Abstract: Mechanisms are provided for determining values to associate with medical conditions of a patient. A patient assessment is received that comprises a natural language question and a corresponding answer, about a patient, provided in response to the question. Cognitive natural language processing is performed on the patient assessment to extract features from the natural language question and corresponding answer. The extracted features are evaluated within a context of a pre-existing electronic medical record of the patient. A value for a medical condition of the patient is determined based on results of the evaluation and stored in the electronic medical record for the patient.

Abstract: Mechanisms are provided for modifying a patient care plan or care provider workflow based on a patient risk assessment. The mechanisms analyze a patient medical record in a patient registry to identify at least one clinical measure for a corresponding patient and calculate a risk assessment value based on the at least one clinical measure value. The risk assessment value indicates a risk level for development of a medical condition or the occurrence of a medical event. The mechanisms select at least one of an action item or work flow to be performed to mitigate the risk level indicated by the risk assessment value based on the risk assessment value and a category of the risk assessment value. The mechanisms perform one or more operations for causing the action item to be performed or for performing the work flow.

Abstract: Mechanisms are provided for monitoring treatment of patients. The mechanisms generate a patient registry comprising a plurality of patient registry records, each patient registry record being associated with a corresponding patient and comprising attributes specifying personal and medical information about the corresponding patient. The mechanisms collect, for each patient registry record, treatment data regarding treatment of the corresponding patient. The mechanism analyzes the treatment data to identify successful treatments administered to the corresponding patients. The mechanisms generate one or more cohorts of corresponding patients based on attributes of the patient registry records associated with corresponding patients for which successful treatments were administered. The mechanisms perform at least one patient management operation based on the generated one or more cohorts.

Abstract: Mechanisms are provided for determining values to associate with medical conditions of a patient. A patient assessment is received that comprises a natural language question and a corresponding answer, about a patient, provided in response to the question. Cognitive natural language processing is performed on the patient assessment to extract features from the natural language question and corresponding answer. The extracted features are evaluated within a context of a pre-existing electronic medical record of the patient. A value for a medical condition of the patient is determined based on results of the evaluation and stored in the electronic medical record for the patient.

Abstract: Mechanisms are provided for determining values to associate with medical conditions of a patient. A patient assessment is received that comprises a natural language question and a corresponding answer, about a patient, provided in response to the question. Cognitive natural language processing is performed on the patient assessment to extract features from the natural language question and corresponding answer. The extracted features are evaluated within a context of a pre-existing electronic medical record of the patient. A value for a medical condition of the patient is determined based on results of the evaluation and stored in the electronic medical record for the patient.

Abstract: Mechanisms are provided for determining the meaning of medical values in natural language content. The mechanisms receive patient information from a source which includes at least one of text or a numerical value indicative of a medical value corresponding to a patient. The mechanisms perform cognitive natural language processing on a context of the text or numerical value in the patient information and determine a meaning of the medical value based on results of the cognitive natural language processing. The mechanisms then process the patient information from the source based on the determined meaning of the medical value.

Abstract: Mechanisms are provided for caching patient information in a variable list and using the variable list to evaluate patient rules. The mechanisms generate a patient registry comprising a plurality of patient registry records, each patient registry record being associated with a corresponding patient and comprising personal and medical information about the corresponding patient. The mechanisms compile at least one variable list for an input variable of one or more clinical rules. The variable list comprises a plurality of instances of personal and medical information from the plurality of sources that are associated with the variable. The mechanisms also process at least one clinical rule in the one or more clinical rules that references the variable as a criteria for evaluating the at least one clinical rule, based on the variable list associated with the variable to determine if the criteria is satisfied by one or more instances.

Abstract: Mechanisms are provided for communicating with a patient. The mechanisms generate a patient registry comprising a plurality of patient registry records, each patient registry record being associated with a corresponding patient and comprising personal and medical information about the corresponding patient. The mechanisms collect, for each patient registry record, first data regarding treatment of the corresponding patient and second data regarding communications made with the corresponding patient. The mechanisms determine, based on analysis of the first data and second data associated with each of the patient registry records, a mode of communication that is most likely to result in a successful treatment of patients. The mechanisms initiate a communication with a patient of interest using the determined mode of communication based on the results of the determination.

Abstract: A medical campaign method, computer program product, and system are provided. A patient registry record in a patient registry is analyzed to identify at least one trend of a clinical measure for a corresponding patient and correlate the at least one trend with one or more medical campaigns directed to improving the clinical measure. A medical campaign is selected from the one or more medical campaigns for application to the patient. The medical campaign is executed to apply the medical campaign to the patient. Executing the medical campaign involves sending a communication to a communication device associated with the patient.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a clinical decision support system. A priority calculation component executing within the clinical decision support system receives weighted priority configuration for a health care facility and a set of patients with care gaps. The priority calculation component assigns a priority value to each given patient in the set of patients based on a risk score of at least one condition for the given patient, a number of days out of compliance for the at least one condition for the given patient, and an organizational goal factor associated with the at least one condition for the health care organization. The priority calculation component generates a prioritized communication sequence for the set of patients.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory. The at least one memory comprises instructions executed by the at least one processor to cause the at least one processor to implement a clinical decision support system. The clinical decision support system receives a set of input data about a plurality of patients. The clinical decision support system identifies a target patient within the plurality of patients seeking guidance for a surgical procedure that has been recommended by a physician. A cluster analysis component executing within the clinical decision support system determines a cluster of patients within the plurality of patents that are similar to the target patient based on the set of input data. The cluster analysis component groups the cluster of patients into a plurality of sub-clusters of patients each being associated with a different level of complications.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory. The at least one memory comprises instructions executed by the at least one processor to cause the at least one processor to implement a clinical decision support system. The clinical decision support system receives a set of input data about a plurality of patients. The clinical decision support system identifies a target patient within the plurality of patients seeking guidance for a surgical procedure that has been recommended by a physician. A cluster analysis component executing within the clinical decision support system determines a cluster of patients within the plurality of patents that are similar to the target patient based on the set of input data. The cluster analysis component groups the cluster of patients into a plurality of sub-clusters of patients each being associated with a different level of complications.

