Analyzing Prescription Monitoring Program Data

Overview

Development stage

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

High-impact designation

Presumed high-impact, determined notSource: b) Presumed high-impact, but determined not high impact

Impact justification

Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.

Start date

Not reported

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Law Enforcement

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

DEA investigators currently rely on manual manipulation of data in spreadsheets to conduct Prescription Monitoring Program (PMP) analysis. This requires 8-10 hours of labor (data extraction, cleaning, cross referencing, narrative synthesis), and, since investigators analyze data in different ways, results vary. There is little consistency across the agency or even between investigators in terms of what data is examined and there is no easy way to identify patterns such as MMEs, combinations, and early fills.

Expected benefits

Increases operational efficiency by automating data preparation, risk identification, and initial narrative generation. Average time spent to produce a PMP decreased from ten to two hours (minus 80%). By employing consistent, data-driven risk analysis, increases the proportion of high-value investigations that result in successful enforcement or administrative outcomes. Reallocates Diversion Investigator effort to higher-value tasks (e.g., field operations, strategic planning), producing a labor cost benefit in the thousands per Diversion Investigator based on average pay and number of PMPs analyzed. Faster identification of prescribing anomalies supports earlier public health interventions and reduces community exposure to diverted pharmaceuticals. A single, centrally managed AI service can be provisioned to all DEA field offices, ensuring uniform analytic standards.

System outputs

Quantitative data reports and analyses

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