Evidence map›Paper›PMID 35901314›Full record

ReviewAnnual review of medicine2023

Origins of Racial and Ethnic Bias in Pulmonary Technologies.

Michael W Sjoding, Sardar Ansari, Thomas S Valley

Abstract readReview
In one paragraph

Review in Annual review of medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Accuracy and Interpretation of Transcutaneous Carbon Dioxide Monitoring in Critically Ill Children.Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies · 2024
    Observational
  5. Article
  6. Article
  7. Article
  8. Z60.5/(En)Coded.Health equity · 2023
    Article
  9. 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

3 authors.

Michael W SjodingDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA; email: msjoding@umich.edu, valleyt@med.umich.edu.
Sardar AnsariWeil Institute for Critical Care Research and Innovation, Ann Arbor, Michigan, USA; email: sardara@med.umich.edu.
Thomas S ValleyDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA; email: msjoding@umich.edu, valleyt@med.umich.edu.

Funding

Optimizing Clinician and AI Collaborations to Support Timely Management of Acute DyspneaR01HL158626 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Michael William Sjoding, Jenna Wiens · 2021 to 2026
$3.8M
Promoting Equity via Change in Practice for Respiratory Failure (PRECIPICE)R01HL157361 · NHLBI · NEW YORK UNIVERSITY · PI ARMSTRONG-HOUGH, MARI, VALLEY, THOMAS SEBASTIAN · 2022 to 2025
$2.7M
Preventing avoidable deaths from pneumonia by improving use of intensive careK23HL140165 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI VALLEY, THOMAS SEBASTIAN · 2018 to 2022
$915k
Data-Driven Identification of the Acute Respiratory Distress SyndromeK01HL136687 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI SJODING, MICHAEL WILLIAM · 2017 to 2021
$862k
Rapid Understanding of Best Practices in Rural Intensive Care (RUBRIC)R01HS028038 · AHRQ · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI VALLEY, THOMAS SEBASTIAN · 2021 to 2022
$776k
AHRQ HHS R01 HS028038NHLBI NIH HHS K01 HL136687NHLBI NIH HHS K23 HL140165NHLBI NIH HHS R01 HL157361NHLBI NIH HHS R01 HL158626
6 · The paper itself

Abstract

Understanding how biases originate in medical technologies and developing safeguards to identify, mitigate, and remove their harms are essential to ensuring equal performance in all individuals. Drawing upon examples from pulmonary medicine, this article describes how bias can be introduced in the physical aspects of the technology design, via unrepresentative data, or by conflation of biological with social determinants of health. It then can be perpetuated by inadequate evaluation and regulatory standards. Research demonstrates that pulse oximeters perform differently depending on patient race and ethnicity. Pulmonary function testing and algorithms used to predict healthcare needs are two additional examples of medical technologies with racial and ethnic biases that may perpetuate health disparities.

Indexed as

EthnicityHealthcare DisparitiesBiasHumansalgorithmsdisparitiesmedical devicespulmonary function testingpulse oximetrystructural racism

Identifiers

PMID35901314
PMCPMC9883596

What OpenQuestion holds

Textmetadata
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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.