Electronic Mammogram Reporting (eMAMR)

Overview

Development stage

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

High-impact designation

High-impactSource: a) High-impact

Impact justification

Not reported

Start date

Not reported

Withheld from public reporting

Not reported

Mission

Topic area

Health and Medical

Operational functions

Document processing and extraction, Clinical and medical Derived by keyword rules; see methodology

Problem the AI is intended to solve

Clinical data extraction from diverse and unstructured sources remains a bottleneck for healthcare providers and agencies. For example, providers may need to re-enter the same patient information across different consults, which is inefficient and takes away valuable clinical time. Existing processes are manual, time-consuming, and prone to errors, which leads to delays in integrating critical patient information (such as PDFs and scanned records) into Electronic Health Record (EHR) systems like CPRS/VistA. This limits care coordination, slows decision-making, and increases administrative workload. The proposed AI solution will automate the workflow for clinical data extraction and integration using OpenAI’s GPT-4o API for computer vision and generative tasks. Leveraging Databricks and AWS, the system processes PDFs and images—extracting clinical data, converting it to structured formats (JSON), and transforming it into standardized clinical notes (TIU/SMART notes) for seamless integration into CPRS.

Expected benefits

- Efficiency Gains: Automation dramatically reduces manual data entry and reconciliation, saving time for clinical staff. - Improved Data Quality: AI-driven extraction increases accuracy, consistency, and completeness of patient records. - Faster Care Coordination: Rapid data integration leads to quicker and more informed clinical decisions, supporting timely patient care. - Higher Scalability and Support of VA Mission: For VA, these outcomes will directly advance its mission to provide faster, safer, and more connected healthcare to the Veterans.

System outputs

- Extracted JSON Files: Clinical data extracted from incoming fax/PDF images using OpenAI GPT-4o. - TIU/SMART Notes: Automated note creation in CPRS/VistA with mirrored SMART notes and health factors. - Consult Closure Actions: Automated documentation supporting the closure of consults. - Enriched Clinical Data: Data contextualized for clinical use and stored for retrieval in Databricks and AWS S3.

Technology

AI classification

Natural language processingSource: Natural Language Processing: AI that processes, interprets, and shares information in human language.

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