Abstract: Systems and methods for data warehouse management for medical information is provided. The system receives a set of medical record documents. These documents include evidence for one or more findings which are identified using natural language processing of evidence. The data set is probabilistically transformed into a structured data set (often as a table). This structured data set includes embedded links that reference the source document where the evidence is located. If the finding is supported by multiple articles of evidence, the link will direct the user to the source document with the highest confidence ranking. Evidence in the source document is highlighted and may include associated annotations. Evidence, findings and annotations may be updated by the user.

Abstract: A medical information navigation engine (“MINE”) is capable of inferring referral activity not reported into a referral workflow system by utilizing intent-based clustering of medical information. The intent based clustering reconciles received medical data, from a variety of sources, and then clusters the data by applying one or more clustering rules. After the referrals not otherwise reported are inferred, they may be utilized to generate metrics that can be utilized to enhance patient care, and reduce costs. Metrics may be generated for both in-network and out-of-network referrals in order to distinguish differences in reporting activity.

Abstract: Systems and methods to improve the optical character recognition of records, and in particular health records, are provided. An image of a medical record is received, and an initial optical image recognition (OCR) on the image is performed to identify text information. The OCR signal quality may be measured, and areas of insufficient OCR signal quality may be isolated. The signal quality is determined by a weighted average of semantic analysis of the resulting text, and/or OCR accuracy measures. The OCR process may be repeated on the isolated regions of lower signal quality, each time using a different OCR transform, until all regions are completed with a desired degree of signal quality (accuracy). All the regions of the document may then be recompiled into a single document for outputting.

Abstract: An electronic medical record (EMR) analysis machine automatically clusters electronic medical records to produce an initial EMR analysis model and to identify high-value EMR documents such that human analysts can focus effort on labeling only high-value EMR documents to iteratively and extremely efficiently train an EMR analysis model. High-value sample EMR documents are identified as those whose membership in one or more clusters is most ambiguous, i.e., nearest the cluster boundary.

Abstract: Systems and methods for managing audit risks utilizing the true state of the patient are provided. A number of medical records for a patient are subjected to predictive modeling for various conditions (known as patient ‘true state’). The true state is then cross referenced by the eligible Medicare documentation, and any findings that are being submitted to MediCare for reimbursement. The result of this cross referencing is the ability to classify each finding and/or true state into a “green, “yellow”, or “red zone”. The green zone is where the finding, documentation and true state are in good alignment. A red zone is where the finding and the true state are entirely at odds. The yellow zone is where the findings and the true state are in agreement, but where there is still audit risk that may be resolved through one or more “opportunities”.

Abstract: A medical information navigation engine (“MINE”) is provided. In some embodiments, the system computes a current patient encounter vector for a current patient encounter, and then an optimal patient encounter vector is computed by assuming a best case patient encounter in accordance with the organizational objectives. The system is then able to compute the difference between the best case encounter and the current patient encounter. This difference is used to compute a corresponding payoff using an intelligent matrix.

Abstract: To achieve the foregoing and in accordance with the present invention, systems and methods for workflow management are provided. In some embodiments, a set of data elements, which are extracted from medical information, are received. The elements are bundled according to one or more similar attributes to form work items. Work items are bundles of data elements for which value is to be extracted for a particular user objective. Next the workflows are configured according to event history for the work items, current action, and user context. The work items may then be routed through the workflow based upon the work items' “energy”. Work item energy is the probability and degree to which a next action taken in a workflow to that particular work item will further a user objective.

Abstract: The present invention relates to systems and methods for hypothetical testing of a supply chain optimization. The computerized method for hypothetical optimization of a supply chain receives a hypothetical optimization query, generates variable definitions responsive to the query, generates scope definitions responsive to the query, generates presentation definitions and generates an optimization parameter set using the variable definitions and the scope definitions. The parameter set is used to optimize a hypothetical supply chain via an artificial intelligence (AI) modeling platform. One or more recommendations are generated based upon the optimized hypothetical supply chain.

Abstract: Systems and methods for generating customized annotations of a medical record are provided. The system receives a medical record and processes it using a predictive model to identify evidence of a finding. The system then determines whether to have a recall enhancement or validation of a specific finding. Recall enhancement is used to tune or develop the predictive model, while validation is used to rapidly validate the evidence. The source document is provided to the user and feedback is requested. When asking for validation, the system also highlights the evidence already identified and requests the user to indicate if the evidence is valid for a particular finding. If recall enhancement is utilized, the source document is provided and the user is asked to find evidence in the document for a particular finding. The user may then highlight the evidence that supports the finding. The user may also annotate the evidence using free form text.

Abstract: Systems and methods for personalizing medicine utilizing the true state of the patient are provided. A number of medical records for a patient are subjected to predictive modeling for various conditions (known as patient ‘true state’). The patient personal information, previous care, and true state may be provided into a state machine in order to determine the resources needed for the patient. The medical resources may be any of laboratory services, diagnostics, therapies and medications. Using the true state information, and number of activities may be performed for the patient based upon the patient's needs. These activities include scheduling lab or diagnostic procedures in advance of an appointment, filling in documentation gaps, identifying items that require additional documentation using the true state, and tracking follow-up. It may also be beneficial to validate the true state.

