Development of a natural language processing algorithm to extract social determinants of health from clinician notes.

Disparities in access to the organ transplant waitlist are well-documented, but research into modifiable factors has been limited due to a lack of access to organized prewaitlisting data. This study aimed to develop a natural language processing (NLP) algorithm to extract social determinants of health (SDOH) from free-text notes and quantify the association of SDOH with access to the transplant waitlist. We collected 261 802 clinician notes from 11 111 adults referred for kidney or liver transplants between 2016 and 2022 at the Duke University Health System. An SDOH ontology and a rule-based NLP algorithm were created to extract and organize terms. Education, transportation, and age were the most frequent terms identified. Negative sentiment and refer were the most negatively associated features with listing in both kidney and liver transplant patients. Income and employment for the kidney, and judgment and positive sentiment for liver were the most positively associated features with the listing. This study suggests that the integration of NLP tools into the transplant clinical workflow could help improve collection and organization of SDOH and inform center-level efforts at resource allocation, potentially improving access to the transplant waitlist and posttransplant outcomes.