Purpose: We provide an initial description and validation of some public domain patient-reported outcome (PRO) items to assess cancer symptom burden to address immediate barriers to symptom assessment use in clinical practice and facilitate future research.
Methods: We created the Open Symptom Framework (OSF), a flexible tool for clinical cancer-related symptom assessment. The items comprise six components: recall period, concept, symptom, qualifier(s), a definition, and a 5-point Likert-type response.
Background: Cancer patients often experience treatment-related symptoms which, if uncontrolled, may require emergency department admission. We developed models identifying breast or genitourinary cancer patients at the risk of attending emergency department (ED) within 30-days and demonstrated the development, validation, and proactive approach to in-production monitoring of an artificial intelligence-based predictive model during a 3-month simulated deployment at a cancer hospital in the United States.
Methods: We used routinely-collected electronic health record data to develop our predictive models.