CC BY-NC-ND 4.0 · Yearb Med Inform 2022; 31(01): 307-316
DOI: 10.1055/s-0042-1742542
Research & Education

Equitable Research PRAXIS: A Framework for Health Informatics Methods

Tiffany C. Veinot
1   School of Information, University of Michigan Ann Arbor, MI, USA
2   Department of Health Behavior and Health Education, School of Public Health, University of Michigan, Ann Arbor, MI, USA
,
Phillipa J. Clarke
3   Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
4   Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
,
Daniel M. Romero
1   School of Information, University of Michigan Ann Arbor, MI, USA
5   Division of Computer Science and Engineering, College of Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
6   Center for the Study of Complex Systems, College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, USA
,
Lorraine R. Buis
1   School of Information, University of Michigan Ann Arbor, MI, USA
7   Department of Family Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
,
Tawanna R. Dillahunt
1   School of Information, University of Michigan Ann Arbor, MI, USA
5   Division of Computer Science and Engineering, College of Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
,
Vinod V.G. Vydiswaran
1   School of Information, University of Michigan Ann Arbor, MI, USA
8   Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA
,
Ashley Beals
1   School of Information, University of Michigan Ann Arbor, MI, USA
,
Lindsay Brown
1   School of Information, University of Michigan Ann Arbor, MI, USA
,
Olivia Richards
1   School of Information, University of Michigan Ann Arbor, MI, USA
,
Alicia Williamson
1   School of Information, University of Michigan Ann Arbor, MI, USA
,
Marcy G. Antonio
1   School of Information, University of Michigan Ann Arbor, MI, USA
› Author Affiliations

Summary

Objectives: There is growing attention to health equity in health informatics research. However, the literature lacks a comprehensive framework outlining critical considerations for health informatics research with marginalized groups.

Methods: Literature review and experiences from nine equity-focused health informatics conducted in the United States and Canada. Studies focus on disparities related to age, disability or chronic illness, gender/sex, place of residence (rural/urban), race/ethnicity, sexual orientation, and socioeconomic status.

Results: We found four key equity-related methodological considerations. To assist informaticists in addressing equity, we contribute a novel framework to synthesize these four considerations: PRAXIS (Participation and Representation, Appropriate methods and interventions, conteXtualization and structural competence, Investigation of Systematic differences). Participation and representation refers to the necessity for meaningful participation of marginalized groups in research, to elevate the voices of marginalized people, and to represent marginalized people as they are comfortable (e.g., asset-based versus deficit-based). Appropriate methods and interventions mean targeting methods, instruments, and interventions to reach and engage marginalized people. Contextualization and structural competence mean avoiding individualization of systematic disparities and targeting social conditions that (re-)produce inequities. Investigation of systematic differences highlights that experiences of people marginalized according to specific traits differ from those not so marginalized, and thus encourages studying the specificity of these differences and investigating and preventing intervention-generated inequality. We outline guidance for operationalizing these considerations at four research stages.

Conclusions: This framework can assist informaticists in systematically addressing these considerations in their research in four research stages: project initiation; sampling and recruitment; data collection; and data analysis. We encourage others to use these insights from multiple studies to advance health equity in informatics.

Supplementary Material



Publication History

Article published online:
04 December 2022

© 2022. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Georg Thieme Verlag KG
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  • References

