Cited by 24Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AICultural Competency in Health Care

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Rhee, Kyu, Dankwa-Mullan, Irene, Brennan, Virginia, Clark, Cheryl·2021·Journal of Health Care for the Poor and Underserved

Abstract

Three seminal reports, the 2001 Institute of Medicine's Crossing the Quality Chasm, the 2003 report Unequal Treatment,1 and the 2020 National Academy of Medicine's (formerly Institute of Medicine) Artificial Intelligence in Healthcare2 represented inflection points in highlighting the substantial disparities in access, clinical care, and outcomes, and recommended that equity in health care and health technology must be achieved to deliver quality care.3 Though Crossing the Quality Chasm set up the STEEEP framework, which explicitly called out equity as one of six health care quality domains (alongside safety, timeliness, effectiveness, efficiency, and patient-centered care) the issue of inequities in health care delivery was truly laid bare in Unequal Treatment, which also called upon health care institutions and providers to develop strategies to confront disparities in care.4 Artificial Intelligence in Healthcare introduced the Quintiple Aim where and Inclusion was added to the Quadruple Aim. Equity Dashboards The application of analytics to demonstrate health care quality in the domains of safety, timeliness, effectiveness, efficiency, and patient-centeredness has been common in diverse dashboards for hospital ratings and other key health care certifications (e.g., National Committee for Quality Assurance, Joint Commission);however, equity has often been overlooked.17 Peter Drucker, a famous business thinker and writer for the modern company, stated that if you can't measure it, you can't improve it. [...]we must move AI from being a black box to a clear box with AI factsheets like nutrition labels where buyers and end-users of AI algorithms can transparently see who trained the AI, what datasets were used, and what specific AI algorithms and models were used.28 We must assure transparent, ethical, fair, and equitable AI. Institute of Medicine, Board on Health Sciences Policy, Committee on Understanding and Eliminating Racial and Ethnic Disparities in Health Care.

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Research works printed but not linked: 3

  1. Thomas Craig KJ, McKillop MM, Huang HT, et al. BMC Health Serv Res. 2020 Jul 10;20(1):640. https:// doi .org/ 10.1186/ s12913-020-05503-z PMid:32650759 · DOI
  2. Sikka R, Morath JM, Leape L. The Quadruple Aim: care, health, cost and meaning in work. BMJ Qual Saf. 2015;24:608-10. https:// doi .org/ 10.1136/ bmjqs-2015-004160 PMid:26038586 · DOI
  3. Miller DD. The medical AI insurgency: what physicians must know about data to practice with intelligent machines. NPJ Digit Med. 2019 Jun 28;2:62. https:// doi .org/ 10.1038/ s41746-019-0138-5 PMid:31388566 · DOI

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Authors
Rhee, K., Dankwa-Mullan, I., Brennan, V., & Clark, C.
Venue
Journal of Health Care for the Poor and Underserved
Publisher
Project MUSE
Published
2021
Volume
32
Issue
2S
Pages
xiii-xviii
ISSN
  • 1049-2089 (ISSN-L)
  • 1548-6869
OpenAlex
W3158495376
Cited by
24
References
10
Citations by year
2026: 2 · 2025: 5 · 2024: 4 · 2023: 9 · 2022: 3 · 2021: 1
FWCI
1.26
Type
journal article
Language
en
Open access
Locations
  • Journal of Health Care for the Poor and Underserved (publishedVersion)
  • Project Muse (Johns Hopkins University) (submittedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Sociology and Political Science › Cultural Competency in Health Care
Keywords
health equity, health care disparities, artificial intelligence in healthcare, algorithmic fairness, AI transparency, Quadruple Aim, Quintuple Aim, racial and ethnic disparities, health technology

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