Implementation of Anxiety UK's Ask Anxia Chatbot Service: Lessons Learned.

JMIR Hum Factors

Linguistics and English Language, Lancaster University, Lancaster, United Kingdom.

Published: June 2024

Chatbots are increasingly being applied in the context of health care, providing access to services when there are constraints on human resources. Simple, rule-based chatbots are suited to high-volume, repetitive tasks and can therefore be used effectively in providing users with important health information. In this Viewpoint paper, we report on the implementation of a chatbot service called Ask Anxia as part of a wider provision of information and support services offered by the UK national charity, Anxiety UK. We reflect on the changes made to the chatbot over the course of approximately 18 months as the Anxiety UK team monitored its performance and responded to recurrent themes in user queries by developing further information and services. We demonstrate how corpus linguistics can contribute to the evaluation of user queries and the optimization of responses. On the basis of these observations of how Anxiety UK has developed its own chatbot service, we offer recommendations for organizations looking to add automated conversational interfaces to their services.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11217701PMC
http://dx.doi.org/10.2196/53897DOI Listing

Publication Analysis

Top Keywords

chatbot service
12
user queries
8
implementation anxiety
4
anxiety uk's
4
uk's anxia
4
chatbot
4
anxia chatbot
4
service lessons
4
lessons learned
4
learned chatbots
4

Similar Publications

Purpose: The integration of artificial intelligence (AI), particularly deep learning (DL), with optical coherence tomography (OCT) offers significant opportunities in the diagnosis and management of glaucoma. This article explores the application of various DL models in enhancing OCT capabilities and addresses the challenges associated with their clinical implementation.

Methods: A review of articles utilizing DL models was conducted, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, and large language models (LLMs).

View Article and Find Full Text PDF

Exploring online patient-doctor interactions. An epistemic and pragmatic analysis of Q&A patterns in an Italian "Ask to the doctor" medical forum.

Patient Educ Couns

January 2025

Department of Education, Cultural Heritage and Tourism, University of Macerata, Piazzale L. Bertelli, 1, Macerata 62100, Italy. Electronic address:

The main objective of this research is to investigate the epistemic and pragmatic management of patient-doctor interactions in Italian online health communities. To achieve this goal, an advanced web scraping methodology was used to extract from an Italian Q&A service (within the healthcare platforms, Il Mio Dottore) 200 pairs of questions and answers concerning two pathological conditions: anxiety and hypothyroidism. We first tagged the two sub-corpora and analyzed them both quantitatively and qualitatively to establish (i) what types of questions were used by patients, and what epistemic attitude and pragmatic function they convey; (ii) whether doctors' replies were aligned or not; (iii) whether there were differences between the two sub-corpora.

View Article and Find Full Text PDF

Background Generative artificial intelligence (AI), such as Chat Generative Pre-trained Transformer (ChatGPT), has shown potential in various medical applications, including answering licensing examination questions. However, its performance in rehabilitation medicine remains underexplored. This study aimed to evaluate the accuracy of ChatGPT4o in answering questions from the Japanese Board-Certified Physiatrist Examination and assess its potential as an educational and clinical support tool.

View Article and Find Full Text PDF

Large Language Models in Healthcare: A Bibliometric Analysis and Examination of Research Trends.

J Multidiscip Healthc

January 2025

Department of Computer Engineering, Afyon Kocatepe University, Faculty of Engineering, Afyonkarahisar, Turkey.

Background: The integration of large language models (LLMs) in healthcare has generated significant interest due to their potential to improve diagnostic accuracy, personalization of treatment, and patient care efficiency.

Objective: This study aims to conduct a comprehensive bibliometric analysis to identify current research trends, main themes and future directions regarding applications in the healthcare sector.

Methods: A systematic scan of publications until 08.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!