomes rather than on databases.
As a result, they partnered with a team of engineers and began working on structured reporting using natural language processing technology. Realizing how difficult it can be to change a process, they focused on extracting and analyzing data being input into existing workflows that providers are comfortable with.
Today, the Thynk Health platform uses natural language processing and artificial intelligence to automate the data entry and data collection processes, thus improving the efficiency and effectiveness of lung cancer screening programs at hospital systems around the nation, helping more hospitals screen, diagnose and treat more at-risk patients.