Abstract: Systems and methods of exchanging Healthcare Quality Measures (HQM) are disclosed. Healthcare providers can define one or more HQMs by constructing expressions using an expression builder according to a funding organization's requirements. Once a measure is derived, it can be converted to a common HQM data format and exchanged with the organization via an intermediary HQM service. The HQM data can then be converted into the organization's proprietary format. Thus, HQM data exchanges among providers and organizations are simplified.

Abstract: Data is combined from multiple independent data stores, and is then queried using a data correlation engine. Contemplated engines preferably keep track of previously run correlations, and then makes those correlations available to clinicians for their own use. For example, a preferred system might provide a listing of correlations run by other clinicians in a particular medical specialty, or a particular community, whether geographic or otherwise. In another example, a preferred system might provide a listing of correlations sorted by popularity, so that the most frequently accessed correlations appear near the top of the list. In any case a clinician could simply view the list, and check off which correlations he/she would like to have run for his/her practice, or practice community.

Abstract: A medical guideline integration engine is presented. The engine imports medical guideline information from disparate sources based on various guideline standards into a knowledge base. The knowledge base houses the information in a database. Upon request, the engine retrieves the information from the knowledge base and converts the information into a desired guideline format.

Abstract: Systems and methods of exchanging Healthcare Quality Measures (HQM) are disclosed. Healthcare providers can define one or more HQMs by constructing expressions using an expression builder according to a funding organization's requirements. The expression builder advantageously allows the Healthcare provider to build measure expressions without the need to know a complex programming language. Once a measure is derived, it can be converted to a common HQM data format and exchanged with the organization via an intermediary HQM service. The HQM data can then be converted into the organization's proprietary format. Thus, HQM data exchanges among providers and organizations are simplified.

Abstract: A hub-spoke patient information service in which a patient's data can be obtained from a network of independent data sources through a centralized service. The hub of the service stores patient identification information and the spokes store the patient's clinical data. In a preferred embodiment, the spokes are independent sources (e.g. each spoke is generally unrelated to other spokes) and include hospitals, doctor offices, clinics, insurance companies, or other entities that store or track clinical data. Preferably, the data sources use a first authentication to properly identify or authentic the service. Once access is granted, the spoke sources are queried for the patient's data. In response, each data source provides the type of patient data available from the source. The user is shown the types of data available without generally showing the actual data. The user can then select the specific patient data to retrieve from the spoke sources.

Abstract: A medical guideline integration engine is presented. The engine imports medical guideline information from disparate sources based on various guideline standards into a knowledge base. The knowledge base houses the information in a database. Upon request, the engine retrieves the information from the knowledge base and converts the information into a desired guideline format.

Abstract: A hub-spoke patient information service in which a patient's data can be obtained from a network of independent data sources through a centralized service. The hub of the service stores patient identification information and the spokes store the patient's clinical data. In a preferred embodiment, the spokes are independent sources (e.g. each spoke is generally unrelated to other spokes) and include hospitals, doctor offices, clinics, insurance companies, or other entities that store or track clinical data. Preferably, the data sources use a first authentication to properly identify or authentic the service. Once access is granted, the spoke sources are queried for the patient's data. In response, each data source provides the type of patient data available from the source. The user is shown the types of data available without generally showing the actual data. The user can then select the specific patient data to retrieve from the spoke sources.

Abstract: Systems and methods of exchanging Healthcare Quality Measures (HQM) are disclosed. Healthcare providers can define one or more HQMs by constructing expressions using an expression builder according to a funding organization's requirements. The expression builder advantageously allows the Healthcare provider to build measure expressions without the need to know a complex programming language. Once a measure is derived, it can be converted to a common HQM data format and exchanged with the organization via an intermediary HQM service. The HQM data can then be converted into the organization's proprietary format. Thus, HQM data exchanges among providers and organizations are simplified.

Abstract: Systems and methods of exchanging Healthcare Quality Measures (HQM) are disclosed. Healthcare providers can define one or more HQMs by constructing expressions using an expression builder according to a funding organization's requirements. Once a measure is derived, it can be converted to a common HQM data format and exchanged with the organization via an intermediary HQM service. The HQM data can then be converted into the organization's proprietary format. Thus, HQM data exchanges among providers and organizations are simplified.

Abstract: A medical guideline integration engine is presented. The engine imports medical guideline information from disparate sources based on various guideline standards into a knowledge base. The knowledge base houses the information in a database. Upon request, the engine retrieves the information from the knowledge base and converts the information into a desired guideline format.

Abstract: Data is combined from multiple independent data stores, and is then queried using a data correlation engine. Contemplated engines preferably keep track of previously run correlations, and then makes those correlations available to clinicians for their own use. For example, a preferred system might provide a listing of correlations run by other clinicians in a particular medical specialty, or a particular community, whether geographic or otherwise. In another example, a preferred system might provide a listing of correlations sorted by popularity, so that the most frequently accessed correlations appear near the top of the list. In any case a clinician could simply view the list, and check off which correlations he/she would like to have run for his/her practice, or practice community.

Abstract: Methods of aggregating and distributing medical records are presented. Patient data is aggregated from one or more medical providers where the patient data is incomplete with respect to a medical data exchange record (MDER). A data repository, preferrably a third party relative to the providers or a recipient of the data, stores the patient data. When a recipient requests the patient data and is properly authenticated, the patient data is presented to the recipient in standard MDER format. Preferrably, the recipient accesses the data via a limit session link.

Abstract: A hub-spoke patient information service in which a patient's data can be obtained from a network of independent data sources through a centralized service. The hub of the service stores patient identification information and the spokes store the patient's clinical data. In a preferred embodiment, the spokes are independent sources (e.g. each spoke is generally unrelated to other spokes) and include hospitals, doctor offices, clinics, insurance companies, or other entities that store or track clinical data. Preferably, the data sources use a first authentication to properly identify or authentic the service. Once access is granted, the spoke sources are queried for the patient's data. In response, each data source provides the type of patient data available from the source. The user is shown the types of data available without generally showing the actual data. The user can then select the specific patient data to retrieve from the spoke sources.