Abstract: The present invention relates to systems and methods for ingesting of raw client data into a supply chain optimization system. The client enterprise data system (EDS) provides raw data, typically in tabular format across many files, to a data management module of the optimization system. This data is then profiled. and a set of mapping AI models is applied to the profiled data. Each of the plurality of mapping AI models corresponds to a single input feature of the optimization model. As the mapping models are applied, a live preview is generated. This live preview is reviewable by a human operator, and human input can be provided. When there is input, the individual mapping model is updated (without impacting the other mapping models), and a new live preview for that given feature is generated. The resulting standardized feature set may be consumed by the supply chain optimization model.

Abstract: A medical information navigation engine (“MINE”) is provided. In some embodiments, the system computes a current patient encounter vector for a current patient encounter, and then an optimal patient encounter vector is computed by assuming a best case patient encounter in accordance with the organizational objectives. The system is then able to compute the difference between the best case encounter and the current patient encounter. This difference is used to compute a corresponding payoff using an intelligent matrix.

Abstract: A medical information navigation engine (“MINE”) includes a medical information interface, a reconciliation engine, and an intent-based presentation engine. The medical information interface receives medical information from a plurality of medical sources, which is subsequently reconciled by the reconciliation engine. The intent-based presentation engine clusters the reconciled medical information by applying at least one clustering rule to the reconciled medication information. The clustered reconciled medical information can be presented to a user.

Abstract: The present invention relates to systems and methods for intelligently optimizing supply chain is provided. In particular, the systems and methods provide the capability to configure supply chain systems so as to: balance between cost and service is optimized and profitability maximized; configure system parameters to respond to both current and future risks; ensure that variability is built into plans enabling maximized efficiency; and human error and bias are eliminated from the planning process such that pro-active rather than reactive behavior becomes the norm.

Abstract: A medical information navigation engine (“MINE”) includes a medical information interface, a reconciliation engine and an intent-based presentation engine. The medical information interface receives medical information from a plurality of medical sources, which is subsequently reconciled by the reconciliation engine. The intent-based presentation engine clusters the reconciled medical information by applying at least one clustering rule to the reconciled medication information. The clustered reconciled medical information can be presented to a user.

Abstract: A medical information navigation engine (“MINE”) is provided. In some embodiments, the system computes a current patient encounter vector for a current patient encounter, and then an optimal patient encounter vector is computed by assuming a best case patient encounter in accordance with the organizational objectives. The system is then able to compute the difference between the best case encounter and the current patient encounter. This difference is used to compute a corresponding payoff using an intelligent matrix.

Abstract: A medical information navigation engine (“MINE”) is capable of inferring referral activity not reported into a referral workflow system by utilizing intent-based clustering of medical information. The intent based clustering reconciles received medical data, from a variety of sources, and then clusters the data by applying one or more clustering rules. After the referrals not otherwise reported are inferred, they may be utilized to generate metrics that can be utilized to enhance patient care, and reduce costs. Metrics may be generated for both in-network and out-of-network referrals in order to distinguish differences in reporting activity.

Abstract: Systems and methods for managing audit risks utilizing the true state of the patient are provided. A number of medical records for a patient are subjected to predictive modeling for various conditions (known as patient ‘true state’). The true state is then cross referenced by the eligible Medicare documentation, and any findings that are being submitted to MediCare for reimbursement. The result of this cross referencing is the ability to classify each finding and/or true state into a “green, “yellow”, or “red zone”. The green zone is where the finding, documentation and true state are in good alignment. A red zone is where the finding and the true state are entirely at odds. The yellow zone is where the findings and the true state are in agreement, but where there is still audit risk that may be resolved through one or more “opportunities”.

Abstract: To achieve the foregoing and in accordance with the present invention, systems and methods for workflow management are provided. In some embodiments, a set of data elements, which are extracted from medical information, are received. The elements are bundled according to one or more similar attributes to form work items. Work items are bundles of data elements for which value is to be extracted for a particular user objective. Next the workflows are configured according to event history for the work items, current action, and user context. The work items may then be routed through the workflow based upon the work items' “energy”. Work item energy is the probability and degree to which a next action taken in a workflow to that particular work item will further a user objective.

Abstract: Systems and methods for data warehouse management for medical information is provided. The system receives a set of medical record documents. These documents include evidence for one or more findings which are identified using natural language processing of evidence. The data set is probabilistically transformed into a structured data set (often as a table). This structured data set includes embedded links that reference the source document where the evidence is located. If the finding is supported by multiple articles of evidence, the link will direct the user to the source document with the highest confidence ranking. Evidence in the source document is highlighted and may include associated annotations. Evidence, findings and annotations may be updated by the user.