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

A Feasibility Study to Improve Colorectal Cancer Screening Among Racially Diverse Zip Codes in a Persistent Poverty County Using Navigation and Machine Learning Predictive Algorithms

Status
Completed
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT05383976
Acronym
PCSNaP
Enrollment
385
Registered
2022-05-20
Start date
2022-03-29
Completion date
2024-11-01
Last updated
2026-02-11

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Colorectal Cancer

Brief 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).

Detailed description

Specifically, the goals of this study are to: 1) Adapt a previously validated electronic health record (EHR)-based machine learning algorithm to predict colorectal cancer (CRC) detection by retraining the model using data from patients seen in primary care clinics serving zip codes with a high proportion of racial and ethnic minorities living in Philadelphia County, a persistent poverty county; and 2) Implement and evaluate the feasibility and effectiveness of an algorithm-based CRC navigation program to increase colorectal cancer screening among patients in Philadelphia county who are at high risk of CRC and have uncompleted colonoscopies. Together, these novel projects aim to be the first to combine use of machine learning algorithms and patient navigation to increase guideline-based cancer screening in order to reduce the burden of CRC among high-risk individuals living in a persistent poverty county through targeted, culturally-sensitive navigation that addresses social factors that prevent CRC screening.

Interventions

OTHERMachine 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

Abramson Cancer Center at Penn Medicine
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Healthy volunteers
Yes

Inclusion criteria

* Patients residing in 18 zip codes in Western and Southwestern Philadelphia who have primary care providers in 4 Penn Medicine Internal Medicine practices and 3 Penn Medicine Family Medicine Practices * Patients who have had a colonoscopy order placed in the past 6 months and have not scheduled, cancelled, or no-showed to their colonoscopy

Exclusion criteria

* Not applicable

Design outcomes

Primary

MeasureTime frameDescription
Enrollment in Navigator Program (Feasibility)During the three month enrollment periodNumber of patients that participate in the navigation program
Completion of Colorectal Cancer ScreeningWithin the three month enrollment period and three month follow-up periodNumber of patients that have completed their colonoscopy or Fecal Immunochemical Test (FIT)
Number of Participants With Adenoma DetectionWithin the three month enrollment period and three month follow-up periodRate of adenomas after completion of colonoscopy

Countries

United States

Contacts

PRINCIPAL_INVESTIGATORCarmen Guerra, MD

University of Pennsylvania

Baseline characteristics

Characteristic
Age, Categorical
<=18 years
0 Participants
Age, Categorical
>=65 years
105 Participants
Age, Categorical
Between 18 and 65 years
280 Participants
Age, Continuous61.28 years
Ethnicity (NIH/OMB)
Hispanic or Latino
18 Participants
Ethnicity (NIH/OMB)
Not Hispanic or Latino
168 Participants
Ethnicity (NIH/OMB)
Unknown or Not Reported
0 Participants
Race (NIH/OMB)
American Indian or Alaska Native
1 Participants
Race (NIH/OMB)
Asian
4 Participants
Race (NIH/OMB)
Black or African American
134 Participants
Race (NIH/OMB)
More than one race
4 Participants
Race (NIH/OMB)
Native Hawaiian or Other Pacific Islander
0 Participants
Race (NIH/OMB)
Unknown or Not Reported
13 Participants
Race (NIH/OMB)
White
63 Participants
Region of Enrollment
United States
184 participants
Sex: Female, Male
Female
243 Participants
Sex: Female, Male
Male
74 Participants

Adverse events

Event typeEG000
affected / at risk
EG001
affected / at risk
deaths
Total, all-cause mortality
0 / 00 / 0
other
Total, other adverse events
0 / 00 / 0
serious
Total, serious adverse events
0 / 00 / 0

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 12, 2026