  • 1 Veinot TC, Ancker JS, Bakken S. Health informatics and health equity: Improving our reach and impact. J Am Med Inform Assoc 2019 Aug 1;26(8-9):689-95
  • 2 Johnson KB, Bright TJ, Clark CR. Overview of the issue. J Health Care Poor Underserved 2021;32(2):1-4
  • 3 Siek K, Veinot TC, Mynatt E. Research opportunities in sociotechnical interventions for health disparity reduction Washington, DC: CRA; 2019 Available from: https://arxiv.org/abs/1908.01035
  • 4 Gibbons MC, Wilson RF, Samal L, Lehmann CU, Dickersin K, Lehmann HP, et al. Consumer health informatics: Results of a systematic evidence review and evidence based recommendations. Transl Behav Med 2011 Mar;1(1):72-82
  • 5 Veinot TC, Mitchell H, Ancker JS. Good intentions are not enough: How informatics interventions can worsen inequality. J Am Med Inform Assoc 2018 Aug 1;25(8):1080-8.
  • 6 Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 2019 Oct 25;366(6464):447-53
  • 7 Unertl KM, Schaefbauer CL, Campbell TR, Senteio C, Siek KA, Bakken S, et al. Integrating community-based participatory research and informatics approaches to improve the engagement and health of underserved populations. J Am Med Inform Assoc 2016 Jan;23(1):60-73.
  • 8 Liang CA, Munson SA, Kientz JA. Embracing four tensions in human-computer interaction research with marginalized people. ACM Transactions on Computer-Human Interaction (TOCHI) 2021;28(2):Article 14
  • 9 Hardy J, Wyche S, Veinot TC. Rural HCI research: Definitions, distinctions, methods, and opportunities. Vancouver, BC: CSCW Conf Comput Support Coop Work. 2019
  • 10 Vines J, Pritchard G, Wright P, Olivier P, Brittain K. An age-old problem: Examining the discourses of ageing in HCI and strategies for future research. ACM Trans Comput Hum Interact 2015;22(1):1-27
  • 11 Spiel K, Haimson OL, Lottridge D. How to do better with gender on surveys. Interactions 2019;26(4):62-5.
  • 12 Gilbey D, Morgan H, Lin A, Perry Y. Effectiveness, acceptability, and feasibility of digital health interventions for LGBTIQ+ young people: Systematic review. J Med Internet Res 2020 Dec 3;22(12):e20158.
  • 13 US Department of Health and Human Services. Healthy People 2030: Social determinants of health Washington: DHHS; 2020. Available from: https://health.gov/healthypeople/priority-areas/social-determinants-health
  • 14 Metzl JM, Hansen H. Structural competency: theorizing a new medical engagement with stigma and inequality. Soc Sci Med 2014 Feb;103:126-33
  • 15 Larson NI, Story MT, Nelson MC. Neighborhood environments: disparities in access to healthy foods in the U.S. Am J Prev Med 2009 Jan;36(1):74-81.
  • 16 Diez Roux AV, Merkin SS, Arnett D, Chambless L, Massing M, Nieto FJ, et al. Neighborhood of residence and incidence of coronary heart disease. N Engl J Med 2001 Jul 12;345(2):99-106
  • 17 Conderino SE, Feldman JM, Spoer B, Gourevitch MN, Thorpe LE. Social and economic differences in neighborhood walkability across 500 U.S. cities. Am J Prev Med 2021 Sep;61(3):394-401
  • 18 Kephart L. How racial residential segregation structures access and exposure to greenness and green space: A review. Environ Justice 2021.
  • 19 Lewis TT, Cogburn CD, Williams DR. Self-reported experiences of discrimination and health: Scientific advances, ongoing controversies, and emerging issues. Annu Rev Clin Psychol 2015;11:407-40
  • 20 Veinot TC, Ancker JS, Cole-Lewis H, Mynatt ED, Parker AG, Siek KA, et al. Leveling up: On the potential of upstream health informatics interventions to enhance health equity. Med Care 2019 Jun;57 Suppl 6 Suppl 2:S108-S114
  • 21 Agency for Healthcare Research and Quality. 2019 National healthcare quality and disparities report. Rockville, MD: AHRQ; 2021
  • 22 Bauer GR, Churchill SM, Mahendran M, Walwyn C, Lizotte D, Villa-Rueda AA. Intersectionality in quantitative research: A systematic review of its emergence and applications of theory and methods. SSM Popul Health 2021 Apr 16;14:100798
  • 23 Vyas DA, Eisenstein LG, Jones DS. Hidden in plain sight - Reconsidering the use of race correction in clinical algorithms. N Engl J Med 2020 Aug 27;383(9):874-82.
