Evidence map›Paper›PMID 40417587›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024

Pruning the Path to Optimal Care: Identifying Systematically Suboptimal Medical Decision-Making with Inverse Reinforcement Learning.

Inko Bovenzi, Adi Carmel, Michael Hu, Rebecca Hurwitz, Fiona McBride, Leo Benac, José Roberto Tello Ayala, Finale Doshi-Velez

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PubMed Full text at PMC
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 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
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.

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

8 authors.

Inko BovenziHarvard University, Cambridge, MA, USA.
Adi CarmelHarvard University, Cambridge, MA, USA.
Michael HuHarvard University, Cambridge, MA, USA.
Rebecca HurwitzHarvard University, Cambridge, MA, USA.
Fiona McBrideHarvard University, Cambridge, MA, USA.
Leo BenacHarvard University, Cambridge, MA, USA.
José Roberto Tello AyalaHarvard University, Cambridge, MA, USA.
Finale Doshi-VelezHarvard University, Cambridge, MA, USA.

Funding

Data-driven subtyping in major depressive disorderR01MH123804 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI PERLIS, ROY H. · 2021 to 2024
$3.1M
NIMH NIH HHS R01 MH123804NIOSH CDC HHS T42 OH008416
6 · The paper itself

Abstract

In aims to uncover insights into medical decision-making embedded within observational data from clinical settings, we present a novel application of Inverse Reinforcement Learning (IRL) that identifies suboptimal clinician actions based on the actions of their peers. This approach centers two stages of IRL with an intermediate step to prune trajectories displaying behavior that deviates significantly from the consensus. This enables us to effectively identify clinical priorities and values from ICU data containing both optimal and suboptimal clinician decisions. We observe that the benefits of removing suboptimal actions vary by disease and differentially impact certain demographic groups.

Indexed as

Clinical Decision-MakingReinforcement, PsychologyHumansIntensive Care Units

Identifiers

PMID40417587
PMCPMC12099440

What OpenQuestion holds

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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.