Evidence map›Paper›PMID 29788380›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2018

Good intentions are not enough: how informatics interventions can worsen inequality.

Tiffany C Veinot, Hannah Mitchell, Jessica S Ancker

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 400 papers, 11 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
400citing papers in PubMed, 11 pooled it
–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

400 citing papers in PubMed, 11 syntheses or guidelines pooled it.

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340 more citing papers are in PubMed but not listed here.

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.

Tiffany C VeinotSchool of Information and Department of Health Behavior and Health Education, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
Hannah MitchellDepartment of Healthcare Policy & Research, Division of Health Informatics, Weill Cornell Medical College, New York, New York, USA.
Jessica S AnckerDepartment of Healthcare Policy & Research, Division of Health Informatics, Weill Cornell Medical College, New York, New York, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Health informatics interventions are designed to help people avoid, recover from, or cope with disease and disability, or to improve the quality and safety of healthcare. Unfortunately, they pose a risk of producing intervention-generated inequalities (IGI) by disproportionately benefiting more advantaged people. In this perspective paper, we discuss characteristics of health-related interventions known to produce IGI, explain why health informatics interventions are particularly vulnerable to this phenomenon, and describe safeguards that can be implemented to improve health equity. We provide examples in which health informatics interventions produced inequality because they were more accessible to, heavily used by, adhered to, or effective for those from socioeconomically advantaged groups. We provide a brief outline of precautions that intervention developers and implementers can take to guard against creating or worsening inequality through health informatics. We conclude by discussing evaluation approaches that will ensure that IGIs are recognized and studied.

Indexed as

Consumer Health InformaticsHealthcare DisparitiesHealth Services AccessibilityDigital DivideHumansSocial Determinants of Health

Identifiers

PMID29788380
PMCPMC7646885

What OpenQuestion holds

Textmetadata
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.