Abstract: Systems and methods are disclosed for determining the appropriateness of medical interventions. In one embodiment, a machine learning system for determining the appropriateness of a selected medical intervention includes health-related data sources, the health-related data sources providing at least one data file of a first type, and a second data file of a second type. A machine learning module is configured to receive the first and second data files, perform a normalization procedure on at least one of the first and second data files, and apply at least one previously trained machine learning model to the normalized data files to produce a prediction output. The prediction output may include a confidence level associated with an appropriateness of the selected medical intervention.

Abstract: Systems and methods are disclosed for determining the appropriateness of medical interventions. In one embodiment, a machine learning system for determining the appropriateness of a selected medical intervention includes health-related data sources, the health-related data sources providing at least one data file of a first type, and a second data file of a second type. A machine learning module is configured to receive the first and second data files, perform a normalization procedure on at least one of the first and second data files, and apply at least one previously trained machine learning model to the normalized data files to produce a prediction output. The prediction output may include a confidence level associated with an appropriateness of the selected medical intervention.

Abstract: Systems and methods are disclosed for determining the appropriateness of medical interventions. In one embodiment, a machine learning system for determining the appropriateness of a selected medical intervention includes health-related data sources, the health-related data sources providing at least one data file of a first type, and a second data file of a second type. A machine learning module is configured to receive the first and second data files, perform a normalization procedure on at least one of the first and second data files, and apply at least one previously trained machine learning model to the normalized data files to produce a prediction output. The prediction output may include a confidence level associated with an appropriateness of the selected medical intervention.

Abstract: Machine learning systems and methods are provided for predicting outcomes of a selected medical intervention. The system includes a number of health-related data sources, the health-related data sources providing at least one data file of a first type, and a second data file of a second type. The system further includes a normalization module configured to receive the first and second data files and perform a normalization procedure on at least one of the first and second data files, and a previously trained machine learning model configured to receive the normalized data files and produce a prediction output including a set of confidence levels associated with a respective set of patient outcomes.

Abstract: Systems and methods are disclosed for surgery prediction. One method includes receiving, from a patient interacting with a survey user interface, a set of survey results, then applying at least one previously trained machine learning model (e.g., one or more artificial neural networks) to the survey results to generate a prediction output. The prediction output includes (i) a first confidence level associated with whether the patient is a surgical candidate for a particular surgical procedure; and, optionally, (ii) a set of second confidence levels associated with a respective set of surgical outcomes. Such systems and methods may be used, for example, by surgeons, health care providers, and insurers performing utilization review.

Abstract: A system and method for payment of health care costs. A host operates a web-based marketplace that may be accessed by a plurality of patients and health care providers. In one embodiment, patients may search for health care providers at the marketplace, utilizing one or more of a plurality of criteria, including price. In one embodiment, payments by patients to health care providers may be made using funds from a health savings account.

Abstract: An implant and method for fusion of adjacent vertebra. The implant has a curved plate having bores for reception of bone screws. In one embodiment, aligned medial sots extend longitudinally in the plate. An interbody graft is attached to or is integrally formed with the plate. In use, retraction post are temporarily secured to adjacent vertebrae and the slots aligned with posts. The graft is inserted and adjacent vertebrae are compressed and held until permanent screw fixation is completed. The compression tool and the posts are removed leaving the vertebrae compressed against the graft to promote healing. In an alternate embodiment, the plate carries multiple grafts which are slidably relative to the plate.