MétaCan
Menu
Retour à la cohorte
Enregistrement W4390610817 · doi:10.1111/1471-0528.17746

ChatGPT compared with Google Search and healthcare institution as sources of postoperative patient instructions after gynecological surgery

2024· letter· en· W4390610817 sur OpenAlexaff
Raanan Meyer, Kacey M. Hamilton, Mireille Truong, Kelly N. Wright, Matthew T. Siedhoff, Yoav Brezinov, Gabriel Levin

Notice bibliographique

RevueBJOG An International Journal of Obstetrics & Gynaecology · 2024
Typeletter
Langueen
DomaineHealth Professions
ThématiqueHealthcare Quality and Satisfaction
Établissements canadiensMcGill UniversityJewish General Hospital
Organismes subventionnairesnon disponible
Mots-clésInstitutionMedicineHealth careSurgeryGeneral surgeryPolitical science

Résumé

récupéré en direct d'OpenAlex

The use of ChatGPT, an artificial intelligence language model, has the potential to improve healthcare delivery.1 The mode of delivering postoperative instructions has been shown to affect patients' actions and satisfaction.2, 3 As online search engines are common sources for patients' medical information,4 we aimed to study the value of ChatGPT-generated postoperative instructions for gynaecological procedures. We analysed postoperative patient instructions for five common gynaecological procedures—laparoscopic hysterectomy, laparoscopic myomectomy, hysteroscopy, colposcopy and dilatation and curettage. We obtained postoperative instructions from ChatGPT version 3.5, Google Search, and from our institution (Appendix S1). To generate instructions from ChatGPT and Google Search the following natural language sentence was entered: ‘Please provide postoperative instructions for a patient who just underwent a (procedure name)’. We used the first non-sponsored response result in Google Search for the analysis. Institutional resources used were standard, authentic hospital discharge instructions for the surgical procedures. We blinded the results by standardising fonts and removing any source identifiers. The primary outcome was the instructions’ Understandability and Actionability. We used the Patient Education Materials Assessment Tool–printable (PEMAT-P) to evaluate postoperative instructions.5 Two researchers, blinded to the source of the instructions, scored the instructions individually. The final scores are the average of both reviewers’ scores. Secondary outcomes included instruction word count, and whether detailed concerning signs and symptoms that should prompt urgent consultation were provided. We compared continuous parameters using Kruskal–Wallis one-way analysis of variance. Chi-square test was used to compare categorical parameters and a post-hoc test was performed. The Institutional Review Board waived the need for approval. Overall, understandability scores ranged from 70.9% to 91.7%, Actionability scores ranged from 60.0% to 80.0% and total PEMAT-P scores ranged from 67.7% to 88.3% (Table S1). ChatGPT scores ranged from 87.5% to 91.7% for Understandability, from 60% to 70% for Actionability, and from 79.4% to 82.4% for total PEMAT-P scores. In the comparison of ChatGPT with Google Search and Institution instructions, Understandability scores were similar across groups (Table 1). Actionability scores were significantly lower in the ChatGPT group compared with Institution instructions (median score 60.0 versus 80.0, respectively, p = 0.007). Total PEMAT-P scores were comparable among groups. Word count was significantly lower in ChatGPT instructions compared with Google Search (median 243.0 versus 658.0, respectively, p = 0.008). ChatGPT related less to concerning signs and symptoms when compared with Google search and Institution (20% versus 100% in both Google Search and Institution, p = 0.048). ChatGPT postoperative instructions scored lower in Actionability, underperformed in providing concerning signs and symptoms and generated approximately one-third of the text-length of the other sources studied. However, Understandability scores were similar across all postoperative instruction sources. ChatGPT was limited in providing patients with actionable instructions. These may include tangible tools such as checklists and specific medications instructions. Although short text length, as provided by ChatGPT, may provide succinct, targeted information, it is limited in providing specific actionable items. Moreover, important instructions regarding ‘red flags’ that should prompt an urgent consultation with a medical provider were lacking. Therefore, relying solely on ChatGPT's instructions may compromise patient safety. Despite these limitations, ChatGPT's strengths include its availability and ability to provide concise responses to specific medical questions that can be tailored for populations with different levels of cultural and social backgrounds. Limitations of the study include the number of procedures analysed, the use of only one set of instructions per procedure from the different sources, and the use of English language only. In addition, ChatGPT responses change with time and inputs, limiting the reproducibility of the study. ChatGPT may also provide incorrect information presented as genuine facts. Although we did not observe inaccuracies in our study, this should be acknowledged by users. In conclusion, a current ChatGPT version provided concise and clear postoperative instructions for specific gynecological procedures. RM, GL- conception, design, data acquisition, analysis and interpretation, manuscript drafting. MT, KW, MS, YB- data interpretation, critical revision of the manuscript draft. All authors approved the final version of the manuscript and are accountable for all aspects of the work. None. Cedars Sinai Medical Center, Los Angeles, CA, USA. MT is a consultant for Ethicon, Medtronic, Heracure Medical and Cooper Surgical; KW is a consultant for Aqua Therapeutics, Hologic, Ethicon and Karl Storz; MS is a consultant for Applied Medical and Intuitive Surgical; and RM is a consultant for Intuitive Surgical. All other authors—none declared. The institutional review board office determined that IRB review and approval is not required. The data that support the findings of this study are available from the corresponding author upon reasonable request. Appendix S1. Table S1. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesIntégrité de la recherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,320
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0020,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,005
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,085
Tête enseignante GPT0,405
Écart entre enseignants0,320 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations11
Publié2024
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueBJOG An International Journal of Obstetrics & GynaecologyMême sujetHealthcare Quality and SatisfactionTravaux en français237 207