Primary Care Practices Need Help to Survive the COVID-19 Pandemic

Ken Terry

Paul Grundy

By PAUL GRUNDY, MD and KEN TERRY

Date: June 20, 2022.

The Smithsonian National Museum of Natural History has reported its biggest number of visitors in more than 2 ½ years. There’s a string of new Broadway musicals that are well-attended every night. It’s safe to shop in malls, eat out in restaurants and go to movie theaters again.

Of course, this has all been made possible by an effective vaccine against COVID-19 that was widely administered in the fall of 2021. Vaccinated citizens of the world are now confident that it’s safe to go out in public, albeit with appropriate precautions.

However, U.S. residents who have health problems are facing a new challenge. Five years ago, in 2017, the median wait time of new patients for doctor appointments was six days. In 2022, the wait time is six months or more.

The reason for this is no mystery. While life has started to return to what we think of as the new normal, the U.S. healthcare system has taken an enormous financial hit, and primary care practices have been especially affected. Many primary care physicians have closed their practices and have retired or gone on to other careers. Consequently, the shortage of primary care has been exacerbated, and access to doctors has plummeted. Urgent care centers, retail clinics and telehealth have not filled this gap.

Because of the long waiting times for primary care appointments, many more people now seek care in emergency departments (EDs). The waiting rooms of these EDs are overcrowded with people who have all types of complaints, including chronic and routine problems as well as emergencies. And this is not just a common sight in inner-city areas, as it once was; it’s now the same pretty much everywhere.

A significant number of people don’t want to spend hours waiting in an ED, even if they need to be seen by a doctor. Untreated conditions are the primary reason behind the spike in hospitalizations and readmissions, and most of these conditions can be traced back to causes that could have been addressed earlier. Many people with chronic diseases are doing the best they can to manage their health and avoid complications on their own. But, as was the case during the pandemic, too many people are still having bad outcomes as a result of not receiving timely medical care.

The telemedicine boom that began during the COVID-19 crisis has continued, and most payers now cover virtual visits. However, the results have been less than optimal in many cases. Although patients can use virtual encounters to access care without a long wait, telemedicine is still mainly valuable for diagnosing and treating minor acute problems and for chronic care follow-ups. For other kinds of complaints, patients are being told to go to emergency rooms.

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This is not a page out of a dystopian work of fiction. It’s a very real possibility that might just become reality if primary care practices in the U.S. don’t receive the infusion of government aid they need to survive. Despite the recent rebound in office visits, primary care visits are still 25% below pre-pandemic levels, and a growing number of primary care physicians are wondering how long they can hang on.

The pandemic spotlights primary care crisis

The COVID-19 pandemic has already claimed over 93,000 lives in America. Primary care clinicians are among the healthcare workers on the frontlines, battling the virus, triaging concerned and infected patients and caring for those who are ill.

However, primary care has long depended economically on people showing up, and in-person visits have dropped precipitously since the COVID-19 outbreak began. A recent report from the Commonwealth Fund states that visits to primary care physicians—both in-person and virtual–have declined by 30 percent since the coronavirus outbreak after rebounding from a 60 percent drop. Primary care physicians (PCPs) have been particularly affected by the resultant falloff in practice revenues.

For their part, many patients are avoiding in-person visits, as they don’t want to risk exposure to the virus. Some practices are actively discouraging patients to come in for routine care. Since most practices rely on a fee-for-service payment model, and telemedicine has not made up for the drop in in-person visits, practice revenues have plunged.

As a result of this decline in cash flow, up to 60,000 family physicians will shut their businesses by the end of June, the American Academy of Family Physicians (AAFP) predicts. To put things in perspective on a smaller scale, one in three PCPs in California think they might have to close their practice.

Even for PCPs employed by hospitals and health systems, the situation is increasingly bleak. According to Becker’s Hospital CFO Report, by mid-May 258 hospitals had furloughed some of their staff in an effort to remain financially stable amid the pandemic. Many hospitals have suspended elective procedures to abide by state regulations and save capacity that they need to treat COVID-19 patients.

