Analyze pathology reports and identify positive cancer diagnoses in real-time so navigators can reach out sooner.
Azra AI’s Cancer Patient Identification module leverages advanced AI and natural language processing (NLP) to detect cancer diagnoses directly from pathology reports in real-time. Designed in collaboration with leading healthcare institutions, this module automates the manual process of casefinding, delivering accurate and immediate results. By reducing the time from diagnosis to treatment, this module not only improves patient outcomes but also enhances operational efficiency across your oncology service line.
Unstructured data from pathology reports often hides crucial information, delaying the identification of cancer patients. Azra AI’s platform automatically ingests these reports, analyzes them using cutting-edge machine learning algorithms, and flags positive cancer diagnoses with unparalleled precision.
The cancer-positive reports are immediately added to a “Care Queue”, which is a simple interface that shows all positive diagnoses in order of priority and urgency.
Navigators can then reach out to the patient and start coordinating care in real-time.
The Care Queue can be tuned as each system wishes in order to reduce or increase the volume of reports that are initially shown.
Enables first contact from the physician and entry into navigator pathway within 24 hours of diagnosis.
Reduce the time to treat cancer patients, improving survivorship and other treatment outcomes.
Augment navigators and care coordinators enabling them to spend more time with patients and handle larger caseloads.
Real-time identification allows navigators to quickly engage with patients and provide care guidance, leading to reduced patient out-migration.
Helps nurse navigator teams find efficiencies in both internal processes and patient care, helping reduce patient leakage.
