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Improving Colorectal Cancer Screening in Racially Diverse Zip Codes Using Navigation and Machine Learning (PCSNaP)

NCT05383976 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 385

Last updated 2026-02-11

Study results available
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Summary

The overarching goal of the "PCSNaP" Research Study is to support the Abramson Cancer Center (ACC) of the University of Pennsylvania in carrying out its mission to increase colorectal cancer (CRC) screening completion among high-risk individuals living in a persistent poverty county by designing, conducting, disseminating and evaluating an electronic health record-based automated identification program to target effective, culturally-sensitive CRC screening navigation to individuals who have not completed an ordered colonoscopy or fecal immunochemical test (FIT).

Conditions

Interventions

OTHER

Machine Learning Algorithm with Existing Penn Medicine CRC Patient Navigation Program

This intervention will utilize the existing Penn Medicine CRC patient navigation program. There will be a monthly list of patients with unfilled coloscopies provided, that are risk-stratified according to the machine learning algorithm and select high-risk criteria. The navigation team will prioritize timely outreach and navigation to high-risk patients according to a script that communicates risk.

Sponsors & Collaborators

  • Abramson Cancer Center at Penn Medicine

    lead OTHER

Principal Investigators

  • Carmen Guerra, MD · University of Pennsylvania

Eligibility

Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2022-03-29
Primary Completion
2024-11-01
Completion
2024-11-01

Countries

  • United States

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05383976 on ClinicalTrials.gov