Advertisement

Standardizing variation: Scaling up clinical genomics in Australia

  • Stephanie Best
    Correspondence
    Correspondence and requests for materials should be addressed to Stephanie Best, Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales 2203, Australia
    Affiliations
    Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia

    Australian Genomics, Murdoch Children’s Research Institute, Melbourne, Victoria, Australia
    Search for articles by this author
  • Janet C. Long
    Affiliations
    Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia
    Search for articles by this author
  • Jeffrey Braithwaite
    Affiliations
    Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia
    Search for articles by this author
  • Natalie Taylor
    Affiliations
    School of Population Health, UNSW Sydney, Sydney, New South Wales, Australia
    Search for articles by this author
Published:January 31, 2022DOI:https://doi.org/10.1016/j.gim.2022.01.004

      Abstract

      Purpose

      Clinical genomics demands close interaction of physicians, laboratory scientists, and genetic professionals. Taking genomics to scale requires an understanding of the underlying processes from the perspective of nongenetic physicians who are new to the field. We identified components of the processes amenable to adaptation when scaling up clinical genomics.

      Methods

      Semistructured interviews informed by the Theoretical Domains Framework with nongenetic physicians, who were using clinical genomics in practice, were guided by an annotated process map with 7 steps following the patient’s journey. Findings from the individual maps were synthesized into an overview process map and a series of individual maps by common location and specialty. Interviews were analyzed using the Theoretical Domains Framework.

      Results

      In total, 16 nongenetic physicians (eg, nephrologists, immunologists) participated, generating 1 overview and 10 individual process maps. Sixteen common steps were identified across clinical specialties and locations, with variations over 9 steps. We report the potential for standardization across these 9 steps.

      Conclusion

      When scaling up complex interventions, it is essential to identify steps where variation can be accommodated. With these results we show how process mapping can be used to identify steps where variation is acceptable during scale up to accommodate adaptation to local context, allowing for the inevitable evolution of factors influencing ongoing implementation and sustainability.

      Graphical abstract

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      ACMG Member Login

      Are you an ACMG Member? Sign in for online access.

      Subscribe:

