Intelligent Medical Objects, Inc.

Michael Decaro

Frank Naeymi-Rad

Matthew C Cardwell

Andrew Stuart Kanter

Regis Charlot

Alina E Oganesova

David O Haines

David Alvin

Steven Rube

Eric Rose

David Parks

James Thompson

Daniel Emmons

Erina Paul

Yiqing Wang

A system and method for extracting data from an electronic health record to provide provider and patient data similarity scoring includes: encoding a problem list for a plurality of patients with concepts from a common electronic health record ontology. In one aspect, the patients have electronic health records maintained by a plurality of providers. The system and method then may parse the concepts into a plurality of clusters or categories and determining, for each of the providers, a total number of patients that have at least one problem in a cluster or category or determining, for each patient, which of the plurality of clusters or categories correspond to at least one concept encoded in the patient's problem list. The system and method then may calculate for each pair of providers or patients, a distance between the providers or patients.

Method and system for extracting data from a plurality of electronic data stores of patient data to provide provider and patient data similarity scoring

A medical classification code modeling system and method that generates visual maps in response to user queries, where the visual maps represent combinations of modifiers of an interface terminology that, taken together, map to medical classification code elements. The system and method may present multiple visual maps on the same display to permit visual analysis of multiple mapping revisions or versions. The system and method further may integrate into an electronic health record such that the user's ultimate selection of a mapping results in the corresponding medical classification code being inserted into the record of a patient at an appropriate location, such as on the patient's problem list.

System and method for medical classification code modeling

A system and process for record duplication analysis that relies on a multi-membership Bayesian analysis to determine the probability that records within a data set are matches. The Bayesian calculation may rely on objective data describing the data set as well as subjective assessments of the data set. In addition, a system and process for record duplication analysis may rely on the predetermination of probabilistic patterns, where the system only searches for patterns exceeding a chosen threshold. Work flow may include selecting which fields within each record should be analyzed, normalizing the values within those fields and removing default data, calculating possible patterns and their match probabilities, analyzing record pairs to determine which have patterns exceeding a chosen threshold to determine the presence of duplicates, and merging duplicates, closing transactions reflecting non-duplicates, identifying records having insufficient data to determine the existence or lack of a match, and/or rolling back accidental merges.

System and process for record duplication analysis

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Mount Prospect, IL, United States of America

Michael Decaro