A Cross-sectional Study Characterizing Pediatric Temperature Percentiles in Children at Well-Child Visits

Dang, Rebecca; Schroeder, Alan R.; Weng, Yingjie; Wang, Marie E.; Patel, Anisha I.

doi:10.1016/j.acap.2022.07.015

Cited by 1 publication

(1 citation statement)

References 24 publications

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“…However, it is important to note that temperature norms vary depending on the method of measurement, and this distinction should be clearly communicated to avoid confusion. (51).…”

Section: Discussionmentioning

confidence: 99%

Assessing Artificial Intelligence-Generated Patient Discharge Information for the Emergency Department: A Pilot Study

De Rouck,

Wille,

Gilbert

et al. 2024

Preprint

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Background: Effective patient discharge information (PDI) in emergency departments (EDs) is vital and often more crucial than the diagnosis itself. Patients who are well informed at discharge tend to be more satisfied and experience better health outcomes. The combination of written and verbal instructions tends to improve patient recall. However, creating written discharge materials is both time-consuming and costly. With the emergence of generative artificial intelligence (AI) and large language models (LMMs), there is potential for the efficient production of patient discharge documents. This study aimed to investigate several predefined key performance indicators (KPIs) of AI-generated patient discharge information. Methods: This study focused on three significant patients’ complaints in the ED: nonspecific abdominal pain, nonspecific low back pain, and fever in children. To generate the brochures, we used an English query for ChatGPT-4 (an LLM) and DeepL software to translate the brochures to Dutch. Five KPIs were defined to assess these PDI brochures: quality, accessibility, clarity, correctness and usability. The brochures were evaluated for each KPI by 8 experienced emergency physicians using a rating scale from 1 (very poor) to 10 (excellent). To quantify the readability of the brochures, frequently used indices were employed: the Flesch Reading Ease, Flesch-Kincaid Grade Level, Simple Measure of Gobbledygook, and Coleman-Liau Index on the translated text. Results: The brochures generated by ChatGPT-4 were well received, scoring an average of 7 to 8 out of 10 across all evaluated aspects. However, the results also indicated a need for some revisions to perfect these documents. Readability analysis indicated that brochures require high school- to college-level comprehension, but this is likely an overestimation due to context-specific reasons as well as features inherent to the Dutch language. Conclusion: Our findings indicate that AI tools such as LLM could represent a new opportunity to quickly produce patient discharge information brochures. However, human review and editing are essential to ensure accurate and reliable information. A follow-up study with more topics and validation in the intended population is necessary to assess their performance.

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“…However, it is important to note that temperature norms vary depending on the method of measurement, and this distinction should be clearly communicated to avoid confusion. (51).…”

Section: Discussionmentioning

confidence: 99%