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Analytics For Employers: A Tutorial (Part 2)

A succinct guide on how to save money on employer-sponsored healthcare

A succinct guide on how to save money on employer-sponsored healthcare

Over the past 3 years, the most common question we have heard from employers and brokers is this: health analytics is good, but what do we do with the dataWell, we are going to answer that question in this very post. That’s right, we’re sharing all the most actionable areas we look at for self-insured employers to help them to gain control over their spending. Some may say that we’re giving away our secrets, but we don’t see it that way. Our mission for employers is to make them savvy health care consumers, so making information transparent is what we do. Furthermore, it’s important to open up conversations on how organizations are using analytic data, because these conversations will help to advance insight and foresight so employers can use their data to create, track and refine a long-term strategy for the benefits they offer.

In Part 1 of this post we established that for employers, the best strategy for using health analytics moves beyond simply looking at spending to enter the realm of strategic benefit planning. This is the limitation of traditional healthcare analytics. Over the next decade, we will continue to see employers move away from watching spending go up and down and move towards looking at data in a way that provides both insight and foresight into population health. The next evolution of employer analytics informs a deeper understanding of who associates are, the benefits that will attract the best talent, and identifying the optimal strategy for funding these next-generation benefits packages.

To start, we pulled together a list of areas that any employer can explore if they want to ensure they’re using data to guide their spending decisions on health benefits. We've broken this into 3 sections: 1) Goals, 2) What's Actionable? and 3) Areas of Insight.

Beginning with the end in mind, here are the top goals that self-insured employers have when it comes to monitoring their health spending.

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Goals:

1.      To cut excess and wasteful healthcare spending and to accurately project future spending. Approximately 20% of an employer’s healthcare spending is wasted due to unnecessary and preventable costs. Open access to data helps to inform employers on exactly what areas are driving wasteful spending and how to better predict future spending.

2.      Identify strategies to support associates on their health journeys. While 5% of people drive 51% of health costs, 50% of plan members account for only 3% of health spending. Understanding how to support the unique, complex health needs of members affects a company’s bottom line in both healthcare costs and employee productivity.

3.      Track progress on the current healthcare and wellbeing strategy. An unbiased evaluation of a healthcare program is eye-opening. Not only does it guide the strategic evolution of an employer's healthcare strategy, it may reveal opportunities to recoup hefty vendor performance guarantees.

4.      Make sure members are getting the preventative medical attention they need. We consistently see that between 10%-20% of members never see a doctor. It’s within this group of people who are not driving costs today where an employer’s greatest future healthcare risks can lie.

In order to meet these goals, an organization needs to identify what exactly can be actionable. It's easy to spot the costs that stick out, but when is it too late to intervene on a cost-driver? Here are the most common areas where employers can focus to influence spending, care quality, preventative care, and effectiveness of condition management.

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What Is Actionable?

·        The plan’s pharmacy formulary (with some limitations based on the PBM partner).

·        The plan’s rules surrounding specialty medications.

·        The healthcare partners the employer selects (health plan, PBM, condition management services, smoking cessation, behavioral health services, direct primary care, centers of excellence).

·        Plan contributions, deductibles and coinsurance paid by employees for their healthcare benefit, emergency room surcharges, spousal surcharges, smoker surcharges, stop loss arrangements.

·        Cost variation among high cost and/or high volume services (MRIs, musculoskeletal surgeries, cancer care, etc).

·        Effectiveness of member education on health benefits.

·        Targeted wellbeing services offered to members.

Now that we’ve laid out the goals of using data and the areas that are actionable, here are some specific questions to answer when looking at the data.

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Areas of insight.

1.      Which conditions and medications represent the largest population health risks? How do these conditions vary by both dollars spent and number of people affected?

2.      Can amending prescription drug policies surrounding step therapy, specialty drugs, generics, place of service/purchase lead to savings for members?

3.      Does the member base have a problem with emergency room (ER) misuse and are certain locations or member categories driving ER costs?

4.      Do changes in member risk score, medication adherence and prevalent disease states such as diabetes show that your investments in condition management, smoking cessation and wellbeing interventions are working? Could performance guarantee fees be recovered from vendors?

5.      Are there trends noticeable related to members who are not engaging with physicians at all? Through looking at healthcare utilization among work location, salary bands, plan types—can we identify trends as to why certain people are not using necessary health services? These barriers to accessing care could be cost, lack of understanding of benefits, and even corporate culture, among others.

6.      What percentage of people are receiving preventative care and age-appropriate screenings among various member demographics?

7.      What are the largest cost variances that can be actionable? For example, could costs associated with procedures such as joint and hip replacement surgery or even MRIs be standardized via options that are available to your members? (Could centers of excellence be leveraged?)

8.      Could a direct primary care model have benefit for the member population?

9.      Are there actionable insights with respect to absence data and workers compensation claims?

10.  What is the size and scope (in dollars and members) of opioid use and dependency-related costs?

In the same way that reading an abstract is not the same as reading the book, please keep in mind that this is a very brief overview of a complex subject.* Every employer has unique challenges related to population health and health spending, so there's is no real "one size fits all" approach. The data drives the discussion in a unique direction for each employer.

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About BetaXAnalytics:

We combine data science with clinical, pharmacy and wellness expertise to guide employers and providers into a data deep-dive that is more comprehensive than any data platform on the market today. BetaXAnalytics uses the power of their health data "for good” to improve the cost and quality of health care. For more insights on using data to drive healthcare, pharmacy and wellbeing decisions, follow BetaXAnalytics on Twitter @betaxanalytics, Facebook @bxanalytics and LinkedIn at BetaXAnalytics.

