Finding hard-to-find patients: Integrating real-world data and AI

Identifying patients'outside the clinic' can provide significant benefits for researchers and population health managers. Better understanding of patient cohorts can shed light on patient journeys, help optimize decisions around treatment choice and timing and inform the development of new therapies and intervention programs. Even without'in-clinic' insight - the ability to directly interact with the patient, to confirm their membership in a cohort - we have plenty of population-level tools we can use to try to identify patients. Even so, patient identification in many cases can become a challenging, even impossible task. For example, we may want to find a cohort with a certain health condition: in the simplest circumstances, if the condition is well-documented in structured medical records, we can query large real-world datasets to isolate the cohort using standardized coding.