Mechanisms are provided for monitoring quality and correctness of content in communications handled by a vendor. The mechanisms sample a set of communications handled by the vendor to generate a sample set of communications and extract content from the sample set of communications. The mechanisms compare the extracted content with expected content of communications handled by the vendor and analyze the extracted content and the expected content to thereby identify differences between the extracted content and the expected content based on results of the analysis. In addition, the mechanisms determine a level of significance of the differences and generate a notification of whether or not to modify the communications, or an operation of the vendor, based on the determined level of significance of differences.

Evaluating Vendor Communications for Accuracy and Quality

Mechanisms are provide for modifying the selection of modes of communication for communicating with patients based on a simulation of vendor communications. A communication simulation is configured with configuration parameters defining expected results of a plurality of communications with patients handled by a communication vendor system associated with a personalized care plan creation and monitoring (PCPCM) system. The plurality of communications are simulated to generate simulated results and a response of the PCPCM system to the simulated results of the plurality of communications is determined. The response of the PCPCM system is evaluated to determine if the PCPCM system responds correctly to the simulated results, and an operation of the PCPCM system is modified based on results of the evaluation.

Modifying Patient Communications Based on Simulation of Vendor Communications

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.

Multi-modal communication with patients based on historical analysis

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.

Interpreting the meaning of clinical values in electronic medical records

In one embodiment, computer implemented method identifies a risk of developing a condition for a particular patient. First, an initial variable set is developed by utilizing one or more patient databases. Second, an enhanced model predictive of a selected condition is created using machine learning. With the enhanced model developed, patient features vectors are created from a patient health information database for the initial variable set. The enhanced model is applied to these patient feature vectors to predict development of the condition. Patients predicted to have the condition can be enrolled in an appropriate intervention program.

Patient condition identification and treatment

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.

Patient Risk Assessment Based on Machine Learning of Health Risks of Patient Population

Mechanisms are provided for performing sequential multi-modal communication with 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 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 sequence of modes of communication that is most likely to result in a successful treatment of patients. The mechanisms initiate a plurality of communications with a patient of interest using the determined sequence of modes of communication based on the results of the determination. The plurality of communications follow the sequence specified in the sequence of modes of communication.

Personalized Sequential Multi-Modal Patient Communication Based on Historical Analysis of Patient Information

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.

Evaluating Risk of a Patient Based on a Patient Registry and Performing Mitigating Actions Based on Risk

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.

Driving patient campaign based on trend patterns in patient registry information

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