CC BY-NC-ND 4.0 · Yearb Med Inform 2022; 31(01): 094-099
DOI: 10.1055/s-0042-1742504
Special Section: Inclusive Digital Health
Working Group Contributions

The Untapped Potential of Nursing and Allied Health Data for Improved Representation of Social Determinants of Health and Intersectionality in Artificial Intelligence Applications: A Rapid Review

IMIA Student and Emerging Professionals Group
Charlene Esteban Ronquillo
1   School of Nursing, University of British Columbia Okanagan, kelowna, Canada
,
James Mitchell
2   School of Computing and Mathematics, Keele University, UK
,
Dari Alhuwail
3   Information Science Department, Kuwait University, Kuwait and Health Informatics Unit, Dasman Diabetes Institute, Kuwait
,
Laura-Maria Peltonen
4   Department of Nursing Science, University of Turku, Finland
,
Maxim Topaz
5   School of Nursing, Columbia University, New York, USA
,
Lorraine J. Block
6   School of Nursing University of British Columbia Vancouver, BC, Canada
› Author Affiliations

Summary

Objectives: The objective of this paper is to draw attention to the currently underused potential of clinical documentation by nursing and allied health professions to improve the representation of social determinants of health (SDoH) and intersectionality data in electronic health records (EHRs), towards the development of equitable artificial intelligence (AI) technologies.

Methods: A rapid review of the literature on the inclusion of nursing and allied health data and the nature of health equity information representation in the development and/or use of artificial intelligence approaches alongside expert perspectives from the International Medical Informatics Association (IMIA) Student and Emerging Professionals Working Group.

Results: Consideration of social determinants of health and intersectionality data are limited in both the medical AI and nursing and allied health AI literature. As a concept being newly discussed in the context of AI, the lack of discussion of intersectionality in the literature was unsurprising. However, the limited consideration of social determinants of health was surprising, given its relatively longstanding recognition and the importance of representation of the features of diverse populations as a key requirement for equitable AI.

Conclusions: Leveraging the rich contextual data collected by nursing and allied health professions has the potential to improve the capture and representation of social determinants of health and intersectionality. This will require addressing issues related to valuing AI goals (e.g., diagnostics versus supporting care delivery) and improved EHR infrastructure to facilitate documentation of data beyond medicine. Leveraging nursing and allied health data to support equitable AI development represents a current open question for further exploration and research.



Publication History

Article published online:
02 June 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
Rüdigerstraße 14, 70469 Stuttgart, Germany

