The Worst-Case COVID-19 Predictions Turned Out To Be Wrong. So Did the Best-Case Predictions.

http://www.reason.com/2021/06/22/

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An argument for humility in the face of pandemic forecasting unknown unknowns.

“Are we battling an unprecedented pandemic or panicking at a computer generated mirage?” I asked at the beginning of the COVID-19 pandemic on March 18, 2020. Back then the Imperial College London epidemiological model’s baseline scenario projected that with no changes in individual behaviors and no public health interventions, more than 80 percent of Americans would eventually be infected with novel coronavirus and about 2.2 million would die of the disease. This implies that 0.8 percent of those infected would die of the disease. This is about 8-times worse than the mortality rate from seasonal flu outbreaks.

Spooked by these dire projections, President Donald Trump issued on March 16 his Coronavirus Guidelines for America that urged Americans to “listen to and follow the directions of STATE AND LOCAL AUTHORITIES.” Among other things, Trump’s guidelines pressed people to “work or engage in schooling FROM HOME whenever possible” and “AVOID SOCIAL GATHERINGS in groups of more than 10 people.” The guidelines exhorted Americans to “AVOID DISCRETIONARY TRAVEL, shopping trips and social visits,” and that “in states with evidence of community transmission, bars, restaurants, food courts, gyms, and other indoor and outdoor venues where people congregate should be closed.”

Let’s take a moment to recognize just how blindly through the early stages of the pandemic we—definitely including our public health officials—were all flying at the time. The guidelines advised people to frequently wash their hands, disinfect surfaces, and avoid touching their faces. Basically, these were the sort of precautions typically recommended for influenza outbreaks. On July 9, 2020, an open letter from 239 researchers begged the World Health Organization and other public health authorities to recognize that COVID-19 was chiefly spread by airborne transmission rather than via droplets deposited on surfaces. The U.S. Centers for Disease Control and Prevention (CDC) didn’t update its guidance on COVID-19 airborne transmission until May 2021. And it turns out that touching surfaces is not a major mode of transmission for COVID-19.

The president’s guidelines also advised, “IF YOU FEEL SICK, stay home. Do not go to work.” This sensible advice, however, missed the fact that a huge proportion of COVID-19 viral transmission occurred from people without symptoms. That is, people who feel fine can still be infected and, unsuspectingly, pass along their virus to others. For example, one January 2021 study estimated that “59% of all transmission came from asymptomatic transmission, comprising 35% from presymptomatic individuals and 24% from individuals who never develop symptoms.”

The Imperial College London’s alarming projections did not go uncontested. A group of researchers led by Stanford University medical professor Jay Bhattacharya believed that COVID-19 infections were much more widespread than the reported cases indicated. If the Imperial College London’s hypothesis were true, Bhattacharya and his fellow researchers argued, that would mean that the mortality rate and projected deaths from the coronavirus would be much lower, making the pandemic much less menacing.

The researchers’ strategy was to blood test people in Santa Clara and Los Angeles Counties in California to see how many had already developed antibodies in response to coronavirus infections. Using those data, they then extrapolated what proportion of county residents had already been exposed to and recovered from the virus.

Bhattacharya and his colleagues preliminarily estimated that between 48,000 and 81,000 people had already been infected in Santa Clara County by early April, which would mean that COVID-19 infections were “50-85-fold more than the number of confirmed cases.” Based on these data the researchers calculated that toward the end of April “a hundred deaths out of 48,000-81,000 infections corresponds to an infection fatality rate of 0.12-0.2%.” As I optimistically reported at the time, that would imply that COVID-19’s lethality was not much different than for seasonal influenza.

Bhattacharya and his colleagues conducted a similar antibody survey in Los Angeles County. That study similarly asserted that COVID-19 infections were much more widespread than reported cases. The study estimated 2.8 to 5.6 percent of the residents of Los Angeles County had been infected by early April. That translates to approximately 221,000 to 442,000 adults in the county who have had the infection. “That estimate is 28 to 55 times higher than the 7,994 confirmed cases of COVID-19 reported to the county by the time of the study in early April,” noted the accompanying press release. “The number of COVID-related deaths in the county has now surpassed 600.” These estimates would imply a relatively low infection fatality rate of between 0.14 and 0.27 percent. 

