Evidence map›Paper›PMID 41028226›Full record

ArticleNPJ digital medicine2025

Generative AI costs in large healthcare systems, an example in revenue cycle.

Michael L Burns, Ssu-Ying Chen, Chu-An Tsai, John Vandervest, Balaji Pandian, Paige Nong, David A Hanauer, Andrew Rosenberg, Jodyn Platt

Abstract read
In one paragraph

Article in NPJ digital medicine, 2025. 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.

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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

9 authors.

Michael L BurnsDepartment of Anesthesiology, University of Michigan, Ann Arbor, MI, USA. mlburns@med.umich.edu.
Ssu-Ying ChenDepartment of Anesthesiology, University of Michigan, Ann Arbor, MI, USA.
Chu-An TsaiDepartment of Anesthesiology, University of Michigan, Ann Arbor, MI, USA.
John VandervestDepartment of Anesthesiology, University of Michigan, Ann Arbor, MI, USA.
Balaji PandianDepartment of Anesthesiology, Weill Cornell Medicine, New York, NY, USA.
Paige NongDivision of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
David A HanauerDepartment of Learning Health Sciences, University of Michigan, Ann Arbor, MI, USA.
Andrew RosenbergDepartment of Anesthesiology, University of Michigan, Ann Arbor, MI, USA.
Jodyn PlattDepartment of Learning Health Sciences, University of Michigan, Ann Arbor, MI, USA.

Funding

Public trust of artificial intelligence in the precision CDS health ecosystem - Administrative SupplementR01EB030492 · NIBIB · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI PLATT, JODYN ELIZABETH · 2021 to 2024
$3.1M
National Institutes of Health, The National Institute of Biomedical Imaging and Bioengineering (NIBIB), Public Trust of Artificial Intelligence in the Precision CDS Health Ecosystem 1-RO1-EB030492NIBIB NIH HHS R01 EB030492
6 · The paper itself

Abstract

Application of large language models in healthcare continues to expand, specifically for medical free-text classification tasks. While foundation models like those from ChatGPT show potential, alternative models demonstrate superior accuracy and lower costs. This study underscores significant challenges, including computational costs and model reliability. Amidst rising healthcare expenditures and AI's perceived potential to reduce costs, a combination of local and commercial models might offer balanced solutions for healthcare systems.

Identifiers

PMID41028226
PMCPMC12485018

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.