Creating and Evaluating Chatbots as Eligibility Assistants for Clinical Trials

Ching Hua Chuan, Susan Morgan

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Clinical trials are important tools to improve knowledge about the effectiveness of new treatments for all diseases, including cancers. However, studies show that fewer than 5% of cancer patients are enrolled in any type of research study or clinical trial. Although there is a wide variety of reasons for the low participation rate, we address this issue by designing a chatbot to help users determine their eligibility via interactive, two-way communication. The chatbot is supported by a user-centered classifier that uses an active deep learning approach to separate complex eligibility criteria into questions that can be easily answered by users and information that requires verification by their doctors. We collected all the available clinical trial eligibility criteria from the National Cancer Institute's website to evaluate the chatbot and the classifier. Experimental results show that the active deep learning classifier outperforms the baseline k-nearest neighbor method. In addition, an in-person experiment was conducted to evaluate the effectiveness of the chatbot. The results indicate that the participants who used the chatbot achieved better understanding about eligibility than those who used only the website. Furthermore, interfaces with chatbots were rated significantly better in terms of perceived usability, interactivity, and dialogue.

Original languageEnglish (US)
Article number6
JournalACM Transactions on Computing for Healthcare
Issue number1
StatePublished - Jan 2021


  • active learning
  • Chatbots
  • clinical trials
  • convolution neural networks
  • eligibility criteria

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Information Systems
  • Medicine (miscellaneous)
  • Health Informatics
  • Health Information Management
  • Biomedical Engineering


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