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What Insurers Can Learn From Tech Giants


The competitive advantage of the world’s leading tech companies resides in the way they use data. Whether you’re searching the web for an answer to an obscure question, or you’re seeing ads for the exact product you’ve been thinking of buying, technology’s ability to anticipate our needs is becoming astoundingly accurate.

Imagine if insurance companies could get this good at knowing who potential customers are, even at predicting a customer’s behavior and lifetime value. If insurers could apply big data and artificial intelligence as the leading tech companies, they could know right away which individuals are in their target customer segment, well before the bureaucracy of filed ratings and underwriting guidelines.

In the insurance industry, successes are built on the ability to forecast risk. And, now the industry has the opportunity to apply advanced data science to create the new frontier of risk forecasting, customer experience design and loss predictions. In fact, it is an imperative. So what is holding them back?

Insurance companies have a long history of struggling to predict what are known as unpriceable risks. This small percentage of every insurance company’s book of business ends up having a large impact on the central metric of their profitability–the loss ratio. Whether these unpriceable risks are fraudsters, litigious insureds, or people who generate high dollar claims, these are the risks that historically have been both the most difficult to predict. Since most insurance companies are not leveraging the same data and artificial intelligence capabilities to identify these risk profiles before the individual becomes a customer, they must take reactionary measures, where possible, such as non-renewal once it’s too late– the loss has occurred.

This is not a new problem, and the industry has tried to solve it in a variety of ways. In the 1990s, insurance companies began to introduce credit-based insurance scores into rating plans. To date, there had been no single factor that existed that was as highly correlated to predicting loss frequency and severity. Considering an applicant’s credit history was, at the time, the best available piece of information that could help the insurer to understand an individual’s behaviors and level of conscientiousness. The hypothesis, which they were able to prove in actuarial filings, was that the higher a person’s credit score, the less likely they would be to file a claim.

However, credit-based insurance scoring may not be here to stay. According to the Insurance Information Institute, some have suggested that the use of insurance scores might unfairly discriminate against certain demographic or economically disadvantaged groups. States such as California, Massachusetts, Hawaii and Maryland place restrictions on the use of credit, and more states are considering moving in a similar direction.

In his book The Four: The Hidden DNA of Amazon, Apple, Facebook, and Google, New York University professor and tech thought leader Scott Galloway cited that between 2010 and 2015, there were 13 companies that managed to outperform the S&P 500 each year. And when we take a look at this elite group, we 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. A research report from Accenture found that artificial intelligence has the potential to increase corporate profitability in 16 industries by an average of 38% by the year 2035. Kate Smaje, a senior partner at McKinsey and global co-leader of McKinsey Digital said, “Data is providing the fuel to power better and faster decisions. High-performing organizations are three times more likely than others to say their data and analytics initiatives have contributed at least 20 percent to EBIT.” It is hard to deny that success in our respective businesses is a function of how well we make use of the data available to us.

Now that we are entering a new era of advanced predictive capabilities, the future can be predicted earlier and with incredible accuracy. So back to the original question: What is holding insurers back from leveraging these advancements?

The first thing holding insurers back is history and culture. The insurance industry has been slow adopters, but fast followers. Although some insurers are already using data to better understand and segment risk profiles of an individual — wide adoption and significant impact remains elusive. According to McKinsey, for Property and Casualty insurers who are leveraging big data and AI “leading insurers can see loss ratios improve three to five points, new business premiums increase 10 to 15 percent, and retention in profitable segments jump 5 to 10 percent.” And, the message from every one of the big 4 consulting firms on the biggest opportunities in the insurance industry today is that the new normal for insurance companies is to leverage sophisticated big data, and to use advanced AI to harness the predictive power of this data.

However, leveraging the power of advanced AI and sophisticated data sets is difficult to do. The guidance and trends are clear here as well, engaging with partners that can deliver immediate value is a way to deliver bottom-line results and escape “pilot traps”. This is where InsurTechs and insurance partners like Pinpoint Predictive excel.

Behavioral economics pertains to Pinpoint’s proprietary, first-party data, which correlates with insurance outcomes and also reveals the explainable features underlying risk-related behaviors. Pinpoint’s platform leverages behavioral economics and deep learning, providing leading insurers with superior risk-selection scores at the beginning of the customer journey, as well as a deeper understanding of individual-level risks. Whereas traditional risk segmentation puts people into blanket categories that can penalize people for their credit, location, or age, a more individualized approach removes potentially discriminating categories that may penalize people for their financial status or the neighborhood they live in. Because of the vastness of big data and behavioral profiles, behavioral-economic profiles remain relatively stable over time. 

It’s the data equivalent of truly knowing a person, understanding their personality, and quantifying their propensity for key risk behaviors.

Armed with this knowledge, insurance companies can get smarter about how to find and keep the right customers. A better understanding of risk informs better decisions in shaping their book of business. They can precisely prioritize prospects that think like their best customers, a vast improvement over targeting prospects with look-alike modeling.

If insurance companies get this good at knowing who potential customers are, they can identify unpriceable risks, such as fraud, litigation, and high cost claims at the beginning of the customer journey and use this information to augment the performance of their risk models. They then can tailor a customer’s journey, creating relevant experiences based on more accurate risk predictions and lifetime value.

