Om1, Inc.

Richard Gliklich

Constantinos Ioannis Boussios

Christopher Francois Paul

Scott Bradley

Francis Thomas O'Donovan

Charles Kossi Kekeh

Health care information for multiple patients is processed to classify patients into categories. Additional data fields related to a category in which a patient is classified are added to the patient record. These data fields are populated in part by automatically processing the existing patient data. Such automatic processing can result in a probability that the underlying data supports having a particular value stored in one of the added data fields, and this probability also can be stored. Over time, additional data can be obtained from patients, caregivers and other sources, for structured data fields based on data entry forms for patient reported outcomes, caregiver reported outcomes, events of interest, survival and resource utilization. A set of factor scores is computed for each patient, for each category in which the patient is classified. An outcome score is computed for each patient for each category in which the patient is classified, using an outcome function defined for that category, as a weighted function of one or more of the factor scores. The outcome function for a category is standardized across all patients classified in that category.

Health care information system providing additional data fields in patient data

A predictive model can be applied to data representing a history of events for an entity to compute a value indicative of an outcome related to a reference time for that entity. The effect of an event from an entity's history of events on an outcome for the entity at a reference time can vary based on the type of event and relative time of that event with respect to the reference time. The effect of an event from an entity's history of events on an outcome for the entity also can vary due to other characteristics of the entity in combination with the event. These effects are captured as weights. For an entity, functions of sets of events from the history of events are computed for the entity and a set of weights for events. The computed results are inputs to the predictive model.

Growing community of inventors

Weston, MA, United States of America

Richard Gliklich