Evidence map›Paper›PMID 39081915›Full record

ArticleIndian journal of anaesthesia2024

Comparing patient education tools for chronic pain medications: Artificial intelligence chatbot versus traditional patient information leaflets.

Prakash Gondode, Sakshi Duggal, Neha Garg, Surrender Sethupathy, Omshubham Asai, Pooja Lohakare

Abstract read
In one paragraph

Article in Indian journal of anaesthesia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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.

Prakash GondodeDepartment of Anesthesiology Pain Medicine and Critical Care, All India Institute of Medical Sciences (AIIMS) New Delhi, India.ORCID https://orcid.org/0000-0003-1014-8407
Sakshi DuggalDepartment of Anesthesiology Pain Medicine and Critical Care, All India Institute of Medical Sciences (AIIMS) New Delhi, India.ORCID https://orcid.org/0000-0002-8865-0854
Neha GargDepartment of Anesthesiology Pain Medicine and Critical Care, All India Institute of Medical Sciences (AIIMS) New Delhi, India.ORCID https://orcid.org/0000-0003-4817-9807
Surrender SethupathyDepartment of Anesthesiology Pain Medicine and Critical Care, All India Institute of Medical Sciences (AIIMS) New Delhi, India.ORCID https://orcid.org/0000-0003-4711-1835
Omshubham AsaiDepartment of Anesthesiology, All India Institute of Medical Sciences, Nagpur, Maharashtra, India.ORCID https://orcid.org/0000-0001-5776-1730
Pooja LohakareDepartment of Microbiology, Mahatma Gandhi Institute of Medical Sciences, Wardha, Maharashtra, India.ORCID https://orcid.org/0009-0003-9190-9219

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Aims: Artificial intelligence (AI) chatbots like Conversational Generative Pre-trained Transformer (ChatGPT) have recently created much buzz, especially regarding patient education. Such informed patients understand and adhere to the management and get involved in shared decision making. The accuracy and understandability of the generated educational material are prime concerns. Thus, we compared ChatGPT with traditional patient information leaflets (PILs) about chronic pain medications. Methods: Patients' frequently asked questions were generated from PILs available on the official websites of the British Pain Society (BPS) and the Faculty of Pain Medicine. Eight blinded annexures were prepared for evaluation, consisting of traditional PILs from the BPS and AI-generated patient information materials structured similar to PILs by ChatGPT. The authors performed a comparative analysis to assess materials' readability, emotional tone, accuracy, actionability, and understandability. Readability was measured using Flesch Reading Ease (FRE), Gunning Fog Index (GFI), and Flesch-Kincaid Grade Level (FKGL). Sentiment analysis determined emotional tone. An expert panel evaluated accuracy and completeness. Actionability and understandability were assessed with the Patient Education Materials Assessment Tool. Results: Traditional PILs generally exhibited higher readability ( Conclusion: While AI chatbots offer efficient information delivery, ensuring accuracy and readability, patient-centeredness remains crucial. It is imperative to balance innovation with evidence-based practice.

Indexed as

AIanalgesiaartificial intelligenceChatGPTchronic painmedication adherencepatient educationreadability

Identifiers

PMID39081915
PMCPMC11285886

What OpenQuestion holds

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LicenceCC BY-NC-SA
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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.