Abstract: A mechanism is provided in a data processing system comprising least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a clinical decision support system. A priority calculation component executing within the clinical decision support system receives weighted priority configuration for a health care facility and a set of patients with care gaps. The priority calculation component assigns a priority value to each given patient in the set of patients based on a risk score of at least one condition for the given patient, a number of days out of compliance for the at least one condition for the given patient, and an organizational goal factor associated with the at least one condition for the health care organization. The priority calculation component generates a prioritized communication sequence for the set of patients.

Abstract: Mechanisms are provided for modifying a patient care plan or care provider workflow based on a patient risk assessment. The mechanisms analyze a patient medical record in a patient registry to identify at least one clinical measure for a corresponding patient and calculate a risk assessment value based on the at least one clinical measure value. The risk assessment value indicates a risk level for development of a medical condition or the occurrence of a medical event. The mechanisms select at least one of an action item or work flow to be performed to mitigate the risk level indicated by the risk assessment value based on the risk assessment value and a category of the risk assessment value. The mechanisms perform one or more operations for causing the action item to be performed or for performing the work flow.

Abstract: Mechanisms are provided for modifying a patient care plan or care provider workflow based on a patient risk assessment. The mechanisms analyze a patient medical record in a patient registry to identify at least one clinical measure for a corresponding patient and calculate a risk assessment value based on the at least one clinical measure value. The risk assessment value indicates a risk level for development of a medical condition or the occurrence of a medical event. The mechanisms select at least one of an action item or work flow to be performed to mitigate the risk level indicated by the risk assessment value based on the risk assessment value and a category of the risk assessment value. The mechanisms perform one or more operations for causing the action item to be performed or for performing the work flow.

Abstract: Mechanisms are provided for stratifying risk of a patient population. The mechanisms receive patient information for a plurality of patients in the patient population and perform a machine learning operation to train a risk scoring algorithm for scoring a risk of adverse conditions for the patient population using the patient information. The mechanisms determine, for each patient in the patient population, a risk score based on an application of the risk scoring algorithm to patient information for the patient. The mechanisms classify each patient into a risk classification category, in a plurality of risk classifications categories, based on a risk score generated by the application of the risk scoring algorithm to the patient information for the patient. The mechanisms generate an output indicating membership of patients in the plurality of risk classification categories.

Abstract: Mechanisms are provided for mapping local medical codes to standardized medical codes. Patient information is received from a source. The patient information comprises at least one local medical code that is local to the source and is not standardized across multiple sources of patient information. Cognitive natural language processing is performed on a context of the at least one local medical code to determine a meaning of the at least one local medical code. A standardized medical code is selected based on the determined meaning and a mapping between the at least one local medical code and the selected standardized medical code is generated. The standardized medical codes are common to a plurality of sources of patient information. The patient information is then processed based on the mapping data structure.

Abstract: A medical campaign method, computer program product, and system are provided. A patient registry record in a patient registry is analyzed to identify at least one trend of a clinical measure for a corresponding patient and correlate the at least one trend with one or more medical campaigns directed to improving the clinical measure. A medical campaign is selected from the one or more medical campaigns for application to the patient. The medical campaign is executed to apply the medical campaign to the patient. Executing the medical campaign involves sending a communication to a communication device associated with the patient.

Abstract: Mechanisms are provided for determining values to associate with medical conditions of a patient. A patient assessment is received that comprises a natural language question and a corresponding answer, about a patient, provided in response to the question. Cognitive natural language processing is performed on the patient assessment to extract features from the natural language question and corresponding answer. The extracted features are evaluated within a context of a pre-existing electronic medical record of the patient. A value for a medical condition of the patient is determined based on results of the evaluation and stored in the electronic medical record for the patient.

Abstract: Mechanisms are provided for monitoring treatment of patients. The mechanisms generate a patient registry comprising a plurality of patient registry records, each patient registry record being associated with a corresponding patient and comprising attributes specifying personal and medical information about the corresponding patient. The mechanisms collect, for each patient registry record, treatment data regarding treatment of the corresponding patient. The mechanism analyzes the treatment data to identify successful treatments administered to the corresponding patients. The mechanisms generate one or more cohorts of corresponding patients based on attributes of the patient registry records associated with corresponding patients for which successful treatments were administered. The mechanisms perform at least one patient management operation based on the generated one or more cohorts.