Over the next decade, most insurance companies will abandon traditional, rudimentary approaches to risk segmentation based on sparse data points. Instead, they will be leveraging deep learning before underwriting a risk to make accurate risk propensity predictions at the beginning of the application process. Pinpoint is at the forefront of driving this transformation. The data modeling predictions that make the most successful tech companies so good at making assessments of who a person is, what they like, and how they buy will be the norm for insurers, not the exception. In this new competitive environment, insurers will be able to directly tie their data strategy to their loss ratios as they get better and better at targeting customers, segmenting risk, and tailoring their customer experience at an individualized level.

Pinpoint is already driving the insurance industry towards this future, and putting the predictive power of big tech in the hands of insurers to help them win. Ready to learn more? Reach out to me at

This piece was originally posted via Pinpoint Predictive.

About Pinpoint Predictive

Pinpoint Predictive brings individualized intelligence to insurers by applying deep learning and behavioral economics to accurately predict the risk propensity of an individual insured without using financial-based credit scores.

Their AI-powered platform has revealed $100s of millions in bottom-line value to Top 10 insurers by quantifying key points of risk in areas including likeliness to file a claim, cancellation, likelihood to commit fraud, and litigiousness.

By utilizing their proprietary Thinkalike Scores in pre-eligibility and pre-renewal decisions, carriers are able to leverage the predictive power of deep learning and Pinpoint’s custom database representing thousands of sophisticated, privacy safe data points on approximately 260 million US adults in real time, resulting in significant improvement in their ability to target their most profitable insureds and improvement in their loss ratio.

Discover the powerful results of using individualized intelligence to boost profitability at

Leadership Presence


What is the most important lesson you have learned about being a good leader?

Developing Leadership Presence is the topic for Professor Lori Coakley’s class on Organizational Behavior next week at Bryant University.

For this class, I was asked to share some things I’ve learned about developing leadership presence. I became a manager long before I felt ready at the ripe old age of 22.

Taking a walk down memory lane has been a CRINGE-WORTHY rewind to 2002, remembering the many mistakes I made as a new leader.

My biggest discoveries from my 20s:

1. Learn who you are as a leader by forcing yourself to get outside of your comfort zone and do hard things—as often as possible.
2. It’s not about you. Being a leader is about helping others to become successful.
3. Be a role model—but don’t forget to be yourself.

You can hear more on these lessons (and some personal stories!) in the full talk here.

100 Books to Read Before You Die

100 Books to Read Before You Die

1984 by George Orwell, England, (1903-1950)

A Doll’s House by Henrik Ibsen, Norway (1828-1906)

A Sentimental Education by Gustave Flaubert, France, (1821-1880)

Absalom, Absalom! by William Faulkner, United States, (1897-1962)

The Adventures of Huckleberry Finn by Mark Twain, United States, (1835-1910)

The Aeneid by Virgil, Italy, (70-19 BC)

Anna Karenina by Leo Tolstoy, Russia, (1828-1910)

Beloved by Toni Morrison, United States, (b. 1931)

Berlin Alexanderplatz by Alfred Doblin, Germany, (1878-1957)

Blindness by Jose Saramago, Portugal, (1922-2010)

The Book of Disquiet by Fernando Pessoa, Portugal, (1888-1935)

The Book of Job, Israel. (600-400 BC)

The Brothers Karamazov by Fyodor M Dostoyevsky, Russia, (1821-1881)

Buddenbrooks by Thomas Mann, Germany, (1875-1955)

Canterbury Tales by Geoffrey Chaucer, England, (1340-1400)

The Castle by Franz Kafka, Bohemia, (1883-1924)

Children of Gebelawi by Naguib Mahfouz, Egypt, (b. 1911)

Collected Fictions by Jorge Luis Borges, Argentina, (1899-1986)

Complete Poems by Giacomo Leopardi, Italy, (1798-1837)

The Complete Stories by Franz Kafka, Bohemia, (1883-1924)

The Complete Tales by Edgar Allan Poe, United States, (1809-1849)

Confessions of Zeno by Italo Svevo, Italy, (1861-1928)

Crime and Punishment by Fyodor M Dostoyevsky, Russia, (1821-1881)

Dead Souls by Nikolai Gogol, Russia, (1809-1852)

The Death of Ivan Ilyich and Other Stories by Leo Tolstoy, Russia, (1828-1910)

Decameron by Giovanni Boccaccio, Italy, (1313-1375)

The Devil to Pay in the Backlands by Joao Guimaraes Rosa, Brazil, (1880-1967)

Diary of a Madman and Other Stories by Lu Xun, China, (1881-1936)

The Divine Comedy by Dante Alighieri, Italy, (1265-1321)

Don Quixote by Miguel de Cervantes Saavedra, Spain, (1547-1616)

Essays by Michel de Montaigne, France, (1533-1592)

Fairy Tales and Stories by Hans Christian Andersen, Denmark, (1805-1875)

Faust by Johann Wolfgang von Goethe, Germany, (1749-1832)

Gargantua and Pantagruel by Francois Rabelais, France, (1495-1553)

Gilgamesh Mesopotamia, (c 1800 BC)

