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What’s the “secret sauce”? How implementation variation affects the success of colorectal cancer screening outreach

BACKGROUND: Mailed fecal immunochemical testing (FIT) programs can improve colorectal cancer (CRC) screening rates, but health systems vary how they implement (i.e., adapt) these programs for their organizations. A health insurance plan implemented a mailed FIT program (named BeneFIT), and participa...

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Autores principales: Coury, Jennifer, Miech, Edward J., Styer, Patricia, Petrik, Amanda F., Coates, Kelly E., Green, Beverly B., Baldwin, Laura-Mae, Shapiro, Jean A., Coronado, Gloria D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7802298/
https://www.ncbi.nlm.nih.gov/pubmed/33431063
http://dx.doi.org/10.1186/s43058-020-00104-7
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author Coury, Jennifer
Miech, Edward J.
Styer, Patricia
Petrik, Amanda F.
Coates, Kelly E.
Green, Beverly B.
Baldwin, Laura-Mae
Shapiro, Jean A.
Coronado, Gloria D.
author_facet Coury, Jennifer
Miech, Edward J.
Styer, Patricia
Petrik, Amanda F.
Coates, Kelly E.
Green, Beverly B.
Baldwin, Laura-Mae
Shapiro, Jean A.
Coronado, Gloria D.
author_sort Coury, Jennifer
collection PubMed
description BACKGROUND: Mailed fecal immunochemical testing (FIT) programs can improve colorectal cancer (CRC) screening rates, but health systems vary how they implement (i.e., adapt) these programs for their organizations. A health insurance plan implemented a mailed FIT program (named BeneFIT), and participating health systems could adapt the program. This multi-method study explored which program adaptations might have resulted in higher screening rates. METHODS: First, we conducted a descriptive analysis of CRC screening rates by key health system characteristics and program adaptations. Second, we generated an overall model by fitting a weighted regression line to our data. Third, we applied Configurational Comparative Methods (CCMs) to determine how combinations of conditions were linked to higher screening rates. The main outcome measure was CRC screening rates. RESULTS: Seventeen health systems took part in at least 1 year of BeneFIT. The overall screening completion rate was 20% (4–28%) in year 1 and 25% (12–35%) in year 2 of the program. Health systems that used two or more adaptations had higher screening rates, and no single adaptation clearly led to higher screening rates. In year 1, small systems, with just one clinic, that used phone reminders (n = 2) met the implementation success threshold (≥ 19% screening rate) while systems with > 1 clinic were successful when offering a patient incentive (n = 4), scrubbing mailing lists (n = 4), or allowing mailed FIT returns with no other adaptations (n = 1). In year 2, larger systems with 2–4 clinics were successful with a phone reminder (n = 4) or a patient incentive (n = 3). Of the 10 systems that implemented BeneFIT in both years, seven improved their CRC screening rates in year 2. CONCLUSIONS: Health systems can choose among many adaptations and successfully implement a health plan’s mailed FIT program. Different combinations of adaptations led to success with health system size emerging as an important contextual factor.
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spelling pubmed-78022982021-01-21 What’s the “secret sauce”? How implementation variation affects the success of colorectal cancer screening outreach Coury, Jennifer Miech, Edward J. Styer, Patricia Petrik, Amanda F. Coates, Kelly E. Green, Beverly B. Baldwin, Laura-Mae Shapiro, Jean A. Coronado, Gloria D. Implement Sci Commun Research BACKGROUND: Mailed fecal immunochemical testing (FIT) programs can improve colorectal cancer (CRC) screening rates, but health systems vary how they implement (i.e., adapt) these programs for their organizations. A health insurance plan implemented a mailed FIT program (named BeneFIT), and participating health systems could adapt the program. This multi-method study explored which program adaptations might have resulted in higher screening rates. METHODS: First, we conducted a descriptive analysis of CRC screening rates by key health system characteristics and program adaptations. Second, we generated an overall model by fitting a weighted regression line to our data. Third, we applied Configurational Comparative Methods (CCMs) to determine how combinations of conditions were linked to higher screening rates. The main outcome measure was CRC screening rates. RESULTS: Seventeen health systems took part in at least 1 year of BeneFIT. The overall screening completion rate was 20% (4–28%) in year 1 and 25% (12–35%) in year 2 of the program. Health systems that used two or more adaptations had higher screening rates, and no single adaptation clearly led to higher screening rates. In year 1, small systems, with just one clinic, that used phone reminders (n = 2) met the implementation success threshold (≥ 19% screening rate) while systems with > 1 clinic were successful when offering a patient incentive (n = 4), scrubbing mailing lists (n = 4), or allowing mailed FIT returns with no other adaptations (n = 1). In year 2, larger systems with 2–4 clinics were successful with a phone reminder (n = 4) or a patient incentive (n = 3). Of the 10 systems that implemented BeneFIT in both years, seven improved their CRC screening rates in year 2. CONCLUSIONS: Health systems can choose among many adaptations and successfully implement a health plan’s mailed FIT program. Different combinations of adaptations led to success with health system size emerging as an important contextual factor. BioMed Central 2021-01-11 /pmc/articles/PMC7802298/ /pubmed/33431063 http://dx.doi.org/10.1186/s43058-020-00104-7 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Coury, Jennifer
Miech, Edward J.
Styer, Patricia
Petrik, Amanda F.
Coates, Kelly E.
Green, Beverly B.
Baldwin, Laura-Mae
Shapiro, Jean A.
Coronado, Gloria D.
What’s the “secret sauce”? How implementation variation affects the success of colorectal cancer screening outreach
title What’s the “secret sauce”? How implementation variation affects the success of colorectal cancer screening outreach
title_full What’s the “secret sauce”? How implementation variation affects the success of colorectal cancer screening outreach
title_fullStr What’s the “secret sauce”? How implementation variation affects the success of colorectal cancer screening outreach
title_full_unstemmed What’s the “secret sauce”? How implementation variation affects the success of colorectal cancer screening outreach
title_short What’s the “secret sauce”? How implementation variation affects the success of colorectal cancer screening outreach
title_sort what’s the “secret sauce”? how implementation variation affects the success of colorectal cancer screening outreach
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7802298/
https://www.ncbi.nlm.nih.gov/pubmed/33431063
http://dx.doi.org/10.1186/s43058-020-00104-7
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