  Objective: The n2c2/UW SDOH Challenge explores the extraction of social
determinant of health (SDOH) information from clinical notes. The objectives
include the advancement of natural language processing (NLP) information
extraction techniques for SDOH and clinical information more broadly. This
paper presents the shared task, data, participating teams, performance results,
and considerations for future work.
  Materials and Methods: The task used the Social History Annotated Corpus
(SHAC), which consists of clinical text with detailed event-based annotations
for SDOH events such as alcohol, drug, tobacco, employment, and living
situation. Each SDOH event is characterized through attributes related to
status, extent, and temporality. The task includes three subtasks related to
information extraction (Subtask A), generalizability (Subtask B), and learning
transfer (Subtask C). In addressing this task, participants utilized a range of
techniques, including rules, knowledge bases, n-grams, word embeddings, and
pretrained language models (LM).
  Results: A total of 15 teams participated, and the top teams utilized
pretrained deep learning LM. The top team across all subtasks used a
sequence-to-sequence approach achieving 0.901 F1 for Subtask A, 0.774 F1
Subtask B, and 0.889 F1 for Subtask C.
  Conclusions: Similar to many NLP tasks and domains, pretrained LM yielded the
best performance, including generalizability and learning transfer. An error
analysis indicates extraction performance varies by SDOH, with lower
performance achieved for conditions, like substance use and homelessness, that
increase health risks (risk factors) and higher performance achieved for
conditions, like substance abstinence and living with family, that reduce
health risks (protective factors).
