Big Data Strives for Individualized Coverage

Gina Battaglia
Published: Tuesday, Feb 21, 2017
Anne-Marie Meyer, PhD

Anne-Marie Meyer, PhD

Healthcare spending is one of the largest expenses for a company and, more and more, employers are using population-based Big Data to ensure their employees have coverage tailored to their needs at a reasonable cost. Employers are hiring experts in healthcare analytics to help obtain insurance policies and benefits that are aligned with historical claims data from their employees. These analytics companies are attempting to improve cancer coverage options via this method, but the complexity of this disease and its variable costs have so far proved to be a barrier.

“On the employee side, health plans are getting more complex,” said Aaron Huang, vice president of marketing at Lumity of San Mateo, CA, which does data analysis to develop a selection of ideal plan choices for employees. “There’s a whole variety of plans that employees have a hard time choosing from, and based on the fluctuating needs of an employer, the health plan recommendation could change from year to year.” There’s huge waste in cancer treatment, Huang says, and he believes Big Data will eventually help to streamline care and provide greater savings.

Other health data companies in this field include Benefits Science Technologies, Oscar, and Zenefits. Lumity serves as a broker for payers and does not charge employers. The company analyzes data from employees’ prescription claims to detect patterns in usage and estimate an employer’s optimal cost of healthcare coverage based on different coverage options. Much depends on the amount and quality of medical data available. For companies with 250 or more workers, insurance companies will provide access to employees’ medical claims data, which leads to a more precise picture of costs, Huang said. “With that level of information, you can actually do some pretty accurate forecasting of one-time and chronic conditions.” For smaller companies, such information is harder to come by.

Currently, Lumity’s ability to improve employer-specific coverage for cancer and other high-cost conditions is limited. Huang hopes that in the future Lumity and companies like it will be able to use data to identify patients with cancer and pair them with advocates who can help with navigation. Huang believes that unnecessary spending for chronic conditions such as cancer amounts to 20% to 30% of the medical tab, which can easily top $1 million for a patient. “That’s a lot of overbilling that could be preempted with the right type of advocates,” he said.

However, the complexity and high cost of cancer treatment makes it very difficult—even when armed with Big Data—to bring costs down, Huang said. “Cancer coverage is very much a case-by-case basis with carriers. With cancer, there are a lot of clinical trials that carriers will decide to cover on a one-off basis. Many of these things are more up to the insurance companies than they are up to the design of the bene ts programs themselves.” Huang said it would be possible to design a plan for a specific type of cancer, but it would raise the costs for everyone in the company. “There’s not an easy answer to providing better cancer coverage today. A lot of it comes down to how they can improve the clinical trial process and fast-track some of these experimental new treatments for people who have rare or hard-to-treat types of cancer.”

Direct costs of cancer screening and treatment, which often include advanced diagnostic testing, planned and unplanned hospital admissions, and multiple therapies that include specialty pharmaceuticals, can be difficult to predict because they are complex and vary considerably, agreed Anne-Marie Meyer, PhD, research assistant professor in cancer epidemiology at UNC-Chapel Hill, NC. In order to find better coverage options, companies such as Lumity would have to take into account the great variability among patients with cancer, she said. “Patients, especially very sick patients, are going through really intensive and diverse treatments, from surgery to chemotherapy to radia- tion. It’s a lot more complicated than other Big Data.”


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