Described herein are methods for processing data in order to assess the likelihood that a patient belongs within a specified cohort. In general, the method may include the steps of receiving a plurality of data elements from multiple data sets, wherein at least a portion of the plurality of data elements are unstructured data elements; and assessing the likelihood that the patient belongs within the specified cohort using at least a portion of the plurality of data elements including at least one unstructured data element. In some embodiments, the method may further include the step of processing the unstructured data elements. In some embodiments, the method may further include the step of querying at least a portion of the plurality of data elements including at least one unstructured data element to assess the likelihood that the patient belongs within the specified cohort.

A system for predicting a reason for a patient's next exam include a clinical database storing one or more clinical documents including clinical data. A natural language processing engine processes the clinical documents to detected clinical data. A normalization engine semantically normalizes the clinical data with respect to an internal data structure and/or an ontology. A pattern recognition engine generates a mapping from a set of known reasons for exam from the normalized clinical data. A prediction engine generates a prediction for a reason for the patient's next exam.

A method for displaying medical data includes receiving physiological data from a first medical monitoring device. The physiological data is obtained on a continuous basis. Physiological data is received from a second medical monitoring device. The physiological data from the second medical monitoring device is obtained on a non-continuous basis. The physiological data received from the first medical monitoring device and the physiological data received from the second medical monitoring device are displayed on a central display station. The central display station is located centrally within a care unit of a caregiving facility.

Systems and methods related to the field of cardiac resuscitation, and in particular to devices for assisting rescuers in performing cardio-pulmonary resuscitation (CPR) are described herein.

System and method used to provide fast and accurate retrieval of findings results from large amounts or report documents (the corpus), such as medical record documents. The system maintains a dynamic list of the characteristics of no-finding called no-finding descriptors, each identified by a tag. Upon entering the corupus, the sentences of each new document are searched, and each sentence the content of which is similar to one of the descriptors is tagged. When search is conducted, the user enters a word or phrase, which expresses the subject of search. This subject is searched for in the corpus and from which a list of all sentences that contain the subject—the initial result list. The initial results list includes both finding and no-finding results. The final result list is obtained by extracting from the initial result list all occurences of the tagged no-finding sentences.

Techniques and apparatus for managing contributions to an accruable health spending account in an employer-sponsored plan offering a member an employer-funded defined contribution, at least one insurance premium option and the ability to specify an allocation of the defined contribution for payment of option premiums and in turn, a directed contribution amount designated to such accruable account are disclosed. The accruable account may be used to reimburse the member for qualified medical expenses, and the member may pay any premium shortfall using a tax-advantaged process such as a premium only payment plan. Also disclosed are techniques and apparatus directed to presenting member-specific out-of-pocket expenses for a selected procedure offered by at least one health-care provider.

A method electronically validates credentials information pertaining to applicants. The method collects credentials information pertaining to applicants in one or more of a plurality of formats and converts the credentials information into a common format of collected credentials information. The converting utilizes a common object model. The method stores the collected credentials information in a database, validates for a plurality of applicants the collected credentials information with external sources through an electronic interface, automatically electronically updates the database with the retrieved data, automatically electronically notifies a user of inconsistencies between obtained third-party credentials data and the collected credentials information pertaining to the applicant, and provides selective electronic access to the third-party credentials data over a communication network to one or more users and to the applicant to which the third-party credentialing data pertains.

Mechanisms are provided for matching patients with care providers. Patient information for a current patient, from at least one patient information source, is analyzed and a medical need of the current patient is determined. Patient information for a plurality of other patients associated with a plurality of care providers is analyzed and, for each care provider in the plurality of care providers, a measure of strength for a type of medical care corresponding to the at least one medical need is determined, based on results of the analysis of patient information for the plurality of other patients associated with the care provider. At least one care provider is selected for inclusion in a care team for the current patient based on the determined strengths of each care provider in the plurality of care providers. A care team data structure comprising information about the at least one care provider is output.

Methods, systems, and computer-readable media are provided to detect similarities between two or more clinical documents. It is determined that a clinician is currently inputting data into a first clinical document that is associated with a patient. A selectable option is provided on a user interface into which the clinician is currently inputting the data. An indication is received that the selectable option has been selected by the clinician. An algorithm is applied to identify the second clinical document from a plurality of clinical documents. At least a portion of the inputted data in the first clinical document and at least a portion of data in the second clinical document are transformed to generate a new representation of the first clinical document that indicates the similarities that are potentially inaccurate or inappropriate between the first clinical document and the second clinical document.

Methods, systems, and computer-readable media are provided to detect similarities between two or more clinical documents. It is determined that a clinician is currently inputting data into a first clinical document that is associated with a patient. A selectable option is provided on a user interface into which the clinician is currently inputting the data. An indication is received that the selectable option has been selected by the clinician. An algorithm is applied to identify the second clinical document from a plurality of clinical documents. At least a portion of the inputted data in the first clinical document and at least a portion of data in the second clinical document are transformed to generate a new representation of the first clinical document that indicates the similarities that are potentially inaccurate or inappropriate between the first clinical document and the second clinical document.