  • 24 Dillahunt TR, Veinot TC. Getting there: Barriers and facilitators to transportation access in underserved communities. ACM Trans Comput Hum Interact 2018;25(5):1-39
  • 25 Mertens DM. Transformative paradigm: Mixed methods and social justice. J Mixed Methods Res 2007;1(3):212-25
  • 26 Mertens DM. Transformative mixed methods: Addressing inequities. Am Behav Sci 2012;56(6):802-13
  • 27 Antonio M, Lau F, Davison K, Devor A, Queen R, Courtney K. Toward an inclusive digital health system for sexual and gender minorities in Canada. J Am Med Inform Assoc 2022 Jan 12;29(2):379-84
  • 28 Lau F, Devor A, Antonio M, et al. A proposed action plan to modernize gender, sex and sexual orientation information practices in Canadian electronic health record systems; 2021. Available from: https://infocentral.infoway-inforoute. ca/en/resources/docs/sex-gender/sex-gender-action-plan/3496-gsso-action-plan-full-document
  • 29 Buis L, Hirzel L, Dawood RM, Dawood KL, Nichols LP, Artinian NT, et al. Text messaging to improve hypertension medication adherence in African Americans from primary care and emergency department settings: Results from two randomized feasibility studies. JMIR Mhealth Uhealth 2017 Feb 1;5(2):e9
  • 30 Hardeman RR, Homan PA, Chantarat T, Davis BA, Brown TH. Improving the measurement of structural racism to achieve antiracist health policy. Health Aff (Millwood) 2022 Feb;41(2):179-86.
  • 31 Sun M, Oliwa T, Peek ME, Tung EL. Negative patient descriptors: documenting racial bias In The electronic health record. Health Aff (Millwood) 2022 Feb;41(2):203-11
  • 32 Himmelstein G, Bates D, Zhou L. Examination of stigmatizing language in the electronic health record. JAMA Netw Open 2022 Jan 4;5(1):e2144967
  • 33 Dirks LG, Beneteau E, Sabin J, Pratt W, Lane C, Bascom E, et al. Battling bias in primary care encounters: Informatics designs to support clinicians. Ext Abstr Hum Factors Computing Syst 2022 Apr;2022:386
  • 34 Kuo PY, Saran R, Argentina M, Heung M, Bragg-Gresham JL, Chatoth D, et al. Development of a checklist for the prevention of intradialytic hypotension in hemodialysis care: Design considerations based on activity theory. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems; 2019. p. 1-14
  • 35 Willis MA, Hein LB, Hu Z, Saran R, Argentina M, Bragg-Gresham J, et al. Feeling better on hemodialysis: User-centered design requirements for promoting patient involvement in the prevention of treatment complications. J Am Med Inform Assoc 2021 Jul 30;28(8):1612-31
  • 36 Iott BE, Loveluck J, Benton A, Golson L, Kahle E, Lam J, et al. The impact of stigma on HIV testing decisions for gay, bisexual, queer and other men who have sex with men: A qualitative study. BMC Public Health 2022 Mar 9;22(1):471
  • 37 Iott BE, Veinot TC, Loveluck J, Kahle E, Golson L, Benton A. Comparative analysis of recruit­ment strategies in a study of men who have sex with men (MSM) in metropolitan Detroit. AIDS Behav 2018 Jul;22(7):2296-311
  • 38 Antonio, MG, Williamson, AK, Kameswaran, V, Ankrah, E., Goulet, S., Wang, I, et al. Reducing patients’ cognitive load for telehealth video visits through student-delivered helping sessions at a United States Federally Qualified Health Center: A pilot intervention study. JMIR [submitted 12 Sep 2022]
  • 39 Toscos T, Drouin M, Pater J, Flanagan M, Pfafman R, Mirro MJ. Selection biases in technology-based intervention research: Patients’ technology use relates to both demographic and health-related inequities. J Am Med Inform Assoc 2019 Aug 1;26(8-9):835-9
  • 40 Consolvo S, McDonald DW, Toscos T, Chen MY, Froehlich J, Harrison B, et al. Activity sensing in the wild: A field trial of ubifit garden. In: Proceedings of the 2008 SIGCHI conference on human factors in computing systems; 2008. p. 1797-806
  • 41 Puri R. Mitigating bias in AI models. IBM Research Blog. 2018. Available from: https://www.ibm.com/blogs/research/2018/02/mitigating-bias-ai-models/
  • 42 Grossman LV, Masterson Creber RM, Benda NC, Wright D, Vawdrey DK, Ancker JS. Interventions to increase patient portal use in vulnerable populations: a systematic review. J Am Med Inform Assoc 2019 Aug 1;26(8-9):855-70
