Segmentation platform

A computerized method comprising:

• (a) receiving, by a intake server, a plurality of current data sets corresponding to a plurality of claims including input data received from a graphical input display generated for receiving data input, each of the plurality of current data sets including one or more of a field having a value indicative of diagnosis data, a field having a value indicative of recovery profile data, a field having a value indicative of complexity data, and a field indicative of recovery likelihood data;
• (b) interposing a segmentation platform between the intake server and a plurality of remote user-accessible computers;
• (c) storing, in one or more data storage devices, (1) historical data sets including a data table including at least a payment data field, a demographic data field, a medical code data field, an injury data field, and an employment data field, (2) current data sets, and (3) user data corresponding to a plurality of users, the user data including fields having values indicative of user identifier data, user workload data, user experience data, and user expertise data;
• (d) applying, by the model training component computer processor, a dimensionality reduction process to the historical data sets to reduce the dimensionality of the historical data sets to generate reduced historical data sets;
• (e) training, by the model training component computer processor based upon the reduced historical data sets, the predictive model to segment current claims;
• (f) storing, in a segment rules database, segment rules to divide the plurality of claims into a plurality of segment classifications, at least one segment rule configured to:
• segment the claim to a higher complexity claim segment when the data set is indicative of at least two of the following conditions:
• the value of the diagnosis data is true, the value of the recovery profile data is true, and a level of complexity value corresponding to the complexity data exceeds a pre-determined level;
• and
• segment the claim to a lower complexity claim segment when the data set is indicative of either:
• the value of the likelihood of recovery is below a first pre-determined threshold value or the value of the likelihood of recovery is above a second pre-determined threshold value;
• (g) receiving, by a triage processor, a current data set corresponding to a claim received by the intake server, the current data set including one or more fields having data indicative of values of at least one of the diagnosis data, the recovery profile data, the complexity data, and the recovery likelihood data;
• (h) accessing, by the triage processor, the segment rule database to retrieve the at least one segment rule;
• (i) segmenting, by the triage processor, based on application of the predictive model and the at least one segment rule to the current data set corresponding to the claim, the current data set to a claim segment;
• (j) dynamically updating user current workload data in the one or more data storage devices based upon continually received current workload data for each of the plurality of users;
• (k) linking, by the triage processor, a user identifier, corresponding to a user, to the claim based upon the assigned claim segment and the user data including the dynamically determined updated current workload data for each of the plurality of users;
• (l) responsive to linking of the user identifier to the claim, automatically routing, by the triage processor, an electronic file corresponding to the claim to the remote user-accessible computer associated with the linked user identifier via the communication network;
• (m) repeating, by the triage processor, steps (g)-(l) for additional current data sets and continuously adapting the predictive model by activating the model training component computer processor to update the predictive model with the current data set and results corresponding to the additional current data sets.