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Qualitative Methods for Refining a Web-Based Educational Tool for Patients Focused on Inherited Cancer Predisposition


To address the increasing demand for inherited cancer genetic testing, we developed and evaluated a web-based educational tool to streamline genetic counseling (GC). Consented patients viewed the initial prototype containing core content (Version 1-Core) and provided feedback through three open-ended survey questions. Additional data were collected through individual interviews from a subgroup who viewed an enhanced version (Version 1-Enhanced), consisting of the same core content and additional optional content. Data were coded to synthesize most commonly repeated themes and conceptualize action items to guide refinement strategies. Of 305 participants, 80 responded to open-ended survey questions to suggest refinement strategies, after viewing Version 1-Core. Interviews with a subgroup of seven participants, who viewed Version 1-Enhanced, provided additional feedback. Of 11 unique action items identified, five overlapped across datasets (provide instructions, simplify language, improve visuals, embed knowledge questions with explanations, include more insurance-related information), three were identified only through open-ended survey data (incorporate automatic progression, clarify test result information, increase interactive content), and three were identified only through interviews (ensure core content is viewed, incorporate progress bar, feature embedded optional content at the end of the tool). Ten action items aligned with underlying tool objectives to provide an interactive online pre-test GC solution and were used to guide refinement strategies. Our results demonstrate the value of rigorous qualitative data collection and analysis in health research and the use of the self-directed learning framework and eHealth strategies to leverage technology in scaling up and innovating the delivery of pre-test GC for inherited cancer.

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  1. 1.

    Breese U, French R (2012) Adult learning theory and patient education for low back pain: a national survey of physical therapists. J Allied Health 41(4):198–203

    PubMed  Google Scholar 

  2. 2.

    Clauser SB, Wagner EH, Aiello Bowles EJ, Tuzzio L, Greene SM (2011) Improving modern cancer care through information technology. Am J Prev Med 40(5 Suppl 2):S198–S207.

    Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Cragun D, Weidner A, Tezak A, Zuniga B, Wiesner GL, Pal T (2020) A web-based tool to automate portions of pretest genetic counseling for inherited cancer. J Natl Compr Cancer Netw 18(7):841–847.

    Article  Google Scholar 

  4. 4.

    Curran MK (2014) Examination of the teaching styles of nursing professional development specialists, part I: best practices in adult learning theory, curriculum development, and knowledge transfer. J Contin Educ Nurs 45(5):233–240.

    Article  PubMed  Google Scholar 

  5. 5.

    Genetic/Familial High-risk Assessment: Breast, Ovarian, and Pancreatic V.1.2020 (2019) NCCN Practice Guidelies. Accessed Dec 4 2019

  6. 6.

    Glaser BG, Strauss AL (1967) The discovery of grounded theory: strategies for qualitative research. Aldine Transaction: a Division of Transaction Publishers, New Brunswick

    Google Scholar 

  7. 7.

    Guest G, MacQueen KM, Namey EE (2012) Applied thematic analysis. SAGE Publications, Inc., Los Angeles

    Book  Google Scholar 

  8. 8.

    Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O'Neal L, McLeod L, Delacqua G, Delacqua F, Kirby J, Duda SN, REDCap Consortium (2019) The REDCap consortium: building an international community of software platform partners. J Biomed Inform 95:103208.

    Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Hernan R, Cho MT, Wilson AL, Ahimaz P, Au C, Berger SM, Guzman E, Primiano M, Shaw JE, Ross M, Tabanfar L, Chilton I, Griffin E, Ratner C, Anyane-Yeboa K, Iglesias A, Pisani L, Roohi J, Duong J, Martinez J, Appelbaum P, Klitzman R, Ottman R, Chung WK, Wynn J (2020) Impact of patient education videos on genetic counseling outcomes after exome sequencing. Patient Educ Couns 103(1):127–135.

    Article  PubMed  Google Scholar 

  10. 10.

    Merriam SB (2002) Andragogy and self-directed learning: pillars of adult learning theory. New Dir Adult Contin Educ 2001(Special Issue 89: The New Update on Adult Learning Theory):3–14.

    Article  Google Scholar 

  11. 11.

    Merriam SB, Bierema LL (2014) Adult learning: linking theory and practice. Jossey-Bass: A Wiley Brand, San Francisco

    Google Scholar 

  12. 12.

    Mitchell ML, Courtney M (2005) Improving transfer from the intensive care unit: the development, implementation and evaluation of a brochure based on Knowles' adult learning theory. Int J Nurs Pract 11(6):257–268.

    Article  PubMed  Google Scholar 

  13. 13.

    O'Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA (2014) Standards for reporting qualitative research: a synthesis of recommendations. Acad Med 89(9):1245–1251.

    Article  PubMed  Google Scholar 

  14. 14.