  • 43 Buis LR, Dawood K, Kadri R, Dawood R, Richardson CR, Djuric Z, et al. Improving blood pressure among african americans with hypertension using a mobile health approach (the MI-BP app): Protocol for a randomized controlled trial. JMIR Res Protoc 2019 Jan 25;8(1):e12601
  • 44 McPherson M, Smith-Lovin L, Cook JM. Birds of a feather: Homophily in social networks. Ann Rev Sociol 2001;27(1):415-44
  • 45 Heckathorn DD. Respondent-driven sampling: a new approach to the study of hidden populations. Soc Probl 1997;44(2):174-99
  • 46 Lin JJ, Mamykina L, Lindtner S, Delajoux G, Strub HB. Fish’n’Steps: Encouraging physical activity with an interactive computer game. In: International conference on ubiquitous computing 2006. Berlin, Heidelberg: Springer; 2006. p. 261-78.
  • 47 Veinot TC. Regional HIV/AIDS information environments and information acquisition success. The Information Society 2013;29(2):88-112
  • 48 Veinot TC, Kim YM, Meadowbrooke CC. Health information behavior in families: Supportive or irritating? Proceedings of the American Society for Information Science and Technology 2011;48(1):1-10
  • 49 Brown LK, Veinot TC. Information behavior and social control: Toward an understanding of conflictual information behavior in families managing chronic illness. J Assoc Inf Sci Technol 2021;72(1):66-82
  • 50 Muhib FB, Lin LS, Stueve A, Miller RL, Ford WL, Johnson WD, et al; Community interven­tion trial for youth study team. A venue-based method for sampling hard-to-reach populations. Public Health Rep 2001;116 Suppl 1(Suppl 1):216-22
  • 51 Vydiswaran VGV, Romero DM, Zhao X, Yu D, Gomez-Lopez I, Lu JX, et al Uncovering the relationship between food-related discussion on Twitter and neighborhood characteristics. J Am Med Inform Assoc 2020 Feb 1;27(2):254-64
  • 52 Harrington CN, Borgos-Rodriguez K, Piper AM. Engaging low-income African American older adults in health discussions through community-based design workshops. In: Proceedings of the 2019 chi conference on human factors in computing systems; 2019. p.1-15
  • 53 Majid U, Kandasamy S, Ramlakhan J. How to design an arts-based health services research study: A participatory qualitative study on the determinants of telehealth adoption. Thousand Oaks, CA: SAGE; 2020
  • 54 Sutton-Brown CA. Photovoice: A Methodological Guide. Photography & Culture 2015;7(2):169-85
  • 55 Bugos E, Frasso R, FitzGerald E, True G, Adachi-Mejia AM, Cannuscio C. Practical guidance and ethical considerations for studies using photo-elicitation interviews. Prev Chronic Dis 2014 Oct 30;11:E189
  • 56 Greyson D, O’Brien H, Shoveller J. Information world mapping: A participatory arts-based elicitation method for information behavior interviews. Libr Inf Sci Res 2017;39(2):149-57
  • 57 Howard T. Journey mapping: A brief overview. Communication Design Quarterly Review 2014;2(3):10-3
  • 58 Demirbilek O, Demirkan H. Universal product design involving elderly users: A participatory design model. Appl Ergon 2004;35(4):361-70
  • 59 Khoshkesht S, Nikbakht Nasrabadi A, Mardanian Dehkordi L. Digital storytelling: The new arts-based research method. Iran J Public Health 2020 Jul;49(7):1395-6
  • 60 Haimson OL, Veinot TC. Coming out to doctors, Coming out to “everyone”: Understanding the average sequence of transgender identity dis­closures using aocial media data. Transgend Health 2020 Sep 2;5(3):158-65
  • 61 Juhn YJ, Ryu E, Wi CI, King KS, Malik M, Romero-Brufau S, et al. Assessing socioeconomic bias in machine learning algorithms in health care: A case study of the HOUSES index. J Am Med Inform Assoc 2022 Jun 14;29(7):1142-51
  • 62 Yip J, Clegg T, Bonsignore E, Gelderblom H, Rhodes E, Druin A. Brownies or bags-of-stuff? Domain expertise in cooperative inquiry with children. In: Proceedings of the 12th International Conference on Interaction Design and Children, 2013. New York: ACM; 2013. p. 201-10.
  • 63 Guha ML, Druin A, Chipman G, Fails JA, Simms S, Farber A. Mixing ideas: A new technique for working with young children as design partners. In: Proceedings of the 2004 conference on Interaction design and children: building a community; 2004. p. 35-42
