Chatbots are software applications to simulate a conversation with a person. The effectiveness of chatbots in facilitating the recruitment of study participants in research, specifically among racial and ethnic minorities, is unknown. The objective of this study is to compare a chatbot versus telephone-based recruitment in enrolling research participants from a predominantly minority patient population at an urban institution. We randomly allocated adults to receive either chatbot or telephone-based outreach regarding a study about vaccine hesitancy. The primary outcome was the proportion of participants who provided consent to participate in the study. In 935 participants, the proportion who answered contact attempts was significantly lower in the chatbot versus telephone group (absolute difference -21.8%; 95% confidence interval [CI] -27.0%, -16.5%; P < 0.001). The consent rate was also significantly lower in the chatbot group (absolute difference -3.4%; 95% CI -5.7%, -1.1%; P = 0.004). However, among participants who answered a contact attempt, the difference in consent rates was not significant. In conclusion, the consent rate was lower with chatbot compared to telephone-based outreach. The difference in consent rates was due to a lower proportion of participants in the chatbot group who answered a contact attempt.
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http://dx.doi.org/10.1093/jamia/ocab240 | DOI Listing |
J Craniofac Surg
November 2024
Division of Plastic Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Great Neck, NY.
Maxillofacial trauma is a significant concern in emergency departments (EDs) due to its high prevalence and the complexity of its management. However, many ED physicians lack specialized training and confidence in handling these cases, leading to a high rate of facial trauma referrals and increased stress on consult services. Recent advancements in artificial intelligence, particularly in large language models such as ChatGPT, have shown potential in aiding clinical decision-making.
View Article and Find Full Text PDFBr Ir Orthopt J
November 2024
Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences Saveetha University, Chennai, India.
Eur Urol Open Sci
December 2024
Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Background And Objective: Data on interaction of patients with artificial intelligence (AI) are limited, primarily derived from small-scale studies, cross-sectional surveys, and qualitative reviews. Most patients have not yet encountered AI in their clinical experience. This study explored patients' confidence in AI, specifically large language models, after a direct interaction with a chatbot in a clinical setting.
View Article and Find Full Text PDFJMIR Form Res
October 2024
Department of Medical Informatics, Korea University College of Medicine, Seoul, Republic of Korea.
Background: Patients often struggle with determining which outpatient specialist to consult based on their symptoms. Natural language processing models in health care offer the potential to assist patients in making these decisions before visiting a hospital.
Objective: This study aimed to evaluate the performance of ChatGPT in recommending medical specialties for medical questions.
JMIR Med Educ
October 2024
Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 200 1st st sw, Rochester, MN, 55905, United States, 1 507 594 4700.
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