US 11,657,922 B1
Artificial intelligence system for modeling drug trends
Christopher G. Lehmuth, St. Louis, MO (US); and Alexi E. Makarkin, Ballwin, MO (US)
Assigned to Express Scripts Strategic Development, Inc., St. Louis, MO (US)
Filed by Express Scripts Strategic Development, Inc., St. Louis, MO (US)
Filed on Jul. 15, 2019, as Appl. No. 16/511,833.
Int. Cl. G16H 70/40 (2018.01); G16H 50/20 (2018.01)
CPC G16H 70/40 (2018.01) [G16H 50/20 (2018.01)] 21 Claims
OG exemplary drawing
 
1. A method comprising:
receiving historical data collected from a client of a health plan, wherein the client includes a plurality of members, and wherein the historical data includes per member per month costs for the client of the health plan and demographic information for the plurality of members of the health plan;
identifying N number of therapeutic classes for the client based on the per member per month costs and the demographic information;
segmenting the historical data into a respective data set for each therapeutic class, wherein the respective data set for the respective therapeutic class includes per member per month data corresponding to the respective therapeutic class;
for each therapeutic class of the N number of therapeutic classes:
determining a pattern for the per member per month data corresponding to the respective therapeutic class;
generating a respective predictive model based on the pattern, wherein the respective predictive model is configured to generate, as output for the respective therapeutic class, a drug spending trend prediction and a per member per month spending prediction for an input period of future time; and
training a neural network of the respective predictive model using a two-stage training process, wherein a first stage of the two-stage training process trains the respective predictive model with the historical data collected from the client of the health plan, wherein a second stage of the two-stage training process trains the respective predictive model with drug market event data, and wherein the training of the neural network includes:
obtaining the drug market event data and historical market data characterizing an impact of the historical drug market event data on drug spending trends and per member per month spending;
determining a predicted impact for the obtained drug market event data based on the historical market data;
generating training data for the second stage of the two-stage training process that includes the obtained drug market event data associated with the predicted impact; and
training the respective predictive model during the second stage of the two-stage training using the training data generated for the second stage training process;
generating predictions for the N number of therapeutic classes by utilizing the predictive models of each therapeutic class; and
providing the predictions via a web portal in at least one of a displayable graphical format and a downloadable data structure.