 
  • References

  • 1 Hamilton IA. An AI tool which reconstructed a pixelated picture of Barack Obama to look like a white man perfectly illustrates racial bias in algorithms. June 22, 2020 ed2020. Available from: https://www.businessinsider.com/depixelator-turned-obama-white-illustrates-racial-bias-in-ai-2020-6
  • 2 Kino S, Hsu Y-T, Shiba K, Chien Y-S, Mita C, Kawachi I, et al. A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects. SSM Popul Health 2021 Jun 5;15:100836.
  • 3 Cook LA, Sachs J, Weiskopf NG. The quality of social determinants data in the electronic health record: a systematic review. J Am Med Inform Assoc 2021 Dec 28;29(1):187-96.
  • 4 Wang, T, Zhao J, Yatskar M, Chang K-W, Ordonez V. Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations. Proceedings of the IEEE/CVF International Conference on Computer Vision; 2019. p. 5310-9.
  • 5 Zhang BH, Lemoine B, Mitchell M. Mitigating unwanted biases with adversarial learning. Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society; 2018. p. 335-40.
  • 6 Zhao Q, Adeli E, Pohl KM. Training confounder-free deep learning models for medical applications. Nat Commun 2020 Nov 26;11(1):6010.
  • 7 Benjamin R. Assessing risk, automating racism. Science 2019;366(6464):421-2.
  • 8 Thompson E, Ejdjoc R, Atchessi N, Striha M, Gabrani-Juma I, Dawson T. COVID-19: A case for the collection of race data in Canada and abroad. Can Commun Dis Rep 2021 Jul 8;47(7-8):300-4.
  • 9 Braveman P. What is Health Equity? And What Difference Does a Definition Make? National Collaborating Centre for Determinants of Health; 2017.
  • 10 Crenshaw K. Demarginalizing the intersection of race and sex: A Black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. University of Chicago Legal Forum; 1989. p139-67.
  • 11 Collins PH. Black Feminist Thought: Knowledge, Consciousness, and the Politics of Empowerment. New York: Routledge; 1991.
  • 12 Cyrus K. Multiple minorities as multiply marginalized: Applying the minority stress theory to LGBTQ people of color. J Gay Lesbian Ment Health 2017;21(3):194-202.
  • 13 Vu M, Li J, Haardörfer R, Windle M, Berg CJ. Mental health and substance use among women and men at the intersections of identities and experiences of discrimination: insights from the intersectionality framework. BMC Public Health 2019 Jan 23;19(1):108.
  • 14 Allana S, Ski CF, Thompson DR, Clark AM. Intersectionality and heart failure: what clinicians and researchers should know and do. Curr Opin Support Palliat Care 2021 Jun 1;15(2):141-6.
  • 15 Naqvi JB, Helgeson VS, Gary-Webb TL, Korytkowski MT, Seltman HJ. Sex, race, and the role of relationships in diabetes health: intersectionality matters. J Behav Med 2020 Feb;43(1):69-79.
  • 16 Viruell-Fuentes EA, Miranda PY, Abdulrahim S. More than culture: Structural racism, intersectionality theory, and immigrant health. Soc Sci Med 2012 Dec;75(12):2099-106.
  • 17 Dobbins M. Rapid review guidebook. Natl Collab Cent Method Tools 2017;13:25.
  • 18 NIH National Library of Medicine. Searching PubMed Using MeSH Search Tags: National Institutes of Health; 2020 [updated 2019]. Available from: https://www.nlm.nih.gov/bsd/disted/meshtutorial/searchingpubmedusingmeshtags/index.html
  • 19 Campbell KA, Mackinnon K, Dobbins M, Jack SM. Nurse-Family Partnership and Geography: An Intersectional Perspective. Glob Qual Nurs Res 2020 Jan 21;7:2333393619900888.
  • 20 Bauer GR, Lizotte DJ. Artificial Intelligence, Intersectionality, and the Future of Public Health. Am J Public Health 2021 Jan;111(1):98-100.
  • 21 Nixon SA. The coin model of privilege and critical allyship: implications for health. BMC Public Health 2019 Dec 5;19(1):1637.
  • 22 Reeves RM, Christensen L, Brown JR, Conway M, Levis M, Gobbel GT, et al. Adaptation of an NLP system to a new healthcare environment to identify social determinants of health. J Biomed Inform 2021 Aug;120:103851.
  • 23 Hancock A-M. When Multiplication Doesn't Equal Quick Addition: Examining Intersectionality as a Research Paradigm. Perspectives on Politics 2007;5(1):63-79.
  • 24 McCall L. The Complexity of Intersectionality. Signs 2005;30(3):1771-800.
  • 25 Taylor B, Robertson D, Wiratunga N, Craw S, Mitchell D, Stewart E. Using computer aided case based reasoning to support clinical reasoning in community occupational therapy. Comput Methods Programs Biomed 2007 Aug;87(2):170-9.
  • 26 Jain A, Way D, Gupta V, Gao Y, de Oliveira Marinho G, Hartford J, et al. Development and Assessment of an Artificial Intelligence-Based Tool for Skin Condition Diagnosis by Primary Care Physicians and Nurse Practitioners in Teledermatology Practices. JAMA Netw Open 2021 Apr 1;4(4):e217249.
