Accepted for/Published in: JMIR Cancer
Date Submitted: Apr 1, 2021
Open Peer Review Period: Apr 6, 2021 - Jun 6, 2021
Date Accepted: Dec 18, 2021
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Enabling Patients to Receive Clinical Practice Guideline Recommendations for Hereditary Cancer Risk Using Chatbots, Family History, Application Programming Interfaces (API), Ontologies, and Owlready2: System Description
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 because providers lack time and training to collect and assess family health history. Consequently, patients at risk are not receiving the genetic counseling and testing they need to determine the preventive steps they should take to mitigate their risk.
Objective:
Enable patients to receive clinical practice guideline recommendations for their hereditary cancer risk based on their family health history with mobile friendly technology.
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 over 5,000 family health history cases. We created 192 test cases to ensure concordance with clinical practice guidelines. The average test case completes in 4.5 seconds, the longest in 19.6 seconds, and the shortest in 2.9 seconds.
Conclusions:
By engaging the chatbot, patients can collect and assess their family health history prior to visiting with their provider. Earlier identification of patients at risk of hereditary cancer leads to earlier and more effective preventive actions for managing hereditary cancer risk.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.