Tag: data

Analytics For Employers: A Tutorial (Part 2)

Healthcare

A succinct guide on how to use analytics to save money on 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:

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 notdriving 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.

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.

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.

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Analytics For Employers: A Tutorial (Part 1)

DataHealthcare

It’s been almost 3 years since we started BetaXAnalytics with the goal of using data science to offer strategic guidance to employers and providers on healthcare spending and services. Since opening our doors, we’ve spent a lot of time talking with companies who pay for healthcare for their employees, as well as the brokers and consultants who help to guide these decisions. At the same time, we’ve spent time taking a look at many of the analytic tools that are on the market right now—these are the technology platforms that provide spending transparency to employers and their brokers.

From day 1 when we started these customer interviews, one resounding theme was apparent. The biggest question we heard from employers and their brokers is simply this: Having data is good…but what do you do with it?

3 years later, this is still the most common question we hear. We see this recurring question from employers and their brokers as a symptom of the early-stage maturity of the employer health analytics market. In short, over the past decade as more self-insured employers use health data to help to manage their spending, we haven’t moved too far from the starting line.

Anyone who is familiar with the general progression of analytics will recognize the analytic maturity model below.

At its most basic level, health care analytics is often pigeonholed into “counting things.” Counting dollars, counting medications, counting members…and watching these numbers go up and down. So every time we get the question, “What do you do with the data?” this just reaffirms that most employers are still in the dark with respect to using data to drive their benefits strategy. This type of “analytics” examines only the past and gives very little insight into the 4 critical areas of focus as a healthcare purchaser (which we explain in Part 2 of this post).

After seeing many of the analytic tools on the market today, we can confidently assert that that the market for using health analytics to control employer healthcare spending is here:


Having data is good…but what do you do with it? This recurring question is a symptom of the low analytic maturity of the current state of employer health analytics—that is to say, we pay for access to data, but it’s rarely actionable. “Analytics” in this stage is synonymous with “counting” and data is hindsight-focused on reporting what has already happened.

The current state of employer analytics is a good start, but it barely scratches the surface of the strategic potential of analytics. Tracking spending is important, but true analytics go far beyond just spending to understand insights into your population health, designing and tracking programs to target conditions and support mental health, and even to provide insight into how well benefits are being communicated to employees. When we move past hindsight analytics to incorporate insight and foresight, we move past counting things, and to the realm of strategic benefit planning. This means developing 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. As with so many initiatives that fall under the Human Resources / Human Capital umbrella—including talent acquisition and retention, compensation, healthcare, engagement, benefits, wellbeing—the most strategic analytics should consider all of these areas. This is the future of analytics—and the future is here.

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.

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

Data

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 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. Before investing in any data and analytics tools, implement these best practices to lay the groundwork for a sound approach to using the data you already have. 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.

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How Much Personal Data Did I Give Up to Take This Facebook Quiz?

Data

I’m about to reveal a big secret about myself. I love a good Facebook quiz. Whether I’m finding out what I will look like in 20 years or what my leprechaun name is, it’s fun to do these mindless games on Facebook and compare results with friends. If you’ve ever done one of these, you know it’s easy–you click one button to agree to share information about yourself, information in your Facebook profile, and information on your Facebook friends. What could be the harm? We figure, “Of course this information is needed if we’re looking to find the accurate answer to ‘What will my Hollywood movie poster look like?'” It seems harmless, so we trust it.   

The Facebook platform collects massive amounts of data on us, and it does so in a brilliant way. Imagine having a stranger come knocking on your door and asking you for a list of all your family, your friends along with photos and everything you know about them.  No one would ever fall for this. But now that Facebook is such a familiar and popular way to connect with people, it doesn’t feel like a stranger to us. We “trust” Facebook, and we use it to store massive amounts of information about ourselves and the people we know. In fact, we trust it so much that when it comes to their “privacy agreement,” we agree to it without even reading its terms.

The reason why the Facebook/Cambridge Analytica debacle has people angry is because people assumed there was no risk in how their data from Facebook would be used. But in this case, to the shock of the world, Facebook exposed data on 50 million Facebook users to a researcher who worked at Cambridge Analytica. And, as another piece of the puzzle, Cambridge Analytica worked for the Trump campaign. So as the public is wielding pitchforks at Facebook’s door, the first lesson for us all is this:

#1: Any data that we’re publicly sharing will be used.

