Abstract: Systems and methods are described for procedurally-based decision support using ontology-based classification of database records. The procedural decision support can facilitate extraction of contextually relevant data from a database. The data may be formatted for compatibility with a knowledge-based data library using one or more scripts and populated in the library as an entity. Classification of the entity can be reasoned using the available data. One or more classifications of the entity may be returned to the procedural decision support to facilitate computation of a recommendation.

Abstract: A system processes clinical guidance to enhance patient care management. The system receives clinical guidance in digital text form from authoritative medical sources. A large language model analyzes the received clinical guidance to extract key information and relationships. The system generates a structured pathway based on the analyzed clinical guidance. The generated pathway represents a comprehensive summary for a specific disease state, organized into logical steps. These steps encompass treatment goals, management strategies, and measures for preventing complications. The system integrates the generated pathway information into an electronic health record (EHR) system. Within the EHR, the system produces a patient chart incorporating the derived pathway information.

Abstract: A system and method for enhancing electronic health record (EHR) systems through integration of proprietary and standardized medical terminologies. Embodiments proprietary terminology concepts from electronic sources and identifies matching standardized medical terminology concepts. A knowledge graph is generated, incorporating both proprietary and standardized concepts along with their relationships. The system receives patient data, including problem lists, medication lists, and lab results. This data is analyzed against the knowledge graph to generate suggestions for the EHR system. These suggestions are then provided to the EHR system for display, enhancing clinical decision support and improving patient care.

Abstract: Systems and methods determine consumability scores of data to be transformed from a source clinical data schema to a target clinical data schema. Determining the consumability score can include calculating values for characteristics of the source data set transformed from the clinical data schema, weighting the individual scores, and aggregating the weighted scores. The consumability score may indicate a predicted suitability of the transformed data for a target use. Further, the system can generate recommendations for using the target data set based on the consumability score. The determination can include predicting whether the transformation produces elements of a target data set are sufficient for the intended purposes of users of the target data set, whether the source data set includes sufficient information that can be mapped to the target clinical data schema, and whether the transformation captures the source data set in sufficient quantity and quality.

Abstract: Systems and methods are provided for facilitating the dispensing of a medication from a dispensing station. An order is received and includes the medication and a total dosage for the medication to be dispensed. The medication is mapped to a table that stores a list of a plurality of medications that are available for dispensing at one or more dispensing stations at different physical locations. Each of the plurality of medications are stored in the table with a corresponding unit usable to formulate a dosage. A particular medication is identified in the plurality of medications in the table that corresponds to the medication of the order. The particular medication also corresponds to the total dosage based on the corresponding unit in the table associated with the particular medication. An instruction is communicated to the one or more dispensing stations to dispense the particular medication.

Abstract: Methods, systems, and computer-readable media are disclosed herein that employ a contextually intelligent framework. In accordance with embodiments, a knowledge model having rules, axioms, and a domain ontology is evaluated to determine rules that are redundant to other rules and axioms, to determines those rules thresholds that may be refactored to generate composite rules and reduce the overall quantity of rules in the knowledge model, and to generate and add new concepts as axioms to the domain ontology as determined through refactoring. Methods, systems, and computer-readable media are disclosed herein that use the refactored and improved knowledge model to reconcile information currently stored in one system with information imported from a plurality of diverse systems, in order to generate recommendations that promote continuity of care in clinical settings.

Abstract: Methods, systems, and computer-readable media are disclosed herein that employ a contextually intelligent framework. In accordance with embodiments, a knowledge model having rules, axioms, and a domain ontology is evaluated to determine rules that are redundant to other rules and axioms, to determines those rules thresholds that may be refactored to generate composite rules and reduce the overall quantity of rules in the knowledge model, and to generate and add new concepts as axioms to the domain ontology as determined through refactoring. Methods, systems, and computer-readable media are disclosed herein that use the refactored and improved knowledge model to reconcile information currently stored in one system with information imported from a plurality of diverse systems, in order to generate recommendations that promote continuity of care in clinical settings.

Abstract: Methods, systems, and computer-readable media are disclosed herein that employ a contextually intelligent framework. In accordance with embodiments, a knowledge model having rules, axioms, and a domain ontology is evaluated to determine rules that are redundant to other rules and axioms, to determines those rules thresholds that may be refactored to generate composite rules and reduce the overall quantity of rules in the knowledge model, and to generate and add new concepts as axioms to the domain ontology as determined through refactoring. Methods, systems, and computer-readable media are disclosed herein that use the refactored and improved knowledge model to reconcile information currently stored in one system with information imported from a plurality of diverse systems, in order to generate recommendations that promote continuity of care in clinical settings.

