Value-based Integrated Recommendation Software Guiding Ovarian Cancer Treatment (VIRGO2)

This study will evaluate the use of a mobile application in improving the patient-reported health outcome measures (PROMIS) for patients diagnosed with advanced stage ovarian, fallopian tube, and primary peritoneal cancer. The application will incorporate clinical data from the patient's medical chart as well as capture patient-reported outcome measures on an ongoing basis to better inform physicians and the care team so that necessary interventions may be implemented.

The study will employ the complete utility of the mobile application by incorporating highly coordinated ovarian cancer care pathways, associated evidence-based recommendations, and delivering these 'at the fingertips' of providers and patients when appropriate. The AI-based mobile application requires both clinical data-input as well as continuously captured patient-reported outcome measures (PROMs) including those related to disease progression, medication side effects, medication adherence, anxiety and depression, and quality of life. The continuous assessment of outcome measures will provide ongoing monitoring that is delivered directly to the electronic medical record (EMR). This data allows abnormal outcome measures to trigger immediate expert-based recommendations for care management with one click in the EMR through implementation of the AI-driven ovarian cancer care pathways. Provider recommendations will be continuously generated for the optimization of care that is based upon individual risk profiles, disease stage, and health outcomes, resulting in dynamic and risk-dependent recommendations. Remote patient monitoring will also allow for improved education and instruction, including appointment reminders and medication adherence optimization. The application will also provide nutritional support, mental support, and caregiver connectivity. Given ovarian cancer will be a chronic condition for 80% of patients, the critical challenge is to deliver high level care that improves patient outcomes while not increasing the cost of health care. This project will assess a process by which this can be done with the electronic medical record, a patient application, and AI-generated patient care pathways. The development of such AI-powered care pathways designed for ovarian cancer will be coordinated throughout the induction and maintenance treatment phases of ovarian cancer management.

Eligibility Criteria:
Inclusion Criteria:

1. Female ≥18 years of age at Screening Visit.
2. Diagnosed with ovarian cancer, fallopian tube, or primary peritoneal cancer
3. Undergoing active treatment at some point during the study period including chemotherapy, immunotherapy, targeted agent or hormonal therapy. If active treatment has not yet started at time of screening, active treatment must be anticipated to begin within 30 days of enrollment.
4. Written informed consent (and assent when applicable) obtained from subject or subject's legal representative and ability for subject to comply with the requirements of the study.
5. Access to IOS or Android-based smart phone

Exclusion Criteria:

1. Unwilling or unable to adhere to the protocol
2. Unwilling or unable to adhere to the informed consent
3. Age \<18yo
4. Concurrent non-gynecologic cancer diagnosis requiring active treatment at enrollment
5. Presence of a condition or abnormality that in the opinion of the Investigator would compromise the safety of the patient or the quality of the data.