The Golden Notebook by Doris Lessing, England, (b.1919)

Great Expectations by Charles Dickens, England, (1812-1870)

Gulliver’s Travels by Jonathan Swift, Ireland, (1667-1745)

Gypsy Ballads by Federico Garcia Lorca, Spain, (1898-1936)

Hamlet by William Shakespeare, England, (1564-1616)

History by Elsa Morante, Italy, (1918-1985)

Hunger by Knut Hamsun, Norway, (1859-1952)

The Idiot by Fyodor M Dostoyevsky, Russia, (1821-1881)

The Iliad by Homer, Greece, (c 700 BC)

Independent People by Halldor K Laxness, Iceland, (1902-1998)

Invisible Man by Ralph Ellison, United States, (1914-1994)

Jacques the Fatalist and His Master by Denis Diderot, France, (1713-1784)

Journey to the End of the Night by Louis-Ferdinand Celine, France, (1894-1961)

King Lear by William Shakespeare, England, (1564-1616)

Leaves of Grass by Walt Whitman, United States, (1819-1892)

The Life and Opinions of Tristram Shandy by Laurence Sterne, Ireland, (1713-1768)

Lolita by Vladimir Nabokov, Russia/United States, (1899-1977)

Love in the Time of Cholera by Gabriel Garcia Marquez, Colombia, (b. 1928)

Madame Bovary by Gustave Flaubert, France, (1821-1880)

The Magic Mountain by Thomas Mann, Germany, (1875-1955)

Mahabharata, India, (c 500 BC)

The Man Without Qualities by Robert Musil, Austria, (1880-1942)

The Mathnawi by Jalal ad-din Rumi, Afghanistan, (1207-1273)

Medea by Euripides, Greece, (c 480-406 BC)

Memoirs of Hadrian by Marguerite Yourcenar, France, (1903-1987)

Metamorphoses by Ovid, Italy, (c 43 BC)

Middlemarch by George Eliot, England, (1819-1880)

Midnight’s Children by Salman Rushdie, India/Britain, (b. 1947)

Moby-Dick by Herman Melville, United States, (1819-1891)

Mrs. Dalloway by Virginia Woolf, England, (1882-1941)

Njaals Saga, Iceland, (c 1300)

Nostromo by Joseph Conrad, England,(1857-1924)

The Odyssey by Homer, Greece, (c 700 BC)

Oedipus the King Sophocles, Greece, (496-406 BC)

Old Goriot by Honore de Balzac, France, (1799-1850)

The Old Man and the Sea by Ernest Hemingway, United States, (1899-1961)

One Hundred Years of Solitude by Gabriel Garcia Marquez, Colombia, (b. 1928)

The Orchard by Sheikh Musharrif ud-din Sadi, Iran, (c 1200-1292)

Othello by William Shakespeare, England, (1564-1616)

Pedro Paramo by Juan Rulfo Juan Rulfo, Mexico, (1918-1986)

Pippi Longstocking by Astrid Lindgren, Sweden, (1907-2002)

Poems by Paul Celan, Romania/France, (1920-1970)

The Possessed by Fyodor M Dostoyevsky, Russia, (1821-1881)

Pride and Prejudice by Jane Austen, England, (1775-1817)

The Ramayana by Valmiki, India, (c 300 BC)

The Recognition of Sakuntala by Kalidasa, India, (c. 400)

The Red and the Black by Stendhal, France, (1783-1842)

Remembrance of Things Past by Marcel Proust, France, (1871-1922)

Season of Migration to the North by Tayeb Salih, Sudan, (b. 1929)

Selected Stories by Anton P Chekhov, Russia, (1860-1904)

Sons and Lovers by DH Lawrence, England, (1885-1930)

The Sound and the Fury by William Faulkner, United States, (1897-1962)

The Sound of the Mountain by Yasunari Kawabata, Japan, (1899-1972)

The Stranger by Albert Camus, France, (1913-1960)

The Tale of Genji by Shikibu Murasaki, Japan, (c 1000)

Things Fall Apart by Chinua Achebe, Nigeria, (b. 1930)

Thousand and One Nights, India/Iran/Iraq/Egypt, (700-1500)

The Tin Drum by Gunter Grass, Germany, (b.1927)

To the Lighthouse by Virginia Woolf, England, (1882-1941)

The Trial by Franz Kafka, Bohemia, (1883-1924)

Trilogy: Molloy, Malone Dies, The Unnamable by Samuel Beckett, Ireland, (1906-1989)

Ulysses by James Joyce, Ireland, (1882-1941)

War and Peace by Leo Tolstoy, Russia, (1828-1910)

Wuthering Heights by Emily Brontë, England, (1818-1848)

Zorba the Greek by Nikos Kazantzakis, Greece, (1883-1957)

Will Amazon Pharmacy Disrupt Rx?


5 Myths and What Amazon Pharmacy Will Mean For Your Wallet

Photo by Joshua Coleman on Unsplash

Let’s cut through all the confusing pharmacy jargon and get to the big idea-Amazon is not doing anything fundamentally different in the prescription drug space…yet.

