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| ID | Type | Description | Link |
|---|---|---|---|
| 850205 | Other Identifier | IRB |
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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).
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Patients Residing in 18 zip codes in Western and Southwestern Philadelphia | 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. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Machine Learning Algorithm with Existing Penn Medicine CRC Patient Navigation Program | Other | 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. |
| 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 Fecal Immunochemical Test (FIT) | Within the three month enrollment period and three month follow-up period |
| Number of Participants With Adenoma Detection | Rate of adenomas after completion of colonoscopy | Within the three month enrollment period and three month follow-up period |
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Inclusion Criteria:
Exclusion Criteria:
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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. Patients will have had a colonoscopy order placed in the past 6 months and have not scheduled, cancelled, or no-showed to their colonoscopy.
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| Name | Affiliation | Role |
|---|---|---|
| Carmen Guerra, MD | University of Pennsylvania | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Pennsylvania | Philadelphia | Pennsylvania | 19104 | United States |
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| ID | Title | Description |
|---|---|---|
| FG000 | Patients Residing in 18 Zip Codes in Western and Southwestern Philadelphia | 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. Machine Learning Algorithm with Existing Penn Medicine CRC Patient Navigation Program: This intervention will utilize the existing Penn Medicine Colorectal Cancer 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. |
| Title | Milestones | Reasons Not Completed | |||||
|---|---|---|---|---|---|---|---|
| Overall Study |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Oct 26, 2021 |
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| FG001 | Controls | The cohort will consist of matched controls for the patients in the intervention cohort, also residing in the same 18 zip codes in Western and Southwestern Philadelphia with primary care providers in same 4 Penn Medicine Internal Medicine practices and 3 Penn Medicine Family Medicine Practices. Using clinical and sociodemographic characteristics, we will create matched cohorts of patients between the intervention participants and historical controls from the same clinics in the year prior. |
| COMPLETED |
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| NOT COMPLETED |
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| ID | Title | Description |
|---|---|---|
| BG000 | Patients Residing in 18 Zip Codes in Western and Southwestern Philadelphia | 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. 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. |
| BG001 | Controls | The cohort will consist of matched controls for the patients in the intervention cohort, also residing in the same 18 zip codes in Western and Southwestern Philadelphia with primary care providers in same 4 Penn Medicine Internal Medicine practices and 3 Penn Medicine Family Medicine Practices. Using clinical and sociodemographic characteristics, we will create matched cohorts of patients between the intervention participants and historical controls from the same clinics in the year prior. |
| BG002 | Total | Total of all reporting groups |
| Units | Counts |
|---|---|
| Participants |
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| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Age, Categorical | Count of Participants | Participants |
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| Age, Continuous | Mean | Full Range | years |
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| Sex: Female, Male | Count of Participants | Participants |
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| Ethnicity (NIH/OMB) | Count of Participants | Participants |
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| Race (NIH/OMB) | Count of Participants | Participants |
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| Region of Enrollment | Number | participants |
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| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | ||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | Enrollment in Navigator Program (Feasibility) | Number of patients that participate in the navigation program | Population consists of intervention patients who were correctly identified by the machine learning algorithm as needing to complete colorectal cancer screening and were eligible for navigation. | Posted | Count of Participants | Participants | During the three month enrollment period |
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| Primary | Completion of Colorectal Cancer Screening | Number of patients that have completed their colonoscopy or Fecal Immunochemical Test (FIT) | Population consists of intervention and control patients identified by the machine learning algorithm as needing to complete colorectal cancer screening. | Posted | Count of Participants | Participants | Within the three month enrollment period and three month follow-up period |
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| Primary | Number of Participants With Adenoma Detection | Rate of adenomas after completion of colonoscopy | Population consists of patients who completed a colonoscopy during the study period. | Posted | Count of Participants | Participants | Within the three month enrollment period and three month follow-up period |
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Adverse event data were not collected for this study.
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | Patients Residing in 18 Zip Codes in Western and Southwestern Philadelphia | 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. 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. | 0 | 0 | 0 | 0 | 0 | 0 |
| EG001 | Controls | The cohort will consist of matched controls for the patients in the intervention cohort, also residing in the same 18 zip codes in Western and Southwestern Philadelphia with primary care providers in same 4 Penn Medicine Internal Medicine practices and 3 Penn Medicine Family Medicine Practices. Using clinical and sociodemographic characteristics, we will create matched cohorts of patients between the intervention participants and historical controls from the same clinics in the year prior. | 0 | 0 | 0 | 0 | 0 | 0 |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Yvette Frimpong | Abramson Cancer Center at Penn Medicine | (215) 573-5107 | Yvette.Frimpong@pennmedicine.upenn.edu |
| Feb 9, 2026 |
| Prot_SAP_000.pdf |
| ID | Term |
|---|---|
| D015179 | Colorectal Neoplasms |
| ID | Term |
|---|---|
| D007414 | Intestinal Neoplasms |
| D005770 | Gastrointestinal Neoplasms |
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D004066 | Digestive System Diseases |
| D005767 | Gastrointestinal Diseases |
| D003108 | Colonic Diseases |
| D007410 | Intestinal Diseases |
| D012002 | Rectal Diseases |
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| ID | Term |
|---|---|
| D000098435 | Machine Learning Algorithms |
| ID | Term |
|---|---|
| D000465 | Algorithms |
| D055641 | Mathematical Concepts |
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| >=65 years |
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| Male |
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| Not Hispanic or Latino |
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| Unknown or Not Reported |
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| Asian |
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| Native Hawaiian or Other Pacific Islander |
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| Black or African American |
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| White |
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| More than one race |
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| Unknown or Not Reported |
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