Optimizing colorectal cancer screening outreach: A probability-based strategy by Temple University Hospital.

e13502 Background: Temple University Hospital has created an initiative to increase colorectal cancer (CRC) screening to a historically underserved population in Northern Philadelphia through the administration of Fecal Immunochemical Tests (FIT). FIT testing possesses the advantage of being inexpensive and easy to use. Moreover, FIT testing serves as an additional opportunity for underserved patients to be integrated into a health network to receive lifesaving care when utilized by a large health system such as Temple Health. This study demonstrates how healthcare institutions, such as Temple Health, can employ probability-based modeling created from empiric data to navigate uncertainty and establish goals for the number of colonoscopies they aim to perform per a specific quantity of FIT tests distributed within the community. By doing so, they can significantly enhance the overall screening efforts in Northern Philadelphia and provide an essential service to those who need it most. Methods: FIT tests were distributed to the participants at community engagement events. FIT response rates, positivity rates, and progression to colonoscopy rates were determined and fit to a binomial distribution in Palisades @Risk software and underwent a Monte Carlo simulation of 10,000 iterations to determine the different probabilities of response rates, positivity rate, and colonoscopies performed per 1000 FIT tests distributed. Results: A total of 292 tests were distributed with a response rate of 56.5% (165 patients) and a positivity rate of 8.9% (26 patients). Out of 292 patients, 2.4% (7 patients) have had a colonoscopy or are scheduled to have a colonoscopy at Temple University Hospital. Probability curves were created to demonstrate the probability of number of responses, number of positive tests, and number of colonoscopies performed per 1000 FIT tests administered to the community. Lastly, we modeled the number of FIT tests that need to be administered to the community to perform at least 100 colonoscopies with different levels of certainty (Table). Conclusions: Here we show how hospital systems, like Temple Health, can use probability distributions based on empiric data to set tangible goals regarding their community engagement efforts to increase access to CRC screening. Through the utilization of probability distributions, healthcare organizations can gain deeper insights into resource allocation needs and strategically direct their initiatives, ultimately fostering a more inclusive and equitable approach to population-based CRC screening. Table: see text