Reliability and Quality of the Nursing Care Planning Texts Generated by ChatGPT

Background: The research on ChatGPT-generated nursing care planning texts is critical for enhancing nursing education through innovative and accessible learning methods, improving reliability and quality.

Purpose: The aim of the study was to examine the quality, authenticity, and reliability of the nursing care planning texts produced using ChatGPT.

Methods: The study sample comprised 40 texts generated by ChatGPT selected nursing diagnoses that were included in NANDA 2021-2023. The texts were evaluated by using descriptive criteria form and DISCERN tool to evaluate health information.

Results: DISCERN total average score of the texts was 45.93 ± 4.72. All texts had a moderate level of reliability and 97.5% of them provided moderate quality subscale score of information. A statistically significant relationship was found among the number of accessible references, reliability ( r = 0.408) and quality subscale score ( r = 0.379) of the texts ( P < .05).

Conclusion: ChatGPT-generated texts exhibited moderate reliability, quality of nursing care information, and overall quality despite low similarity rates.