Abstract: In one aspect: A non-healthcare data source associated with a patient may be identified, and a non-healthcare dataset may be extracted from the non-healthcare data source. A target subset of healthcare data may be selected from a set of healthcare data based on the non-healthcare dataset. A query may be executed on the target subset of healthcare data to identify a candidate patient information dataset. A patient information data structure may be created or updated based on the candidate patient information dataset associated with the patient. In another aspect: A match score may be computed between a non-healthcare dataset and a candidate patient information dataset, and responsive to the match score meeting a threshold, the candidate patient information dataset may be selecting as a patient information dataset. The patient information dataset may be stored in the patient information data structure in association with the patient.

Abstract: Methods, systems, and computer-readable media are disclosed herein to provide rule-based reconciliation of records. Specifically, rules are utilized to reconcile one or more records and identify duplicates therein. Once duplicate records are identified, one or more ranking sets can be utilized to identify which of the duplicate records to write to the system.

Abstract: Systems and methods are described for procedurally-based decision support using ontology-based classification of database records. The procedural decision support can facilitate extraction of contextually relevant data from a database. The data may be formatted for compatibility with a knowledge-based data library using one or more scripts and populated in the library as an entity. Classification of the entity can be reasoned using the available data. One or more classifications of the entity may be returned to the procedural decision support to facilitate computation of a recommendation.

Abstract: Techniques for enabling a user, having little or no code-writing experience, to build and configure a user-customized software application configured to extract, process, and display information associated with user-selected health records or portions of health records is disclosed. The techniques may include operations such as receiving a selection of a first resource defined by a first EHR system, analyzing metadata associated with the first resource to identify a first set of characteristics corresponding to the first resource, identifying configuration fields associated with the first resource based on the first set of characteristics, presenting the configuration fields associated with the first resource, receiving configuration values for the configuration fields, and generating code for executing API calls to extract data associated with the first resource in accordance with the configuration values and for presenting the data associated with the first resource.

Abstract: Methods, systems, and computer-readable media are disclosed herein to provide rule-based reconciliation of records. Specifically, rules are utilized to reconcile one or more records and identify duplicates therein. Once duplicate records are identified, one or more ranking sets can be utilized to identify which of the duplicate records to write to the system.

Abstract: Systems and methods are described for procedurally-based decision support using ontology-based classification of database records. The procedural decision support can facilitate extraction of contextually relevant data from a database. The data may be formatted for compatibility with a knowledge-based data library using one or more scripts and populated in the library as an entity. Classification of the entity can be reasoned using the available data. One or more classifications of the entity may be returned to the procedural decision support to facilitate computation of a recommendation.

Abstract: Systems and methods determine consumability scores of data to be transformed from a source clinical data schema to a target clinical data schema. Determining the consumability score can include calculating values for characteristics of the source data set transformed from the clinical data schema, weighting the individual scores, and aggregating the weighted scores. The consumability score may indicate a predicted suitability of the transformed data for a target use. Further, the system can generate recommendations for using the target data set based on the consumability score. The determination can include predicting whether the transformation produces elements of a target data set are sufficient for the intended purposes of users of the target data set, whether the source data set includes sufficient information that can be mapped to the target clinical data schema, and whether the transformation captures the source data set in sufficient quantity and quality.

Abstract: Systems and methods are described for procedurally-based decision support using ontology-based classification of database records. The procedural decision support can facilitate extraction of contextually relevant data from a database. The data may be formatted for compatibility with a knowledge-based data library using one or more scripts and populated in the library as an entity. Classification of the entity can be reasoned using the available data. One or more classifications of the entity may be returned to the procedural decision support to facilitate computation of a recommendation.

Abstract: Methods, systems, and computer-readable media are disclosed herein to provide rule-based reconciliation of records. Specifically, rules are utilized to reconcile one or more records and identify duplicates therein. Once duplicate records are identified, one or more ranking sets can be utilized to identify which of the duplicate records to write to the system.

Abstract: An alert optimizer subsystem for a HIT system modifies, reconciles, and/or prioritizes candidate clinical alerts. The optimized alerts may be filtered, prioritized, enriched, and/or formatted so that the alerts are more relevant and/or more actionable for a system user.

Abstract: Methods, systems, and computer-readable media are disclosed herein to provide rule-based reconciliation of records. Specifically, rules are utilized to reconcile one or more records and identify duplicates therein. Once duplicate records are identified, one or more ranking sets can be utilized to identify which of the duplicate records to write to the system.

Abstract: Methods, systems, and computer-readable media are disclosed herein that employ a contextually intelligent framework. In accordance with embodiments, a knowledge model having rules, axioms, and a domain ontology is evaluated to determine rules that are redundant to other rules and axioms, to determines those rules thresholds that may be refactored to generate composite rules and reduce the overall quantity of rules in the knowledge model, and to generate and add new concepts as axioms to the domain ontology as determined through refactoring. Methods, systems, and computer-readable media are disclosed herein that use the refactored and improved knowledge model to reconcile information currently stored in one system with information imported from a plurality of diverse systems, in order to generate recommendations that promote continuity of care in clinical settings.