2_use_case_name: AccessGUDID Data Validation
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: The artificial intelligence (AI) system for AccessGUDID will focus on enhancing data quality assurance by detecting anomalies in medical device metrics, such as inconsistent units of measurement or unusual device dimensions. It will analyze both structured data, like numerical measurements, and unstructured data, such as free text entries. The AI will not modify data directly but will flag potential issues for human review. Integrated into the U.S. Food and Drug Administration (FDA)'s processing workflow, it will leverage standard codes for precise grouping and employ advanced language models to process unstructured data. Deployed within the regulatory compliance and data management areas of NLM, this AI system aims to streamline data validation processes, reduce manual workload, and maintain the integrity of the AccessGUDID database, without overstepping into areas requiring human oversight.

The key problem the AI aims to address is the presence of inconsistent or incorrect data entries, such as mismatched units of measurement or outlier device dimensions, which can lead to regulatory compliance issues and potential risks in device usage. By automating the anomaly detection process, the AI will improve the efficiency of data validation, significantly reducing the manual effort required to identify and correct errors. This will not only enhance the accuracy of the data but also speed up the processing and integration of new submissions into the AccessGUDID database. Improved data integrity will lead to better decision-making for manufacturers, healthcare providers, and regulators, ensuring that medical devices on the market meet safety and performance standards. Ultimately, the positive outcomes of this AI application include increased trust in the medical device information provided to end users, which contributes to the broader goal of improving public health and ensuring patient safety across the nation.
12_outputs: For inputs, it handles structured data, such as numerical metrics (e.g., device dimensions, units of measurement), and unstructured data, such as free-hand text entries describing medical devices. This data is sourced from the AccessGUDID database, which aggregates information submitted by manufacturers to the FDA. The frequency of data processing aligns with the batch submissions from the FDA, typically occurring daily or whenever new data is received. The system processes large volumes of data to detect anomalies, making it scalable to accommodate growing datasets. For outputs, the AI generates predictions and classifications, specifically identifying potential anomalies in the data, such as incorrect units of measurement or outlier device specifications. The results are presented as flags or labels indicating the presence of an anomaly, which are then reviewed by human analysts. The AI produces these results in real-time as part of the batch processing workflow, ensuring that each data submission is evaluated promptly before being fully integrated into the AccessGUDID database. The output helps in streamlining the data validation process and improving overall data quality.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 3/1/2024