Evidence map›Paper›PMID 38422082›Full record

ArticlePloS one2024

Who should decide how limited healthcare resources are prioritized? Autonomous technology as a compelling alternative to humans.

Jonathan J Rolison, Peter L T Gooding, Riccardo Russo, Kathryn E Buchanan

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 97% of its field
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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

4 authors at 1 institution in 2 countries.

Jonathan J RolisonDepartment of Psychology, University of Essex, Colchester, United Kingdom.ORCID 0000-0001-6219-2878
Peter L T GoodingDepartment of Psychology, University of Essex, Colchester, United Kingdom.
Riccardo RussoDepartment of Psychology, University of Essex, Colchester, United Kingdom.
Kathryn E BuchananDepartment of Psychology, University of Essex, Colchester, United Kingdom.
University of Essex · GB

Funding

Knowledge Transfer Partnership (KTP) KTP11196
6 · The paper itself

Abstract

Who should decide how limited resources are prioritized? We ask this question in a healthcare context where patients must be prioritized according to their need and where advances in autonomous artificial intelligence-based technology offer a compelling alternative to decisions by humans. Qualitative (Study 1a; N = 50) and quantitative (Study 1b; N = 800) analysis identified agency, emotional experience, bias-free, and error-free as four main qualities describing people's perceptions of autonomous computer programs (ACPs) and human staff members (HSMs). Yet, the qualities were not perceived to be possessed equally by HSMs and ACPs. HSMs were endorsed with human qualities of agency and emotional experience, whereas ACPs were perceived as more capable than HSMs of bias- and error-free decision-making. Consequently, better than average (Study 2; N = 371), or relatively better (Studies 3, N = 181; & 4, N = 378), ACP performance, especially on qualities characteristic of ACPs, was sufficient to reverse preferences to favor ACPs over HSMs as the decision makers for how limited healthcare resources should be prioritized. Our findings serve a practical purpose regarding potential barriers to public acceptance of technology, and have theoretical value for our understanding of perceptions of autonomous technologies.

Indexed as

Artificial IntelligenceData AccuracyEmotionsHealth FacilitiesHumansTechnology

Identifiers

PMID38422082
PMCPMC10903831
OpenAlexW4392283157

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.