Big data. You hear this phrase everywhere lately. Social media companies collecting and selling big data based on your scrolling habits to advertisers. Governments are using big data from CCTV to create facial recognition technology. Car makers are gathering intel from driving patterns and using big data to inform future autonomous vehicle technology.
They aren’t the only ones. The healthcare industry is primed for big data innovations, too. Industry experts are speculating about better and faster disease diagnoses, improved electronic medical records, reduced addition or adverse drug interactions, and more. Everything from medical protocols to the technology used in hospitals to the practical, day-to-day way healthcare centers run will likely be impacted by the era of big data.
So how will big data make an impact on the benefits industry? Let’s find out.
First, let’s look at what the term “big data” really means. Early mentions of big data defined it as large, unwieldy data sets that were too big to store, analyze, or process using traditional data analytics tools. Nowadays, big data is a popular term for any big data set that can provide insights, trends, or patterns that inform our larger understanding of the world. Millions upon millions of rows in a spreadsheet, hundreds of gigabytes in a feed, or thousands of data points collected can all be used to determine larger patterns or answer complex questions.
Here’s a good example from the benefits industry. Artemis Health works with a few “big data” providers to offer valuable insights to self-insured employers, benefits brokers, and/or benefits consultants. Milliman provides us with a big data set (benchmarks) that helps our customers compare themselves to others. With millions of data points, employers and advisors can find out how their health and wellness stacks up in comparison to the population at large. They can benchmark against things like risk scores, costs, case management utilization, and more. That’s big data in action in the benefits industry.
Self-insured employers are on the forefront of innovation with big data thanks to their utilization of healthcare analytics strategies. They take on the financial risk of insuring large populations of employees and their families, and they’re always on the hunt for ways to make healthcare better.
Employers are motivated by the Triple Aim of Healthcare: better health, better care, and better value. They want to foster a culture of health and wellness for a simple reason: healthy employees are more likely to show up to work, be productive, and lead happy lives. Employers are also focused on better care because they have a financial incentive to ensure providers are offering quality care. They want employees and their families to be diagnosed promptly and correctly, avoid long hospitalizations, or be readmitted because a treatment or surgery wasn’t successful. Finally, self-insured employers are motivated to reduce the cost of healthcare and keep prices in check because they are literally paying the claims.
For all these reasons, employers are turning to their health and wellness data. Using claims data from their employee population can give them unique insights into the cost and quality of their employee benefits. Let’s look at an example.
This analysis from the Artemis Platform shows the costs associated with the common Type II Diabetes drug Fortamet. We’ve broken it out by subscriber and spouse, and the total spent per year is about $100,000, and it’s about $1300 in employer paid claims each time someone fills this prescription. The identical generic drug Metformin costs approximately $11,000 and year, just $3 dollars per prescription. By adjusting their formulary, the employer and member can both count on substantial savings without a disruption to the quality of care.
This is a good example of how employers can use the power of healthcare analytics to find savings and still offer quality benefits to employees and their families.
Providers like hospital networks, physicians, physical therapists, pharmacists, and more are also increasingly using big data analytics to offer better outcomes and better value to patients. We mentioned disease diagnosis earlier, and we’ll come back to it now. Do you think health data analytics could diagnose cancer faster and more accurately than traditional medical tests?
New research from a team in Singapore suggests that big data holds the key to accurate, fast cancer diagnoses. The scientists used data from thousands of biopsies to find markers and data points consistent with cancer, and they’ve developed a tool any provider can use as a results: a scorecard.
From the article:
“To develop and validate the TMI “scorecard”, Dr Lim used big data and predictive analysis of over 30,000 patient-derived biopsies. Using public datasets of healthy individuals and cancer patients, the team noticed that cancer patients had a higher set of TMI scores. Testing a person’s TMI signature can determine if someone has cancer or not.”
Not only is this an innovative way to use big data, it’s also familiar and consistent with how doctors diagnose disease today. When you come in with a sore throat, your primary care physician will go down a list of symptoms with you. Fever? Cough? Trouble swallowing? White spots at the back of the mouth? This process is essentially a scorecard derived from thousands of similar interactions with other patients. Big data is bringing this same process to more complex diseases to help patients get treatment plans faster and more accurately
While brokers and consultants in the benefits industry are doing similar things with big data to their employer clients, they’re doing it for a different reason. They’re finding savings, they’re measuring program performance, and they’re predicting future costs. But they are doing to save time and offer more value to the self-insured employers they serve.
Finding the time for benefits data analytics isn’t on a broker or consultant’s to-do list unless clients specifically ask for it. But what if employers didn’t have to demand it, and brokers could provide it as a value-add? What if they were able to give clients actionable insights as a matter of course without spending dozens of hours a week finding these insights? If advisors could spend just one hour a week on benefits reporting and roll the same report out to their entire book of business, that would certainly maximize their value and effectiveness as a consultant.
Big data solutions, especially those designed just for the benefits industry, can provide out-of-the-box recommendations that take a vast amount of data and distill it into actionable information. For example, instead of a spreadsheet with thousands of rows with emergency room claims, advisors would be much more successful working with a built-in data model that takes ER claims, categorizes them by top diagnosis codes, and recommends possible solutions. Artemis Health’s Actionable Overspending App does just that, plus it focuses on other areas of inefficient spending, too.
Here’s another example. Let’s say a client had a hunch that their population needed more help with behavioral or mental health conditions. Without a big data tool, this might take weeks to pull together and compare data across feeds to get an accurate answer. A great data solution can turn weeks into minutes:
In this analysis, we can see clearly that the “cohort” of members with depression and anxiety are all struggling compared to the rest of the population. They are missing more work, taking more disability, and visiting the emergency room more often. An advisor can create an analysis like this for one client, then proactively share the findings with all their other clients. It saves them time and offers their clients better value from their benefits data.
So we can see from these examples that big data has an outsized role to play in the future of employee benefits. Payers, providers, and advisors are all taking on healthcare data analytics as part of their strategies to provide better care, better outcomes, and better value for patients.