Evidence map›Paper›PMID 42836173›Full record

ArticleProceedings of the SIGCHI conference on human factors in computing systems. CHI Conference2019

Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes.

Qian Yang, Aaron Steinfeld, John Zimmerman

Abstract read
In one paragraph

Article in Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference, 2019. 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. Investigating Why Clinicians Deviate from Standards of Care: Liberating Patients from Mechanical Ventilation in the ICU.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2024
    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.

Qian YangHCI Institute, Carnegie Mellon University.
Aaron SteinfeldRobotics Institute, Carnegie Mellon University.
John ZimmermanHCI Institute, Carnegie Mellon University.

Funding

CORA_TM_A Personalized Cardiac Counselor for Optimal TherapyR01HL122639 · NHLBI · CORNELL UNIVERSITY · PI ANTAKI, JAMES F., MURALI, SRINIVAS · 2015 to 2018
$3.5M
NHLBI NIH HHS R01 HL122639
6 · The paper itself

Abstract

Clinical decision support tools (DST) promise improved healthcare outcomes by offering data-driven insights. While effective in lab settings, almost all DSTs have failed in practice. Empirical research diagnosed poor contextual fit as the cause. This paper describes the design and field evaluation of a radically new form of DST. It automatically generates slides for clinicians' decision meetings with subtly embedded machine prognostics. This design took inspiration from the notion of

Indexed as

Decision Support SystemsHealthcareUser Experience

Identifiers

PMID42836173
PMCPMC13637767

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.