Comparison of a Cancer Family History Collection and Risk Assessment Tool - ItRunsInMyFamily - with Risk Assessment by Health-Care Professionals

iKNOWgynetics - A web-based learning concept to empower primary care gynecologists to participate in the care of patients with a family history of breast and ovarian cancer

Familial cancer burden and genetics play an increasingly important role in the early detection and prevention of gynecological cancers. However, people with hereditary cancer risks are often identified late when they already have cancer. We aimed at developing and evaluating a training concept for primary care gynecologists-iKNOWgynetics-to improve their knowledge and awareness of genetic cancer syndromes and their ability to identify patients with increased familial cancer risks based on up-to-date evidence and current guidelines (in Germany, primary care includes all doctors treating patients on an outpatient basis without a clear separation of the expertise of the doctor or of their specialty). Starting off with a needs assessment among primary care gynecologists, we developed and evaluated an online training concept-using a web-based learning platform in combination with a live virtual seminar-to convey practice-relevant knowledge about familial cancer. After registration, participants get access to the web-based learning platform (www.iknowgynetics.de) to prepare for the virtual seminars and to use it as online reference to re-access the contents after the training. Evaluation included multiple-choice (MC) questions on knowledge and participants' self-efficacy to implement the acquired knowledge, which were administered in a pre-post design. Of 109 participants, 103 (94.5%) filled out pre- and post-questionnaires. Eighty-five participants were gynecologists in primary care from Berlin (81.2%) and Brandenburg (18.8%) and had an average of 24.1 years (SD = 8.5 years) of professional experience. After the training, participants answered significantly more knowledge questions correctly (M = 15.2 of 17, SD = 1.3) than before (M = 13.8 of 17, SD = 1.7) (p < 0.01) and felt more confident to be able to apply referral criteria for specialized counseling in practice (p < 0.001). The online-based training iKNOWgynetics considers the busy schedule of primary care gynecologists and supports them in acquiring practice-relevant information on familial cancer risks and on how to identify healthy persons at risk, which may ultimately help to improve the prevention of gynecological cancers. In future studies, the reported concept could be transferred to other entities.

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

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.