ChatGPT is not the solution to physicians’ documentation burden

Preiksaitis, Carl; Sinsky, Christine A.; Rose, Christian

doi:10.1038/s41591-023-02341-4

Cited by 20 publications

(13 citation statements)

References 3 publications

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“…The potential role for AI in crafting LORs has been previously suggested; however, this is the first study that demonstrates the feasibility of this application for AI 12,13 . Human‐authored letters have documented, problematic tendencies toward bias, and AI is suggested as a potential solution to reducing bias in LOR, though contrary arguments and data exist 3,11,14–18 . Although our study only examined biased language related to gender and was not powered to examine this aspect in detail, we observed signal of gender‐biased language in both human and AI‐authored LORs.…”

Section: Discussionmentioning

confidence: 68%

Brain versus bot: Distinguishing letters of recommendation authored by humans compared with artificial intelligence

Preiksaitis,

Nash,

Gottlieb

et al. 2023

AEM Education and Training

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ObjectivesLetters of recommendation (LORs) are essential within academic medicine, affecting a number of important decisions regarding advancement, yet these letters take significant amounts of time and labor to prepare. The use of generative artificial intelligence (AI) tools, such as ChatGPT, are gaining popularity for a variety of academic writing tasks and offer an innovative solution to relieve the burden of letter writing. It is yet to be determined if ChatGPT could aid in crafting LORs, particularly in high‐stakes contexts like faculty promotion. To determine the feasibility of this process and whether there is a significant difference between AI and human‐authored letters, we conducted a study aimed at determining whether academic physicians can distinguish between the two.MethodsA quasi‐experimental study was conducted using a single‐blind design. Academic physicians with experience in reviewing LORs were presented with LORs for promotion to associate professor, written by either humans or AI. Participants reviewed LORs and identified the authorship. Statistical analysis was performed to determine accuracy in distinguishing between human and AI‐authored LORs. Additionally, the perceived quality and persuasiveness of the LORs were compared based on suspected and actual authorship.ResultsA total of 32 participants completed letter review. The mean accuracy of distinguishing between human‐ versus AI‐authored LORs was 59.4%. The reviewer's certainty and time spent deliberating did not significantly impact accuracy. LORs suspected to be human‐authored were rated more favorably in terms of quality and persuasiveness. A difference in gender‐biased language was observed in our letters: human‐authored letters contained significantly more female‐associated words, while the majority of AI‐authored letters tended to use more male‐associated words.ConclusionsParticipants were unable to reliably differentiate between human‐ and AI‐authored LORs for promotion. AI may be able to generate LORs and relieve the burden of letter writing for academicians. New strategies, policies, and guidelines are needed to balance the benefits of AI while preserving integrity and fairness in academic promotion decisions.

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Section: Discussionmentioning

confidence: 68%

Brain versus bot: Distinguishing letters of recommendation authored by humans compared with artificial intelligence

Preiksaitis,

Nash,

Gottlieb

et al. 2023

AEM Education and Training

Self Cite

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show abstract

“…Approximately 57.8% of the generated responses were assessed as accurate or nearly correct. This outcome underscores the imperative for exercising caution when solely relying on AI-generated medical information and the need for continuous evaluation, as others have noted [ 16 ]. However, in another study by Walker et al [ 17 ] aimed at evaluating the reliability of medical information provided by ChatGPT-4, multiple iterations of their queries executed through the model yielded a remarkable 100% internal consistency among the generated outputs [ 17 ].…”

Section: Discussionmentioning

confidence: 74%

Using ChatGPT-4 to Create Structured Medical Notes From Audio Recordings of Physician-Patient Encounters: Comparative Study

Kernberg,

Gold,

Mohan

2024

J Med Internet Res

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Background Medical documentation plays a crucial role in clinical practice, facilitating accurate patient management and communication among health care professionals. However, inaccuracies in medical notes can lead to miscommunication and diagnostic errors. Additionally, the demands of documentation contribute to physician burnout. Although intermediaries like medical scribes and speech recognition software have been used to ease this burden, they have limitations in terms of accuracy and addressing provider-specific metrics. The integration of ambient artificial intelligence (AI)–powered solutions offers a promising way to improve documentation while fitting seamlessly into existing workflows. Objective This study aims to assess the accuracy and quality of Subjective, Objective, Assessment, and Plan (SOAP) notes generated by ChatGPT-4, an AI model, using established transcripts of History and Physical Examination as the gold standard. We seek to identify potential errors and evaluate the model’s performance across different categories. Methods We conducted simulated patient-provider encounters representing various ambulatory specialties and transcribed the audio files. Key reportable elements were identified, and ChatGPT-4 was used to generate SOAP notes based on these transcripts. Three versions of each note were created and compared to the gold standard via chart review; errors generated from the comparison were categorized as omissions, incorrect information, or additions. We compared the accuracy of data elements across versions, transcript length, and data categories. Additionally, we assessed note quality using the Physician Documentation Quality Instrument (PDQI) scoring system. Results Although ChatGPT-4 consistently generated SOAP-style notes, there were, on average, 23.6 errors per clinical case, with errors of omission (86%) being the most common, followed by addition errors (10.5%) and inclusion of incorrect facts (3.2%). There was significant variance between replicates of the same case, with only 52.9% of data elements reported correctly across all 3 replicates. The accuracy of data elements varied across cases, with the highest accuracy observed in the “Objective” section. Consequently, the measure of note quality, assessed by PDQI, demonstrated intra- and intercase variance. Finally, the accuracy of ChatGPT-4 was inversely correlated to both the transcript length (P=.05) and the number of scorable data elements (P=.05). Conclusions Our study reveals substantial variability in errors, accuracy, and note quality generated by ChatGPT-4. Errors were not limited to specific sections, and the inconsistency in error types across replicates complicated predictability. Transcript length and data complexity were inversely correlated with note accuracy, raising concerns about the model’s effectiveness in handling complex medical cases. The quality and reliability of clinical notes produced by ChatGPT-4 do not meet the standards required for clinical use. Although AI holds promise in health care, caution should be exercised before widespread adoption. Further research is needed to address accuracy, variability, and potential errors. ChatGPT-4, while valuable in various applications, should not be considered a safe alternative to human-generated clinical documentation at this time.

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“…The expansive datasets also show an emergent trend: while LLMs have found robust applications in patient communication, medical writing, and auxiliary diagnosis, there remains latent potential in realms such as medical education and training, especially in simulating patient-doctor interactions. 10 , 85 , 93 , 94 , 95 In addition, the challenges posed by linguistic and cultural nuances in LLMs underscore the importance of region-specific model training and data integration.…”

Section: Discussionmentioning

confidence: 99%

The application of large language models in medicine: A scoping review

Meng,

Yan,

Zhang

et al. 2024

iScience

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ChatGPT is not the solution to physicians’ documentation burden

Cited by 20 publications

References 3 publications

Brain versus bot: Distinguishing letters of recommendation authored by humans compared with artificial intelligence

Brain versus bot: Distinguishing letters of recommendation authored by humans compared with artificial intelligence

Using ChatGPT-4 to Create Structured Medical Notes From Audio Recordings of Physician-Patient Encounters: Comparative Study

The application of large language models in medicine: A scoping review

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