And once our data is out there, absent restrictions, we have little control over how it is being used. Data is valuable to companies, both in utility and in dollars. So when it comes to any platform that collects and stores any data on you, you can assume this data will be used in some way or sold to a 3rd party. 

#2: So much more of our personal data exists than what we realize.

It’s scary, I know. Data on you and me is everywhere. And if you have watched my talk for TEDxProvidence, you know how the amount of data we’re able to capture has increased exponentially in just the last 15 years. According to Google’s former CEO Eric Schmidt, the same amount of data created from the beginning of time to 2003 is what was generated in the last 2 days. 

Our data is used by marketers, by election strategists, by grocery stores, and by prescription drug companies. It’s used by every social media platform, and our data is used by their affiliated companies as well. Simply put, most companies are using our personal data in some way.

#3: Not only are most companies using our data, but the most successful companies are built on data. 

There are 13 companies in the S&P 500 that have managed to outperform the entire S&P 500 5 years in a row. The majority of these companies are “algorithmically driven,” meaning they gather data from their users and they update the consumer experience almost automatically. These are companies like Facebook, Amazon and Google. Global business investments in data and analytics will surpass $200 billion a year by the year 2020. In the future, we will see more and more businesses moving data to the core of their competitive strategy.

What does this mean to us? The time is right for the public to champion a universal code of ethics surrounding our data use.

#4: Our data should be protected by a common code of ethics.

Now that we have just a glimpse of what can happen when data is available unrestricted in the hands of others, we need to have a common set of rules to govern data use. DJ Patil, the first Chief Data Scientist for the White House, reminded us that “with great power comes great responsibility” in his February 2018 call to action “A Code Of Ethics for Data Science.” This post coincidentally was published over a month before the Facebook/Cambridge Analytica Scandal hit the press. The weighty responsibility of using data appropriately weighs on the minds of many within the data science community.

When my partners and I formed our company BetaXAnalytics, our founding principle is that we wanted to use the power of data “for good” to improve the cost and quality of healthcare in the United States. Since we had a deep experience in clinical and pharmacy data science, we knew there was a resounding need for ethical transparency for those who are paying for health services. We wanted to provide the actionable insight that our clients need to make decisions regarding healthcare services and care coordination.

Since my company BetaXAnalytics works with healthcare data, the way we protect data is governed by HIPAA; this legislation ensures both the privacy and safeguard of people’s health-related information. A large amount of our time and resources are put towards our focus of maintaining data security and privacy. The data we use is governed by strict contracts with our clients and we never sell data to third parties.

As a company whose business is built on interpreting health data, we live by the mantra “with great power comes great responsibility.” We hope to see this movement grow both within and outside the data science community to work towards using the powers of data “for good.”

 Shannon Shallcross is Co-Founder and CEO of BetaXAnalytics

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How To Make Data-Driven Decisions When You Don’t Have Data

Data

In 1934, T.S. Eliot famously lamented the empty soul of modern work life. Though he wrote “Choruses from the Rock” over 80 years ago, he hits a nerve in our present-day struggles by asking, “Where is the wisdom we have lost in knowledge? Where is the knowledge we lost in information?” In current times, we have so much data at our fingertips, but does that mean we are making better decisions? Today, the core of data analytics is simply using information to make well-informed decisions. The only difference today from 80 years ago is that we simply have more information available to make decisions and more sophisticated methods to use this information. 

A question that I get time and time again from managers is “How do I make data-driven decisions when I don’t have any data?” As a decision maker, it’s incredibly frustrating to feel hampered by a lack of data.  Despite wide availability of information, companies might not put data into the hands of decision makers for a couple reasons. Maybe the organization does not have an effective way of capturing data—this happens in companies that have older technology in key areas of the business. Or maybe the data they have is too messy—for instance, perhaps they can track customer quotes online, but they have no way of cleaning up the 30 different customer quotes that actually were generated by the same person. In other cases, data is kept sectioned off in certain parts of a company, but it is not shared widely with people whose decisions depend on the information. For whatever the reason that managers feel like they do not have access to information to make an informed decisions, there are a few guidelines you can follow to ensure that you are making the right decisions.