To keep their practices going, 85% of primary care clinicians are now delivering some care virtually through either video-based or telephone-based encounters. Medicare has taken the lead in covering these virtual visits, but many commercial insurers lag behind. Also, the telehealth infrastructure is still not all there. In a recent survey by the Primary Care Collaborative, 72% of primary care doctors reported that their most vulnerable patients do not have capabilities for virtual visits outside of telephone calls.

Even before the COVID-19 outbreak began, primary care physicians were an endangered species. A 2019 report from the Association of American Medical Colleges (AAMC) projected a shortage of 21,000 to 55,200 primary care physicians over the next decade, and the number of young doctors going into primary care is falling. Meanwhile, only a third of America’s physicians deliver primary care, compared to half or more in other countries. So we need more, not less primary care.

With all hands needed on deck right now, primary care physicians are more important more than ever. If a significant number of PCPs retired or closed their practices, the pandemic would be harder to defeat, and the healthcare system would be much worse than it is today. Access to care would be greatly diminished, and untreated health conditions and unnecessary deaths would become common.

Time to save primary care

Many primary care practices in America are small businesses, and they require immediate relief for stabilization. However, what has been done for them so far falls far short of the mark.

The federal government provided almost $40 billion in forgivable loans to healthcare and social assistance organizations, accounting for 11.7% of total funding in the Paycheck Protection Program. Yet, some practices were denied loans as other businesses depleted funds from the program in the first round. Moreover, none of the federal rescue packages passed so far has included any funds directly targeted to primary care.

The Centers for Medicare and Medicaid Services (CMS) has also failed to come to the aid of primary care practices. A Medicare program that provided advance and accelerated payments to physician practices was criticized because practices were required to pay back the loans later on. However, the funds were missed when CMS abruptly discontinued the program in late April, and the American Medical Group Association now supports bills that would bring it back with a lengthened period of loan repayment.

Some experts say the solution to primary care’s financial woes is monthly capitation payments that would occur regardless of how many in-person visits a practice received. Last year CMS announced the Primary Care First demonstration program, which includes capitation payments. But the program won’t be implemented until 2021, and many primary care practices could go under by then.  

The Commonwealth Fund, America’s Health Insurance Plans, the primary care medical societies, and other organizations are now begging Congress to appropriate money directly to primary care practices in the next stimulus package. We think that’s a necessary first step to right the ship of primary care before it goes down. In addition, it’s time for commercial insurers to offer telemedicine-friendly coverage and institute primary care contracting strategies that reward practices for providing high-quality and coordinated care.     

Losing a significant chunk of our primary care capacity would hurt both patients and the healthcare system. A greater investment in primary care would result in lower costs, higher patient satisfaction, fewer hospitalizations and ED visits, and lower mortality.

The primary care landscape will be very different after the pandemic, and many fear that it may not be for the better. While an immediate cash infusion is desperately needed, PCPs may face a near-complete market consolidation stemming from the financial repercussions of the coronavirus. That won’t necessarily happen: some healthcare systems, facing a cash crunch of their own, may let their employed doctors go back to private practice. But if consolidation increases even further, we can expect higher healthcare costs and less choice of providers.

What’s clear at this point is that primary care must be saved if we are to emerge from this pandemic with the essential infrastructure of health care intact. Everybody needs primary care, and we all need access to healthcare. Congress must make health policy decisions with an eye to the kind of healthcare system we want to have when it’s really safe to go out again.

Paul Grundy, MD, is the Chief Transformation Officer at Innovaccer and the Convenor of the virtual healthcare community, Care As One.

Ken Terry is a journalist and author who has covered health care for more than 25 years. His latest book, Physician-Led Health Care Reform: A New Approach to Medicare for All, will be published in June.

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What’s a diagnosis about? COVID-19 and beyond

By MICHEL ACCAD

Last month marked the 400th anniversary of the birth of John Graunt, commonly regarded as the father of epidemiology.  His major published work, Natural and Political Observations Made upon the Bills of Mortality, called attention to the death statistics published weekly in London beginning in the late 16th century.  Graunt was skeptical of how causes of death were ascribed, especially in times of plagues.  Evidently, 400 years of scientific advances have done little to lessen his doubts! 

