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      Challenges and strategies for implementing genomic services in diverse settings: experiences from the Implementing GeNomics In pracTicE (IGNITE) network

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          Abstract

          Background

          To realize potential public health benefits from genetic and genomic innovations, understanding how best to implement the innovations into clinical care is important. The objective of this study was to synthesize data on challenges identified by six diverse projects that are part of a National Human Genome Research Institute (NHGRI)-funded network focused on implementing genomics into practice and strategies to overcome these challenges.

          Methods

          We used a multiple-case study approach with each project considered as a case and qualitative methods to elicit and describe themes related to implementation challenges and strategies. We describe challenges and strategies in an implementation framework and typology to enable consistent definitions and cross-case comparisons. Strategies were linked to challenges based on expert review and shared themes.

          Results

          Three challenges were identified by all six projects, and strategies to address these challenges varied across the projects. One common challenge was to increase the relative priority of integrating genomics within the health system electronic health record (EHR). Four projects used data warehousing techniques to accomplish the integration. The second common challenge was to strengthen clinicians’ knowledge and beliefs about genomic medicine. To overcome this challenge, all projects developed educational materials and conducted meetings and outreach focused on genomic education for clinicians. The third challenge was engaging patients in the genomic medicine projects. Strategies to overcome this challenge included use of mass media to spread the word, actively involving patients in implementation (e.g., a patient advisory board), and preparing patients to be active participants in their healthcare decisions.

          Conclusions

          This is the first collaborative evaluation focusing on the description of genomic medicine innovations implemented in multiple real-world clinical settings. Findings suggest that strategies to facilitate integration of genomic data within existing EHRs and educate stakeholders about the value of genomic services are considered important for effective implementation. Future work could build on these findings to evaluate which strategies are optimal under what conditions. This information will be useful for guiding translation of discoveries to clinical care, which, in turn, can provide data to inform continual improvement of genomic innovations and their applications.

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          Most cited references27

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          CPIC: Clinical Pharmacogenetics Implementation Consortium of the Pharmacogenomics Research Network.

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            Use of concept mapping to characterize relationships among implementation strategies and assess their feasibility and importance: results from the Expert Recommendations for Implementing Change (ERIC) study

            Background Poor terminological consistency for core concepts in implementation science has been widely noted as an obstacle to effective meta-analyses. This inconsistency is also a barrier for those seeking guidance from the research literature when developing and planning implementation initiatives. The Expert Recommendations for Implementing Change (ERIC) study aims to address one area of terminological inconsistency: discrete implementation strategies involving one process or action used to support a practice change. The present report is on the second stage of the ERIC project that focuses on providing initial validation of the compilation of 73 implementation strategies that were identified in the first phase. Findings Purposive sampling was used to recruit a panel of experts in implementation science and clinical practice (N = 35). These key stakeholders used concept mapping sorting and rating activities to place the 73 implementation strategies into similar groups and to rate each strategy’s relative importance and feasibility. Multidimensional scaling analysis provided a quantitative representation of the relationships among the strategies, all but one of which were found to be conceptually distinct from the others. Hierarchical cluster analysis supported organizing the 73 strategies into 9 categories. The ratings data reflect those strategies identified as the most important and feasible. Conclusions This study provides initial validation of the implementation strategies within the ERIC compilation as being conceptually distinct. The categorization and strategy ratings of importance and feasibility may facilitate the search for, and selection of, strategies that are best suited for implementation efforts in a particular setting. Electronic supplementary material The online version of this article (doi:10.1186/s13012-015-0295-0) contains supplementary material, which is available to authorized users.
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              Implementing genomic medicine in the clinic: the future is here

              Although the potential for genomics to contribute to clinical care has long been anticipated, the pace of defining the risks and benefits of incorporating genomic findings into medical practice has been relatively slow. Several institutions have recently begun genomic medicine programs, encountering many of the same obstacles and developing the same solutions, often independently. Recognizing that successful early experiences can inform subsequent efforts, the National Human Genome Research Institute brought together a number of these groups to describe their ongoing projects and challenges, identify common infrastructure and research needs, and outline an implementation framework for investigating and introducing similar programs elsewhere. Chief among the challenges were limited evidence and consensus on which genomic variants were medically relevant; lack of reimbursement for genomically driven interventions; and burden to patients and clinicians of assaying, reporting, intervening, and following up genomic findings. Key infrastructure needs included an openly accessible knowledge base capturing sequence variants and their phenotypic associations and a framework for defining and cataloging clinically actionable variants. Multiple institutions are actively engaged in using genomic information in clinical care. Much of this work is being done in isolation and would benefit from more structured collaboration and sharing of best practices. Genet Med 2013:15(4):258–267
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                Author and article information