The key is not to get more data – it’s to get the right data.

It’s important to keep in mind you can have all the data in the world and still not have the information you need. The key is not to get more data – it’s to get the right data. In research from the book Stop Spending, Start Managingexecutives reported wasting an average of $7,731 per day—or $2.8 million per year—on wasteful “analytics.” The first step to making sound decisions is to recognize what that “right” data is for your business. Once you identify this, you can cut your time looking at reports significantly because now you have a strategy. You know exactly what you need to see to make a decision, and you can see through the noise of mountains of data that don’t add value to your decisions. 

Executives reported wasting an average of $7,731 per day—or $2.8 million per year—on wasteful “analytics.”

If you don’t have access to the data you need at work, here are some steps you can take:

1.      Identify your business goals.  Here’s your opportunity to start at square one and holistically rethink how your decisions are made. This entails taking a 50,000 foot view of your business to make sure that you’re asking the right questions. We often get in the habit of process, and we repeat process patterns of looking at old reports that don’t tell us what we really need to know. If your business unit always looked at a set group of metrics, it’s easy to get tunnel vision and to see it as a bad decision to stop looking at a certain report. But I recommend taking a step back to ensure you’re asking the following questions before even looking at any data:

·        What are the business objectives for which we are responsible? (In other words,what are our goals?)

·        What are the crucial areas of the business that we need to be tracking?

2.      Identify which data you need to track progress on your goals. What data do you need to see to be able to track progress on these goals and to make sound decisions? In most cases, every business goal you cite has one or multiple metrics that will help you to gauge progress against that goal.

3.      Examine your data access. Identify which of these must-have pieces of data you have access to. For the data you don’t currently have access to, identify how you can get access. This can be as easy as requesting access from another department, or as hard as implementing a way to capture new data.

4.      If needed in the short term, identify proxy data for the information to which you don’t have access. When you can’t access crucial data, is there a proxy measure that would tell you the same thing? For instance, if you have no way today of tracking the number of customers who are calling with a particular complaint, can you poll your front line customer service representatives to identify trends in complaint themes? Finding a short-term proxy for needed data will provide you with some useful information. The proxy is not a perfect solution, but in the short term it’s better than using no information at all.

5.      Start the process of gaining access to the data that you need. As simple as this sounds, if you’re in a situation where you don’t have access to crucial data, the goal is to exit this reality as soon as possible. Whether this means insourcing or outsourcing to gain access to data you need, there’s simply no business case for continuing to manage without the right information.

The guiding principle of how to manage your data is to identify what data aligns with your goals—if you don’t have access to this data today, the best place to be is somewhere on the track to gaining access to this data. Identifying proxy data is a bridge to dealing with an undesirable situation, and moving towards one that puts you on the right path. But it is important to not accept a lack of data within your company simply because it’s “the way it’s always been done.” If you find yourself clamoring for meaningful metrics, creating a process to get this data involves some work–but there are huge rewards for your business in the end.Y

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BetaXAnalytics is a healthcare data consulting firm that helps payers and providers to maximize their CMS reimbursements and helps employers to reduce their healthcare spending through proven strategies to contain costs. For more insights on using data to drive healthcare, pharmacy and wellbeing decisions, follow BetaXAnalytics on Twitter @betaxanalytics, Facebook @bxanalytics and LinkedIn at BetaXAnalytics.

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2 Reasons Why Your Data is Lying to You

Data

Big Da·ta noun

An overused buzzword, which, despite its lofty sound, basically means “lots and lots of data.” A Mount Everest of tangled data. 

The term “Big Data” gets thrown around all too often these days, but anyone who works closely with healthcare data is intimately aware of its shortcomings. From lack of sharing patient data between providers to inconsistencies with recording patient data, the more we know about the problem, the more impossible it seems to unlock the powerful potential that lies in healthcare data. But at the heart of the issue, there are 2 main reasons why people don’t get accurate insights from their data.

