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Benefits experts agree that using data analytics is an important part of their strategies. In fact, our recent research study with Employee Benefit News/Arizent found that over 80% of benefits leaders are using top sources of non-traditional data alongside their medical and pharmacy data. Benefits data has the potential to unlock insights into population health, tackle inefficient spending, and improve the point solution programs being offered to employees and their families.
But is benefits analytics a win-win for both employers and the members on their plans? Artemis works with employers, advisors, and health plans, and they all share stories about how their analytics efforts have directly helped employees. We want to showcase some ways that benefits data wins trickle down to employees and their families.
Jeff, 50, is a long-haul truck driver with a large national logistics company, and his work takes him all over the country. He likes that his work allows him to travel, but he gets tired of eating mostly in truck stops and fast food restaurants. He doesn’t get as much exercise as he likes because of his long hours on the road.
He began noticing that he was unusually thirsty, and because his dad had Type II Diabetes, he knew this might be an early sign. Using his employer-sponsored plan, he got checked out and was distressed to learn he had also developed Type II Diabetes. He couldn’t stop thinking about the inconvenience of testing his blood sugar, keeping his supplies in the truck, injecting himself with insulin, and trying to maintain a normal eating schedule while still doing his job.
Jeff’s employer had a special program to help diabetic patients manage their care, and he got an email inviting him to use it. He set up a call with a coach, who helped him learn to use the testing equipment, get on the right balance of medication, and manage his diabetes. The coach helped him identify healthier foods and snacks he could find at gas stations and keep on hand in his truck. Jeff even learned a quick, no equipment exercise routine he could do while he is on the road, and he managed to drop 15 pounds in the last year.
The program Jeff uses was added by his employer after they found that many Type II diabetics in their population were not complying with their medications and other treatment best practices. They used their benefits data to justify the program, and they are tracking the success of patients like Jeff to ensure the program is delivering value.
Marianna, 34, works as a finance director at a software company. She’s very busy at work and at home, with two children under the age of 5. She often finds herself laying awake at night, unable to fall asleep because she’s thinking about everything she has to do the next day. During the day, she’s easily distracted, and she just isn’t as productive as she used to be. She feels like she’s drowning in stress, and she can’t see any way to make a change.
Her friend suggests she try finding a therapist, so she takes a look at in-network doctors on her employer’s plan. She calls a few, and most say they aren’t accepting new patients. When she finally finds a practice that will take her, they set her first appointment for four months out. She feels hopeless.
Marianna’s company sends out an annual health risk assessment survey, and she shares her struggles with stress and anxiety in the comments. The company’s benefit leaders see many comments like hers, and they decide to look into their data. They find that stress, anxiety, depression, and other mood disorders are among their top 5 most common diagnoses. By looking at data beyond medical/rx claims, they find that employees with these conditions are 12 times more likely to miss work than their peers. They decide to implement a new mental health wellness program, expand their network of therapists, and offer a digital therapy program.
Marianna decides to try the digital therapy app, as it suits her busy lifestyle. She finds relief in sharing her experiences, and begins taking medication for anxiety. She learns some breathing exercises and meditation techniques to help her sleep, and she begins to feel like her old self again.
Emma, 43, works for a manufacturing company at their corporate headquarters. Her son Carson is 14, and he’s a competitive ski racer. She’s incredibly proud of his success on the junior racing circuit, but she worries about what will happen if he has a serious crash. And then it happens. He catches an edge at a race and tumbles head over feet down the steepest section of the course. He has broken his tibia in a spiral fracture and will require surgery.
Carson’s surgery goes smoothly, and he’s ready to begin physical therapy. However, he’s a busy high schooler. In addition to his ski racing, he’s in the jazz band and he works after school at the city library. Between his activities and his parents’ busy jobs, they don’t know how they’re going to fit in PT.
Emma mentions this to a coworker, who tells her that they have an online physical therapy benefit. Their benefits team implemented the new program because many employees live in rural areas where they might have to drive an hour to get to a physical therapy facility. She looks into it and it seems like it will work well for Carson. He can download an app on his phone and do live exercises and stretches with his physical therapist. It fits their family’s busy schedule, and it provides the care Carson needs to get back to his ski racing.
All these stories have one thing in common: a benefits team that puts a priority on data-driven decisions. One focused on gaps in care compliance, another on how employee health affected absenteeism, and another on the demographics that affected their population. They used data to find opportunities, roll out new programs, and make sure they were meeting patients’ needs.