Om1, Inc.

Outcome Sciences, Inc.

Massachusetts Eye and Ear Infirmary

Om1, Lnc.

Richard E Gliklich

Constantinos Ioannis Boussios

Christopher Francois Paul

Scott Bradley

Francis Thomas O'Donovan

Charles Kossi Kekeh

Jigar Bandaria

William Matthew White

A classification method and system for medical conditions based on the concept of subtypes, which are classes of patients whose medical fact patterns as analyzed in an N-dimensional space places them closer to other patients belonging to the same subtype than to patients who belong to different subtypes and, who share similar likelihood of certain specified outcomes. A computer system processes patient data for a plurality of patients from a set of patients called a cohort. The computer system processes the patient data for the cohort to group patients into sub-cohorts of similar patients, i.e., each sub-cohort includes patients who have similar medical fact patterns in their patient data. Patients in different sub-cohorts generally, but not necessarily, have significant differences in their patient data. The computer system generates quantitative definitions, describing the patients in the sub-cohorts.

Identification of patient sub-cohorts and corresponding quantitative definitions of subtypes as a classification system for medical conditions

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 standardized outcome scores across patients

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.

Applying predictive models to data representing a history of events

A health care information system generates information that describes how different inputs to a model affect the output of the model, by creating a localized model for a given entity, and determining how the output of the localized model for the given entity changes in response to different inputs. The computer system builds the localized model for a given entity based on the model and on data values for the given entity. The computer system inputs one or more different data values for selected input features of the localized model, while data values for the remaining input features of the localized model are fixed to data values for the given entity, and obtains corresponding outputs from the localized model. The results from this localized model for the given entity indicate which of the selected input features have the most impact on the output of the model for that entity.

Information system providing explanation of models

A data processing system is provided for determining clinical outcomes of medical data gathered by the system. The system can allow a person to define a medical study and can then administer the medical study and can collect and analyze data from potentially geographically diverse doctors, patients and other people associated with a study. Users enter sets of medical information. The system can analyze the medical data according to any number of clinical algorithms that may be custom defined and edited before and during the study. The system conditionally outputs the clinical outcome to the user. The clinical outcome can be used for treatment of patients participating in the study immediately after the data is input and analyzed. The medical outcomes can indicate such things as performance comparisons, composite outcomes, and risk stratification and assessments for such things as treatments, drugs, illnesses, doctors, patients and physician groups.

Apparatus and methods for determining and processing medical outcomes

A data processing system is provided for determining clinical outcomes of medical data gathered by the system. The system can allow a person (e.g. a doctor) to define a medical study and can then administer the medical study and can collect and analyze data in real-time from potentially geographically diverse doctors, patients and other people associated with a study. The system can analyze the medical data in real-time according to any number of clinical algorithms that may be custom defined and edited before and during the study. The clinical algorithms produce clinical outcome data that can be used for treatment of patients participating in the study immediately after the data is input and analyzed. The medical outcomes can indicate such things as performance comparisons, composite outcomes, and risk stratification and assessments for such things as treatments, drugs, illnesses, doctors, patients and physicians groups.

A method and system for delivering energy to affect the signs of aging is described. Energy is delivered to a target volume of a muscle such that the energy creates a lesion in the target volume. A characteristic of the lesion is controlled to affect movement of the muscle.

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Boston, MA, United States of America

Richard E Gliklich