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

Study Overview
=================
Brief Summary
-----------------
The overarching goals 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).

Official Title
-----------------
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

Conditions
-----------------
Colorectal Cancer

Intervention / Treatment
-----------------
* Other: Machine Learning Algorithm with Existing Penn Medicine CRC Patient Navigation Program


Participation Criteria
=================
Eligibility Criteria
-----------------
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
Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
Accepts Healthy Volunteers

Study Plan
=================
How is the study designed?
-----------------

Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Patients Residing in 18 zip codes in Western and Southwestern Philadelphia<br>The cohort will consist of 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. | Other: Machine Learning Algorithm with Existing Penn Medicine CRC Patient Navigation Program<br>* 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.<br>|

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Enrollment in Navigator Program (Feasibility) | Number of patients that participate in the navigation program | During the three month enrollment period | 
| Completion of Colorectal Cancer Screening | Number of patients that have completed their colonoscopy or FIT | Within the three month enrollment period and three month follow-up period | 
| Adenoma Detection Rate | Rate of adenomas after completion of colonoscopy | Within the three month enrollment period and three month follow-up period |