Real-time Analytics
The company wanted to predict seasonal fluctuations of illnesses to reduce patient wait times and better balance the workloads of its medical professionals.
Reported impact
The telehealth provider has cut patient wait times by more than 50 percent and significantly reduced the need for provider incentives during patient demand surges by using Azure Machine Learning and AIDAN forecasting.
MDLIVE for Cigna built its Microsoft solution to improve analytics and decision-support workflows. The company wanted to predict seasonal fluctuations of illnesses to reduce patient wait times and better balance the workloads of its medical professionals. Key components included Azure Machine Learning ran model inference or heavy processing, and Azure DevOps coordinated workflows, prompts, or deployment. The company also used Azure DevOps to automate and iterate rapidly, enhancing teamwork. The governed Microsoft stack gave MDLIVE for Cigna a reusable foundation for ai and machine learning, devops while keeping deployment, security, and day-to-day operations inside the Microsoft estate. The telehealth provider has cut patient wait times by more than 50 percent and significantly reduced the need for provider incentives during patient demand surges by using Azure Machine Learning and AIDAN forecasting.
"In 2022, we saw about 25 percent higher patient volume than in 2021," says Bergquist.
It has reduced patient wait times by more than 50 percent even as usage has grown each year.
It takes 90 to 120 days for MDLIVE for Cigna to verify the credentials of its providers, so it must have a way to predict future demand if it is to meet patient needs.
Telehealth provider MDLIVE for Cigna serves 62 million people in 50 US states and 10 territories.
The company wanted to predict seasonal fluctuations of illnesses to reduce patient wait times and better balance the workloads of its medical professionals.
The telehealth provider has cut patient wait times by more than 50 percent and significantly reduced the need for provider incentives during patient demand surges by using Azure Machine Learning and AIDAN forecasting.
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