Evidence map›Paper›PMID 41639200›Full record

ReviewNPJ digital medicine2026

Medicare advantage becoming a disadvantage with use of artificial intelligence in prior authorization review.

Sara Raza, Sara Gerke, Christina Silcox, Rachele Hendricks-Sturrup, Carmel Shachar

Abstract readReview
In one paragraph

Review in NPJ digital medicine, 2026. 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. Review
  2. Review
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

5 authors.

Sara RazaCenter for Health Law and Policy Innovation, Harvard Law School, Cambridge, MA, USA. sararaza@uw.edu.
Sara GerkeCollege of Law and European Union Center, University of Illinois Urbana-Champaign, Champaign, IL, USA.
Christina SilcoxDuke-Robert J. Margolis Institute for Health Policy, Duke University, Washington, DC, USA.
Rachele Hendricks-SturrupDuke-Robert J. Margolis Institute for Health Policy, Duke University, Washington, DC, USA.
Carmel ShacharCenter for Health Law and Policy Innovation, Harvard Law School, Cambridge, MA, USA. cshachar@law.harvard.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Reported instances of AI-assisted, blanket denials of coverage have increased in recent years, particularly for Medicare Advantage plans, resulting in insurers facing criticism, class action lawsuits, investigations from Congress, and key providers leaving their networks. To ensure a fair healthcare system, action is needed to improve transparency in how AI tools approve or deny claims, and address provider burnout and patient burden due to navigating prior authorization requests and appeals.

Identifiers

PMID41639200
PMCPMC12979811

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.