In November 2020, Amazon Pharmacy entered the world of prescription drugs-this is a market sized in excess of $500 billion in the United States. The Amazon Pharmacy benefit is being offered to all 126 million Amazon Prime members, in addition to their many other member perks such as free 2-day shipping and online streaming of Amazon Video.

The benefit of Amazon Pharmacy to Amazon Prime members is the promise of 80% off generic drugs, 40% off brand name medications, as well as a savings card that can be used at up to 50,000 brick and mortar pharmacies including CVS, Walgreens and Walmart. You can compare prices both with and without insurance at checkout. In some cases, Amazon Pharmacy could make your out of pocket cost for a prescription lower than when using insurance. Amazon says it works with most insurance plans.

Sounds pretty amazing, right? There’s a big caveat here: this is not fundamentally different from what some other players are doing in the prescription drug space. In fact, this model is exactly the same as what Good Rx does. What’s more, it is so similar to Good Rx that the two companies launched using the exact same partner to administer their prescription discount program-Inside Rx (Inside Rx is owned by Express Scripts, and is a subsidiary of Cigna’s Evernorth). Yes, hiding within Amazon Pharmacy are all the usual players to ensure Amazon can play the prescription drug game by the rules, be able to work with 3rd party payers and to keep prices competitive.

So let’s look at some assumptions which are actually myths about Amazon Pharmacy.

  1. Amazon’s dive into pharmacy has disrupted the prescription drug market

For now, Amazon has just “entered the ring,” and disruption has not happened yet. There is currently nothing they are doing that is fundamentally changing the prescription drug market other than accessing their 126 million Amazon Prime members and offering this option as an additional benefit. While what they are doing in the prescription drug space is not substantially different from other players in the market, the mere fact that Amazon is the leader in distribution logistics with an already impressive nationwide U.S. household brand saturation positions them to have a high potential to disrupt the prescription drug market in the future.

2. Amazon Pharmacy’s goal is to make prescriptions more affordable for uninsured Americans

Unfortunately this is not the exact problem Amazon Pharmacy is solving right now. Keep in mind this benefit is only available to those who are paying $119/year for their Amazon Prime membership. To take this further, according to Ge Bai, Associate Professor at John Hopkins Bloomberg School of Public Health, the relatively small number of uninsured patients as a market do not have enough clout to be a viable business model for Amazon. Cash buyers make up less than 5% of the overall retail prescription spending. Amazon is targeting customers who have commercial prescription coverage (usually through employers) and people covered through Medicare-combined, these groups make up 79% of retail prescription drug spending. While not exclusively targeting the uninsured, Amazon Pharmacy is certainly providing a consumer-friendly option for cash buyers to buy prescription drugs.

3. Amazon Pharmacy is going to fix out-of-control prescription drug prices in the U.S.

Not exactly. Amazon Prime members will be able to enjoy lower prices on some drugs, however these discounts are still covered by the same PBMs who are covering the very high cost of drugs in the first place. What Amazon Pharmacy is doing is offering value to their members through price transparency and making it easier for Prime members to price compare the cost of medications paying cash versus using their insurance coverage. They are offering convenience to Prime members with quick and reliable shipping, and perhaps more competitive prices on select drugs, but they are not currently tackling the underlying runaway drug pricing issue in the U.S..

4. Amazon Pharmacy’s prices will be the cheapest prescription prices available

This will not always be true. It appears that Amazon Pharmacy is aiming to make drug prices competitive for some popular drugs such as Humira, Metformin, and Januvia, however other medications could be more expensive than competitors. To illustrate this price variability, Dr. Eric Bricker has done a nice job illustrating price comparisons between Good Rx and Amazon Pharmacy. His analysis shows that Amazon and Good Rx prices vary by drug, and neither company could be blanket dubbed to always be “the cheapest option.”

Source: YouTube AHealthcareZ — Healthcare Finance Videos

5. Amazon Pharmacy will cut out Pharmacy Benefit Managers (PBMs) such as CVS Caremark, Express Scripts and Optum Rx

This is not the case. While Amazon Pharmacy is new on the prescription drug scene, they need to play by the same rules to navigate the complexity of the prescription drug market in the U.S.. Keep in mind Amazon Pharmacy is using Inside Rx to administer their prescription discounts; they are working within the PBM model, so they are not currently doing anything to drastically change the system people currently use to get their prescription drugs. And because the largest portion of prescription drug spending comes from commercial payers and Medicare, maintaining the ability to effectively deal with 3rd party payers is needed to enable people to access their coverage when they order medications from Amazon Pharmacy.

Amazon’s Challenge in Rx: Behavior Change

Amazon Pharmacy is looking to bring convenience and more transparent pricing to Amazon Prime users by offering free, 2-day delivery for medications at scale. It is useful to note here that there is one nut Amazon needs to crack to truly become disruptive in the prescription drug space- behavior change.

Mail order pharmacy, while long understood to be convenient and in some cases less expensive for some medications, in 2019 it made up roughly 5%of the prescription drug market. Even in light of the pandemic over the past year when people have limited their trips outside of their homes, it is still unknown whether the use of mail order prescriptions will see a sustained increase in the future.