  • 64 Walsh G, Druin A, Guha ML, Foss E, Golub E, Hatley L, et al. Layered elaboration: A new technique for co-design with children. In: Proceedings of the 2010 SIGCHI Conference on Human Factors in Computing Systems; 2010. p. 1237-40
  • 65 Dillahunt TR, Maestre JF, Kameswaran V, Poon E, Osorio Torres J, Gallardo M, et al. Trust, reciprocity, and the role of timebanks as inter­mediaries: Design implications for addressing healthcare transportation barriers. In: 2022 CHI Conference on Human Factors in Computing Systems; 2022. p. 1-22
  • 66 Lewis K, Kaufman J, Gonzalez M, Wimmer A, Christakis N. Tastes, ties, and time: A new social network dataset using Facebook.com. Soc Networks 2008;30(4):330-42
  • 67 Romero DM, Uzzi B, Kleinberg J. Social networks under stress. In: Proceedings of the 25th International Conference on World Wide Web 2016. p.9-20
  • 68 Lu S, Zhao J, Wang H. Academic failures and co-location social networks in campus. EPJ Data Sci 2022;11(1):10
  • 69 Burt RS. Network items and the general social survey. Soc Networks 1984;6(4):293-339.
  • 70 Duchowny K, Clarke P, Gallagher NA, Adams R, Rosso AL, Alexander NB. Using mobile, wearable, technology to understand the role of built environment demand for outdoor mobility. Environ Behav 2019; 51(6):671-88
  • 71 Clarke P, Gallagher NA. Optimizing mobility in later life: The role of the urban built environment for older adults aging in place. J Urban Health 2013 Dec;90(6):997-1009.
  • 72 Ferraro KF, Shippee TP. Aging and cumulative inequality: how does inequality get under the skin? Gerontologist 2009 Jun;49(3):333-43.
  • 73 Handel G. Family Worlds and Qualitative Family Research. Marriage Fam Rev 1997;24(3-4):335-48.
  • 74 Eggenberger SK, Nelms TP. Family interviews as a method for family research. J Adv Nurs 2007 May;58(3):282-92
  • 75 Barbarin AM, Klasnja P, Veinot TC. Good or bad, ups and downs, and getting better: Use of personal health data for temporal reflection in chronic illness. Int J Med Inform 2016 Oct;94:237-45
  • 76 Barbarin A, Veinot TC, Klasnja P. Taking our time: chronic illness and time-based objects in families. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing 2015. p. 288-301
  • 77 Wolf CT, Veinot TC. Struggling for space and finding my place: An interactionist perspective on everyday use of biomedical information. J Assoc Inf Sci Technol 2015;66(2):282-96
  • 78 Willis M, Brand Hein L, Hu Z, Saran R, Argentina M, et al. IUsability evaluation of a tablet-based intervention to prevent intradialytic hypotension in dialysis patients during in-clinic dialysis: Mixed methods study. JMIR Hum Factors 2021 Jun 14;8(2):e26012.
  • 79 James TG, Sullivan MK, Butler JD, McKee MM. Promoting health equity for deaf patients through the electronic health record. J Am Med Inform Assoc 2021 Dec 28;29(1):213-6.
  • 80 Welch VA, Norheim OF, Jull J, Cookson R, Sommerfelt H, Tugwell P; CONSORT-Equity and Boston Equity Symposium. CONSORT-Equity 2017 extension and elaboration for better reporting of health equity in randomised trials. BMJ 2017 Nov 23;359:j5085
  • 81 Clarke P. National Neighborhood Data Archive (NaNDA). Ann Arbor, MI: ICPSR; 2000-2020. Available from: https://www.openicpsr.org/openicpsr/nanda
  • 82 Berkowitz SA, Karter AJ, Corbie-Smith G, Seligman HK, Ackroyd SA, Barnard LS, et al. Food insecurity, food “deserts,” and glycemic control in patients with diabetes: A longitudinal analysis. Diabetes Care 2018 Jun;41(6):1188-95
  • 83 Maestre JF, Dillahunt T, Theisz A, Furness M, Kameswaran V, Veinot TC, et al. Examining mobility among people living with HIV in rural areas. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Yokohama. ACM; 2021. p. 1-17
  • 84 Frey WR, Patton DU, Gaskell MB, McGregor K. Artificial intelligence and inclusion: Formerly gang-involved youth as domain experts for analyzing unstructured Twitter data. Soc Sci Comput Rev 2020;38(1):42-56
  • 85 Corlett S, Mavin S. Reflexivity and researcher positionality. In: Cassell C, Cunliffe A, Grandy G. The SAGE handbook of qualitative business and management research methods. Thousand Oaks, CA: SAGE; 2018. p. 377-98