Unfortunately, from the vantage of 14 months, those hopeful results have not been borne out. Santa Clara County public health officials report that there have been 119,712 diagnosed cases of COVID-19 so far. If infections were really being underreported by 50-fold, that would suggest that roughly 6 million Santa Clara residents would by now have been infected by the coronavirus. The population of the county is just under 2 million. Alternatively, extrapolating a 50-fold undercount would imply that when 40,000 diagnosed cases were reported on July 11, 2020, all 2 million people living in Santa Clara County had been infected by that date.

Los Angeles County reports 1,247,742 diagnosed COVID-19 cases cumulatively. Again, if infections were really being underreported 28-fold, that would imply that roughly 35 million Angelenos out of a population of just over 10 million would have been infected with the virus by now. Again turning the 28-fold estimate on its head, that would imply that all 10 million Angelenos would have been infected when 360,000 cases had been diagnosed on November 21, 2020.

COVID-19 cases are, of course, being undercounted. Data scientist Youyang Gu has been consistently more accurate than many of the other researchers parsing COVID-19 pandemic trends. Gu estimates that over the course of the pandemic, U.S. COVID-19 infections have roughly been 4-fold greater than diagnosed cases. Applying that factor to the number of reported COVID-19 cases would yield an estimate of 480,000 and 5,000,000 total infections in Santa Clara and Los Angeles respectively. If those are ballpark accurate, that would mean that the COVID-19 infection fatality rate in Santa Clara is 0.46 percent and is 0.49 percent in Los Angeles. Again, applying a 4-fold multiplier to take account of undercounted infections, those are both just about where the U.S. infection fatality rate of 0.45 percent is now.

The upshot is that, so far, we have ended up about half-way between the best case and worst case scenarios sketched out at the beginning of the pandemic.

Permanent expansions of government data collection will support policy innovation

We rarely see the impact of policies reflected in data in real time. The COVID-19 pandemic changed that. In the present moment, a range of government, private, and academic sources catalogue household-level health and economic information to enable rapid policy analysis and response. To continue promoting periodic findings, identifying vulnerable populations, and maintaining a focus on public health, frequent national data collection needs to be improved and expanded permanently.

Knowledge accumulates over time, facilitating new advancements and advocacy. While mRNA biotechnology was not usable decades ago, years of public research helped unlock highly effective COVID-19 vaccines. The same can be true for advancing effective socioeconomic policies. More national, standardized data like the Census Bureau’s Household Pulse Survey will accelerate progress. At the same time, there are significant issues with national data sources. For instance, COVID-19 data reported by the CDC faced notable quality issues and inconsistencies between states.

Policymakers can’t address problems that they don’t know exist. Researchers can’t identify problems and solutions without adequate data. We can better study how policies impact population health and inform legislative action with greater federal funding dedicated to wide-ranging, systematized population surveys.

Broader data collection enables more findings and policy development

Evidence-based research is at the core of effective policy action. Surveillance data indicates what problems families face, who is most affected, and which interventions can best promote health and economic well-being. These collections can inform policy responses by reporting information on the demographics disproportionately affected by socioeconomic disruptions. Race and ethnicity, age, gender, sexual orientation, household composition, and work occupation all provide valuable details on who has been left behind by past and present legislative choices.

Since March 2020, COVID-19 cases and deaths, changes in employment, and food and housing security have been tracked periodically with detailed demographic information through surveys like the   Both cumulative statistical compilations and representative surveillance polling have been instrumental to analyses.  Our team has recorded over 200 state-level policies in the COVID-19 US State Policy (CUSP) database to further research and journalistic investigations. We have learned a number of policy lessons, from the health protections of eviction moratoria to the food security benefits of social insurance expansions. Not to be forgotten is the importance of documented evidence to these insights.

Without this comprehensive tracking, it would be difficult to determine the number of evictions occurring despite active moratoria, what factors contribute to elevated risk of COVID-19, and the value of pandemic unemployment insurance programs in states. The wider number of direct and indirect health outcomes measured have bolstered our understanding of the suffering experienced by different demographic groups. These issues are receiving legislative attention, in no small part due to the broad statistical collection and subsequent analytical research on these topics.

Insufficient data results in inadequate understanding of policy issues

The more high-quality data there is, the better. With the state-level policies present in CUSP, our team and other research groups quantified the impact of larger unemployment insurance benefit sizes, greater minimum wages, mask mandates, and eviction freezes. These analyses have been utilized by state and federal officials. None would have been possible without increased data collection.

However, our policy investigations are constrained by the data availability and quality on state and federal government websites, which may be improved with stimulus funds allocated to modernize our public health data infrastructure. Some of the most consequential decision-making right now relates to vaccine distribution and administration, but it is difficult to disaggregate state-level statistics. Many states lack demographic information on vaccine recipients as well as those that have contracted or died from COVID-19. Even though racial disparities are present in COVID-19 cases, hospitalizations, and deaths nationally, we can’t always determine the extent of these inequities locally. These present issues are a microcosm of pre-existing problems.

Data shortcomings present for years, in areas like occupational safety, are finally being spotlighted due to the pandemic. Minimal national and state workplace health data translated  to insufficient COVID-19 surveillance in workplace settings. Studies that show essential workers are facing elevated risk of COVID-19 are often limited in scope to individual states or cities, largely due to the lack of usable and accessible data. More investment is needed going forward beyond the pandemic to better document a   Otherwise there will continue to be serious blind spots in the ability to evaluate policy decisions, enforce better workplace standards, and hold leaders accountable for choices.

These are problems with a simple solution: collect more information. Now is not the time to eliminate valuable community surveys and aggregate compilations, but to expand on them. More comprehensive data will provide a spotlight on current and future legislative choices and improve the understanding of policies in new ways. It is our hope that   are built upon and become the new norm.

Disclosure: Funding received from Robert Wood Johnson Foundation was used to develop the COVID-19 US State Policy Database.

Deepening the role of Big Tech in analyzing clinical data

How Big Tech Is Changing the Way Hospitals Are Run | Technology Networks

HCA Healthcare, the nation’s largest for-profit hospital chain, which operates 185 hospitals and more than 2,000 care sites across 20 states, announced a landmark deal with search giant Google this week, aimed at extracting and analyzing data from more than 32M annual patient encounters.

The multiyear partnership will involve data scientists from both companies working together to develop care algorithms and clinical alerts to improve outcomes and efficiency. Data from HCA’s electronic health records will be integrated with Google’s cloud computing service, and the companies have pledged to adhere to strict limitations to protect individual patient privacy—a key concern raised by regulators after Google announced a similar partnership with another national health system, Ascension, at the end of 2019.

Despite those assurances, some experts pointed to this week’s announcement as further evidence that existing privacy protections are insufficient in the face of the deepening relationships between tech companies, like Google and Microsoft, and healthcare providers, who manage the sensitive health information of millions of patients.
 
We’d agree—we’re overdue for a major rethink of how patient privacy is handled. The healthcare industry spent much of the last decade “wiring” the health system, converting from paper records to electronic ones, and building vast storehouses of clinical data along the way. We’ve now reached a new phase, and the primary task ahead is to harness all of that data to actually improve care. That will require extensive data sharing, such as a recently announced initiative among several major health systems, and will also entail tapping the expertise of “big data” companies from beyond healthcare—the very same companies whose business practices have sometimes raised privacy concerns in the broader social context. But health information is different—more personal and more sensitive—than data about shopping preferences and viewing habits, requiring more rigorous regulation. 

As more big data deals are inked in healthcare, the question of patient privacy will become increasingly pressing.

JPMorgan launches healthcare company, Morgan Health

JPMorgan Chase launches new healthcare-focused unit for U.S. employees |  Reuters

JPMorgan Chase on May 20 unveiled its new healthcare company, dubbed Morgan Health, which its top executive told Becker’s Hospital Review can be viewed as a continuation of Haven, an ambitious healthcare venture that recently disbanded

“We learned a lot from the Haven experience,” Dan Mendelson, CEO of Morgan Health, said. “The Haven experience focused us on primary care, digital medicine and specific populations. … You can see this as a continuation of the work that was started at Haven.” 

However, Mr. Mendelson said there are several key differences between Morgan Health and Haven, the healthcare venture launched by Amazon, Berkshire Hathaway and JPMorgan Chase in 2018. For one, it has a much more simplified business structure, as it is a unit of JPMorgan Chase. Second, it has a philosophy of striking partnerships to meet its goals rather than working from the ground up. 

“We don’t want to create things from scratch,” Mr. Mendelson said. “We are going to be collaborating with outstanding healthcare organizations nationally to accomplish our objectives. That’s another piece that differentiates this effort from the prior one.” 

Morgan Health said its new business is focused on improving employer-sponsored healthcare in the U.S. and bringing meaningful innovation into the industry by targeting insurance and keeping populations healthy. Success for the company will be measured by whether it improves the Triple Aim: quality of care, access to care and cost to deliver care, Mr. Mendelson said. Morgan Health initially will focus its efforts on improving care for JPMorgan Chase employees, but its long-term goals are to become a leader at improving healthcare in the U.S. and to create a successful model other employers can adopt.

“We come at this with the benefit of having 285,000 employees and dependents,” Mr. Mendelson said. “We have a very strong interest in driving quality improvements for them and also creating models that are reproducible across organizations. We are looking to take a leadership role to improve care in the United States.” 

Morgan Health said it has three core focus areas at its launch: improving healthcare by investing $250 million into organizations that are improving employer-sponsored healthcare; piloting new benefits for employees; and promoting healthcare equity for its employees and the broader community. 

One employee benefit Morgan Health will be piloting is advanced primary care, Mr. Mendelson said. Morgan Health said it is working to create improved primary care capacity to enable employees to better navigate the healthcare system. One example of this is instead of having employees see just a primary care physician, they would be directed to a clinic that leverages more healthcare talent, such as pharmacists and nurses, to improve health outcomes. 

Morgan Health said it will work with a range of partners, including provider groups, health plans and other employers. One such organization is CVS Health/Aetna, which is one of JPMorgan Chase’s insurance carriers, Mr. Mendelson said. 

“CVS Health has a lot of innovation within the organization that we are not currently tapping into,” Mr. Mendelson said. “It’s a great example of a great American company that is ripe for further partnership and innovation in this effort.” 

Morgan Health initially will have 20 dedicated employees, but Mr. Mendelson said the healthcare unit is tapping talent from other existing departments at JPMorgan Chase, including its legal, communications and benefits departments. 

“This is a company that is very passionate about leading; there’s a very deep reservoir of support from the organization to accomplish the objectives,” Mr. Mendelson said. “These are objectives that are hard — it will take us time to accomplish and to show meaningful improvement. But there’s a sense that this is so important that there’s going to be a sustained effort in this regard and that we will achieve our objectives together.” 

Prior to joining Morgan Health, Mr. Mendelson served as an operating partner at private equity firm Welsh, Carson, Anderson & Stowe. He also is the founder and former CEO of healthcare advisory firm Avalere Health and worked in the White House Office of Management and Budget during the Clinton administration. 

Mr. Mendelson said his passion for establishing collaborative partnerships in healthcare will help him succeed in his new role. 

The Big Tech of Health Care

https://prospect.org/health/big-tech-of-health-care-united-optum-change-merger/

Optum, a subsidiary of UnitedHealth, provides data analytics and infrastructure, a pharmacy benefit manager called OptumRx, a bank providing patient loans called Optum Bank, and more.

It’s not often that the American Hospital Association—known for fun lobbying tricks like hiring consultants to create studies showing the benefits of hospital mergers—directly goes after another consolidation in the industry.

But when the AHA caught wind of UnitedHealth Group subsidiary Optum’s plans, announced in January 2021, to acquire data analytics firm Change Healthcare, they offered up some fiery language in a letter to the Justice Department. The acquisition … will concentrate an immense volume of competitively sensitive data in the hands of the most powerful health insurance company in the United States, with substantial clinical provider and health insurance assets, and ultimately removes a neutral intermediary.”

If permitted to go through, Optum’s acquisition of Change would fundamentally alter both the health data landscape and the balance of power in American health care. UnitedHealth, the largest health care corporation in the U.S., would have access to all of its competitors’ business secrets. It would be able to self-preference its own doctors. It would be able to discriminate, racially and geographically, against different groups seeking insurance. None of this will improve public health; all of it will improve the profits of Optum and its corporate parent.

Despite the high stakes, Optum has been successful in keeping this acquisition out of the public eye. Part of this PR success is because few health care players want to openly oppose an entity as large and powerful as UnitedHealth. But perhaps an even larger part is that few fully understand what this acquisition will mean for doctors, patients, and the health care system at large.

If regulators allow the acquisition to take place, Optum will suddenly have access to some of the most secret data in health care.

UnitedHealth is the largest health care entity in the U.S., using several metrics. United Healthcare (the insurance arm) is the largest health insurer in the United States, with over 70 million members, 6,500 hospitals, and 1.4 million physicians and other providers. Optum, a separate subsidiary, provides data analytics and infrastructure, a pharmacy benefit manager called OptumRx, a bank providing patient loans called Optum Bank, and more. Through Optum, UnitedHealth also controls more than 50,000 affiliated physicians, the largest collection of physicians in the country.

While UnitedHealth as a whole has earned a reputation for throwing its weight around the industry, Optum has emerged in recent years as UnitedHealth’s aggressive acquisition arm. Acquisitions of entities as varied as DaVita’s dialysis physicians, MedExpress urgent care, and Advisory Board Company’s consultants have already changed the health care landscape. As Optum gobbles up competitors, customers, and suppliers, it has turned into UnitedHealth’s cash cow, bringing in more than 50 percent of the entity’s annual revenue.

On a recent podcast, Chas Roades and Dr. Lisa Bielamowicz of Gist Healthcare described Optum in a way that sounds eerily similar to a single-payer health care system. “If you think about what Optum is assembling, they are pulling together now the nation’s largest employers of docs, owners of one of the country’s largest ambulatory surgery center chains, the nation’s largest operator of urgent care clinics,” said Bielamowicz. With 98 million customers in 2020, OptumHealth, just one branch of Optum’s services, had eyes on roughly 30 percent of the U.S. population. Optum is, Roades noted, “increasingly the thing that ate American health care.”

Optum has not been shy about its desire to eventually assemble all aspects of a single-payer system under its own roof. “The reason it’s been so hard to make health care and the health-care system work better in the United States is because it’s rare to have patients, providers—especially doctors—payers, and data, all brought together under an organization,” OptumHealth CEO Wyatt Decker told Bloomberg. “That’s the rare combination that we offer. That’s truly a differentiator in the marketplace.” The CEO of UnitedHealth, Andrew Witty, has also expressed the corporation’s goal of “wir[ing] together” all of UnitedHealth’s assets.

Controlling Change Healthcare would get UnitedHealth one step closer to creating their private single-payer system. That’s why UnitedHealth is offering up $13 billion, a 41 percent premium on the public valuation of Change. But here’s why that premium may be worth every penny.

Change Healthcare is Optum’s leading competitor in pre-payment claims integrity; functionally, a middleman service that allows insurers to process provider claims (the receipts from each patient visit) and address any mistakes. To clarify what that looks like in practice, imagine a patient goes to an in-network doctor for an appointment. The doctor performs necessary procedures and uses standardized codes to denote each when filing a claim for reimbursement from the patient’s insurance coverage. The insurer then hires a reviewing service—this is where Change comes in—to check these codes for accuracy. If errors are found in the coded claims, such as accidental duplications or more deliberate up-coding (when a doctor intentionally makes a patient seem sicker than they are), Change will flag them, saving the insurer money.

The most obvious potential outcome of the merger is that the flow of data will allow Optum/UnitedHealth to preference their own entities and physicians above others.

To accurately review the coded claims, Change’s technicians have access to all of their clients’ coverage information, provider claims data, and the negotiated rates that each insurer pays.

Change also provides other services, including handling the actual payments from insurers to physicians, reimbursing for services rendered. In this role, Change has access to all of the data that flows between physicians and insurers and between pharmacies and insurers—both of which give insurers leverage when negotiating contracts. Insurers often send additional suggestions to Change as well; essentially their commercial secrets on how the insurer is uniquely saving money. Acquiring Change could allow Optum to see all of this.

Change’s scale (and its independence from payers) has been a selling point; just in the last few months of 2020, the corporation signed multiple contracts with the largest payers in the country.

Optum is not an independent entity; as mentioned above, it’s owned by the largest insurer in the U.S. So, when insurers are choosing between the only two claims editors that can perform at scale and in real time, there is a clear incentive to use Change, the independent reviewer, over Optum, a direct competitor.

If regulators allow the acquisition to take place, Optum will suddenly have access to some of the most secret data in health care. In other words, if the acquisition proceeds and Change is owned by UnitedHealth, the largest health care corporation in the U.S. will own the ability to peek into the book of business for every insurer in the country.

Although UnitedHealth and Optum claim to be separate entities with firewalls that safeguard against anti-competitive information sharing, the porosity of the firewall is an open question. As the AHA pointed out in their letter to the DOJ, “[UnitedHealth] has never demonstrated that the firewalls are sufficiently robust to prevent sensitive and strategic information sharing.”

In some cases, this “firewall” would mean asking Optum employees to forget their work for UnitedHealth’s competitors when they turn to work on implementing changes for UnitedHealth. It is unlikely to work. And that is almost certainly Optum’s intention.

The most obvious potential outcome of the merger is that the flow of data will allow Optum/UnitedHealth to preference their own entities and physicians above others. This means that doctors (and someday, perhaps, hospitals) owned by the corporation will get better rates, funded by increased premiums on patients. Optum drugs might seem cheaper, Optum care better covered. Meanwhile, health care costs will continue to rise as UnitedHealth fuels executive salaries and stock buybacks.

UnitedHealth has already been accused of self-preferencing. A large group of anesthesiologists filed suit in two states last week, accusing the company of using perks to steer surgeons into using service providers within its networks.

Even if UnitedHealth doesn’t purposely use data to discriminate, the corporation has been unable to correct for racially biased data in the past.

Beyond this obvious risk, the data alterations caused by the Change acquisition could worsen existing discrimination and medical racism. Prior to the acquisition, Change launched a geo-demographic analytics unit. Now, UnitedHealth will have access to that data, even as it sells insurance to different demographic categories and geographic areas.

Even if UnitedHealth doesn’t purposely use data to discriminate, the corporation has been unable to correct for racially biased data in the past, and there’s no reason to expect it to do so in the future. A study published in 2019 found that Optum used a racially biased algorithm that could have led to undertreating Black patients. This is a problem for all algorithms. As data scientist Cathy O’Neil told 52 Insights, “if you have a historically biased data set and you trained a new algorithm to use that data set, it would just pick up the patterns.” But Optum’s size and centrality in American health care would give any racially biased algorithms an outsized impact. And antitrust lawyer Maurice Stucke noted in an interview that using racially biased data could be financially lucrative. “With this data, you can get people to buy things they wouldn’t otherwise purchase at the highest price they are willing to pay … when there are often fewer options in their community, the poor are often charged a higher price.”

The fragmentation of American health care has kept Big Data from being fully harnessed as it is in other industries, like online commerce. But Optum’s acquisition of Change heralds the end of that status quo and the emergence of a new “Big Tech” of health care. With the Change data, Optum/UnitedHealth will own the data, providers, and the network through which people receive care. It’s not a stretch to see an analogy to Amazon, and how that corporation uses data from its platform to undercut third parties while keeping all its consumers in a panopticon of data.

The next step is up to the Department of Justice, which has jurisdiction over the acquisition (through an informal agreement, the DOJ monitors health insurance and other industries, while the FTC handles hospital mergers, pharmaceuticals, and more). The longer the review takes, the more likely it is that the public starts to realize that, as Dartmouth health policy professor Dr. Elliott Fisher said, “the harms are likely to outweigh the benefits.”

There are signs that the DOJ knows that to approve this acquisition is to approve a new era of vertical integration. In a document filed on March 24, Change informed the SEC that the DOJ had requested more information and extended its initial 30-day review period. But the stakes are high. If the acquisition is approved, we face a future in which UnitedHealth/Optum is undoubtedly “the thing that ate American health care.”

The High Price of Lowering Health Costs for 150 Million Americans

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https://one.npr.org/?sharedMediaId=968920752:968920754

The Problem

Employers — including companies, state governments and universities — purchase health care on behalf of roughly 150 million Americans. The cost of that care has continued to climb for both businesses and their workers.

For many years, employers saw wasteful care as the primary driver of their rising costs. They made benefits changes like adding wellness programs and raising deductibles to reduce unnecessary care, but costs continued to rise. Now, driven by a combination of new research and changing market forces — especially hospital consolidation — more employers see prices as their primary problem.

The Evidence

The prices employers pay hospitals have risen rapidly over the last decade. Those hospitals provide inpatient care and increasingly, as a result of consolidation, outpatient care too. Together, inpatient and outpatient care account for roughly two-thirds of employers’ total spending per employee.

By amassing and analyzing employers’ claims data in innovative ways, academics and researchers at organizations like the Health Care Cost Institute (HCCI) and RAND have helped illuminate for employers two key truths about the hospital-based health care they purchase:

1) PRICES VARY WIDELY FOR THE SAME SERVICES

Data show that providers charge private payers very different prices for the exact same services — even within the same geographic area.

For example, HCCI found the price of a C-section delivery in the San Francisco Bay Area varies between hospitals by as much as:$24,107

Research also shows that facilities with higher prices do not necessarily provide higher quality care. 

2) HOSPITALS CHARGE PRIVATE PAYERS MORE

Data show that hospitals charge employers and private insurers, on average, roughly twice what they charge Medicare for the exact same services. A recent RAND study analyzed more than 3,000 hospitals’ prices and found the most expensive facility in the country charged employers:4.1xMedicare

Hospitals claim this price difference is necessary because public payers like Medicare do not pay enough. However, there is a wide gap between the amount hospitals lose on Medicare (around -9% for inpatient care) and the amount more they charge employers compared to Medicare (200% or more).

Employer Efforts

A small but growing group of companies, public employers (like state governments and universities) and unions is using new data and tactics to tackle these high prices. (Learn more about who’s leading this work, how and why by listening to our full podcast episode in the player above.)

Note that the employers leading this charge tend to be large and self-funded, meaning they shoulder the risk for the insurance they provide employees, giving them extra flexibility and motivation to purchase health care differently. The approaches they are taking include:


Steering Employees

Some employers are implementing so-called tiered networks, where employees pay more if they want to continue seeing certain, more expensive providers. Others are trying to strongly steer employees to particular hospitals, sometimes know as centers of excellence, where employers have made special deals for particular services.

Purdue University, for example, covers travel and lodging and offers a $500 stipend to employees that get hip or knee replacements done at one Indiana hospital.

Negotiating New Deals

There is a movement among some employers to renegotiate hospital deals using Medicare rates as the baseline — since they are transparent and account for hospitals’ unique attributes like location and patient mix — as opposed to negotiating down from charges set by hospitals, which are seen by many as opaque and arbitrary. Other employers are pressuring their insurance carriers to renegotiate the contracts they have with hospitals.

In 2016, the Montana state employee health planled by Marilyn Bartlett, got all of the state’s hospitals to agree to a payment rate based on a multiple of Medicare. They saved more than $30 million in just three years. Bartlett is now advising other states trying to follow her playbook.

In 2020, several large Indiana employers urged insurance carrier Anthem to renegotiate their contract with Parkview Health, a hospital system RAND researchers identified as one of the most expensive in the country. After months of tense back-and-forth, the pair reached a five-year deal expected to save Anthem customers $700 million.

Legislating, Regulating, Litigating

Some employer coalitions are advocating for more intervention by policymakers to cap health care prices or at least make them more transparent. States like Colorado and Indiana have passed price transparency legislation, and new federal rules now require more hospital price transparency on a national level. Advocates expect strong industry opposition to stiffer measures, like price caps, which recently failed in the Montana legislature. 

Other advocates are calling for more scrutiny by state and federal officials of hospital mergers and other anticompetitive practices. Some employers and unions have even resorted to suing hospitals like Sutter Health in California.

Employer Challenges

Employers face a few key barriers to purchasing health care in different and more efficient ways:

Provider Power

Hospitals tend to have much more market power than individual employers, and that power has grown in recent years, enabling them to raise prices. Even very large employers have geographically dispersed workforces, making it hard to exert much leverage over any given hospital. Some employers have tried forming purchasing coalitions to pool their buying power, but they face tricky organizational dynamics and laws that prohibit collusion.

Sophistication

Employers can attempt to lower prices by renegotiating contracts with hospitals or tailoring provider networks, but the work is complicated and rife with tradeoffs. Few employers are sophisticated enough, for example, to assess a provider’s quality or to structure hospital payments in new ways. Employers looking for insurers to help them have limited options, as that industry has also become highly consolidated.

Employee Blowback

Employers say they primarily provide benefits to recruit and retain happy and healthy employees. Many are reluctant to risk upsetting employees by cutting out expensive providers or redesigning benefits in other ways.recent KFF survey found just 4% of employers had dropped a hospital in order to cut costs.

The Tradeoffs

Employers play a unique role in the United States health care system, and in the lives of the 150 million Americans who get insurance through work. For years, critics have questioned the wisdom of an employer-based health care system, and massive job losses created by the pandemic have reinforced those doubts for many.

Assuming employers do continue to purchase insurance on behalf of millions of Americans, though, focusing on lowering the prices they pay is one promising path to lowering total costs. However, as noted above, hospitals have expressed concern over the financial pressures they may face under these new deals. Complex benefit design strategies, like narrow or tiered networks, also run the risk of harming employees, who may make suboptimal choices or experience cost surprises. Finally, these strategies do not necessarily address other drivers of high costs including drug prices and wasteful care. 

Large health systems band together on monetize clinical data

https://mailchi.mp/41540f595c92/the-weekly-gist-february-12-2021?e=d1e747d2d8

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Fourteen of the nation’s largest health systems announced this week that they have joined together to form a new, for-profit data company aimed at aggregating and mining their clinical data. Called Truveta, the company will draw on the de-identified health records of millions of patients from thousands of care sites across 40 states, allowing researchers, physicians, biopharma companies, and others to draw insights aimed at “improving the lives of those they serve.” 

Health system participants include the multi-state Catholic systems CommonSpirit Health, Trinity Health, Providence, and Bon Secours Mercy, the for-profit system Tenet Healthcare, and a number of regional systems. The new company will be led by former Microsoft executive Terry Myerson, who has been working on the project since March of last year. As large technology companies like Amazon and Google continue to build out healthcare offerings, and national insurers like UnitedHealth Group and Aetna continue to grow their analytical capabilities based on physician, hospital, and pharmacy encounters, it’s surprising that hospital systems are only now mobilizing in a concerted way to monetize the clinical data they generate.

Like Civica, an earlier health system collaboration around pharmaceutical manufacturing, Truveta’s launch signals that large national and regional systems are waking up to the value of scale they’ve amassed over time, moving beyond pricing leverage to capture other benefits from the size of their clinical operations—and exploring non-merger partnerships to create value from collaboration. There will inevitably be questions about how patient data is used by Truveta and its eventual customers, but we believe the venture holds real promise for harnessing the power of massive clinical datasets to drive improvement in how care is delivered.