Modeling to Learn (MTL)

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

Service Delivery

Operational functions

Clinical and medical Derived by keyword rules; see methodology

Problem the AI is intended to solve

Modeling to Learn is designed to address the limited start, flow, and dose of evidence-based psychotherapy and evidence-based pharmacotherapy for Post Traumatic Stress Disorder (PTSD), depression, alcohol use disorder and opioid use disorders.

Expected benefits

Improved patient safety and recovery and efficient use of staff and financial resources to deliver timely access to VHA’s highest quality behavioral healthcare.

System outputs

Care improvement priorities and recommendations identified with Modeling to Learn leverage local VA Medical Center (VAMC) or Community-based Outpatient Clinic (CBOC) a) appointment and patient accumulations, b) rate of care flows and c) frequently made care decisions (e.g., return-to-clinic orders) to achieve policy-consistent, evidence-based episodes of care within local workforce constraints. Decision-makers can review simulated future trends for the next 2 years based on the last 2 years and examine alternative decisions most likely to improve local care to meet VHA standards for access and quality. The Modeling to Learn data user interface follows these steps to prioritize improvement decisions: Prioritize a local workflow, Prioritize the highest volume or greatest increase in diagnoses, Prioritize imbalances between diagnoses and policy-consistent evidence-based care flow, Prioritize evidence-based psychotherapy or pharmacotherapy start and dose imbalance, Prioritize time to detect patient improvement or risk using measurement-based care. The Modeling to Learn simulation user interface follows these add'l steps to prioritize improvement decisions: Prioritize medication management, psychotherapy, team care or team flow, Prioritize evidence-based care episodes balancing flow, Prioritize leverage of small new decisions with large beneficial improvements for the local patient population, Explain why the decision improves care using feedback.

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