Sequential Coverage Algorithm (SCA) and partial Expectation-Maximization (EM) estimation in Record Linkage

Section 1: Use Case Identifiers

Use Case ID: HHS-CDC-00049
Agency: HHS
Op Div/Staff Div: CDC
Use Case Topic Area: Health & Medical
Is the AI use case found in the below list of general commercial AI products and services?
None of the above.
Describe the AI system's outputs.
CDC's National Center for Health Statistics (NCHS) Data Linkage Program has implemented both supervised and unsupervised machine learning (ML) techniques in their linkage algorithms. The Sequential Coverage Algorithm (SCA), a supervised ML algorithm, is used to develop joining methods (or blocking groups) when working with very large datasets. The unsupervised partial Expectation-Maximization (EM) estimation is used to estimate the proportion of pairs that are matches within each block. Both methods improve linkage accuracy and efficiency.
Stage of Development: Operation and Maintenance
Is the AI use case rights-impacting, safety-impacting, both, or neither?
Neither

Section 2: Use Case Summary

Date Initiated: 02/2019
Date when Acquisition and/or Development began: 08/2019
Date Implemented: 08/2020
Date Retired: N/A
Was the AI system involved in this use case developed (or is it to be developed) under contract(s) or in-house?
Developed with contracting resources.
Provide the Procurement Instrument Identifier(s) (PIID) of the contract(s) used.
N/A
Is this AI use case supporting a High-Impact Service Provider (HISP) public-facing service?
N/A
Does this AI use case disseminate information to the public?
No
How is the agency ensuring compliance with Information Quality Act guidelines, if applicable?
N/A
Does this AI use case involve personally identifiable information (PII) that is maintained by the agency?
Yes
Has the Senior Agency Official for Privacy (SAOP) assessed the privacy risks associated with this AI use case?
ongoing

Section 3: Data and Code

Do you have access to an enterprise data catalog or agency-wide data repository that enables you to identify whether or not the necessary datasets exist and are ready to develop your use case?
No
Describe any agency-owned data used to train, fine-tune, and/or evaluate performance of the model(s) used in this use case.
Data used to evaluate the models include data from the National Hospital Care Survey, the National Health and Nutrition Examination Survey, and the National Health Interview Survey as well as linked administrative data.
Is there available documentation for the model training and evaluation data that demonstrates the degree to which it is appropriate to be used in analysis or for making predictions?
Documentation is widely available
Which, if any, demographic variables does the AI use case explicitly use as model features?
Sex/Gender
Does this project include custom-developed code?
Yes
If the code is open-source, provide the link for the publicly available source code.
N/A

Section 4: AI Enablement and Infrastructure

Does this AI use case have an associated Authority to Operate (ATO) for an AI system?
Yes
System Name: Consolidated Statistical Platform
How long have you waited for the necessary developer tools to implement the AI use case?
6-12 months
For this AI use case, is the required IT infrastructure provisioned via a centralized intake form or process inside the agency?
Yes
Do you have a process in place to request access to computing resources for model training and development of the AI involved in this use case?
Yes
Has communication regarding the provisioning of your requested resources been timely?
Yes
How are existing data science tools, libraries, data products, and internally-developed AI infrastructure being re-used for the current AI use case?
Re-use production level code and/or data products
Has information regarding the AI use case, including performance metrics and intended use of the model, been made available for review and feedback within the agency?
Documentation has been developed