Evidence map›Paper›PMID 38957712›Full record

ArticlePlastic and reconstructive surgery. Global open2024

Current Applications of Artificial Intelligence in Billing Practices and Clinical Plastic Surgery.

Christina Zhu, Pradeep K Attaluri, Peter J Wirth, Ellen C Shaffrey, Jeffrey B Friedrich, Venkat K Rao

Abstract read
In one paragraph

Article in Plastic and reconstructive surgery. Global open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 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

6 authors.

Christina ZhuFrom the Division of Plastic and Reconstructive Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wis.
Pradeep K AttaluriFrom the Division of Plastic and Reconstructive Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wis.
Peter J WirthFrom the Division of Plastic and Reconstructive Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wis.
Ellen C ShaffreyFrom the Division of Plastic and Reconstructive Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wis.
Jeffrey B FriedrichDivision of Plastic Surgery, University of Washington, Seattle, Wash.
Venkat K RaoFrom the Division of Plastic and Reconstructive Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wis.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Integration of artificial intelligence (AI), specifically with natural language processing and machine learning, holds tremendous potential to enhance both clinical practices and administrative workflows within plastic surgery. AI has been applied to various aspects of patient care in plastic surgery, including postoperative free flap monitoring, evaluating preoperative risk assessments, and analyzing clinical documentation. Previous studies have demonstrated the ability to interpret current procedural terminology codes from clinical documentation using natural language processing. Various automated medical billing companies have used AI to improve the revenue management cycle at hospitals nationwide. Additionally, AI has been piloted by insurance companies to streamline the prior authorization process. AI implementation holds potential to enhance billing practices and maximize healthcare revenue for practicing physicians.

Identifiers

PMID38957712
PMCPMC11216662

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.