How UnitedHealth Group Is Infusing Deep Learning Into Healthcare Services
In a massive healthcare organization, even a small improvement in workflow can translate to major gains in efficiency. That means lower costs for the healthcare provider and better, faster care for patients.
UnitedHealth Group, one of the largest healthcare companies in the U.S., is turning to GPU-powered AI for these kinds of enhancements. In a talk at the GPU Technology Conference last month, two of the organization’s AI developers shared how it’s adopting deep learning for a variety of applications — from prior authorization of medical procedures to directing phone calls.
“The datasets required to solve these problems are enormous,” said Dima Rekesh, senior distinguished engineer at Optum, the health services platform of UnitedHealth Group. “Deep learning is uniquely suited to solve some of these hard problems through its ability to parse large amounts of data.”
The key challenge for an AI to be usable is getting error rates low enough, Rekesh said. “When you develop a model, you need to cross a threshold of accuracy to the point where you can trust it — to the point where it’s a pleasant experience for someone, whether it’s a call center representative or a medical professional looking at a model’s predictions.”
Deep learning models can meet that high bar, he says.
“AI solutions actually impact not just the operational costs for our company, but also patient services,” said Julie Zhu, chief data scientist and distinguished engineer at Optum. “We could make decisions much earlier, with more accurate treatment recommendations and earlier detection of disease.”
Optum is using a number of NVIDIA GPUs, including a cluster of V100 GPUs and the NVIDIA DGX-1, to power its deep learning work.
This Procedure Is AI Approved
Healthcare providers often need prior authorization, or advance approval from a patient’s insurance plan, before moving forward with a procedure or filling out a prescription. Manually approving procedures currently costs Optum hundreds of labor hours and millions of dollars a year.
In addition to checking whether or not a patient’s insurance plan covers a treatment, the healthcare provider must gather information from several sources to confirm that it’s necessary for a given patient to have a procedure or take a particular medication. With deep learning models, much of this decision-making could eventually be done automatically.
Zhu and her colleagues are developing neural networks that can conduct prior authorization in real time. The AI is currently in production and is being benchmarked against the manual process.
The team found its deep learning model outperforms the traditional machine learning model by a significant margin against a high volume of cases.
“When you have a million cases per year, the impact is really big,” Zhu said. UnitedHealth Group serves 126 million individuals and 80 percent of U.S. hospitals. “Even a small percentage improvement in accuracy will have a huge impact.”
Deep Learning on the Other End of the Line
More than a million people dial UnitedHealth Group each day. As with any large organization, callers are greeted by an automatic voice response system — a phone tree interface with prompts like “Press 1 to reach the emergency department” or “Press 6 for radiology.”
This process can be streamlined with deep learning.
By implementing AI in its call system, UnitedHealth Group can use natural language processing models to understand what callers are looking for and answer automatically, or route them to the right department or service representative.
Rekesh is working on developing neural networks that can accomplish these tasks, with the goals of reducing call length and connecting patients and customers to answers more quickly. To do so, he’s using OpenSeq2Seq, an open-source toolkit for NLP and speech recognition developed by NVIDIA researchers.
“In NLP, deep learning is the only option,” he said. “Other solutions just aren’t accurate enough.”
Deep learning models can also be used to streamline the process of authenticating patients’ identities on the call. For customer representatives, an AI-powered interface can help them during the call by pulling up the patient’s records or providing recommendations on the agent’s computer screens.
Optum plans to deploy some of these deep learning models later this year. The organization is also working on neural network tools for multi-disease prediction and medical claim fraud detection.