The treatment of cancer patients is very complex, with treatment decisions made all the more difficult in patients with multiple comorbidities and compounding drug toxicities. Front-line treatments can be straight forward, but disease relapses often require input and expertise from multidisciplinary teams. These teams utilize their collective experience in disease treatment and patient outcomes to determine the best treatment plan for relapsing patients. These treatment plans are based upon the prior experience of a treatment team, and the outcome data generated following the patient treatment generally remains solely with that team. Mayo Clinic investigators have developed a clinical analytics tool (CODE-M) designed to evaluate clinical outcome differences, based upon data obtained from prior treatment decisions, to enable clinicians to identify the optimal treatment plan for an individual at the point of care. CODE-M incorporates the capture of relevant real-world patient data, longitudinal data points (collected throughout patient care) and non-EMR data (e.g. biomarkers, patient reported outcomes, etc.) to yield valuable, patient-specific clinical insights to inform on treatment decisions. This tool has the potential to “build upon itself”, whereby it is constantly receiving new data and utilizing this new data to inform on current patient treatment decisions (i.e. the more this tool is used, the more patient outcome data it can utilize and the “smarter” it becomes). CODE-M is unparalleled in that it harvests an array of treatment and outcome data that was formerly interpreted and utilized by one treatment team and transfers it into an enterprise-wide tool, capable of enabling physicians to make data-driven decisions by understanding how patients similar to their patient fared on various treatment options.