Evidence map›Paper›PMID 38843115›Full record

ReviewPLOS digital health2024

Empowering US healthcare delivery organizations: Cultivating a community of practice to harness AI and advance health equity.

Mark P Sendak, Jee Young Kim, Alifia Hasan, Will Ratliff, Mark A Lifson, Manesh Patel, Iniouluwa Deborah Raji, Ajai Sehgal, Keo Shaw, Danny Tobey and 3 more

Abstract readReview
In one paragraph

Review in PLOS digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Mark P SendakDuke Institute for Health Innovation, Duke Health, Durham, North Carolina, United States of America.ORCID https://orcid.org/0000-0001-5828-4497
Jee Young KimDuke Institute for Health Innovation, Duke Health, Durham, North Carolina, United States of America.ORCID https://orcid.org/0000-0003-4850-9757
Alifia HasanDuke Institute for Health Innovation, Duke Health, Durham, North Carolina, United States of America.
Will RatliffDuke Institute for Health Innovation, Duke Health, Durham, North Carolina, United States of America.
Mark A LifsonCenter for Digital Health, Mayo Clinic, Rochester, Minnesota, United States of America.
Manesh PatelDivision of Cardiology, Duke Health, Durham, North Carolina, United States of America.
Iniouluwa Deborah RajiDepartment of Electrical Engineering and Computer Science, UC Berkeley, Berkeley, California, United States of America.
Ajai SehgalCenter for Digital Health, Mayo Clinic, Rochester, Minnesota, United States of America.ORCID https://orcid.org/0009-0007-7562-8758
Keo ShawDLA Piper, Charlotte, North Carolina, United States of America.
Danny TobeyDLA Piper, Dallas, Texas, United States of America.
Alexandra ValladaresCommunity Representative, Durham, North Carolina, United States of America.
David E VidalCenter for Digital Health, Mayo Clinic, Rochester, Minnesota, United States of America.
Suresh BaluDuke Institute for Health Innovation, Duke Health, Durham, North Carolina, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Healthcare delivery organizations (HDOs) in the US must contend with the potential for AI to worsen health inequities. But there is no standard set of procedures for HDOs to adopt to navigate these challenges. There is an urgent need for HDOs to present a unified approach to proactively address the potential for AI to worsen health inequities. Amidst this background, Health AI Partnership (HAIP) launched a community of practice to convene stakeholders from across HDOs to tackle challenges related to the use of AI. On February 15, 2023, HAIP hosted an inaugural workshop focused on the question, "Our health care delivery setting is considering adopting a new solution that uses AI. How do we assess the potential future impact on health inequities?" This topic emerged as a common challenge faced by all HDOs participating in HAIP. The workshop had 2 main goals. First, we wanted to ensure participants could talk openly without reservations about challenging topics such as health equity. The second goal was to develop an actionable, generalizable framework that could be immediately put into practice. The workshop engaged 77 participants with 100% representation from all 10 HDOs and invited ecosystem partners. In an accompanying Research Article, we share the Health Equity Across the AI Lifecycle (HEAAL) framework. We invite and encourage HDOs to test the HEAAL framework internally and share feedback so that we can continue to refine and maintain the set of procedures. The HEAAL framework reveals the challenges associated with rigorously assessing the potential for AI to worsen health inequities. Significant investment in personnel, capabilities, and data infrastructure is required, and the level of investment needed could be beyond reach for most HDOs. We look forward to expanding our community of practice to assist HDOs around the world.

Identifiers

PMID38843115
PMCPMC11156347

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.