    Robson ME, Bradbury AR, Arun B, Domchek SM, Ford JM, Hampel HL, Lipkin SM, Syngal S, Wollins DS, Lindor NM (2015) American Society of Clinical Oncology policy statement update: genetic and genomic testing for cancer susceptibility. J Clin Oncol 33(31):3660–3667.

    CAS  Article  PubMed  Google Scholar 

  15. 15.

    Rubanovich CK, Cheung C, Mandel J, Bloss CS (2018) Physician preparedness for big genomic data: a review of genomic medicine education initiatives in the United States. Hum Mol Genet 27(R2):R250–r258.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Schmidlen T, Schwartz M, DiLoreto K, Kirchner HL, Sturm AC (2019) Patient assessment of chatbots for the scalable delivery of genetic counseling. J Genet Couns 28(6):1166–1177.

    Article  PubMed  Google Scholar 

  17. 17.

    Sirintrapun SJ, Lopez AM (2018) Telemedicine in cancer care. Am Soc Clin Oncol Educ Book 38:540–545.

    Article  PubMed  Google Scholar 

  18. 18.

    Tavakol M, Torabi S, Akbar Zeinaloo A (2006) Grounded theory in medical education research. Med Educ Online 11(1):4607.

    Article  PubMed  Google Scholar 

  19. 19.

    Taylor DC, Hamdy H (2013) Adult learning theories: implications for learning and teaching in medical education: AMEE guide no. 83. Med Teach 35(11):e1561–e1572.

    Article  PubMed  Google Scholar 

  20. 20.

    Thavaneswaran S, Rath E, Tucker K, Joshua AM, Hess D, Pinese M, Ballinger ML, Thomas DM (2019) Author correction: therapeutic implications of germline genetic findings in cancer. Nat Rev Clin Oncol 16(6):397.

    Article  PubMed  Google Scholar 

  21. 21.

    van Gemert-Pijnen JE, Nijland N, van Limburg M, Ossebaard HC, Kelders SM, Eysenbach G, Seydel ER (2011) A holistic framework to improve the uptake and impact of eHealth technologies. J Med Internet Res 13(4):e111.

    Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Watling CJ, Lingard L (2012) Grounded theory in medical education research: AMEE guide no. 70. Med Teach 34(10):850–861.

    Article  PubMed  Google Scholar 

  23. 23.

    Watson CH, Ulm M, Blackburn P, Smiley L, Reed M, Covington R, Bokovitz L, Tillmanns T (2016) Video-assisted genetic counseling in patients with ovarian, fallopian and peritoneal carcinoma. Gynecol Oncol 143(1):109–112.

    Article  PubMed  Google Scholar 

  24. 24.

    Young SD, Holloway IW, Swendeman D (2014) Incorporating guidelines for use of mobile technologies in health research and practice. Int Health 6(2):79–81.

    Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Zigmont JJ, Kappus LJ, Sudikoff SN (2011) Theoretical foundations of learning through simulation. Semin Perinatol 35(2):47–51.

    Article  PubMed  Google Scholar 

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We thank Courtney Lewis for her efforts in the initial prototype of the tool, and Joy Kechik and other genetic counseling students at the University of South Florida, Genetic Counseling Graduate Program. We thank Carlos Montoya and the University of South Florida, College of Public Health ETA office, for technical assistance with the software used in the development of the tool. We thank the clinical team from the Vanderbilt Hereditary Cancer Clinic.


This work was supported by funding from Ingram Professorship (ID0EQ6AG3405), Kleberg Foundation (ID0ESDBG3406), and Vanderbilt Genetic Institute Departmental Funds (ID0EUHBG3407). This project was also supported by CTSA (award number UL1 TR002243) from the National Center for Advancing Translational Sciences. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences or the NIH.

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Study conception and design: Deborah Cragun and Tuya Pal. Project administration and data collection: Ann Tezak, Brenda Zuniga, and Anne Weidner. Qualitative coding and analysis: Ann Tezak and Brenda Zuniga. Funding acquisition: Tuya Pal. Writing—original draft preparation: Ann Tezak and Tuya Pal. Writing—review and editing: All authors.

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Correspondence to Tuya Pal.

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Interview Guide: The interview guide, used for conducting seven in-depth interviews with participants who viewed Vesion 1-Enhanced, reflects an introductory script followed by open-ended interview questions structured with probes that seek participant input on the following domains: 1) impressions of the tool’s overall content; 2) visual and design appeal; 3) confidence in relaying information learned; 4) suggestions for improvement and/or alternative ways of communicating topics; and 5) functional barriers for the participants and anticipated audience. The guide ends with a closing script and section for interviewer notes. (PDF 1100 kb)

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Tezak, A.L., Zuniga, B., Weidner, A. et al. Qualitative Methods for Refining a Web-Based Educational Tool for Patients Focused on Inherited Cancer Predisposition. J Canc Educ (2021).

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  • Pre-test genetic counseling
  • Inherited cancer
  • Education
  • Care delivery