Artificial intelligence (AI) holds a promise of advancing our ability to improve patient care. Trained on routinely collected healthcare data, AI algorithms can identify patients at risk for a variety of medical events such as lingering misdiagnosis, disease progression, an upcoming severe adverse event, or non-adherence to treatment. Healthcare data can originate from structured fields (e.g., diagnoses captured via ICD-10) but 80% of data in the healthcare setting is unstructured, locked in sources such as radiology reports, clinical notes, discharge summaries and pathology notes.
Applying AI methods to these large data sets can provide a window of clarity for traditionally opaque patient predictors and support prioritization of patient engagement based on risk level and available clinical resources. This webinar will provide an overview of proven AI applications for medical event prediction, covering the lifecycle of solution development: data source selection, study design, training the AI algorithm, efficient integration into the clinical workflow, role of Social Determinants of Health as care gaps, etc. We will showcase real-world applications of such AI algorithms with measured impact to patient care across a variety of therapeutic areas.