Screen for Esophageal Adenocarcinoma

Overview

Development stage

PilotSource: b) Pilot – The use case has been deployed in a limited test or pilot capacity.

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

Health and Medical

Operational functions

Clinical and medical Derived by keyword rules; see methodology

Problem the AI is intended to solve

Veterans disproportionately have many of the important risk factors for esophageal adenocarcinoma (EAC), and research has demonstrated that Veterans are a substantial gap in the prevention of EAC. Healthcare providers can utilize Kettles Esophageal and Cardia Adenocarcinoma prediction tool (K-ECAN) to guide decision-making and identify potential patients who should be offered screening for EAC.

Expected benefits

If EAC is identified at the earliest stage (intramucosal cancer without metastases), the 5-year survival with surgery is greater than 85%, and at least as good with endoscopic therapy alone. Implementation of such an intervention would bridge the gap between population-health screenings and personalized management in a manner that would minimize the impact on payer budget and resources. Since individuals are more commonly up to date for colorectal cancer screening (for instance, with colonoscopy), it is particularly suitable to harness the missed opportunity of EAC screening at the time of screening for colorectal cancer. Also, when an EAC screening with upper endoscopy is performed simultaneously with colonoscopy, it is much less expensive. This project will also serve as a roadmap for how such precision approaches to screening can be applied to other rare cancers.

System outputs

The K-ECAN is an automated prediction tool for EAC that harnesses the electronic health record at the point of contact during opportune moments (such as at the time of scheduling colorectal cancer screening) to guide decision-making and identify patients who should be offered screenings for EAC. The provider would know their patient's predicted annual incidence of EAC or esophagogastric junction adenocarcinoma (EGJAC) (at least [XXX per 100,000) and their predicted annual mortality (at least [YYY] per 100,000). They would also know additional risk factors, such as age, sex, Body Mass Index (BMI), Gastroesophageal reflux disease (GERD), and smoking history.

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

Yes

Public source code

osf.io ↗

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