What are the barriers to more Americans using mail order pharmacy? First mail order pharmacy does not fit in to “the way things have always been.” Right now, most doctors prescribe your medication and send it directly to your local retail pharmacy for same-day pickup. This is the default way many people are getting new prescription medications right now. Also, it is important to note the high proportion of people on Medicare who are using multiple prescription drugs on an ongoing basis. This population has had the lowest penetration by Amazon Prime, and may be less apt to seek alternate methods for getting prescriptions. An additional barrier is that people over age 65 may be less digitally savvy to go online to switch to mail order prescription fulfillment.

With Change Comes Opportunity

Before they are able to disrupt the prescription drug market in the United States, Amazon Pharmacy currently faces the difficult task of changing consumer behavior with respect to how people get their prescription drugs. However, looking beyond today and in to the future, there is new potential on the horizon. As American health care takes on a new trajectory under the Biden administration, Amazon Pharmacy could be poised to respond to the new opportunities that change and health care reform may bring.

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Originally published at

Why The World’s Leading Data Experts Warn Covid-19 Data is Wrong


And how to make better decisions from the data you see

As states are slowly starting to ease lock down restrictions and phasing reopening businesses, data is playing a big role in helping policy makers and leaders to both form and execute these decisions. But there is one question that must be asked in this process–is the data used to make these decisions correct?

In short, the answer is not always.

Enter Exhibit A: one of the leading models in the early phases of the pandemic in the U.S. went from rising star, consulted daily by White House officials, only to be put in a corner with a dunce cap after concerning inaccuracies were brought to light. The University of Washington’s Institute for Health Metrics and Evaluation, or IHME, was being used by White House officials, the Centers for Disease Control (CDC) and state officials around the country. This model formed the basis of NPR’s popular state-by-state peak predictions and was used by many other credible news agencies.

Ruth Etzioni, biostatistician at the Fred Hutch Cancer Center said the IHME model makes her cringe. In a STAT article she stated, “That it is being used for policy decisions and its results interpreted wrongly is a travesty unfolding before our eyes.” Epidemiologist Marc Lipsitch of the Harvard T.H. Chan School of Public Health said of the IHME model, “It’s not a model that most of us in the infectious disease epidemiology field think is well suited” for projecting Covid-19 deaths.

The root of concern from data experts was a glaring issue.

The IHME model had predicted that Covid-19 deaths would reach 60,000 by the end of August. This was problematic because deaths in the US had already reached 68,000 by the beginning of May. On May 4th the IHME called a press conference to release their model update with a new prediction of 134,000 deaths by the end of August, more than double the previous estimates.

Yann LeCun, Facebook’s Chief AI Scientist described IHME’s model in a tweet on May 18 as “pretty much the worst.” 

Youyang Gu is a Data Scientist (MIT ’15) and creator of the model. This model is now one of 17 Covid-19 data models linked on the CDC’s site. Early in the pandemic he repeatedly expressed concerns over the IHME model. 

Due to the mounting concerns over its inaccuracies, on May 1 the CDC quietly removed the IHME model from their website. And just like that, one of the leading data sources used by Americans was put on the shelf. The takeaway for Americans—just because we see data does not mean that it’s correct. Especially in the middle of a pandemic where all we have to go off of is a relatively small amount of very new data.

Harvard Professor of Statistics Xiao-Li Meng warned of the consequences of the poor quality of Covid-19 data that is currently available. He argues in his May 14th publication for the Harvard Data Science Review that academic studies on Covid-19, while conducted thoughtfully, are “dangerous” when researchers do not take into account the low quality of most of the Covid-19 that is available today. According to him, data quality is of utmost importance:

Building elaborated epidemiological or mathematical models without taking into account the weaknesses of the data generating mechanism is still statistically unprincipled, because data quality fundamentally trumps everything else.

Data is like Transformers — there’s more than meets the eye. We need to understand the “more.”

Sadly, this is not the only data fail since the Covid-19 pandemic arrived in the U.S. In their May 21st article “How Could the CDC Make That Mistake?” The Atlantic reported that the CDC and several states including Pennsylvania, Georgia and Texas were mixing viral test data with antibody test data, damaging the public’s ability to understand what is happening in any one state. Harvard Professor of Global Health and director of the Harvard Global Health Institute K. T. Li said that blending viral and antibody tests “will drive down your positive rate in a very dramatic way.” As a consequence of this error, some of the metrics that decision makers have depended on for state reopening plans have been wrong, and we do not actually know how our ability to test people who are sick with Covid-19 has improved. The conflating of viral and antibody tests is a clean cut example of how easy it is to dramatically skew data.

Over the past 3 months, we have all been consuming data daily in an effort to track this pandemic. So to uncover the inaccuracy of key data we have relied on is nothing short of frustrating. But there is a lesson in all this madness: no data is perfect.

Data quality fundamentally trumps everything else

In my 2017 talk for TEDxProvidence I amplified the limitations of data. Having loads of data and data scientists does not guarantee our ability to make accurate predictions. Botched predictions for both the 2016 U.S. Presidential Election and the Brexit decision are sobering examples of this. It’s happened again with Covid-19 projections, and we’ll keep seeing the same pattern repeat in the future. This will continue because the innate nature of data is imperfect.

So what do we do with all of this?

