Abstract: 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.

Abstract: 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.

Abstract: A system, method, and user interface for optimizing encoding of a patient problem list in an electronic medical record or electronic health record. The system is operable on one or more computers with one or more processors that are configured to construct a database of HCC signatures, each HCC signature including one or more ICD-10 and/or SNOMED codes, deconstruct a patient problem list within an electronic medical record or electronic health record to generate an identification of one or more ICD-10 and/or SNOMED codes included within the patient problem list, evaluate the identified ICD-10 and/or SNOMED codes against each HCC signature to determine sufficient matches to one or more HCC signatures, cross-check HCC concepts related to the sufficiently-mapped HCC signatures and eliminate HCC concepts already identified in the record to generate a group of at least one non-eliminated, sufficiently-mapped HCC concepts, and displaying the non-eliminated, sufficiently-mapped HCC concepts.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.