Abstract: Examples described herein generally relate to recommending therapeutic treatment information for a patient, and more particularly to recommendations regarding drugs or drug combinations. A computer system may access a plurality of data records associated with the patient. The system may correlate and consolidate the plurality of records based on an accuracy rating of each data source. The system may provide access, via a first application programming interface (API) to a first plurality of shared machine learning algorithms to perform a respective analysis of the plurality of data records. The system may receive, via a second API, a proprietary set of executable code to perform an analysis of the plurality of data records. The system may execute the first plurality of shared machine learning algorithms and the set of executable code on the plurality of data records for the patient to generate therapeutic treatment information for the patient.

Abstract: Implementations described and claimed herein provide systems and methods for generating an optimized treatment experience for a patient. In one implementation, patient experience data is captured using at least one patient experience device. The patient experience data corresponds to a patient experience factor for the patient. A current level of the patient experience factor is determined using a patient experience processing system. The current level of the patient experience factor is determined based on the patient experience data. A customized therapy for the patient is generated based on the current level of the patient experience factor. The customized therapy is an alternative treatment to a drug therapy administration. An administration of a patient treatment experience is generated based on the customized therapy. The patient treatment experience is generated using the at least one patient experience device and includes one or more of patient sense stimulation and patient cognitive stimulation.