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Identifying factors and causal chains associated with optimal implementation of Lynch syndrome tumor screening: An application of coincidence analysis.
Cragun, Deborah; Salvati, Zachary M; Schneider, Jennifer L; Burnett-Hartman, Andrea N; Epstein, Mara M; Hunter, Jessica Ezzell; Liang, Su-Ying; Lowery, Jan; Lu, Christine Y; Pawloski, Pamala A; Schlieder, Victoria; Sharaf, Ravi N; Williams, Marc S; Rahm, Alanna Kulchak.
Afiliação
  • Cragun D; College of Public Health, University of South Florida, Tampa, FL.
  • Salvati ZM; Department of Genomic Health, Geisinger, Danville, PA.
  • Schneider JL; Center for Health Research, Kaiser Permanente Northwest, Portland, OR.
  • Burnett-Hartman AN; Institute for Health Research, Kaiser Permanente Colorado, Aurora, CO.
  • Epstein MM; Division of Health Systems Science, University of Massachusetts Chan Medical School, Worcester, MA.
  • Hunter JE; Genomics, Ethics, and Translational Research Program, RTI International, Research Triangle Park, NC.
  • Liang SY; Palo Alto Medical Research Foundation, Sutter Health, Palo Alto, CA.
  • Lowery J; University of Colorado Cancer Center, University of Colorado, Aurora, CO.
  • Lu CY; Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA.
  • Pawloski PA; Health Partners, Bloomington, MN.
  • Schlieder V; Department of Genomic Health, Geisinger, Danville, PA.
  • Sharaf RN; Population Health Sciences, Weill Cornell Medicine, New York, NY.
  • Williams MS; Department of Genomic Health, Geisinger, Danville, PA.
  • Rahm AK; Department of Genomic Health, Geisinger, Danville, PA. Electronic address: alanna.kulchakrahm@nih.gov.
Genet Med ; 26(10): 101201, 2024 Jun 28.
Article em En | MEDLINE | ID: mdl-38953292
ABSTRACT

PURPOSE:

This study compared Lynch syndrome universal tumor screening (UTS) across multiple health systems (some of which had 2 or more distinct UTS programs) to understand multilevel factors that may affect the successful implementation of complex programs.

METHODS:

Data from 66 stakeholder interviews were used to conduct multivalue coincidence analysis and identify key factors that consistently make a difference in whether UTS programs were implemented and optimized at the system level.

RESULTS:

The selected coincidence analysis model revealed combinations of conditions that distinguish 4 optimized UTS programs, 10 nonoptimized programs, and 4 systems with no program. Fully optimized UTS programs had both a maintenance champion and a positive inner setting. Two independent paths were unique to nonoptimized programs (1) positive attitudes and a mixed inner setting or (2) limited planning and engaging among stakeholders. Negative views about UTS evidence or lack of knowledge about UTS led to a lack of planning and engaging, which subsequently prevented program implementation.

CONCLUSION:

The model improved our understanding of program implementation in health care systems and informed the creation of a toolkit to guide UTS implementation, optimization, and changes. Our findings and toolkit may serve as a use case to increase the successful implementation of other complex precision health programs.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article