Unravelling ChatGPT's potential in summarising qualitative in-depth interviews.

IF 2.8 3区 医学 Q1 OPHTHALMOLOGY Eye Pub Date : 2024-11-05 DOI:10.1038/s41433-024-03419-0
Mei Hui Adeline Kon, Michelle Jessica Pereira, Joseph Antonio De Castro Molina, Vivien Cherng Hui Yip, John Arputhan Abisheganaden, WanFen Yip
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Abstract

Background/objectives: Qualitative research can be laborious and time consuming, presenting a challenge for practitioners and policymakers seeking rapid, actionable results. Data collection, transcription and analysis are the main contributors to the resource-intensive nature. OpenAI's Chat Generative Pre-trained Transformer (ChatGPT), have demonstrated potential to aid in data analysis. Our study aimed to compare themes generated by ChatGPT (3.5 and 4.0) with traditional human analysis from in-depth interviews.

Methods: Three transcripts from an evaluation study to understand patients' experiences at a community eye clinic were used. Transcripts were first analysed by an independent researcher. Next, specific aims, instructions and de-identified transcripts were uploaded to ChatGPT 3.5 and ChatGPT 4.0. Concordance in the themes was calculated as the number of themes generated by ChatGPT divided by the number of themes generated by the researcher. The number of unrelated subthemes and time taken by both ChatGPT were also described.

Results: The average time taken per transcript was 11.5 min, 11.9 min and 240 min for ChatGPT 3.5, ChatGPT 4.0 and researcher respectively. Six themes were identified by the researcher: (i) clinic's accessibility, (ii) patients' awareness, (iii) trust and satisfaction, (iv) patients' expectations, (v) willingness to return and (vi) explanation of the clinic by referral source. Concordance for ChatGPT 3.5 and 4.0 ranged from 66 to 100%.

Conclusion: Preliminary results showed that ChatGPT significantly reduced analysis time with moderate to good concordance compared with current practice. This highlighted the potential adoption of ChatGPT to facilitate rapid preliminary analysis. However, regrouping of subthemes will still need to be conducted by a researcher.

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解读 ChatGPT 在总结深入定性访谈方面的潜力。
背景/目标:定性研究既费力又费时,对于寻求快速、可操作结果的从业人员和决策者来说是一项挑战。数据收集、转录和分析是造成资源密集型的主要原因。OpenAI 的聊天生成预训练转换器(ChatGPT)在协助数据分析方面已显示出潜力。我们的研究旨在将 ChatGPT(3.5 和 4.0)生成的主题与传统的深度访谈人工分析进行比较:方法:我们使用了一项评估研究中的三份记录,该研究旨在了解患者在社区眼科诊所的就医体验。笔录首先由一名独立研究人员进行分析。然后,将具体的目的、说明和去标识化的记录誊本上传到 ChatGPT 3.5 和 ChatGPT 4.0。主题的一致性以 ChatGPT 生成的主题数除以研究人员生成的主题数来计算。此外,还描述了不相关次主题的数量以及两种 ChatGPT 所耗费的时间:ChatGPT 3.5、ChatGPT 4.0 和研究人员的每份记录誊本平均耗时分别为 11.5 分钟、11.9 分钟和 240 分钟。研究人员确定了六个主题:(i) 诊所的可及性;(ii) 患者的认知度;(iii) 信任度和满意度;(iv) 患者的期望;(v) 再次就诊的意愿;(vi) 转介方对诊所的解释。ChatGPT 3.5 和 4.0 的一致性从 66% 到 100% 不等:初步结果表明,与目前的做法相比,ChatGPT 大大缩短了分析时间,并且具有中等到良好的一致性。这凸显了采用 ChatGPT 进行快速初步分析的潜力。不过,子主题的重新分组仍需由研究人员进行。
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来源期刊
Eye
Eye 医学-眼科学
CiteScore
6.40
自引率
5.10%
发文量
481
审稿时长
3-6 weeks
期刊介绍: Eye seeks to provide the international practising ophthalmologist with high quality articles, of academic rigour, on the latest global clinical and laboratory based research. Its core aim is to advance the science and practice of ophthalmology with the latest clinical- and scientific-based research. Whilst principally aimed at the practising clinician, the journal contains material of interest to a wider readership including optometrists, orthoptists, other health care professionals and research workers in all aspects of the field of visual science worldwide. Eye is the official journal of The Royal College of Ophthalmologists. Eye encourages the submission of original articles covering all aspects of ophthalmology including: external eye disease; oculo-plastic surgery; orbital and lacrimal disease; ocular surface and corneal disorders; paediatric ophthalmology and strabismus; glaucoma; medical and surgical retina; neuro-ophthalmology; cataract and refractive surgery; ocular oncology; ophthalmic pathology; ophthalmic genetics.
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