Abstract: An apparatus for automatically mapping a verbatim narrative to a term in a medical terminology dictionary includes a natural language processor and a comparator. The natural language processor processes terms from the medical terminology dictionary and from a medical coding decision database to generate a processed database that also includes the original terms from the medical terminology dictionary and the medical coding decision database. The natural language processor also processes the verbatim narrative. The comparator compares the processed verbatim narrative to the terms in the processed database and determines whether the processed verbatim narrative is an exact match to a term in the processed database. The verbatim narrative is mapped to the term in the medical terminology dictionary that corresponds to the term in the processed database that is an exact match. The verbatim narratives may include adverse event narratives, concomitant medication narratives, or other types of narratives.

Abstract: An apparatus for automatically mapping a verbatim narrative to a term in a medical terminology dictionary includes a natural language processor and a comparator. The natural language processor processes terms from the medical terminology dictionary and from a medical coding decision database to generate a processed database that also includes the original terms from the medical terminology dictionary and the medical coding decision database. The natural language processor also processes the verbatim narrative. The comparator compares the processed verbatim narrative to the terms in the processed database and determines whether the processed verbatim narrative is an exact match to a term in the processed database. The verbatim narrative is mapped to the term in the medical terminology dictionary that corresponds to the term in the processed database that is an exact match. The verbatim narratives may include adverse event narratives, concomitant medication narratives, or other types of narratives.

Abstract: An improved system for tracking the progress of a clinical study includes a classifier generator, a classifier application subsystem, a study stage annotation subsystem, a progress status models generator, an aggregation module, and a progress status evaluation subsystem.

Abstract: A system for assessing a clinical trial site's subject event reporting rate includes a subject event and visit count processor and a site subject event rate processor. The subject event and visit count processor receives subject event and subject visit data from a plurality of clinical trial sites and calculates for each clinical trial site a total visit count and a total subject event count. The site subject event rate processor receives the total visit count and total subject event count for each clinical trial site, calculates a trial-level subject event rate for the clinical trial, calculates for each clinical trial site an expected total subject event count based on the clinical trial site's total visit count and the trial-level subject event rate, and compares for each clinical trial site the expected total subject event count to the total subject event count to assess the probability that the clinical trial site is under-reporting or over-reporting subject events.

Abstract: A method for monitoring clinical trial progress includes calculating progress curves for clinical trial states. Calculating a progress curve includes assigning values to events for a datapoint in the clinical trial, generating or building pairs of values for each consecutive sequence of the events, summing up the values of pairs of events corresponding to a state change, and determining a state for the datapoint based on the sum of the values. Monitoring clinical trial progress then includes calculating a second progress curve for another clinical trial state and comparing the delay between points of the progress curves. A system for monitoring clinical trial progress is also described.

Abstract: A method for remote site monitoring includes receiving data from a clinical data site, calculating a site-metric risk indicator for each metric, and aggregating the site-metric risk indicators to generate a site-level data quality score. The site-metric risk indicator is calculated using a metric risk profile, which is based on historic clinical study data and study data received from clinical data sites. In some embodiments, each metric risk profile is based on experience levels of the sites. An apparatus for remote site monitoring is also described.

Abstract: A method for remote site monitoring includes receiving data from a data site, converting those data into a site-level quality score using a metric risk profile, and calculating a risk indicator based on the site-level quality score. The metric risk profile may be based on historic data and study data, where the study data is received from a plurality of data sites. In some embodiments, converting the data includes normalizing a value of a metric by applying to it the metric risk profile and aggregating the normalized metric values to calculate the site-level quality score. An apparatus for remote site monitoring is also described.