Improving Your Employees’ Access to Medical Care Using Data Analytics

hospital waiting room

A fast food restaurant chain operating in 24 states in the Southeast and Midwest was struggling with medical cost inflation. Their year-to-year trend had actually outpaced market rates. This caught the CFO’s attention.

Subsequently, they recognized that their population was largely rural, as their restaurants tended to be located on interstate highways. Typical of their industry, they had hourly employees who often took a job with the chain to “get medical benefits.”

They had recently transitioned to a data analytics platform, which pinpointed a few key findings that supported a bold new health outcomes strategy. Enter Big Data.

What Their Data Told Them

A review of utilization patterns revealed an excess rate of emergency room visits and a low rate of physician office visits compared with normative values. Their population’s emergency room visit rate was nearly twice the national average, whereas office visits for primary care were less than half of the predicted norm.

Utilization of local urgent care centers was particularly costly for them. The high rates of ER visits (and low rates of physician office visits) were even more skewed for conditions such as back and neck pain, upper respiratory infections and dermatologic disorders.

What Actions They Took

Recognizing that the restaurants were in largely rural areas, the covered population was not sophisticated in accessing health care, and their medical plan design had a below-market copay for ER visit, the restaurant chain made some key decisions.

  1. First, they agreed to increase the copay for ER visits.
  2. Second, they worked with their carrier to communicate a “When to See Your Doctor,” and “When to go to the E.R,” campaign.
  3. Third, as a direct result of the health data analysis, the restaurant chain was able to make the case to hire a third-party telemedicine vendor to improve access to routine primary care for their member population.

Going forward, the restaurant chain tracked overall reduction in emergency room use, emergency room costs and significant utilization of the new telemedicine capabilities. The end goal was to improve how their employees and their families were using health care, while reducing overall costs.


With big data health analytics, employer plan sponsors can specifically understand key utilization patterns that point to focused solutions, such as:

  • What percentage of diabetics were hospitalized for their condition
  • How many children with asthma had an ER visit in the last year
  • The proportion of members with back pain who have had a CT or MRI for their condition
  • What percentage of members over 50 years of age have not had a primary care visit in the last two years
  • How ER visits resulting in hospitalization have trended over the last three years
  • How behavioral health visit rates compare to expected normative values.

There is an explosion of solutions in the health and benefits marketplace that impact how covered members access care. The traditional methods of provider network narrowing and copay design are matched by newer strategies, including onsite health clinics, telemedicine, cost transparency solutions, medical quality transparency capabilities, and telephonic patient advocacy services.

Add to that the rise of delivery system innovations such as retail medical clinics, the proliferation of urgent care centers, and the growth of accountable care organizations (ACOs), and it’s obvious that employers today have an unprecedented number of options to influence how their members access the care that they need.

And it all starts with Big Data. The new health data analytics is paving the way for employers to do what’s right for the employees they provide medical coverage for, as well as their bottom line: efficiently and effectively improve how their covered members access care.

About Ron Leopold

Ronald S. Leopold, MD, MBA, MPH, is National Practice Leader, Health Outcomes at Willis' Human Capital Practice. He…
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