PestScore VA: AI-Driven Pest Risk Forecasting

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

Not reported

Mission

Topic area

Science

Operational functions

Forecasting and prediction, Risk scoring and triage Derived by keyword rules; see methodology

Problem the AI is intended to solve

VA facilities currently rely on fragmented, inconsistent pest control documentation methods, ranging from hand written logs to variable digital records. This make it difficult to detect early warning signs, track pest trends or ensure system-wide compliance with IPM standards. Additionally, leadership lacks real-time visibility into pest activity across departments or sites. PestScore VA solves this by creating a standardized AI enhanced system that enables predictive insights and uniform pest risk documentation, enhancing transparency, documentation, and proactive care.

Expected benefits

1. Predictive Insight: The AI forecasts pest activity using hybrid logic (trends plus probability), helping teams act before outbreaks escalate. 2. Standardized Reporting: Uniform, clean reports help facilities meet regulatory and environmental standards more easily. 3. Operational Efficiency: Technicians enter familiar data (notes, sightings), and AI handles analysis, saving time without increasing workload. 4. Environmental Health: Supports less toxic, preventive pest strategies that align with patient safety and VA sustainability goals. 5. Leadership Visibility: Allows decision makers to compare zones or hospitals at a glance, helping allocate resources intelligently.

System outputs

1. Risk Scores: Each zone receives a dynamic PestScore (0-100) indicating current and future pest pressure based on conditions, sighting, and predictive logic. 2. Intelligent Summaries: AI writes plain language summaries explaining likely pest causes, routes, and risk drivers, tailored to non-expert readers. 3. Follow-Up recommendations: PestScore suggests reinspection timelines based on severity, location type, and pest species behavior. 4. PDF reports: Branded, timestamped reports are generated instantly, suitable for leadership review, audits, or historical tracking.

Technology

AI classification

Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…

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