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: 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: 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: 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: 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 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 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.