After you are able to find the right model that best learns patterns in your data to predict which members will be high cost, DataRobot makes it easy to deploy the model into your desired decision environment. Decision environments are the ways in which the predictions generated by the model will be consumed by the appropriate stakeholders in your organization, and how these stakeholders will make decisions using the predictions to impact the overall process.
Automation | Augmentation | Blend
In this use case, we are utilizing DataRobots Automated Machine Learning (AutoML) to identify at-risk claimants. This solution can be fully automated, or augmented with a human in loop if desired.
There are a number of ways to implement this model. Because of the low latency of the predictions, performing a drag-and-drop prediction may work for non-technical users. However, this model is often deployed by creating a RESTful API endpoint; in this case, the model is accessed from a BI workstream and the results materialized in a dashboard or application.
Population Health Professionals, Underwriters
Population Health Underwriters
Data Scientists and Engineers
Population health experts can identify at-risk patients and reach out to members to make them aware of different services and programs available to them to get services earlier and reduce costs. Additionally, this information can be used by underwriters to price renewals appropriately.
Models should be tracked by evaluating drift and population stability index (PSI). If the model was trained on the same group that the scoring data comes from, then drift is often stable. Problems can arise when the training data is derived from a broader swath of the population that does not include the group being scored. It is also important to keep track of which at-risk patients intervention was performed on as this data will be useful for future modeling. For example, if this program runs for a year then there will be a year’s worth of data available for predicting the likelihood of an intervention resulting in a reduction in costs; this data can be used to test and refine different intervention strategies.
If, for some reason, the scoring group’s data cannot be used to train the model then a good strategy is to perform a stratified sample of the broader group so that the distributions of the predictor variables closely match the scoring data.
From a deployment perspective, the risks are relatively low as the API deployment allows for flexibility to integrate with a wide range of BI environments and applications. Additionally, if manual batch scoring has to be used this is often a viable option that can provide value while an automated solution can be developed.