  • 86 Wang C, Burris MA. Photovoice: Concept, methodology, and use for participatory needs assessment. Health Educ Behav 1997 Jun;24(3):369-87
  • 87 Barry CA, Britten N, Barber N, Bradley C, Stevenson F. Using reflexivity to optimize teamwork in qualitative research. Qual Health Res 1999 Jan;9(1):26-44.
  • 88 Caretta MA, Pérez MA. When participants do not agree: Member checking and challenges to epistemic authority in participatory research. Field Methods 2019;31(4):359-74
  • 89 Bryk AS, Raudenbush SW. Hierarchical linear models: Applications and data analysis. Newbury: Sage; 1992
  • 90 McCormack M, Anderson E, Adams A. Cohort effect on the coming out experiences of bisex­ual men. Sociology 2014;48(6):1207-23
  • 91 Clarke P, Wheaton B. Mapping social context on mental health trajectories through adulthood. Adv Life Course Res 2005;9:269-301
  • 92 Goodspeed R, Yan X, Hardy J, Vydiswaran VGV, Berrocal VJ, Clarke P, et al. Comparing the data quality of global positioning system devices and mobile phones for assessing relationships between place, mobility, and health: Field study. JMIR Mhealth Uhealth 2018 Aug 13;6(8):e168.
  • 93 Lee G, Choi B, Ahn CR, Lee S. Wearable biosensor and hotspot analysis–based framework to detect stress hotspots for advancing elderly’s mobility. Eng Manag J 2020;36(3):04020010
  • 94 Arif Khan M, Shahmoradi A, Etminani-Ghasrodashti R, Kermanshachi S, Michael Rosenberger J. A Geographically weight regression approach to modeling the determinants of on-demand ride services for elderly and disabled. International Conference on Transportation and Development 2021. p.385-96
  • 95 Burt RS. The network structure of social capital. Res Organ Behav 2000;22:345-423
  • 96 Sadri AM, Ukkusuri SV, Lee S, Clawson R, Aldrich D, Nelson MS, et al. The role of social capital, personal networks, and emergency responders in post-disaster recovery and resilience. Nat Hazards (Dordr) 2018;90(3):1377-406
  • 97 Metaxa-Kakavouli D, Maas P, Aldrich DP. How social ties influence hurricane evacuation behavior. Proc ACM Hum Comput Interact 2018;2(CSCW):1-16
  • 98 Hartel J, Thomson L. Visual approaches and photography for the study of immediate information space. J Assoc Inf Sci Technol 2011;62(11):2214-24
  • 99 Veinot TC, Pierce CS. Materiality in information environments: Objects, spaces, and bodies in three outpatient hemodialysis facilities. J Assoc Inf Sci Technol 2019;70(12):1324-39
  • 100 Veinot TC, Zheng K, Lowery JC, Souden M, Keith R. Using electronic health record systems in diabetes care: emerging practices. In: Proceedings of the 1st ACM International Health Informatics Symposium 2010. p. 240-9
  • 101 Shachak A, Reis S. The impact of electronic medical records on patient-doctor communication during consultation: A narrative literature review. J Eval Clin Pract 2009 Aug;15(4):641-9
  • 102 Vasserman L, Li J, Adams CJ, Dixon L. Unintended bas and identity terms 2018. Available from: https://medium.com/jigsaw/unintended-bias-and-names-of-frequently-targeted-groups-8e0b81f80a23
  • 103 Buolamwini J, Gebru T. Gender shades: Intersectional accuracy disparities in commercial gender classification. In: 2018 Conference on fairness, accountability and transparency. PMLR. p. 77-91.
  • 104 Chouldechova A, Benavides-Prado D, Fialko O, Vaithianathan R. A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. In: 2018 Conference on fairness, accountability and transparency. PMLR. p. 134-48
  • 105 Hovy D, Spruit SL. The social impact of natural language processing. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics 2016 (Volume 2: Short Papers). p. 591-8
  • 106 Dwork C, Hardt M, Pitassi T, Reingold O, Zemel R. Fairness through awareness. Proceedings of the 3rd Innovations in Theoretical Computer Science Conference 2012. Cambridge, MA: ACM. p.214-26
  • 107 Antonio, M. Exploring the role of digital technologies for social connectedness, outcomes and experiences with the chronic obstructive pulmonary disease (COPD) community: A transformative mixed methods research study. Doctoral thesis], Victoria, BC: University of Victoria; 2021
  • 108 Stringer ET. Action research: a handbook for practitioners. Thousand Oaks, CA: SAGE Publications; 1996.