App feedback model for NLP tasks

Overview

Development stage

DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

Not reported

Withheld from public reporting

Not reported

Mission

Topic area

Service Delivery

Operational functions

Clinical and medical Derived by keyword rules; see methodology

Problem the AI is intended to solve

The objective is to utilize Natural Language Processing (NLP) with comment reviews for Appfeedback, specifically to identify named entities (NER), profanity, and stop words, and provide an automated approach to pre-processing and cleaning text for downstream analytics tools. The system uses spaCy's en_core_web_sm model, an open-source software and model, to analyze text reviews from various sources, including external sources (Google and Apple stores) and internal sources (FeedbackUI and VA Mobile). The model provides an out-of-the-box approach for NLP tasks, including NER, profanity detection, and stop word removal. The data sources include: Text reviews from the VA's mobile applications on the Google and Apple stores (external sources) Reviews from FeedbackUI (internal source, available in the OIA_MobileHealth database) Reviews from VA Mobile (internal source, available via CSV files on the Mobile VA's internal website) Users: The users of this system are likely the OCC Data Science Team, who are responsible for developing and maintaining the pipeline. Target Audience: The target audience mobile application developers and other internal stakeholders who may be interested in analyzing and understanding the sentiment and feedback from these reviews. Problem to be solved: Consolidating feedback provided by patients and providers for VA Mobile Apps. Simple aggregation of data that provides a dashboard for clients to view and monitor trends.

Expected benefits

Decrease working hours in manual review of feedback.

System outputs

Identified issues shown within Power BI dashboard.

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

Yes

Public source code

Not reported

Sourcing

How it was built

Developed in-houseSource: b) Developed in-house

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