      Subscribe to Genetics in Medicine
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Best S.
        • Brown H.
        • Stark Z.
        • et al.
        Teamwork in clinical genomics: a dynamic sociotechnical healthcare setting.
        J Eval Clin Pract. 2021; 27: 1369-1380https://doi.org/10.1111/jep.13573
        • Aronson S.J.
        • Rehm H.L.
        Building the foundation for genomics in precision medicine.
        Nature. 2015; 526: 336-342https://doi.org/10.1038/nature15816
        • Rogers E.M.
        Diffusion of innovations.
        Free Press of Glencoe, New York1962
        • Craig P.
        • Dieppe P.
        • Macintyre S.
        • et al.
        Developing and evaluating complex interventions: the new Medical Research Council guidance.
        BMJ. 2008; 337: a1655https://doi.org/10.1136/bmj.a1655
        • Shi L.
        • Wang Z.
        Computational strategies for scalable genomics analysis.
        Genes (Basel). 2019; 10: 1017https://doi.org/10.3390/genes10121017
        • Horton T.J.
        • Illingworth J.H.
        • Warburton W.H.P.
        Overcoming challenges in codifying and replicating complex health care interventions.
        Health Aff (Millwood). 2018; 37: 191-197https://doi.org/10.1377/hlthaff.2017.1161
        • Hoffmann T.C.
        • Glasziou P.P.
        • Boutron I.
        • et al.
        Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide.
        BMJ. 2014; 348: g1687https://doi.org/10.1136/bmj.g1687
        • Pinnock H.
        • Barwick M.
        • Carpenter C.R.
        • et al.
        Standards for reporting implementation studies (StaRI) statement.
        BMJ. 2017; 356: i6795https://doi.org/10.1136/bmj.i6795
        • Sperber N.R.
        • Carpenter J.S.
        • Cavallari L.H.
        • et al.
        Challenges and strategies for implementing genomic services in diverse settings: experiences from the Implementing GeNomics In pracTicE (IGNITE) network.
        BMC Med Genomics. 2017; 10: 35https://doi.org/10.1186/s12920-017-0273-2
        • Hawe P.
        Lessons from complex interventions to improve health.
        Annu Rev Public Health. 2015; 36: 307-323https://doi.org/10.1146/annurev-publhealth-031912-114421
        • von Thiele Schwarz U.
        • Aarons G.A.
        • Hasson H.
        The Value Equation: three complementary propositions for reconciling fidelity and adaptation in evidence-based practice implementation.
        BMC Health Serv Res. 2019; 19: 868https://doi.org/10.1186/s12913-019-4668-y
        • Pérez D.
        • Van der Stuyft P.
        • Zabala M.C.
        • Castro M.
        • Lefèvre P.
        A modified theoretical framework to assess implementation fidelity of adaptive public health interventions.
        Implement Sci. 2016; 11 (Published correction appears in Implement Sci. 2016;11(1):106.): 91
        • Chambers D.A.
        • Norton W.E.
        The Adaptome: advancing the science of intervention adaptation.
        Am J Prev Med. 2016; 51: S124-S131https://doi.org/10.1016/j.amepre.2016.05.011
        • Hawe P.
        • Shiell A.
        • Riley T.
        Complex interventions: how “out of control” can a randomised controlled trial be?.
        BMJ. 2004; 328: 1561-1563https://doi.org/10.1136/bmj.328.7455.1561
      1. Braithwaite J. Wears R.L. Hollnagel E. Resilient Health Care Volume 3: Reconciling Work-as-Imagined and Work-as-Done. 1st edition. CRC Press, 2016
        • Kononowech J.
        • Landis-Lewis Z.
        • Carpenter J.
        • et al.
        Visual process maps to support implementation efforts: a case example.
        Implement Sci Commun. 2020; 1: 105https://doi.org/10.1186/s43058-020-00094-6
        • Morrow A.
        • Hogden E.
        • Kang Y.J.
        • et al.
        Comparing theory and non-theory based implementation approaches to improving referral practices in cancer genetics: a cluster randomised trial protocol.
        Trials. 2019; 20: 373https://doi.org/10.1186/s13063-019-3457-6
        • Taylor N.
        • Best S.
        • Martyn M.
        • et al.
        A transformative translational change programme to introduce genomics into healthcare: a complexity and implementation science study protocol.
        BMJ Open. 2019; 9e024681https://doi.org/10.1136/bmjopen-2018-024681
        • Stark Z.
        • Boughtwood T.
        • Phillips P.
        • et al.
        Australian Genomics: a federated model for integrating genomics into healthcare.
        Am J Hum Genet. 2019; 105: 7-14https://doi.org/10.1016/j.ajhg.2019.06.003
        • Stark Z.
        • Dolman L.
        • Manolio T.A.
        • et al.
        Integrating genomics into healthcare: a global responsibility.
