Automated Clinical Practice Guideline Recommendations for Hereditary Cancer Risk Using Chatbots and Ontologies: System Description

• Authors: Jordon B Ritchie, Medical University of South Carolina; Lewis J Frey, Ralph H. Johnson VA Medical Center; Jean-Baptiste Lamy, Université Sorbonne Paris Nord; Cecelia Bellcross, Emory University; Heath Morrison, Doxy.me, Charleston, SC; Joshua D Schiffman, University of Utah School of Medicine; Brandon M Welch, Medical University of South Carolina
• Journal Title: JMIR Cancer
• Volume: Volume 8, Number 1
• Publisher: JMIR Publications | 2022-01-01, Pages e29289-e29289
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.2196/29289
• Subjects: Health Sciences, Public Health; Health Sciences, Oncology
• Keywords: clinical practice guidelines; consumer health informatics; hereditary cancer; restful API; risk assessment; service-oriented architecture

Abstract

Background: Identifying patients at risk of hereditary cancer based on their family health history is a highly nuanced task. Frequently, patients at risk are not referred for genetic counseling as providers lack the time and training to collect and assess their family health history. Consequently, patients at risk do not receive genetic counseling and testing that they need to determine the preventive steps they should take to mitigate their risk. Objective: This study aims to automate clinical practice guideline recommendations for hereditary cancer risk based on patient family health history. Methods: We combined chatbots, web application programming interfaces, clinical practice guidelines, and ontologies into a web service-oriented system that can automate family health history collection and assessment. We used Owlready2 and Protégé to develop a lightweight, patient-centric clinical practice guideline domain ontology using hereditary cancer criteria from the American College of Medical Genetics and Genomics and the National Cancer Comprehensive Network. Results: The domain ontology has 758 classes, 20 object properties, 23 datatype properties, and 42 individuals and encompasses 44 cancers, 144 genes, and 113 clinical practice guideline criteria. So far, it has been used to assess >5000 family health history cases. We created 192 test cases to ensure concordance with clinical practice guidelines. The average test case completes in 4.5 (SD 1.9) seconds, the longest in 19.6 seconds, and the shortest in 2.9 seconds. Conclusions: Web service-enabled, chatbot-oriented family health history collection and ontology-driven clinical practice guideline criteria risk assessment is a simple and effective method for automating hereditary cancer risk screening.