Selectively redeemable bundled services recommender systems and methods

Abstract

An exemplary healthcare services recommender implementation may be configured to recommend selectively redeemable bundled services comprising care for conditions predicted as likely by a machine learning model based on patient data input. The likely conditions may be predicted for a patient or a patient population comprising a plurality of patients. The machine learning model may comprise a neural network trained to determine a probability distribution of patient condition trends for a future time period based on patient data input. The patient data input may comprise data from an Electronic Medical Record (EMR), diagnosis, lab test, patient-provided information such as survey/symptom data, and/or wearable device data. The machine learning model may be trained using a training data set including at least healthcare outcomes sampled during a past time period from a plurality of patient healthcare episodes each comprising a plurality of procedures performed for the patient in at least one encounter.