The takeaway here is that every person should know that data is always flawed. Whether you’re a CEO or just someone who is trying to make sense of what’s going on, we need to understand a few basic principles when looking at data. Cassie Kozyrkov, Head of Decision Intelligence at Google put together a very succinct and helpful list of “dos” and “don’ts” for interpreting Covid-19 data.

A few takeaways to keep in mind as it relates to pandemic data:

There are many different ways to measure what appears to be the same thing. The fact that some states have been lumping viral and antibody tests together and others have not is a problem. Mistakes like this happen when we don’t question how data is being measured.

Never blindly trust data or a model. While no one model is perfect in its ability to predict the future, we use models as a tool to assist with health care and resource planning. In the case of the IHME model, its inaccuracies were concerning enough to discontinue using it for policy decisions. Just like the imperfect data used to make them, data models are imperfect, too.

A better understanding of the subject matter leads to better understanding of the data. It’s a dangerous trap to fall into when we don’t have a deep knowledge of the type of data we’re looking at. Data is more accurately interpreted by those with a deep understanding of the data sources, clinical measures, and the spread of infectious diseases. There are certain areas where we do need to trust experts.

Finally, when it comes to matters of using data to make personal decisions in a pandemic, safety is the most important thing. No amount of data will make you discover that frequent hand washing, social distancing and wearing a mask are the wrong choice. As the author of The Black Swan Nassim Taleb stated, “It’s a situation where you can’t afford to be wrong even once.”

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Shannon Shallcross is Co-Founder and CEO of BetaXAnalytics

Here Are the Hottest Health Care Issues of 2020


The year 2020 marks the 10th anniversary of the Affordable Care Act. Recognized as the biggest change to our health care system since the creation of Medicare and Medicaid in 1965, many thought the Affordable Care Act (ACA) would make all of our health care problems disappear. On the contrary, it has proven to be a mile marker on a long road towards the finish line. While the ACA has had its share of successes, it also left many health care issues untouched. However one thing is for certain: we can expect a health care shift will take place based on the outcome of the 2020 presidential election.

Already dominating the Democratic debates, here are the hottest health care issues in 2020 that we will be hearing everyone talking about this year:

The Affordable Care Act—What’s Next?

The big question that needs to be decided in 2020 is what to do with the Affordable Care Act. This law was enacted to provide health coverage to many uninsured Americans including those with pre-existing conditions; it also mandated that insurance include coverage for 10 essential services (Preventive care, Maternity care, and behavioral health, to name a few). Today 9 out of 10 of Americans have health insurance. Half of Americans get their coverage through employers, 35% get coverage through Medicare and/or Medicaid, 7% buy direct from insurance companies, and roughly 9% remain uninsured.

The answer to how to move forward with the ACA is largely divided along party lines. While Republicans are looking to reduce federal regulation and funding, Democrats are focusing on expanding coverage. For Republicans, reducing regulation and spending means repealing the ACA, getting rid of Medicaid expansion and eliminating subsidies, and allowing states to respond with their own budgets rather than using federal funds for this purpose. For Democrats, expanding coverage can range from moderate solutions to cover holes left by the ACA such as a public option, to the more extreme solution of Medicare for All. However all of these options come at a cost.

Health Care Affordability

Right now health care in the United States is hitting people where they feel it most—in their wallets. Health spending currently stands at over 17% of the US GDP. Total health spending was estimated to exceed $3.8 trillion in 2019, and it’s projected to increase on average by 5.6% annually to reach almost $6 trillion by the year 2027.

A top concern for individuals are deductibles, or the amount that patients have to pay before their insurance kicks in. In the early 2000s, several economists suggested that if people had more “skin in the game,” they would become better shoppers for their health care. Fast-forward to today, and there is not clear evidence to show that deductibles are making people more responsible for their health. Now that deductibles are climbing to higher amounts in the several thousands of dollars (the average individual deductible is currently $1655), we have a problem. 4 in 10 people don’t have more than $400 set aside for emergencies, according to the Federal Reserve. So the hard truth for Americans is that many are just one illness away from serious financial hardship. One of the problems that the ACA failed to address is the issue of rising deductibles. Insurers knew they had to cover many more benefits under the ACA so many employers increased deductibles to be able to give mandated coverage while limiting the impact to their bottom line.

Surprise Medical Bills

Also in the theme of health care affordability is the issue of “Surprise Billing.” These are bills that patients get from providers outside of their network when there is no way they could have avoided the bill. While the patient may have deliberately chosen an in-network provider for their service, they could receive a surprise bill from a lab, radiologist, an anesthesiologist or assistant surgeon who was involved in their care. This is one issue where there seems to be bi-partisan agreement that a remedy is needed. 19% of Emergency Room admissions and over half of ambulance rides are out of network. This leads to higher premiums for everyone else, and is generally considered a market failure that needs to be fixed. But as with many issues within health care, it’s complicated. Patients are affected by the problem, yet providers are hurt by the reforms.

