Abstract: Methods, systems, and computer-readable media are provided for facilitating record matching and entity resolution and for enabling improvements in record linkage. A power-spectrum-based temporal pattern-specific weight may be incorporated into record linkage methods to enhance the record linkage accuracy and statistical performance. For example, in embodiments, a value-specific weight may be calculated from a population-based frequency of field-specific values and provides an opportunity to capture and measure the relative importance of specific values found in a field. A timeseries-derived Bayesian power spectrum weight may be calculated from the population-based frequency of temporal pattern-specific values in terms of intensities at various frequencies of the power spectrum computed from the timeseries, and further provides an opportunity to capture and measure the relative importance of specific sequences of care episodes.

Abstract: Methods, systems, and computer-readable media are provided for facilitating mapping of semantically similar terms between and among two or more information systems. In particular, to facilitate automatic discovery, establishment, and/or statistical validation of linkages between a plurality of different nomenclatures employed by a plurality of information systems, such as multiple electronic health record systems. In embodiments, the imputation of latent synonymy in corpora comprised of samples of historical records from each system enables automated terminology mapping between disparate systems' records, thereby establishing reliable linkages that may subsequently be utilized for realtime decision support, data mining-based research, or other valuable purposes.

Abstract: Systems, methods and computer-readable media are provided for facilitating patient health care by providing discovery, validation, and quality assurance of nomenclatural linkages between pairs of terms or combinations of terms in databases extant on multiple different health information systems that do not share a set of unified codesets, nomenclatures, or ontologies, or that may in part rely upon unstructured free-text narrative content instead of codes or standardized tags. Embodiments discover semantic structures existing naturally in documents and records, including relationships of synonymy and polysemy between terms arising from disparate processes, and maintained by different information systems. In some embodiments, this process is facilitated by applying Latent Semantic Analysis in concert with decision-tree induction and similarity metrics.

Abstract: Systems, methods and computer-readable media are provided for facilitating clinical decision support and managing patient population health by health-related entities including caregivers, health care administrators, insurance providers, and patients. Embodiments of the invention provide decision support services through new knowledge, including high-value itemsets having a plurality of codified clinical concepts from clinical information of a reference population and determining statistical measures of association between the high-value itemsets and codified clinical concepts in a target patient's electronic health records to determine additional clinical concepts that may be relevant for the patient. The high-value itemsets are discovered from the reference set of clinical information using data-mining approaches that include rare items and are linked to other clinical information to provide more contextually relevant information.

Abstract: Systems, methods, and computer-readable media are provided for facilitating clinical decision making by directing the emission of computer-generated health-care related recommendations towards contexts in which the recipient will likely find the recommendations salient and will likely welcome them and act upon them. ‘Uptake’ of computer-generated recommendations for diagnostic tests or therapeutic interventions is thereby substantially increased, and ‘alert fatigue’ is substantially decreased. Embodiments of our technology overcome certain drawbacks associated with the prior art by providing a means for ascertaining which decision-support recommendations are likely to be favorably considered by the recipient and acted-upon (recommendation ‘uptake’). System and method embodiments for providing a predicted probability of user uptake of a context-specific system-generated recommendation patient are disclosed herein and for applying that information to decide whether or not to emit the relevant recommendation.

Abstract: Systems, methods and computer-readable media are provided for facilitating clinical decision support and managing patient population health by health-related entities including caregivers, health care administrators, insurance providers, and patients.

Abstract: Systems, methods, and computer-readable media for denormalizing healthcare data are provided. An indication a patient has been added to a tracking list is received. The tracking list is determined to be associated with a primed view. Data is retrieved for the patient. The data for the patient is cached into the primed view before a clinician requests the data.

Abstract: Systems, methods and computer-readable media are provided for facilitating patient health care by providing discovery, validation, and quality assurance of nomenclatural linkages between pairs of terms or combinations of terms in databases extant on multiple different health information systems that do not share a set of unified codesets, nomenclatures, or ontologies, or that may in part rely upon unstructured free-text narrative content instead of codes or standardized tags. Embodiments discover semantic structures existing naturally in documents and records, including relationships of synonymy and polysemy between terms arising from disparate processes, and maintained by different information systems. In some embodiments, this process is facilitated by applying Latent Semantic Analysis in concert with decision-tree induction and similarity metrics.

Abstract: Methods, computer systems, and computer readable media for transforming raw healthcare data into relevant healthcare data are provided. Transformation of raw healthcare data into relevant data is accomplished by receiving raw data from a plurality of data collectors, where the plurality of data collectors extract the raw data from a plurality of raw data sources, and sorting the raw data into unstructured raw data, structured data of non-standard nomenclature, and structured data of standard nomenclature. The data is transformed into relevant data through the use of natural language processing, nomenclature and ontology mapping, and adaptive knowledge processing. The relevant data is stored in a plurality of data repositories, and computer applications and services are allowed access to the plurality of data repositories.