Abstract: By extracting clinical data of any format from respective different sources, a data repository normalized to a generic format is created. A medical domain specific language may be used to interact with the data repository for identifying cohorts and gaps in care for the respective cohorts. Any rules for finding gaps in care are converted into the medical domain specific language for determining gaps. This standardization in both the data repository and rule application may allow for a true cost and time to value solution accessible to many different medical practices.

Abstract: By extracting clinical data of any format from respective different sources, a data repository normalized to a generic format is created. A medical domain specific language may be used to interact with the data repository for identifying cohorts and gaps in care for the respective cohorts. Any rules for finding gaps in care are converted into the medical domain specific language for determining gaps. This standardization in both the data repository and rule application may allow for a true cost and time to value solution accessible to many different medical practices.

Abstract: By extracting clinical data of any format from respective different sources, a data repository normalized to a generic format is created. A medical domain specific language may be used to interact with the data repository for identifying cohorts and gaps in care for the respective cohorts. Any rules for finding gaps in care are converted into the medical domain specific language for determining gaps. This standardization in both the data repository and rule application may allow for a true cost and time to value solution accessible to many different medical practices.

Abstract: A computer-implemented system, methods and computer storage media are provided for creating extensible data structures for rule based systems to improve complex event processing. An underlying individual data model is accessed and the content is translated to create singleton values to be utilized in complex event processing to improve the execution fo the complex event processing.

Abstract: By extracting clinical data of any format from respective different sources, a data repository normalized to a generic format is created. A medical domain specific language may be used to interact with the data repository for identifying cohorts and gaps in care for the respective cohorts. Any rules for finding gaps in care are converted into the medical domain specific language for determining gaps. This standardization in both the data repository and rule application may allow for a true cost and time to value solution accessible to many different medical practices.

Abstract: By extracting clinical data of any format from respective different sources, a data repository normalized to a generic format is created. A medical domain specific language may be used to interact with the data repository for identifying cohorts and gaps in care for the respective cohorts. Any rules for finding gaps in care are converted into the medical domain specific language for determining gaps. This standardization in both the data repository and rule application may allow for a true cost and time to value solution accessible to many different medical practices.

Abstract: Adaptive medical data collection for medical entities may involve triggering an analysis of electronic records in response to information input into an Electronic Medical Record (EMR) of a patient. Determining a potential condition for the patient based on the analysis. Identifying additional information indicated as relevant to the potential condition of the patient, and generating a request for the identified additional information.

Abstract: Adaptive medical data collection for medical entities may involve triggering an analysis of electronic records in response to information input into an Electronic Medical Record (EMR) of a patient. Determining a potential condition for the patient based on the analysis. Identifying additional information indicated as relevant to the potential condition of the patient, and generating a request for the identified additional information.

Abstract: Workflows for medical entities are determined and evaluated by determining a plurality of medical tasks based on an analysis of a plurality of electronic medical records of a medical entity. A workflow of the medical entity is determined based on a sequence of medical tasks, the sequence determined based on the analysis of the plurality of electronic medical records, and an evaluation of the workflow is performed based on a predefined criterion.

Abstract: By extracting clinical data of any format from respective different sources, a data repository normalized to a generic format is created. A medical domain specific language may be used to interact with the data repository for identifying cohorts and gaps in care for the respective cohorts. Any rules for finding gaps in care are converted into the medical domain specific language for determining gaps. This standardization in both the data repository and rule application may allow for a true cost and time to value solution accessible to many different medical practices.

Abstract: Workflows for medical entities are determined and evaluated by determining a plurality of medical tasks based on an analysis of a plurality of electronic medical records of a medical entity. A workflow of the medical entity is determined based on a sequence of medical tasks, the sequence determined based on the analysis of the plurality of electronic medical records, and an evaluation of the workflow is performed based on a predefined criterion.

Abstract: Inclusion of a patient in a medical category is determined by triggering an analysis of an electronic medical record of the patient in response to an input of data into the electronic medical record. Identifying characteristics that indicate inclusion in the medical category with the analysis, and determining a probability the patient belongs to the medical category based on the identified characteristics.

Abstract: Adaptive medical data collection for medical entities may involve triggering an analysis of electronic records in response to information input into an Electronic Medical Record (EMR) of a patient. Determining a potential condition for the patient based on the analysis. Identifying additional information indicated as relevant to the potential condition of the patient, and generating a request for the identified additional information.

Abstract: A predictive model of medical knowledge is trained from patient data of multiple different medical centers. The predictive model is machine learnt from routine patient data from multiple medical centers. Distributed learning avoids transfer of the patient data from any of the medical centers. Each medical center trains the predictive model from the local patient data. The learned statistics, and not patient data, are transmitted to a central server. The central server reconciles the statistics and proposes new statistics to each of the local medical centers. In an iterative approach, the predictive model is developed without transfer of patient data but with statistics responsive to patient data available from multiple medical centers. To assure comfort with the process, the transmitted statistics may be in a human readable format.