        Am J Hum Genet. 2019; 104: 13-20https://doi.org/10.1016/j.ajhg.2018.11.014
        • Gaff C.L.
        • Winship I.M.
        • Forrest S.M.
        • et al.
        Preparing for genomic medicine: a real world demonstration of health system change.
        NPJ Genom Med. 2017; 2 (Published correction appears in NPJ Genom Med. 2017;2:31.): 16
        • Curran G.M.
        • Bauer M.
        • Mittman B.
        • Pyne J.M.
        • Stetler C.
        Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact.
        Med Care. 2012; 50: 217-226https://doi.org/10.1097/MLR.0b013e3182408812
        • Cane J.
        • O’Connor D.
        • Michie S.
        Validation of the theoretical domains framework for use in behaviour change and implementation research.
        Implement Sci. 2012; 7: 37https://doi.org/10.1186/1748-5908-7-37
        • Atkins L.
        • Francis J.
        • Islam R.
        • et al.
        A guide to using the Theoretical Domains Framework of behaviour change to investigate implementation problems.
        Implement Sci. 2017; 12: 77https://doi.org/10.1186/s13012-017-0605-9
        • Colligan L.
        • Anderson J.E.
        • Potts H.W.
        • Berman J.
        Does the process map influence the outcome of quality improvement work? A comparison of a sequential flow diagram and a hierarchical task analysis diagram.
        BMC Health Serv Res. 2010; 10: 7https://doi.org/10.1186/1472-6963-10-7
        • Best S.
        • Long J.C.
        • Gaff C.
        • Braithwaite J.
        • Taylor N.
        Investigating the adoption of clinical genomics in Australia. An implementation science case study.
        Genes (Basel). 2021; 12: 317https://doi.org/10.3390/genes12020317
        • Braun V.
        • Clarke V.
        To saturate or not to saturate? Questioning data saturation as a useful concept for thematic analysis and sample-size rationales.
        Qual Res Sport Exer Health. 2021; 13: 201-216https://doi.org/10.1080/2159676X.2019.1704846
        • NSW Agency for Clinical Innovation
        Clinical program design and implementation guide on how to process map. NSW Agency for Clinical Innovation.
        (Published July14, 2015.) (Accessed July 28, 2021.)
        • Castro F.G.
        • Yasui M.
        Advances in EBI development for diverse populations: Towards a science of intervention adaptation.
        Prev Sci. 2017; 18: 623-629https://doi.org/10.1007/s11121-017-0809-x
        • Chambers D.A.
        • Feero W.G.
        • Khoury M.J.
        Convergence of implementation science, precision medicine, and the learning health care system: a new model for biomedical research.
        JAMA. 2016; 315: 1941-1942https://doi.org/10.1001/jama.2016.3867
        • Chambers D.A.
        • Glasgow R.E.
        • Stange K.C.
        The dynamic sustainability framework: addressing the paradox of sustainment amid ongoing change.
        Implement Sci. 2013; 8: 117https://doi.org/10.1186/1748-5908-8-117
        • Wiltsey Stirman S.
        • Baumann A.A.
        • Miller C.J.
        The FRAME: an expanded framework for reporting adaptations and modifications to evidence-based interventions.
        Implement Sci. 2019; 14: 58https://doi.org/10.1186/s13012-019-0898-y
        • Dopp A.R.
        • Mundey P.
        • Beasley L.O.
        • Silovsky J.F.
        • Eisenberg D.
        Mixed-method approaches to strengthen economic evaluations in implementation research.
        Implement Sci. 2019; 14: 2https://doi.org/10.1186/s13012-018-0850-6
        • Clay-Williams R.
        • Austin E.
        • Braithwaite J.
        • Hollnagel E.
        Qualitative assessment to improve everyday activities.
        in: Rapport F. Braithwaite J. Transforming Healthcare with Qualitative Research. Routledge/Taylor & Francis, 2020: 71-82
        • French S.D.
        • Green S.E.
        • O’Connor D.A.
        • et al.
        Developing theory-informed behaviour change interventions to implement evidence into practice: a systematic approach using the Theoretical Domains Framework.
        Implement Sci. 2012; 7: 38https://doi.org/10.1186/1748-5908-7-38
        • Von Thiele Schwarz U.
        • Förberg U.
        • Sundell K.
        • Hasson H.
        Colliding ideals - an interview study of how intervention researchers address adherence and adaptations in replication studies.
        BMC Med Res Methodol. 2018; 18: 36https://doi.org/10.1186/s12874-018-0496-8
        • Anyan F.
        The influence of power shifts in data collection and analysis stages: a focus on qualitative research interview.
        Qual Rep. 2013; 18: 1-9https://doi.org/10.46743/2160-3715/2013.1525