Certification and Testing/Program Administration – AI-enabled Internal Processes

Overview

Development stage

In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

Not reported

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Administrative Functions

Operational functions

Summarization, Procurement and acquisition Derived by keyword rules; see methodology

Problem the AI is intended to solve

ONC’s certification and related program operations rely on many manual, text-heavy, and repetitive tasks (e.g., analyzing surveillance reports, validating certification test results, generating public meeting materials, preparing communications, managing Jira tickets, summarizing standards/IG guidance, and drafting acquisition documents). These activities are time-consuming, error-prone, and difficult to scale as program workload increases. The AI use case is intended to automate or semi-automate routine document drafting, data summarization, basic analysis, and information retrieval across these functions so staff can focus on higher-value review, oversight, and decision-making.

Expected benefits

Expected benefits include: (1) improved efficiency of internal certification and program operations (e.g., faster preparation of surveillance analyses, CHPL artifacts, and meeting materials); (2) reduced risk of omissions and inconsistencies in internal documents through standardized AI-assisted drafting and terminology checks; (3) quicker access to relevant information from CHPL data, Jira tickets, standards implementation guides, and financial spreadsheets; and (4) more timely, clear, and consistent public-facing communications and policy/support documents. Indirectly, these improvements support ONC’s mission to advance safe, interoperable health IT by improving the quality and timeliness of its certification, oversight, and communication activities.

System outputs

AI-generated or AI-assisted outputs include: (1) draft analytical summaries and reports (e.g., surveillance reporting analysis, RWT results validation, SED categorization, data visualizations); (2) draft public-facing and stakeholder communications (e.g., webinar Q&As, plain-language explanations of regulatory or standards text, communication templates); (3) internal operational artifacts (e.g., CHPL backups, release notes, Jira responses and ticket summaries, Excel query results); and (4) first drafts of acquisition and planning documents (e.g., statements of work, market research, memoranda of need, acquisition plans). All outputs are reviewed, edited, and approved by ONC staff before use.

Technology

AI classification

Generative AISource: Generative AI: AI that generates new or synthetic content (e.g., images, videos, audio, text, code).

System name(s)

Not reported

Custom-developed code

Not reported

Public source code

Not reported

Sourcing

How it was built

Not reported

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

Not reported

Privacy Impact Assessment

Not reported

Authorization to Operate

Not reported

Demographic features

Not reported

Training and evaluation data

Not reported

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported