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

Data

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 covid19-projections.com 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

Healthcare

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

DataHealthcare

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)

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.

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.

Healthcare’s Next Disruptors: Employers

Healthcare

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.

Employers Are Using an Innovative Way to Soften the Financial Burden of High Deductible Health Plans

Healthcare

As a healthcare data technology company, the most common question we hear from employers is, “how can we lower our healthcare spending?” The high cost of healthcare has a significant impact on employer expenses. The Milliman Medical Index projects that in 2018 the average premium for a family of four is $28,166. While the magnitude of health costs is a reality for our employer clients, finding an effective way to manage these high costs is often their first priority.

When we drill down into cost-drivers, we consistently see a startling theme across employers—that only 5% of people are driving 51% of healthcare spending. A very small group of people with chronic conditions (such as diabetes, rheumatoid arthritis, or cancer) are contributing to half of the total health spending. While this disproportionate spending is a reality within employer populations, according to the Kaiser Family Foundation Medical Expenditure Panel Survey, this also holds true for Americans as a whole.

As a way to manage the rising cost of healthcare, nearly 40% of adults are covered by what is known as a high-deductible health plan. Under this model, employees share in a greater share of their health expenses, responsible for on average $5,248 of out of pocket medical costs each year. The thinking behind this way of cost-sharing with employees is that when employees are responsible for paying a greater share of their health expenses, that they will become better healthcare consumers by shopping for the best prices and avoiding unnecessary procedures. 

Unfortunately, in many of these cases the high out-of-pocket medical costs cause financial hardship on many people. A survey from the Kaiser Family Health Foundation found that 1/3 of adults have trouble paying their medical bills, and 73% have cut back on spending on food, clothing or basic household items to pay their medical bills. The Report on the Economic Well-Being of U.S. Households, an annual survey conducted by the Federal Reserve Board, found that 44 percent of adult Americans claim they would not have $400 in case of an emergency without turning to credit cards, family and friends, or selling their own possessions. When those who are financially strapped have mounting healthcare bills, the consequences can be personally devastating. A 2015 poll by the Robert Wood Johnson Foundation and the Harvard T.H. Chan School of Public Health found that 26 percent of those who took part in the survey claimed medical bills caused severe damage to their household’s financial wellbeing.

Because the impact of high healthcare deductibles causes such a financial hardship for individuals and their families, many recent studies have shown that this type of health plan causes individuals to delay necessary healthcare. Researchers from UC Berkeley and Harvard studied the results of a large employer’s choice to offer a high deductible plan over 2 years. Instead of finding evidence to support the theory that high-deductible plans make people take more charge of their health spending, they found no evidence to show that employees were comparing costs or cutting unnecessary services once they had a high healthcare deductible. They cut low-value health services at the same rate as they were cutting important medical services, causing the employer to question whether members were making the right choices for their long term health. Additional studies have found that the danger of high deductible health plans is that their members with the highest health risks have shown that they avoid necessary care and medications due to cost. 

On the other side of the phenomenon that 5% of people drive 51% of health costs, we see another theme that is equally surprising—half of plan members contribute to only 3% of total health spending. That’s right—a large proportion of costs come from a small number of people, yet a large number of people contribute very little to overall costs. Why is this? 

At BetaXAnalytics, when we look at employer utilization of health services we consistently find that between 10%-20% of members never see their doctor. These are the employees who either feel they simply “don’t have time” to see the doctor or “don’t have the money” to spend into their annual deductible. But it’s within this group of people who are not driving costs today where an employer’s greatest healthcare risks can lie. 

The answer for employers? Make it as easy as possible for members to get the care they need. One effective way to ensure that people aren’t avoiding necessary care is to remove the traditional financial and convenience barriers that prevent employees from seeing the doctor. Hooray Health provides a template for employers to solve this problem. They afford first dollar coverage for preventative, basic and urgent care visits with $0 deductibles, and $25 copays for all in-network visits. Their innovative network consists of over 2,400 retail clinics and urgent care centers across the country with extended hours and no appointments necessary. They also provide access to telemedicine visits via phone 24-hours a day, 7 days a week at no cost which makes getting necessary care quick and easy, even when work and family schedules make it difficult to go into a doctor’s office. Their app-based tools and live medical concierge are available 24/7 make finding care easy and convenient. As an added benefit to address employee concerns about prescription costs, Hooray Health offers a prescription discount card to ensure employees that they are receiving competitive prices for their medications.

Hooray Health removes financial and convenience barriers that prevent people from getting the care they need by making access to necessary care easy, convenient, and free for employees. This type of solution is particularly useful for employers with high deductible health plans where high out of pocket costs may deter employees and their families from seeking the necessary care that they need. While solutions like these remove barriers to care, they also save employers money by providing an affordable alternative to many of the care services needed by their members. Providing easy-to-use concierge-based access to a network of retail clinics, urgent care centers and telemedicine doctors ensures that employee health won’t neglect their health due to lack of money or lack of time. You can learn more by contacting them at info@hoorayhealthcare.com

* * *

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.

About Hooray Health:

Since its founding in 2017, Hooray Health has been committed to disrupting the health insurance industry by providing employers, individuals and their families with the assurance that their basic healthcare needs are covered. Here at Hooray Health, we believe that healthcare should be simple, honest, and affordable, that’s why whether you apply online or over the phone, the process is always simple, and acceptance is guaranteed. Partnered with over 2,300 urgent care and retail clinics, and a 24/7 medical concierge team, Hooray Health members know that no matter where they are or what time it is, their healthcare is there for them. Starting plans have a low monthly cost with no annual deductible, an affordable copay, and no surprise balance bills. Every day, Hooray Health is smashing the industry norms and bringing healthcare to all.

Image credit: iStockPhoto

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.

Risk Adjustment: 5 Things You Should Know About the Latest in the Unraveling of the ACA

Healthcare

$10.4 billion is in limbo, representing roughly 11% of individual market health insurance premium dollars. This is the latest headline on the unraveling of the Affordable Care Act as the government recently made a disruptive announcement that they would be freezing risk adjustment payments that were expected to be paid in the fall. While on the surface this sounds like a doomsday scenario, healthcare’s future in America may look less bleak than it sounds. Here are 5 things you should know about this trending topic.

1.      What is Risk Adjustment?

Risk adjustment has played a big part in the ACA to encourage insurers to cover people regardless of their health status instead of cherry-picking the healthiest people. This is not to be confused with other ObamaCare subsidy programs. Instead, risk adjustment payments shuffle money from insurers with relatively healthy populations to insurers with unhealthy members. As a way to keep this system fair, insurers with the healthiest members pay money to those with the least healthy members to cover their more expensive costs of care. This system was designed to remain budget-neutral. This most recent decision to freeze risk adjustment payments equates to $10.4 billion tied to the individual and small group health insurance markets for the 2017 plan year. For reference, risk adjustment transfers play a large role in the current insurance system, comprising 11% of total premium dollars in the individual market in 2016. 

2.      What led to this announcement that risk adjustment payments would be suspended?

The government has announced that risk adjustment payments expected for the fall of 2018 would be frozen due to a February 2018 ruling of the US District Court for the District of New Mexico. This ruling pertained to a complaint made by a New Mexico insurer who claimed that the risk adjustment formula was inaccurate and was disproportionately rewarding larger insurers. The New Mexico federal judge on the case ruled that the risk adjustment formula is unfair because it uses the statewide average premium in the calculation, whereas some have argued that using the carrier’s own average premium would be fairer. It is interesting to note that this New Mexico ruling from February 2018 conflicts with a ruling in a similar case from just one month earlier in Massachusetts, where the federal court sided in favor of using the statewide average premiums in the risk adjustment methodology.

3.      Why should we care?

An announcement like this spreads chaos that comes with uncertainty during an already challenging time for American healthcare. Payers who were expecting risk adjustment payments prior to this freeze would lose billions, as presented in CMS’s recent summaryreport of the 2017 risk adjustment program.

Insurers’ 2019 rate filings are in progress, and the $10.4 billion in risk adjustment payments factor heavily in how these health insurance plans are priced. Furthermore, suspension of these payments in 2018 could lead to financial solvency challengers for insurers, which would lead to higher premiums.

4.      What do the experts say?

Despite the current uncertainty, many policy experts believe that the risk adjustment payment freeze will be lifted, and payments will be ultimately made in the fall. The Centers for Medicare and Medicaid Services (CMS) released a statement saying that the agency has asked the New Mexico court to reconsider its decision and expressed hope for a prompt resolution of the issue. 

5.      Where do we go from here?

The government could do a few things that would immediately fix this issue:

·        File an interim final rule which could address the risk adjustment formula concerns raised by the court  

·        Ask for a stay on the impact of the New Mexico court ruling

·        Appeal the ruling

·        Ask the court to clarify that the ruling only applies to plans sold in New Mexico

Whether any of these things will actually happen is still unknown. We can expect to hear the result of CMS’s request for reconsideration of the New Mexico decision, as well as CMS’s further clarification on the status of the risk adjustment payments in the near future.           

For more information on risk adjustment, the following sources provide a well-rounded view of the issue:

Politico – Obamacare Insurance Rate Hikes

CMS 2017 Summary Report Risk Adjustment 2017

Wall Street Journal – Trump’s Latest Affordable Care Act Move Adds to Insurers Uncertainty

The Incidental Economist – Taking a Dive on Risk Adjustment

Fierce Healthcare – Examining the Future of the ACA

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BetaXAnalytics combines clinical data science with emerging technology to improve the cost and quality of health care for payers, providers and employers. Our risk adjustment natural language processing tools help payers and providers to improve coding accuracy and to maximize their financial performance. For more insights on using data to drive healthcare, pharmacy and wellbeing decisions, follow BetaXAnalytics on Twitter @betaxanalytics, Facebook @bxanalytics and LinkedIn at BetaXAnalytics.

How Much of Our Healthcare is “Waste”?

Healthcare

It’s no secret that healthcare costs in the United States are skyrocketing, as the latest estimates put our annual healthcare spending at 18% of the U.S. GDP. Furthermore, the health of Americans ranks among the worst compared to health outcomes in similar developed countries. This complex reality is aggravated by a number of problems. Much of the care that we receive is known as “fee-for-service”—we pay for each medical service or test we receive. What this means for patients is that the more they see their doctor, the more they pay. This is problematic for many Americans, as nearly 40% have a high deductible health plan where individuals are responsible for on average $5,248 of out of pocket medical expenses each year. A survey from the Kaiser Family Health Foundation found that 1/3 of adults have trouble paying their medical bills, and 73% have cut back on spending on food, clothing or basic household items to pay their medical bills. With the cost of healthcare in the U.S. being such a recognized problem, one of the many concerns that contributes to this issue is what is known as “low value care,” or in other terms, wasteful healthcare services. 

Choosing Wisely® is an international initiative that promotes facilitating conversations between patients and their doctors about the clinical need for low value care. The goal is to reduce wasteful medical tests and unnecessary health services.  As part of this initiative, over 80 partners have published an extensive list of clinically-vetted recommendations that represent medical best practices. A 2014 study of the Virginia All Payer Claims Database applied a list of these clinical recommendations and found that low cost, high volume health services contribute the most to unnecessary health spending; wasteful spending in this study was estimated at $538 million.

What does spending on wasteful healthcare services look like at an employer level? One of our clients, a large, self-insured employer, was interested in understanding if wasteful health spending was an area of concern for their members. To answer this question, we looked through 3 years of their medical and pharmacy claims to pull out services that were deemed “clinically wasteful.” From this data, we narrowed our focus to 11 specific measures of wasteful services which are based on areas of waste that are clinically validated under both the Choosing Wisely® initiative and HEDIS. These 11 areas are as follows:

  1. Don’t do imaging for an uncomplicated headache.
  2. Don’t perform PAP smears on women younger than 21.
  3. Don’t perform PAP smears on women who had hysterectomy for non-cancer disease.
  4. Don’t perform routine annual PAP tests in women 30–65 years of age.
  5. Don’t diagnose or manage asthma without spirometry.
  6. Members with a primary diagnosis of low back pain should not have an imaging study (plain x-ray, MRI, CT scan) within 28 days (4 weeks) of diagnosis.
  7. Don’t indiscriminately prescribe antibiotics for uncomplicated acute rhinosinusitis.
  8. Don’t order sinus computed tomography (CT) for uncomplicated acute rhinosinusitis.
  9. In the evaluation of simple syncope and normal neurological examination, don’t obtain brain imaging studies (CT or MRI).
  10. Don’t do CT for evaluation of suspected appendicitis in children until after ultrasound has been considered.
  11. Don’t recommend follow-up imaging for clinically inconsequential adnexal cysts.

Out of the entire member population consisting of employees, spouses and children who were covered under the company’s health benefits, there were 2515 members who received care in the 11 categories of care that we studied. Our study found that half (1274) of these people received medical services that are considered clinically wasteful.

15.5% of spending in these 11 categories was considered to be wasteful. Out of $1.47 million spent on care within these 11 categories, $229k was spent on these clinically wasteful services. 

There are a few limitations to keep in mind while evaluating these measures of waste. First, we examined 11 out of over 500 clinical best practices. This is because we wanted to focus on data points that were more accurately represented by claims data, as EMR notes were not available as part of this study. What we measured were specific types of waste and we did not calculate the total amount of “healthcare waste” in this employer’s spending. Furthermore, our study examined direct claims-based costs and made no assumptions of additional downstream medical costs for those who received low value care services. 

What do these findings mean for this company and others who are paying for healthcare? Healthcare waste (low value care) is a reality in our healthcare system today. Patients, providers, and those paying for care are all affected. Even in this study that examined health spending in one company, out of the 11 areas of clinical waste we studied we found that half of the people receiving care in these categories received low value services. Initiatives such as Choosing Wisely® are effective in raising awareness of low value care and opening conversations between patients and their doctors to avoid unnecessary medical tests and treatments. You can find more information on this educational initiative at www.choosingwisely.org

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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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@SRShallcross on Twitter

Tuesday, September 22nd, 2020 at 11:14am
Whether you're an entrepreneur, a mover/shaker, or just someone looking for a fantastic virtual networking event--Get Started Rhode Island is next Wednesday, September 30 at 5:30.
Registration is open to the public and free via this link --> https://t.co/mmI7Ly7A1A
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Thursday, September 17th, 2020 at 11:09am
Get Started Rhode Island will air virtually on September 30 at 5:30 PM. Register today at https://t.co/Zy28GwfQ3A and then download the Whova App to interact with the finalists/other attendees and watch the event. Discover who wins up to $50,000 in cash and prizes! #GetStartedRI https://t.co/xi356PgVUk SRShallcross photo
Thursday, September 10th, 2020 at 1:42pm
“The U.S. health care system supposedly runs as a free market, yet is really a broken patchwork of competing public, private, state, county, and federal health care entities. “

The interstate highway system as a model for U.S. health care - STAT https://t.co/9xSrZ66Ytz

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