Structuring Notice of Concern Data
Description
How can the Office of Refugee Resettlement (ORR) clear its backlog of notices of concern (NOC) and minimize backlog in the future? Notice of Concern (NOC) forms contain critical information regarding safety of children who have left ORR's care. Some forms are received as scans, with the information not in machine-readable format. ORR receives hundreds of NOCs a day. Due to personnel shortage in the Prevention of Child Abuse and Neglect Team (PCAN) team responsible for reviewing and acting on NOCs, as of October 2024 there was a backlog of over 30,000 NOCs.
Detailed example
Structured data parsed from the subset of NOCs that are scans of documents AI is not used to triage NOCs, just to parse information from scanned documents. The parsed information is presented to the PCAN team alongside the original document for review and action.
AI / analytics pattern
Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
More effective and efficient review of NOCs With AI-enabled structuring of data in NOCs received in scanned formats, ORR can reduce the large backlog that has accumulated.
Audit / financial statement impact
The use of AI is narrowly focused on extracting key data points from unstructured narratives and validating data completeness. The outputs do not serve as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the 6 cases outlined in M-25-21, page 19.
Controls / human review
ATO: Yes; PIA: To be posted on https://www.hhs.gov/pia/index.html, pending HHS OCIO action
Data needed
No training or fine-tuning; we are using secure commercially available LLMs.