Drug pricing

The complex concern of skyrocketing drug prices in the US to was supposed to be tackled in 2019, but industry lobbying and partisan divides prevented movement on this topic last year. The United States is unique because it does not regulate or negotiate the prices of new drugs that come on the market. Elsewhere in the world, countries task a government body to negotiate these prices. Because of our lack of cost-control measures, 23.3% of each health care dollar goes to cover the cost of prescription drugs, while some individual drugs have a price tag in the millions. The cost of free market innovation in this country has led to wildly fluctuating prices, where in some cases the same drugs can be purchased at a fraction of the cost abroad. House speaker Nancy Pelosi is calling to having this issue attached to a package of other expiring health care programs that need to be renewed this year. However it is uncertain whether this issue will be resolved before the looming election.

Each of the these hot topics in 2020 ultimately fit into two categories—issues around cost and issues around coverage. Health care topics continue to come back to the fundamental need to 1) control health care costs in the United States, and 2) control the what, who and how of health coverage. So much of the discussions at this early part of the year focus on which health care delivery model can fix the problems left unsolved by the ACA of affordability, while addressing the outstanding issues of wasteful spending. Adjacent issues that will receive attention are mental health, addiction and reproductive rights—all concerns still at the top of mind for Americans. The other looming issue that will gain more attention over the next 5 years as the American population ages is the unavailability and lack of affordability of long term care. Amidst this broad range of health care hot topics, here’s to hoping that 2020 will bring about meaningful solutions.

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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 health care, pharmacy and well-being decisions, follow BetaXAnalytics on Twitter @betaxanalytics, Facebook @bxanalytics and LinkedIn at BetaXAnalytics.

Why Budgeting For Health Care Is Near Impossible


The average millennial will spend between 1/2 and 2/3 of their lifetime earnings on healthcare. This jaw-dropping estimate, outlined in David Goldhill’s book Catastrophic Care: Why Everything We Think We Know about Health Care Is Wrong, is the perfect picture of how, for Americans, the new normal involves personally budgeting for healthcare expenses. Unfortunately it’s not an easy task to break healthcare costs down to what comes out of our personal pockets.

Divided equally among each person in the U.S., healthcare’s overall price tag averages out to over $10,000 per person each year—a whopping 18% of U.S. GDP. Since employers provide 48% of the healthcare coverage in the U.S. this burden has fallen heavily on their shoulders, and, as a consequence, they have shared this cost burden with employees. The growing popularity of high deductible health plans and copays means employees are sharing a larger portion of these healthcare costs, and as such, the average person needs to budget for these costs in their financial planning.

Healthcare Costs: What Employers Pay, What Employees Pay

The Milliman Medical Index estimates medical costs each year as they relate to employer and employee contributions. Based on data from 2018, healthcare for a family of 4 in the United States costs $28,166. Of this total cost, $15,788 comes from the employer, while the employee contributes on average $7,674, with an additional $4,704 paid by the employee for out of pocket for deductibles and copays.

Here’s a snapshot of the cost breakdown for employer-sponsored health insurance:

The 2018 Milliman Medical Index estimates the total cost of healthcare for a family of 4 to be $28,166; $15,788 of this comes from the employer, $7,674 comes from the employee, and an additional $4,704 is paid by the employee in the form of deductibles and copays.

*2019 ACA Out of Pocket Maximums are $7,900/individual and $15,800/family.

These estimates are sound breakdowns based on large amounts of employer-sponsored plan data from Milliman. But do they truly inform how an individual can budget for their own healthcare expenses? Unfortunately the answer to this question is not so easy.

What Factors Influence Individual Health Spending

To understand how to budget for individual health expenses, we need to look at the levers that influence healthcare costs. And there are several factors which could cause individual health costs to largely vary. These factors are:

1.      Age and Gender. Not surprisingly, actual health costs can vary greatly based on an individual’s age and gender. The figure below from the Peterson Kaiser Health System Tracker breaks down the American population by age, and then demonstrates each age group’s share of overall health spending.

The Peterson Kaiser Health System Tracker breaks down the American population by age, and then demonstrates each age group's share of overall health spending.

2.      Individual health status. Chronic illnesses such as diabetes and cancer have a marked impact on someone’s personal healthcare costs. The Centers for Medicare and Medicaid Services (CMS) report that 90% of the nation’s $3.3 trillion dollars in healthcare spending is for people with chronic and mental health conditions.

3.      Geographic area. Differences in the costs of labor, rents and taxes in different geographic regions affect healthcare costs. Furthermore, areas of the country with more technological advances will have higher utilization rates of healthcare, further contributing to cost differences.

4.      Provider variation. A frequently criticized hallmark of the healthcare industry is that provider costs can vary widely depending on where an individual goes to seek treatment. Furthermore, different payment methodologies, pre-negotiated payment rates and capitated rates can affect healthcare costs.

5.      Insurance coverage. Richer health insurance plans tend to have higher utilization rates than budget options with less coverage. In addition, who is paying for the procedure can affect the ultimate cost. For example, what a provider is paid from Medicare (which, as demonstrated in the figure below, provides 14% of all coverage in the U.S.) and what they are paid under an employer-sponsored plan for the same exact procedure could be two different costs.

CMS breaks down the source of health cost coverage in the United States by coverage provided by employers, Medicare, Medicaid, the individual market, and other forms of coverage, while also factoring in the number of uninsured in the U.S.

