Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-12-04T19:10:29.490Z Has data issue: false hasContentIssue false

Design Issues in E-Consent

Published online by Cambridge University Press:  01 January 2021

Abstract

Electronic informed consent represents an opportunity to redesign the way that participants understand and elect to enroll in clinical research studies. However, electronic consent faces certain barriers common to all informed consent processes and other barriers specific to the technical environment. At Sage Bionetworks, we designed an electronic consent process as a software product and released it as an open source tool. We believe that using contemporary design processes to intentionally create cognitive friction, where potential study participants are confronted with interfaces that require them to slow down and contemplate study concepts, offers a significant opportunity for ethical design as research increasingly uses smartphones and digital methodologies.

Type
Symposium Articles
Copyright
Copyright © American Society of Law, Medicine and Ethics 2018

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Montalvo, W. and Larson, E., “Participant Comprehension of Research for Which They Volunteer: A Systematic Review,” Journal of Nursing Scholarship 46, no. 6 (2014): 423431.Google Scholar
Key stakeholders in government (such as the Office for Human Research Protections, Department of Health and Human Services (HHS), “Use of Electronic Informed Consent in Clinical Investigations, Guidance,” available at <https://www.hhs.gov/ohrp/news/announcements-and-news-releases/2016/use-electronic-informed-consent-clinical-trials/index.html> (last visited January 19, 2018)); Center for Drug Evaluation and Research, Food and Drug Administration (FDA), “Use of Electronic Informed Consent,” available at <https://www.fda.gov/downloads/drugs/guidances/ucm436811.pdf> (last visited January 19, 2018) have issued formal guidance for use of electronic informed consent in clinical investigations, and the world's largest consumer brand—Apple—has released an open source toolkit to support e-consent (Apple, “ResearchKit and CareKit,” available at <https://www.apple.com/research-kit/> (last visited January 19, 2018)). Additionally, a survey of more than 100 biotech, pharmaceutical, CRO, and IRB organizations found that 66% of the global top 50 pharmaceutical companies are engaged in or planning an e-consent initiative, with all of the top 10 already in implementation. See Clinical Leader, “The Current State Of eConsent In Clinical Trials,” available at <https://www.clinicalleader.com/doc/the-current-state-of-econsent-in-clinical-trials-0001> (last visited January 19, 2018).+(last+visited+January+19,+2018));+Center+for+Drug+Evaluation+and+Research,+Food+and+Drug+Administration+(FDA),+“Use+of+Electronic+Informed+Consent,”+available+at++(last+visited+January+19,+2018)+have+issued+formal+guidance+for+use+of+electronic+informed+consent+in+clinical+investigations,+and+the+world's+largest+consumer+brand—Apple—has+released+an+open+source+toolkit+to+support+e-consent+(Apple,+“ResearchKit+and+CareKit,”+available+at++(last+visited+January+19,+2018)).+Additionally,+a+survey+of+more+than+100+biotech,+pharmaceutical,+CRO,+and+IRB+organizations+found+that+66%+of+the+global+top+50+pharmaceutical+companies+are+engaged+in+or+planning+an+e-consent+initiative,+with+all+of+the+top+10+already+in+implementation.+See+Clinical+Leader,+“The+Current+State+Of+eConsent+In+Clinical+Trials,”+available+at++(last+visited+January+19,+2018).>Google Scholar
Decision makers do not make choices in a vacuum. They make them in an environment where many features, noticed and unnoticed, can influence their decisions. The person who creates that environment is, in our terminology, a choice architect. This paper analyzes some of the tools that are available to choice architects. The goal of this paper is to show how choice architecture can be used to help nudge people to make better choices (as judged by themselves) without forcing certain outcomes upon anyone, a philosophy of libertarian paternalism. The tools highlighted here are: defaults, expecting error, understanding mappings, giving feedback, structuring complex choices, and creating incentives. See Thaler, R. H., Sunstein, C. R., and Balz, J. P., “Choice Architecture,” April 2, 2010, available at <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1583509> (last visited January 19, 2018).Google Scholar
Doerr, M., Suver, C., and Wilbanks, J., “Developing a Transparent, Participant-Navigated Electronic Informed Consent for Mobile-Mediated Research,” April 22, 2016, available at <https://ssrn.com/abstract=2769129> (last visited January 19, 2018).CrossRefGoogle Scholar
Ravina, B. et al., “A Longitudinal Program for Biomarker Development in Parkinson's Disease: A Feasibility Study,” Movement Disorders 24, no. 14 (2009): 20812090. See also Parkinson's Foundation, “Longitudinal and Biomarker Study in PD (LABS-PD),” available at <http://parkinson.org/research/Science-News-and-Progress/Scientific-News/augscinews2> (last visited January 19, 2018).CrossRefGoogle Scholar
Bot, B. M. et al., “The mPower Study, Parkinson Disease Mobile Data Collected Using ResearchKit,” Scientific Data 3, no. 160011 (2016): doi: 10.1038/sdata.2016.11.Google Scholar
