Abstract: A computer-implemented method, system, and computer program product monitors clinical research performance. One or more metrics of clinical research performance for investigator/provider/research sites across research studies are collected. The metrics include performance area, characteristic of the performance area with one or more attributes, point values for each attribute, and weight value for the characteristic. A performance score is produced for each of the entities based on the one or more metrics. A machine learning model is trained to determine performance scores based on the produced performance score for each of the entities. A request for entities is processed by applying performance scores from the machine learning model and appropriate corresponding data to a predictive model to determine resulting performance scores, rank and/or match for each of the one or more entities for a given protocol and/or assessment trigger.

Abstract: A computer-implemented method, system, and computer program product monitors clinical research performance. One or more metrics of clinical research performance for investigator/provider/research sites across research studies are collected. The metrics include performance area, characteristic of the performance area with one or more attributes, point values for each attribute, and weight value for the characteristic. A performance score is produced for each of the entities based on the one or more metrics. A machine learning model is trained to determine performance scores based on the produced performance score for each of the entities. A request for entities is processed by applying performance scores from the machine learning model and appropriate corresponding data to a predictive model to determine resulting performance scores, rank and/or match for each of the one or more entities for a given protocol and/or assessment trigger.

Abstract: A computer-implemented method, system, and computer program product monitors clinical research performance. One or more metrics of clinical research performance for investigator/provider/research sites across research studies are collected. The metrics include performance area, characteristic of the performance area with one or more attributes, point values for each attribute, and weight value for the characteristic. A performance score is produced for each of the entities based on the one or more metrics. A machine learning model is trained to determine performance scores based on the produced performance score for each of the entities. A request for entities is processed by applying performance scores from the machine learning model and appropriate corresponding data to a predictive model to determine resulting performance scores, rank and/or match for each of the one or more entities for a given protocol and/or assessment trigger.

Abstract: A computer-implemented method, system, and computer program product monitors clinical research performance. One or more metrics of clinical research performance for investigator/provider/research sites across research studies are collected. The metrics include performance area, characteristic of the performance area with one or more attributes, point values for each attribute, and weight value for the characteristic. A performance score is produced for each of the entities based on the one or more metrics. A machine learning model is trained to determine performance scores based on the produced performance score for each of the entities. A request for entities is processed by applying performance scores from the machine learning model and appropriate corresponding data to a predictive model to determine resulting performance scores, rank and/or match for each of the one or more entities for a given protocol and/or assessment trigger.