Reason #1 Your Data Lies: It’s Dirty

Software expert Hollis Tibbets, formerly the Global Director of Marketing at Dell, estimated that duplicate data and bad data combined cost the U.S. economy over $3 trillion every year. This staggering number is just about two times the national deficit.

Unfortunately, the healthcare industry in particular is a breeding ground for duplicate data. The U.S. Attorney’s office estimated that 14% of healthcare spending is wasted due to dirty data; this includes duplicate and/or incomplete data. With 16% of the U.S. Gross Domestic Product attributed to healthcare spending – or $2.14 Trillion total spend – that would mean that duplicate and dirty data costs the healthcare industry over $300 billion every year. And the sad reality of this issue is that 50% of IT Budgets are spent on data rehabilitation.[1]

Larry English, an acclaimed information quality expert and creator of the Total Information Quality Methodology (TIQM) has estimated that that 15-20% of a company’s operating budget can be wasted due to dirty data. This number is quantified by the exhaustive effort to extract, manipulate, append and scrub data via SQL, Excel or other means. And this estimate is independent of the fact that 30% of healthcare provider records are inaccurate or missing information due to inconsistent entry of codes and inaccurately transposing metrics or patient identifiers.[2]

Reason #2 Your Data Lies: It’s Interpreted by People Who Do Not Understand It

A study by McKinsey has projected that “by 2018, the U.S. alone may face a 50 percent to 60 percent gap between supply and requisite demand of deep analytic talent.” The shortage is already taking hold across industries, including healthcare, finance, aerospace, insurance, and pharmaceuticals. In April 2014, the consulting firm Accenture surveyed its clients on their big-data strategies, and more than 90 percent said they planned to hire more employees with expertise in data science—most within a year. However, 41 percent of the more than 1,000 survey respondents said a lack of talent was their main hurdle.[3]

Data Scientists are important in the process of data cleansing, appending and analysis because they work with unstructured data. These are the people who write algorithms to extract insights from the mounds of disparate data sources, including e-mails, text notes, photos and other user-generated content. They sort through the mess of dirty (messy, incomplete, and inaccurate) data and neatly append it to uncover the true insights.

All analytics must start with data investigation. Since data is inherently messy, the analysis process must start with a multi-faceted cleansing process by someone who, while working with health data, has deep clinical understanding. This knowledge enables them to identify and appropriately treat negative values, reversals, duplication, adjustments, and they understand how to handle data anomalies. This experience also enables them to check for clues throughout the process as to why data may not make sense.  For example, thoroughly examining data may reveal issues with recycling patient IDs and inadvertently mixing patient data together. Yes, this happens. Dirty data is not to be trusted…ever.

Bring Truth Out of Data

It is easy to get caught up in the buzz of “Big Data.” You may have a strategy for collecting data…and maybe even an analytics department. But neither of these efforts means your data is telling the truth. If a significant part of your data management strategy is not allocated to 1) scrubbing data and 2) ensuring those who work with the data truly understand it, your data’s actionable insights (read: truth) may still be hiding.


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Shannon Shallcross is the CEO of BetaXAnalytics, a company that leverages data insights to improve clinical outcomes, improve patient well being and decrease health care costs. They deliver custom tools and data analytics to managed care organizations, providers and employers to reduce costs and improve the quality of healthcare and pharmacy services.

Follow BetaXAnalytics on Twitter @betaxanalytics and LinkedIn at BetaXAnalytics.

[1] Tibbetts, H., 2011. $3 Trillion Problem: Three Best Practices for Today’s Dirty Data Pandemic. [Online] Available at: http://hollistibbetts.sys-con.com/node/1975126.

[2] A Business Case for Fixing Provider Data Issues: Save Money, Reduce Waste and Improve Member Services: Proactive Provider Data Management[Online] Available at: https://www.lexisnexis.com/risk/downloads/whitepaper/fixing-provider-data-issues-whitepaper-wp.pdf.

[3] Orihuela, Rodrigo and Dina Bass. Help Wanted: Black Belts in Data. [Online] Available at: http://www.bloomberg.com/news/articles/2015-06-04/help-wanted-black-belts-in-data.

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