Obar, J. A. and Oeldorf-Hirsch, A., The Biggest Lie on the Internet: Ignoring the Privacy Policies and Terms of Service Policies of Social Networking Services, paper presented at TPRC 44: The 44th Research Conference on Communication, Information, and Internet Policy, August 24, 2016, available at <http://dx.doi.org/10.2139/ssrn.2757465> (last visited January 19, 2018).CrossRef+(last+visited+January+19,+2018).>Google Scholar
Cassileth, B.R., Zupkis, R. V., Sutton-Smith, K., and March, V., “Informed Consent — Why Are Its Goals Imperfectly Realized?” New England Journal of Medicine 302, no. 16 (1980): 896900.Google Scholar
Paasche-Orlow, M. K., Taylor, H. A., and Brancati, F. L., “Readability Standards for Informed-Consent Forms as Compared With Actual Readability,” New England Journal of Medicine 348, no. 8 (2003): 721726.CrossRefGoogle Scholar
See, e.g., Flory, J. and Emanuel, E., “Interventions to Improve Research Participants' Understanding in Informed Consent for Research, A Systematic Review,” JAMA 292, no. 13 (2004): 15931601.Google Scholar
See Obar and Oeldorf-Hirsch, supra note 7.Google Scholar
Weinreich, H., Obendorf, H., Herder, E., and Mayer, M., “Not Quite the Average: An Empirical Study of Web Use,” Association for Computing Machinery Transactions on the Web 2, no. 1 (2008): 126. For the study data reanalyzed, see J. Nielson, “How Little Do Users Read?,” available at <http://www.nngroup.com/articles/how-little-do-users-read> (last visited January 19, 2018).Google Scholar
“Technical debt (also known as design debt or code debt) is a concept in software development that reflects the implied cost of additional rework caused by choosing an easy solution now instead of using a better approach that would take longer.” Wikipedia, “Technical Debt,” available at <https://en.wikipedia.org/wiki/Technical_debt> (last visited January 19, 2018).+(last+visited+January+19,+2018).>Google Scholar
See Doerr, Suver, and Wilbanks, supra note 4.Google Scholar
McCay-Peet, L., Lalmas, L. Mounia, and Navalpakkam, V., “On Saliency, Affect and Focused Attention,” presentation given at the SIGCHI Conference on Human Factors in Computing Systems by the Association for Computing Machinery, 2012.Google Scholar
Doerr, M. et al., “Formative Evaluation of Participant Experience with Mobile eConsent in the App-Mediated Parkinson mPower Study: A Mixed Methods Study,” in Eysenbach, G., ed., Journal of Medical Internet Research mHealth and uHealth 5, no. 2 (2017): doi:10.2196/mhealth.6521.Google Scholar
Sage Bionetworks, “Participant Centered Consent Toolkit,” available at <http://sagebase.org/governance/participant-centered-consent-toolkit/> (last visited January 19, 2018).+(last+visited+January+19,+2018).>Google Scholar
Tourraine, V., “List of all ResearchKit apps,” available at <http://blog.shazino.com/articles/science/researchkit-list-apps/> (last visited January 19, 2018).+(last+visited+January+19,+2018).>Google Scholar
Kaiser Permanente Research Bank, available at <https://researchbank.kaiserpermanente.org/> (last visited January 19, 2018).+(last+visited+January+19,+2018).>Google Scholar
National Institutes of Health, All of Us Research Program,” available at <https://allofus.nih.gov> (last visited January 19, 2018).+(last+visited+January+19,+2018).>Google Scholar
Comstock, J., “NIH awards $120M to Scripps, others, to enroll 350K participants in Precision Medicine Initiative via mobile apps,” available at <http://www.mobihealthnews.com/content/nih-awards-120m-scripps-others-enroll-350k-participants-precision-medicine-initiative-mobile> (last visited January 19, 2018).+(last+visited+January+19,+2018).>Google Scholar
“OpenMarket's Survey Reveals Texting is the #1 Preferred Channel for Two-Way Business-to-Millennial Communications,” available at <https://www.openmarket.com/press/millennials-prefer-sms-business-notifications/> (last visited January 19, 2018). (last visited January 19, 2018).' href=https://scholar.google.com/scholar?q=“OpenMarket's+Survey+Reveals+Texting+is+the+#1+Preferred+Channel+for+Two-Way+Business-to-Millennial+Communications,”+available+at++(last+visited+January+19,+2018).>Google Scholar
Godolphin, W., “Shared Decision-Making,” Healthcare Quarterly 12 (2009): e186e190.CrossRefGoogle Scholar
Terranova, T., “Attention, Economy and the Brain,” Culture Machine 13, no. 1 (2012): 119.Google Scholar
“Cognitive friction” is a phrase coined by Alan Cooper in his 1999 book The Inmates are Running the Asylum: Why High-Tech Products Drive Us Crazy and How to Restore the Sanity (Indianapolis, IN: Sams Publishing, 2004): at 19. In the modern design world, friction is “anything that gets between a user and a task” and is often considered a negative, especially when it's accidental and stops users from their tasks. But friction-on-purpose is a key part of designing a learning curve — and learning curves are at the heart of informed consent design.Google Scholar
Selzer, S., “The Fiction of No Friction: Thoughts on the Future of Human-Centered Design,” available at <https://airbnb.design/the-fiction-of-no-friction-2/> (last visited January 19, 2018).+(last+visited+January+19,+2018).>Google Scholar
Johnson, E. J. et al., “Beyond Nudges: Tools of a Choice Architecture,” Marketing Letters 23 (2012): 487.Google Scholar
Tang, D. et al., “Overlapping Experiment Infrastructure: More, Better, Faster Experimentation,” Google White paper, available at <https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/36500.pdf> (last visited January 19, 2018).+(last+visited+January+19,+2018).>Google Scholar