  • 27 Parker M, Cunningham S, Enderby P, Hawley M, Green P. Automatic speech recognition and training for severely dysarthric users of assistive technology: The STARDUST project. Clin Linguist Phon 2006 Apr-May;20(2-3):149-56.
  • 28 Kokol P, Brumec V, Habjani A, Turk DM, Procter P, Nicklin L. Intelligent systems for nursing education. Stud Health Technol Inform 2001;84(Pt 2):1047-51.
  • 29 Fitzpatrick TB. The validity and practicality of sun-reactive skin types I through VI. Arch Dermatol 1988 Jun;124(6):869-71.
  • 30 Takamine L, Forman J, Damschroder LJ, Youles B, Sussman J. Understanding providers' attitudes and key concerns toward incorporating CVD risk prediction into clinical practice: a qualitative study. BMC Health Serv Res 2021 Jun 7;21(1):561.
  • 31 Duron L, Ducarouge A, Gillibert A, Lainé J, Allouche C, Cherel N, et al. Assessment of an AI Aid in Detection of Adult Appendicular Skeletal Fractures by Emergency Physicians and Radiologists: A Multicenter Cross-sectional Diagnostic Study. Radiology 2021 Jul;300(1):120-9.
  • 32 Arnold J, Davis A, Fischhoff B, Yecies E, Grace J, Klobuka A, et al. Comparing the predictive ability of a commercial artificial intelligence early warning system with physician judgement for clinical deterioration in hospitalised general internal medicine patients: a prospective observational study. BMJ Open 2019 Oct 10;9(10):e032187.
  • 33 Anand V, Carroll AE, Biondich PG, Dugan TM, Downs SM. Pediatric decision support using adapted Arden Syntax. Artif Intell Med 2018 Nov;92:15-23.
  • 34 Canales C, Lee C, Cannesson M. Science without conscience is but the ruin of the soul: the ethics of big data and artificial intelligence in perioperative medicine. Anesth Analg 2020 May;130(5):1234-43.
  • 35 Clark CR, Wilkins CH, Rodriguez JA, Preininger AM, Harris J, DesAutels S, et al. Health Care Equity in the Use of Advanced Analytics and Artificial Intelligence Technologies in Primary Care. J Gen Intern Med 2021 Oct;36(10):3188-93.
  • 36 Marmot M, Allen J, Bell R, Bloomer E, Goldblatt P; Consortium for the European Review of Social Determinants of Health and the Health Divide. WHO European review of social determinants of health and the health divide. Lancet 2012 Sep 15;380(9846):1011-29.
  • 37 Cantor MN, Thorpe L. Integrating data on social determinants of health into electronic health records. Health Aff (Millwood) 2018 Apr;37(4):585-90.
  • 38 Haupeltshofer A, Egerer V, Seeling S. Promoting health literacy: What potential does nursing informatics offer to support older adults in the use of technology? A scoping review. Health Informatics J 2020 Dec;26(4):2707-21.
  • 39 Wang M, Pantell MS, Gottlieb LM, Adler-Milstein J. Documentation and review of social determinants of health data in the EHR: measures and associated insights. J Am Med Inform Assoc 2021 Nov 25;28(12):2608-16.
  • 40 Topaz M, Ronquillo C Peltonen LM, Pruinelli L, Sarmiento RF, Badger MK, et al. Nurse Informaticians Report Low Satisfaction and Multi-level Concerns with Electronic Health Records: Results from an International Survey. AMIA Annu Symp Proc 2017 Feb 10;2016:2016-25.
  • 41 Bako AT, Taylor HL, Wiley K, Jr, Zheng J, Walter-McCabe H, Kasthurirathne SN, et al. Using natural language processing to classify social work interventions. Am J Manag Care 2021 Jan 1;27(1):e24-e31.
  • 42 Topaz M, Lai K, Dowding D, Lei VJ, Zisberg A, Bowles KH, et al. Automated identification of wound information in clinical notes of patients with heart diseases: Developing and validating a natural language processing application. Int J Nurs Stud 2016 Dec;64:25-31.
  • 43 Ronquillo CE, Peltonen LM, Pruinelli L, Chu CH, Bakken S, Beduschi A, et al. Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative. J Adv Nurs 2021 Sep;77(9):3707-17.
  • 44 Barrera A, Gee C, Wood A, Gibson O, Bayley D, Geddes J. Introducing artificial intelligence in acute psychiatric inpatient care: qualitative study of its use to conduct nursing observations. Evid Based Ment Health 2020 Feb;23(1):34-38. Erratum in: Evid Based Ment Health 2021 May;24(2):ebmental-2019-300136corr1.
  • 45 Liao P-H, Hsu P-T, Chu W, Chu W-C. Applying artificial intelligence technology to support decision-making in nursing: A case study in Taiwan. Health Informatics J 2015 Jun;21(2):137-48.
  • 46 Berzin SC, Singer J, Chan C. Practice innovation through technology in the digital age: A grand challenge for social work. American Academy of Social Work & Social Welfare; 2015.
  • 47 Yadav R. Conversation with the Twenty-First Century Social Work: Some ”Post(s)' Perspectives. The British Journal of Social Work 2021.
  • 48 Liu L. Occupational therapy in the fourth industrial revolution. Canadian Journal of Occupational Therapy 2018;85(4):272-83.
  • 49 Huang L, Liu G. Functional motion detection based on artificial intelligence. The Journal of Supercomputing 2021:1-40.