Evidence map›Paper›PMID 37465809›Full record

ArticleCureus2023

When Precision Meets Penmanship: ChatGPT and Surgery Documentation.

Alexander Robinson, Shaurya Aggarwal

Abstract read
In one paragraph

Article in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 3 pooled it
–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

7 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  3. Pooled it
  4. Article
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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

2 authors.

Alexander RobinsonGeneral Surgery, Mid and South Essex NHS (National Health Service) Foundation Trust, Chelmsford, GBR.
Shaurya AggarwalGeneral Surgery, Mid and South Essex NHS (National Health Service) Foundation Trust, Chelmsford, GBR.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

ChatGPT (Chatbot Generative Pre-Trained Transformer) is an artificial intelligence with several potential applications in the field of medicine. As a large language model, it is particularly good at generating text. This study investigates the use of ChatGPT in constructing operation notes for laparoscopic appendicectomy, one of the most common surgical procedures in the UK. We prompted ChatGPT-4, the latest generation of ChatGPT, to produce operation notes for laparoscopic appendicectomy, which were then evaluated against 'Getting It Right First Time' (GIRFT) recommendations. GIRFT is an organisation that has collaborated with the National Health Service (NHS) to improve surgical documentation guidelines. Excluding certain items documented elsewhere in patient records, the generated notes were assessed against 30 key points in GIRFT recommendations. This process was repeated three times to obtain an average score. Our results showed that ChatGPT generated operation notes in seconds, with an average coverage of 78.8% (23.66 out of 30 points) of the GIRFT guidelines, surpassing average compliance with similar guidelines from the Royal College of Surgeons (RCS). However, the quality of ChatGPT's output was found to be dependent on the quality of the prompt, highlighting the need for verification of the generated content. Additionally, secure integration with electronic health records is required before ChatGPT can be adopted into the NHS.

Indexed as

appendicectomychatgptlaparoscopic surgerynhssurgical documentation

Identifiers

PMID37465809
PMCPMC10350653

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.