Predictive analytics using AI and ML

Although real-time data analytics are useful to identify opioid usage trends, the ability to predict risk and fraud based on historical data is key to tackling the crisis proactively. As an example, predicting opioid prescription rates by providers or predicting an average beneficiary risk score based on historical trends and current attributes would be helpful for federal and state agencies to identify focus areas and preventive measures. CMS publishes datasets for these types of analyses and predictions.

AWS artificial intelligence (AI) services include capabilities to enable fraud detection and risk analysis.

Amazon SageMaker

Amazon SageMaker is a fully managed service that provides developers and data scientists with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models. This is in contrast to the Traditional ML development which is a complex, expensive, iterative process and made even harder as there are no integrated tools for the entire machine learning workflow. SageMaker makes it easy to deploy your trained model into production with a single click so that you can start generating predictions for real-time or batch data.

Amazon Forecast

Amazon Forecast is a fully managed service that uses machine learning to deliver highly accurate forecasts. Based on the same technology used at Amazon.com, Amazon Forecast uses machine learning to combine time series data with additional variables to build forecasts. Amazon Forecast requires no machine learning experience to get started. You only need to provide historical data, plus any additional data that you believe may impact your forecasts. For example, Amazon Forecast can be used to forecast trends on budgets and opioid campaign management.