What Healthcare is Costing and What is Coming Out of Our Personal Pockets Are Two Completely Different Things

In short, the reason why budgeting for individual health costs is so challenging is because our system of how we pay for healthcare masks the true cost of healthcare. The subsidization in the health insurance market muddies the waters for anyone trying to budget for their own personal healthcare costs. And just in case this wasn’t confusing enough, the rules that govern this system which determine things like out of pocket maximums, in addition to insurance rates, change every year.

An individual trying to budget for their own expenses can use a best-guess of looking at their annual share of healthcare premiums and their average out of pocket costs each year. This assumes that their own past health expenses are the best way to predict future expenses. But even this approach is not perfect. Understanding how the $170/person cost of healthcare in 1960 made up only 5% of US GDP, compared to healthcare’s current share at 18% of GDP…the past might not always be the best predictor of the future where healthcare is concerned.

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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.

Analytics For Employers: A Tutorial (Part 2)


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:


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.

Analytics For Employers: A Tutorial (Part 1)


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.

Healthcare’s Next Disruptors: Employers


The cost of healthcare in the United States is rapidly rising with no end in sight, and this cost problem is hitting the American worker’s pocket in a more profound way than ever in history. Healthcare in the U.S. is now at 18% of US GDP at a cost of over $10,000 per person annually. To put this into perspective, healthcare costs in 1960 were 5% of U.S. GDP ($170/person). 

Here’s what not everyone knows: on the other side of this problem of rising healthcare costs are pockets of people effecting serious change—these people are employers.

Forward thinking employers and the HR leaders responsible for making their healthcare decisions are thinking outside of the box to find health care models that are more effective at keeping employees healthy. These are people who are rejecting the traditional benefits model of disjointed, piecemeal solutions. Instead, these employers are using data to form a comprehensive strategy to ensure their healthcare dollars are keeping members healthy.

Consider the following developments in 2018 alone as an indication of the trends we’re seeing in the employer market:

  • Solution Scale: Amazon, Berkshire Hathaway and JPMorgan Chase announced in early 2018 their formation of a healthcare alliance to tackle finding more effective care models for their combined employee base of 1.2 million people.
  • Accountability: The National Drug Purchasing Coalition (NDPC), whose members include employers like PepsiCo and ExxonMobil, has partnered with Express Scripts to form a fully-transparent model where the NDPC pays what Express Scripts pays for prescription drugs. In turn, Express Scripts will administer a pay-for-performance clinical care model that shifts the financial risk previously borne by employers onto the prescription drug plan administrator.
  • Emphasis on Health Outcomes: GM announced a deal with the Detroit-based hospital system Henry Ford Health System for a direct contracting healthcare model for its 24,000 employees and family members
  • Using Data to Guide Health Strategy: Morgan Stanley recently announced that they have created a chief medical officer role to oversee their use of HR data and analytics. Said Morgan Stanley’s Chief Human Resources Officer Jeff Brodsky, “Harnessing our HR data, we can achieve better wellness for our employees and address rising healthcare costs.”
  • Making Healthcare Easier for Employees: Amazon and Apple have joined the 30% of employers that offer onsite medical clinics for employees and their families.

If your company is looking to help to effect change in this healthcare revolution, here are a few ways to start:

1.     Shift financial risk. Seek partners who are willing to step outside of the traditional fee-for-service healthcare models that currently put the highest financial risk on the employer (and in turn, employees). Instead, shift financial risk to care providers and other partners who directly impact health outcomes. Direct primary care is a great example of this type of accountable care model. 

2.     Gain data transparency. Get access to timely and ongoing data to drive your healthcare benefit decisions. It is easy to get inundated by mountains of complex data and trying to aggregate it with location and other benefits data, so we recommend that employers assign a strategic data subject matter expert to drive the discussion. This is what the team at BetaXAnalytics does—as data scientists with clinical, pharmacy and wellness expertise, our deep-dive into employer data is more comprehensive than any data platform on the market today.

3.     Remove barriers. Think about ways to remove the barriers that prevent employees and their families from getting the care that they need—the financial barriers, the time constraints and convenience barriers. Onsite clinics and telemedicine are just a couple of examples of strategies to make healthcare convenient and inexpensive for employees.

“Employers taking healthcare into their own hands is the most meaningful way we can change healthcare in this country”

~Bret Jackson, president of The Economic Alliance for Michigan, a member of the National Alliance of Healthcare Purchaser Coalitions

This week we presented at the Strategic HR Conference at Mount Washington to Chief Human Resource Officers and HR leaders throughout the Northeast to share best practices on how HR leaders can use data to drive their health and benefits strategy in a way that maximizes their healthcare budget. If you’re interested in learning more, email me.

Who pays for healthcare in the U.S.? We all do. By way of taxes and out of pocket premiums, we all contribute to these costs that flow largely through the government and employers. And the more informed the people are who are paying for healthcare become, the more we can effect change.

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About BetaXAnalytics:

If you’re an employer who feels there’s got to be a better way to control health care costs, you’re on to something. And we can help. BetaXAnalytics partners with employers to use the power of their health data “for good” to improve the cost and quality of their health care. By combining PhD-level expertise with the latest technology, they help employers to become savvy health consumers, to save health dollars and to better target health interventions to keep employees well. 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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