                Contributors
                nina.sperber@duke.edu
                carpentj@iu.edu
                lcavallari@cop.ufl.edu
                ldamschroder@gmail.com
                dehoff@cop.ufl.edu
                josh.denny@Vanderbilt.Edu
                geoffrey.ginsburg@duke.edu
                yguan@som.umaryland.edu
                carol.horowitz@mountsinai.org
                kdlevy@iu.edu
                mia.levy@Vanderbilt.Edu
                ebony.madden@nih.gov
                michael.matheny@Vanderbilt.Edu
                tpollin@som.umaryland.edu
                vpratt@iu.edu
                mrosenma@iu.edu
                voils@surgery.wisc.edu
                KWeitzel@cop.ufl.edu
                russell.wilke@usd.edu
                ryanne.wu@dm.duke.edu
                lori.orlando@duke.edu
                Journal
                BMC Med Genomics
                BMC Med Genomics
                BMC Medical Genomics
                BioMed Central (London )
                1755-8794
                22 May 2017
                22 May 2017
                2017
                : 10
                : 35
                Affiliations
                [1 ]ISNI 0000 0004 1936 7961, GRID grid.26009.3d, Division of General Internal Medicine, , Duke University School of Medicine, ; Durham, NC USA
                [2 ]ISNI 0000 0001 2287 3919, GRID grid.257413.6, , Indiana University School of Nursing, ; Indianapolis, IN USA
                [3 ]ISNI 0000 0004 1936 8091, GRID grid.15276.37, Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, , University of Florida, ; Gainesville, FL USA
                [4 ]Implementation Pathways, LLC and VA Ann Arbor Center for Clinical Management Research, Ann Arbor, USA
                [5 ]ISNI 0000 0004 1936 8091, GRID grid.15276.37, , University of Florida, College of Pharmacy and Medicine and Center for Pharmacogenomics, ; Gainesville, USA
                [6 ]ISNI 0000 0004 1936 9916, GRID grid.412807.8, , Vanderbilt University Medical Center, ; Nashville, USA
                [7 ]ISNI 0000 0004 1936 7961, GRID grid.26009.3d, Duke Center for Applied Genomics & Precision Medicine, , Duke University, ; Durham, NC USA
                [8 ]ISNI 0000 0001 2175 4264, GRID grid.411024.2, , University of Maryland School of Medicine, ; Baltimore, USA
                [9 ]ISNI 0000 0001 0670 2351, GRID grid.59734.3c, , Icahn School of Medicine at Mount Sinai, ; New York, USA
                [10 ]ISNI 0000 0001 2233 9230, GRID grid.280128.1, , National Human Genome Research Institute (NHGRI), ; Rockville, USA
                [11 ]ISNI 0000 0004 1936 9916, GRID grid.412807.8, , Vanderbilt University Medical Center, Tennessee Valley HealthCare System VA, ; Nashville, USA
                [12 ]ISNI 0000 0004 0420 6882, GRID grid.417123.2, , William S. Middleton Memorial Veterans Hospital, ; Madison, WI USA
                [13 ]ISNI 0000 0001 2167 3675, GRID grid.14003.36, Department of Surgery, , University of Wisconsin-Madison, ; Madison, WI USA
                [14 ]ISNI 0000 0001 2293 1795, GRID grid.267169.d, Sanford School of Medicine, , University of South Dakota, ; Vermillion, USA
                [15 ]Duke University, Duke-National University of Singapore Medical School, 8 College Road, Singapore, 169857 Singapore
                [16 ]VA Health Services Research & Development, Durham VA Health Care System, 411 West Chapel Hill Street, Suite 600, Durham, NC 27701 USA
                Article
                273
                10.1186/s12920-017-0273-2
                5441047
                28532511
                7922ecb2-a9d7-4542-b13f-aee73f84c9fe
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

                History
                : 2 December 2016
                : 10 May 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000051, National Human Genome Research Institute;
                Award ID: U01HG0007282
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

                Genetics
                precision medicine,pharmacogenomics,electronic health record,patient engagement,provider engagement,implementation

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