Using LLM to optimize National Health Interview Survey (NHIS) case note information
Description
The National Health Interview Survey (NHIS) employs Census field representatives (FRs) who use open text fields, referred to as ‘case notes,’ to document their interactions with households during screening and interview processes. These case notes serve as a valuable resource, offering insights into the nature of these interactions and aiding in the identification of ‘base cases’—instances that may reveal significant data quality issues. Currently, the review of case notes is performed manually on a case-by-case basis, which limits opportunities for optimization. The objective of this initiative is to explore how large language models (LLMs) can enhance the efficiency and effectiveness of the case notes review process.
Detailed example
Identifying additional problematic cases not referred by Census; examining all cases from some FR whose case was referred to confirm whether similar issues exist in other cases the FR worked on; identifying themes in the case notes like certain letters/respondent materials that are in use, problematic interview strategies, or respondent confusion with questions.
AI / analytics pattern
Natural Language Processing: AI that processes, interprets, and shares information in human language.
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
Utilizing large language models (LLMs) for case note reviews provides several advantages, including substantial time and cost savings, improved data quality post data collection, and the creation of more effective training programs. These enhancements not only optimize operational efficiency but also support the goals of public health organization by ensuring that high-quality data is readily available for informed decision-making.
Controls / human review
ATO: Yes; PIA: Not published