* A Note on Data Privacy The purpose of using health analytics is to identify actionable areas to target costs and to improve effectiveness of care options on an aggregate level. This is done by looking at trends in data and under no circumstances should insights be presented to an employer in a way where data is individually identifiable. There are a number of data-related best practices that we recommend to remain adherent to privacy laws. Any employer, broker or consultant who is using health analytics should do so under strict adherence to HIPAA regulations and under the advisement of an experienced data privacy attorney.

Forget Flashy Technology: Here Are 3 Data and Analytics Best Practices Any Company Can Use Right Now

Image credit: iStockPhoto, Ryan J. Lane

Image credit: iStockPhoto, Ryan J. Lane

The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades.
— Dr. Hal R. Varian, Chief Economist at Google

Practically everyone is talking about using data and analytics to succeed today in business, but surprisingly companies are only deriving a fraction of the value that’s available to them in their data when they’re making decisions. The reasons for this vary across organizations, but often times it comes down to budget constraints, talent constraints, or lack of recognition from leadership that analytics will help their business to run better. During an interview in 2009, Google’s Chief Economist Dr. Hal R.Varian predicted, "The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades." 

Let’s take a look at some of the highest-performing companies out there today.  Over the past 5 years, there have been 13 companies that have managed to outperform the S&P 500 each year.  And when you take a look at this elite group—which includes companies such as Facebook, Amazon, and Google—you find that the majority of these businesses are algorithmically-driven.  These companies take in data constantly, and use this data in real time to update the user-experience.  In their 2012 feature on big data, Andrew McAfee and Erik Brynjolfsson shared findings from their research that “companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors.”  It is hard to deny that success in our respective businesses is not a function of how well we make use of the data available to us. 

So how does Human Resources (HR) fit in to this picture?  HR may not be the first group that you think of when considering who should have a strategy around using data.  However, HR has the weighty responsibility of managing the top expenses of a company—salaries, healthcare, and benefits.  The 2018 Milliman Medical Index estimates that the cost of healthcare for a family of 4 this year will be upwards of $28,166. Yet approximately 20% of employer-sponsored health care spending is wasted each year due to unnecessary or preventable costs across the continuum of care.  The rise of high deductible health plans mean that decisions made within HR on health plans and benefits are decisions that weigh heavily on their employees pocketbooks as well.   When we look at HR through the expense-management lens, we see that HR carries the company’s fiduciary responsibility to manage these expenses not just for the bottom line of the employer, but also for the sake of their employees’ wallets.

We often see companies who make the decision to start using data and analytics immediately start shopping for a tool to make use of their data.  While this step may be right for some companies, there are a few foundational analytics best-practices that we recommend companies have in place before making any analytic technology investments.

1.       Understand the quality of your data.  One of the biggest mistakes we see companies make is that they assume that just because a report comes from I.T. or from a vendor, that the data is correct.  However, very rarely is the data captured by a company in “ready-to-use” form.  IBM estimates that poor data quality cost American companies $3.1 trillion  in 2016 alone.  A recent study of 75 executives who assessed their own organizations data quality found that only 3% of their companies’ data met basic quality standards.  Furthermore, understanding data quality is a fundamental issue within organizations, executives are more informed to understand how data quality affects their vendor partners as well.  Every bit of data that we review is a piece of a much larger picture, and understanding the limitations of the quality of your company’s data helps to make a more accurate assessment of its insights.

2.       Develop your data strategy.  Take a step back from day to day operations to decide how to data can help to inform your decisions.  This affects what metrics you’re looking at, and how often you’re receiving it.  Many companies are surprised to find that the process of developing a data strategy often means reducing the amount of reports people are looking at.  A common assumption is that the more data we’re looking at, the better off we are.  In reality, when decision-makers are inundated with extraneous reports, they may miss valuable messages that they need to see.  What goals is your division working towards?  Which pieces of data most closely track progress to these goals?  The best way to guide a strategic process for looking at data aligns your business goals with a limited number of key metrics to indicate when changes are needed to reset course. 

3.       Identify a data “expert” on your team.  Given the issues that exist in every organization with data quality, it is valuable to identify someone who is intimately aware of the source and limitations of the data your company assesses.  This person can answer questions on why particular data might be wrong, if duplicate records are skewing the data, or how outliers are affecting results.  Your data expert can help to tell the story of your organization’s data to better frame what actions are needed to meet your operating goals.

Using data to make better business decisions does not need to be cost-prohibitive for your company.  Before investing in any data and analytics tools, implementing these foundational best practices lays the groundwork for a sound approach to using data.  They can be used by any company, regardless of size or budget.  And the best part is, you can start to use these best practices today.

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Bob Selle has led culture change and organizational design for America’s most recognized retailers.  He is currently the Chief Human Resource Officer for the northeast's premier close-out store Ocean State Job Lot, leading a transformation that has named them a Forbes Best Midsize Employer two years in a row.

Shannon Shallcross is a data expert who believes that data interpretation holds the key to solving healthcare’s toughest challenges. As the co-founder and CEO of BetaXAnalytics, her company uses the power of data “for good” to improve the cost, transparency and quality of healthcare for employers.

See Bob and Shannon at the Strategic HR Mt. Washington Conference on October 29th, 2018 during their plenary session, Metrics That Matter: Let Numbers Tell a Story.