A few days ago, Fox News reported that Colorado governor Jared Polis had “pushed back against recent coronavirus death counts, including those conducted by the Centers for Disease Control and Prevention.”  The Centennial State had previously reported a COVID death count of 1,150 but then revised that number down to 878.  That is but one of many reports raising questions about what counts as a COVID case or a COVID death.  Beyond the raw numbers, many controversies also rage about derivative statistics such as “case fatality rates” and “infection fatality rates,” not just among the general public but between academics as well.  

Of course, a large part of the wrangling is due not only to our unfamiliarity with this new disease but also to profound disagreements about how epidemics should be confronted.  I don’t want to get into the weeds of those disputes here.  Instead, I’d like to call attention to another problem, namely, the somewhat confused way in which we think about medical diagnosis in general, not just COVID diagnoses.

The way I see it, there are two concepts at play in how physicians view diagnoses and think about them in relation to medical practice.  These two concepts—one more in line with the traditional role of the physician, the other adapted to modern healthcare demands—are at odds with one another even though they both shape the cognitive framework of doctors.  

The old view: diagnosis as shared conceptual model with therapeutic aims

If we reflect on what doctors traditionally do, we may see that a diagnosis is not so much an objective reality that a doctor uncovers in a patient as it is a kind of tacit convention between physicians about how to refer to an illness that shares common features among those that it affects. 

For a new disease to be identified as such, at least a few people have to be affected with it and the pattern of affliction needs to be distinct enough for doctors to recognize it as different from the vast number of other ways people get sick.  The illness is then given a name, but that name refers more to a shared conceptual model than to an entity that “resides” in the patient.  In a certain sense, when we say “Imelda has COVID-19” we really mean “Dr. Welby has COVID-19 in mind when he thinks about Imelda’s illness.”

The conventional or shared understanding of particular diagnoses invariably changes over time: new knowledge, new testing capabilities, new treatments, new biological theories, and theories about health and disease inevitably modify the meaning of a given diagnosis, even if the specific term itself perdures.  In a recent podcast episode, our guest Adam Rodman gave us a flavor of this in regards to “the flu.” Nosology—the classification of diseases—is quite messy.

Depending on how quickly a new disease emerges or how widespread it becomes, the diagnosis can take more or less time to mature in the consciousness of physicians.  But here’s the rub: no two patients are sick in the same way. To steal an insight from Tolstoy, all healthy persons are alike, but each patient is a patient in his own way.

So, deciding whether a given patient meets the conventional understanding of a particular diagnosis is ultimately a matter of judgment.  The expression “to make a diagnosis” is apt.  A diagnosis is made, i.e., applied to a patient in real time, as appropriate to the circumstances. 

Consider the following scenarios:

• Imelda is admitted to the hospital for a shortness of breath.  She has ground glass opacities on lung CT and tests positive for SARS-CoV2. Does she have COVID-19?
• Imelda is admitted to the hospital for a stroke.  She has no fever and her lungs are clear but she tests positive for SARS-CoV2. Does she have COVID-19?
•  Imelda is in a nursing home minding her own business peacefully and she tests positive for SARS-CoV2 during a routine screen.  Does she have COVID-19?
• Imelda is in hospice care for terminal breast cancer.  She dies.  A nasal swab is positive for SARS-CoV2.  Did she die of COVID-19?

The answer to those questions depends on what we mean by “COVID-19” but, before we can even address that question, we also have to wonder what is a diagnosis for?

If I am Imelda’s physician, I make a diagnosis with the view to act on it in order to help her (as best as I can).  The diagnosis is pretty much tied to the prognosis and treatment.  It is meant to help me provide the best care to restore her health. 

In principle, then, I can also suspend my judgment and withhold making a final diagnosis.  If I don’t know what ails Imelda it may be best if I remain circumspect, at least temporarily.  Or, if Imelda is without complaints and without signs of illness despite evidence of virus in her nasal passages, I may recommend isolating her from others but I need not feel compelled to render a particular diagnosis.

In other words, if a diagnosis is really shorthand for an idealization of a particular form of illness that is shared among physicians, the making of a diagnosis in a particular instance takes place with a therapeutic goal in mind.  In this framework, to make a diagnosis is not so much a pronouncement that is true or false (who knows what Imelda really has?) as it is one that is practically wise or unwise (in the end, things turned out for Imelda as best as could be expected).

Understood properly, then, a diagnosis, is a shared model for “practical reasoning” in the art of medicine.  Done well, practical reasoning manifests what Aristotle called practical wisdom, or phronesis, and what the late Herb Fred brilliantly captured as “Mutton’s Law.”  

The new view: diagnosis as a formality to be discovered

The other, competing conception of the diagnostic process, has taken prominence with the advent of scientific medicine and with the increasing influence that medical research has had on medical education.  

When medical scientists seek to study a disease, they also seek to reduce or eliminate any evaluative steps that are subject to human judgment which is considered a potential source of bias.  For that reason, they have a tendency to formalize disease definitions in order to standardize clinical research. These definitions almost always take the form of “diagnostic criteria” that can be part of inclusion or exclusion rules in research protocols.

Over the last many decades, and under the influence of research-oriented faculty, medical education has largely incorporated the formalization of disease definitions into its curriculum.  Nowadays, medical textbooks often follow the example of clinical research protocols and highlight criteria of symptoms and signs to define particular diseases.  Professional societies which represent various medical specialties often do the same when they promulgate practice guidelines for physicians.

The most glaring example of that formalization is in psychiatry. The Diagnostic and Statistical Manual is precisely a compendium of criteria-defined diagnoses that now rules psychiatric practice. But physical illnesses are also increasingly treated in that way: Infective endocarditis, rheumatoid arthritis, arterial hypertension, Kawasaki disease, POEMS syndrome, and innumerable other illnesses have been codified according to formal diagnostic criteria.  

The formal definition of disease entails a tendency to view the diagnosis as an objective reality residing in the patient: If a set of symptoms and signs defines a disease, and those symptoms and signs are in the patient, then the diagnosis itself must also be found in the patient.  In turn, this entails viewing the diagnostic process as a task of detection or discovery: to determine whether “Imelda has COVID-19” or not, the physician must put that reality to the test.  The diagnosis is “reached” once the criteria have been sufficiently satisfied.  

The formal definition of disease also entails a greater separation between diagnosis and therapy.  Under the traditional understanding of diagnosis, the diagnostic process is integrated to the therapeutic aim.  The physician adjusts or calibrates his diagnostic pronouncements based on the therapeutic options available.  With a formal definition of disease, on the other hand, the diagnostic process tends to be circumscribed and is guided by a set of cognitive tools (“decision analysis”) that can be kept apart from the therapeutic decision-making which enjoys its own distinct set of cognitive tools.

To be clear, I am not submitting that doctors are no longer applying diagnostic judgment in the traditional sense described above.  No one has openly rejected the old concept.  I am mainly proposing that these two distinct conceptions of diagnosis are nowadays conflated: the first one, rooted in the traditional therapeutic activity of the physician, is a conceptual model for practical reasoning which allows a greater degree of judgment and individualization, particularly as it keeps the diagnostic process in close alignment with the therapeutic possibilities.  The other, derived from the emphasis on clinical research and scientific medicine, represents disease as a precise, objective reality to be discovered or detected.  

As we shall soon see, only one or the other can be correct and, in my view, only the former, traditional concept is suitable for genuine medical care.  But at play are also the demands of healthcare systems: Government oversight and insurance payment require that medical disease be formalized and made explicit. The criterion-based definitions of disease, then, serve the bureaucratic needs of the system which can exert its authority and financial power of persuasion on doctors to make them adopt—consciously or unconsciously; enthusiastically or less so—the process of diagnostic standardization that characterizes the scientific approach.

Testing 101 is 2×2

The shift in understanding regarding diagnosis is quite apparent in the current expectation that medical students and residents demonstrate facility with probability concepts and with “decision analysis.” Decision-analysis is a theory initially developed for quality control in manufacturing and engineering, but it also includes tools imported from radar signal-detection science, such as receiver-operating characteristics curves.

Terms such as sensitivity, specificity, and predictive value are now part of common medical jargon and clinicians are increasingly expected to be able to explain the meaning of those terms using a “2×2 table” such as the taken from a recent review:

I won’t get into a detailed explanation of what the 2×2 table purports to indicate (Dr. Koka covered that topic magisterially here), but I would like to draw attention to the fact that 2×2 tables require a “gold standard” in order to have any meaning.  This is indicated at the top of the table above.  A test result is a “true positive” if it is positive in those in whom we know the disease to be present.  

But how do we know that the disease is present or not if a diagnosis is a shared conceptual model of practical reasoning that resides principally in the collective minds of a college of physicians? Do we conduct a poll?  If a diagnosis is an objective reality to be discovered, who has access to that reality?  What can serve as a gold standard?  

In reality, no gold standard for disease can possibly exist.  Test sensitivity and specificity can only refer to the detection of isolated patient characteristics.  What tests do is identify an elevated chemical concentration, an abnormal cellular architecture, a mass, a density, or a germ that are not supposed to be there, etc.  Tests cannot aim to detect a disease per se, even though textbook definitions for test sensitivity and specificity frequently imply or even state that it is the disease itself that is sought by the test.  For example, in its entry titled “Evaluating Diagnostic Tests,” the hugely popular and “evidence-based” online clinical encyclopedia UpToDate, edited and curated by well-established academic physicians, states that “Sensitivity is the probability that an individual with the disease will test positive (emphasis mine),” and the mirror opposite for specificity. 

To be fair, other sources that define concepts of sensitivity and specificity may be more careful to say that what is being “diagnosed” by a test is a “sign,” a “condition,” or a “disorder.”  But what that shows is that the term “diagnosis” has been watered down to refer to any kind of abnormality.  It’s no wonder that loose thinking about diagnosis has led to an epidemic of “overdiagnosis.”  We have let CT scans “diagnose” pulmonary embolism, biopsies “diagnose” thyroid cancers, angiograms “diagnose” coronary disease when, in truth, diagnoses should only be made by physicians, be about individual patients, and take into account the totality of the available evidence, including the personal life circumstances of the affected man or woman.  The cure for overdiagnosis is not to restrict the use of tests but to focus the attention of physicians on a more correct view of what it means to diagnose.

What’s a COVID-19 diagnosis?  

And now we have COVID-19.

Unsurprisingly, the “COVID-19 tests” available to us do not test for the actual disease but for fragments of virus (in the case of RT-PCR tests) or for recent SARS-CoV2 positivity on RT-PCR testing (in the case of antibody tests).  This also means that, at this stage in the game, estimating the incidence or prevalence of COVID-19 is quite a crude exercise.  Not only do the COVID tests not test for the disease, but the collective experience of physicians is still in its infancy.  It is still too early to expect a mature “shared model” of practical reasoning.  

In a few months or perhaps sooner, I imagine that diagnostic criteria will be proposed for COVID-19, perhaps with some kind of scoring system among “major” and “minor” criteria to render a verdict of COVID “definitive” or “probable” or “excluded” in the manner of the criteria for, say, infective endocarditis.  But formal diagnostic criteria will not help objectify the disease and cannot serve as a gold standard either.  In fact, diagnostic criteria themselves can be evaluated according to 2×2 and ROC analysis: Do the criteria capture all cases of COVID-19?  Do they miss any?  If so, how many and according to what other gold standard?  Ultimately, there is no escape from the judgment of clinicians.

To be clear, the widespread suspicion regarding COVID mortality statistics is not directed principally at physicians but mostly at public health officials who apply their own concepts of diagnosis to serve their policy needs.  Still, I don’t think that many doctors, let alone the public, recognize that when a physician fills out a death certificate he or she is not acting in a true medical capacity: there is no patient interest to serve.  A cause of death is not, strictly-speaking, a medical diagnosis.

That only confirms the importance of carefully understanding what a diagnosis means and of safeguarding that meaning for the good of the profession and of society.  Contrary to popular academic belief, the shift from practical reasoning to probabilistic thinking is not serving us well. 

What do we call a medical education that neglects forming wise physicians in favor of training standardized ones for the sake of clinical research and for assembly-line healthcare practice?  At this point, I’ll withhold making a diagnosis.

Michel Accad is a cardiologist based in San Francisco and host of the podcast, The Accad & Koka Report. This post originally appeared on his blog, Alert and Oriented here.

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THCB Gang, Episode 11 LIVE 1PM PT/4 PM ET 5/28

Episode 11 of “The THCB Gang” will be live-streamed on Thursday, May 27th at 1PM PT / 4PM ET 5/28.

Joining me are the regulars: patient safety expert Michael Millenson (MLMillenson), writer Kim Bellard (@kimbbellard), health futurist Ian Morrison (@seccurve), and two new guests: digital health advisor Steven Wardell (@StevenWardell) and MD turned Consultant Maggi Cary (DrMaggiC)! The conversation will revolve around the latest developments around policy and technology in relation to health care and COVID19’s spread.

If you’d rather listen, the “audio only” version is preserved as a weekly podcast available on our iTunes & Spotify channels — Matthew Holt

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Health in 2 Point 00, Episode 125 | Amwell, Mindstrong, Big Sky Health, Medwing

Today on Health in 2 Point 00, we have more than $300 million in deals to cover! On Episode 125, Jess asks me about Amwell raising $194 million although they haven’t gone public (yet), Mindstrong getting $100 million and a new CEO from Uber in another big raise for their mental health app, Big Sky Health landing $8 million for its intermittent fasting, meditation and alcohol consumption apps, Tava Health raising $3 million to expand its teletherapy program, and German startup Medwing getting $30 million to address Europe’s shortage of healthcare workers. Don’t miss Jess speaking at the AHIP discussion on How Digital Self-Care is Transforming Mental Health Care tomorrow at 1pm ET! —Matthew Holt

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The 2020 COVID Election

By KIM BELLARD

Many believe that the 2020 Presidential election will be a referendum on how President Trump has handled the coronavirus pandemic.  Some believe that is why the President is pushing so hard to reopen the economy, so that he can reclaim it as the focal point instead.  I fear that the pandemic will, indeed, play a major role in the election, but not quite in the way we’re openly talking about.  

It’s about there being fewer Democrats.

Now, let me say right from the start that I am not a conspiracy believer.  I don’t believe that COVID-19 came from a Chinese lab, or that China deliberately wanted it to spread.  I don’t even believe that the Administration’s various delays and bungles in dealing with the pandemic are strategic or even deliberate.  

I do believe, though, that people in the Administration and in the Republican party more generally may be seeing how the pandemic is playing out, and feel less incentive to combat it to the fullest extent of their powers.  Let’s start with who is dying, where.

The New York Times put it bluntly: The Coronavirus Is Deadliest Where Democrats Live, as illustrated with their map.

Coronavirus cases in counties won by Clinton in 2016 on left, in counties won by Trump on right; larger bubble means more cases. Data is as of May 21, 2020. By Jugal K. Patel for The New York Times

The article explains: 

The devastation, in other words, has been disproportionately felt in blue America, which helps explain why people on opposing sides of a partisan divide that has intensified in the past two decades are thinking about the virus differently. It is not just that Democrats and Republicans disagree on how to reopen businesses, schools and the country as a whole. Beyond perception, beyond ideology, there are starkly different realities for red and blue America right now.

Potential explanations for the differences include population densities, loci of international travel, and proportion of minority populations.  

Nor is it necessarily true that the current trends will remain; coronarvirus is starting to surge in rural areas.  A Washington Post analysis indicates: “Rural counties now have some of the highest rates of covid-19 cases and deaths in the country, topping even the hardest-hit New York City boroughs and signaling a new phase of the pandemic.”  

It’s not only about red states versus blue states; it is also about who is dying in each.  People of color have been impacted much more.  African-Americans are dying at almost three times the rate of whites or Latinos; in Kansas, the rate is seven times, and five times in Michigan or Missouri.  And not all states are reporting deaths by race, so the problem may be understated.  Latinos and, to a lesser extent, Asian Americans have also been hit harder than whites.  

Figure 1. Covid-19 deaths per 100000.  Source: APM Research Lab

APM Research Lab estimates (as of May 20):

If they had died of COVID-19 at the same rate as White Americans, about 12,000 Black Americans, 1,300 Latino Americans and 300 Asian Americans would still be alive.

Some of this has been attributed to underlying health and/or living conditions, but a new study found that, even controlling for differences in age, sex,income and chronic health problems, African-Americans are hospitalized with COVID-19 at nearly three times the rate of white or Latino patients. Co-author Stephen H. Lockhart told The New York Times:  “The important thing we found in this study is that even when we were accounting for all those things, race mattered.”  

There’s an economic toll as well — as shocking as the jobless rates are generally, they are worse for minorities, and minority-owned businesses are being hit hardest.  This may be due, in part, because such businesses had a much harder time obtaining PPP loans.

Certainly not all minority voters vote Democrat, but African Americans tend to overwhelmingly do so and Latino voters also do, although not quite as monolithically.  A disease like COVID-19 that disproportionately impacts minorities hurts Democrats more than Republicans, whether outright through fatality or just in reducing turnout.   

Turnout by minority votes has long been a problem.  Going to a polling place during a pandemic will be problematic for some voters, and if you are a voter who is in a higher risk group — such as African-American, it may be particularly so.  Many states are pushing for mail-in voting for any resident as a way to assuage such concerns, a tactic that President Trump fiercely opposes.  He cites potential for “massive fraud,” although no evidence exists for this and many states, including ones controlled by Republicans, have allowed it without problems.  

The opposition to mail-in voting is less about safeguarding the integrity of the election and more about trying to control who ends up voting. 

Our reactions to the pandemic are very much splitting along party lines.  For example, wearing a face mask, as public health officials urge, is now seen as a political statement.  That brought North Dakota Governor Doug Burgum, a Republican, to tears in a recent press conference:

President Trump’s 2016 election hinged on razor thin margins in a few swing states. No matter the impact of COVID-19, he isn’t likely to win in hard-hit blue states like New York, California, or Massachusetts, but it might well make the difference in swing states like Michigan, Pennsylvania, or Wisconsin, and could even make a difference in purple states like Illinois, New Jersey, or Minnesota.  How many fewer Democrat voters able/alive to vote would it take?

The strategy is not without risks.  Polls show voters give former Vice President Joe Biden a wide lead on ability to deal with the pandemic, and support for Trump among senior citizens — which had supported Trump in 2016 but who now at greatest risk of contracting and dying from COVID-19 — is weakening.  Statements from Republicans like Texas Lieutenant Governor Dan Patrick suggesting seniors were willing to die from COVID-19 as a trade-off for restarting the economy don’t help.

Let me be clear: I’m not saying President Trump or other Republicans want minority voters to die, and certainly not that they are intentionally trying to make that happen.  But, from a political standpoint, the pandemic is currently is hitting his supporters less hard, and there’s a political calculation may come with that.  Democrats cannot be blind to that.  

COVID-19 is the biggest health crisis in a hundred years.  It has caused perhaps the greatest economic crisis in ninety years.  It would be unfortunate if we allowed it to also cause perhaps the biggest political crisis in our nation’s history.  

Kim is a former emarketing exec at a major Blues plan, editor of the late & lamented Tincture.io, and now regular THCB contributor.

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Catalyst @ Health 2.0 Launches Subsidized Rapid Response Open Calls (RROCs)

SPONSORED POST

By CATALYST @ HEALTH 2.0

In collaboration with the Robert Wood Johnson Foundation, Catalyst @ Health 2.0 is proud to announce funding for health care providers with limited resources and urgent needs to identify and source digital health innovation during COVID-19 through our Rapid Response Open Calls (RROC). RROCs are streamlined calls for applications that connect health care providers to digital health solutions. Deployed as part of Catalyst’s Health Tech Responds to COVID-19 platform, RROCs can be launched within days to meet the host’s needs.

Catalyst created the RROC to address an urgent need from Brigham and Women’s Hospital (BWH) Emergency Department for provider-facing, text-based platforms to help healthcare professionals self-monitor symptoms of coronavirus, report burnout, and access helpful resources. Within one day, the Brigham and Women’s Health RROC was launched. In a 7-day application period, Catalyst received an overwhelmingly positive response with more than 80 quality submissions. BWH was able to evaluate the submissions through a streamlined process and 5 innovators were selected to demo their solutions to the BWH ED team. BWH began pursuing a potential partnership with one of the semi-finalists. 

If you are a healthcare provider with limited resources during COVID-19 (e.g. FQHCs, community health centers, etc.), apply for a subsidized RROC HERE!

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How to Practice High-Quality Telemedicine in the Era of COVID-19

By ANISH MEHTA, MD

My practice received its first question about coronavirus from a patient on January 28, 2020. Though there were over 200 deaths reported in China by that time, no one could have imagined how drastically this would come to disrupt our lives at home.

Thankfully, I had a head start.

As a doctor at an integrated telemedicine and primary care practice in New York City, nearly two out of every three of my medical encounters that month was already virtual.

I spent much of January caring for patients who had contracted seasonal viruses, like influenza or norovirus (i.e. the stomach flu). My patients reached out nearly every day with bouts of fevers, fatigue, diarrhea, and vomiting. Our team did all we could to encourage each of these patients to stay home and avoid spreading their highly contagious virus throughout the community (sound familiar?).

We are now guiding our patients through the COVID-19 outbreak using the same tools we use to guide them through any healthcare need – real-time monitoring, proactive outreach, and team-based care.

After our first COVID-19 question, our team started compiling information about every patient who reached out with symptoms that even slightly resembled COVID-19. This soon turned into a comprehensive patient registry containing the epidemiologic risk factors, clinical risk factors, symptoms, and a follow-up plan for each patient. Based on their total risk level, we follow up with these patients every 24 to 120 hours.

Every day, one provider on the team texts or schedules a video visit with each follow-up patient, reassesses their symptoms, and re-stratifies their risk. Most patients respond with a text message letting us know that their symptoms are the same or slowly improving. But for patients at higher risk, we want more information. We help these patients acquire a thermometer or pulse oximeter to follow up on their respiratory vitals. With this data, our team can provide patients and their families with thresholds on when to seek out a higher level of care.

Our job for these patients is clear: provide treatment at home and only recommend the hospital if there is no other option. By centralizing data and establishing clear triggers for a new plan of care, a single provider can follow up with over 30 COVID-19 patients in a single day.

Of all the patients with COVID-19-like symptoms, so far not one has required hospitalization.

The other providers at our practice spend their days talking with patients via text, phone, or video. They have mastered one of the most valuable diagnostic tools in medicine: taking a history. But no matter how skillful they are at asking the right questions, a solo provider in the current outbreak can be cognitively and emotionally overwhelmed by the information coming in from patients.

Back when our brick-and-mortar medical offices were open, our team used to hold a daily huddle to discuss complex patients on the schedule that day. With the offices closed, this ritual still stands. Instead of discussing whose hypertension is poorly controlled or who is overdue for a Pap smear, now we focus on issues inevitably impacted by COVID-19. We discussed whether to prescribe steroids to a patient with an asthma flare likely due to COVID-19, or how to keep a patient feeling isolated and suicidal safe at home. This routine serves as an important reminder that we are not only providing COVID-19-care. Patients continue to have medical and mental health problems that are amplified by this pandemic.

During my medical residency, a senior doctor once told me that when it comes to sick patients, never worry alone. Our entire clinical team worries about COVID-19 and how the pandemic impacts our patients’ health. More than anything, the frequent huddles create space to share those concerns in the open. Even if there’s not an immediate solution, we can rest a bit more assured knowing that no one is worrying alone. We tackle these challenges as a team.

As the number of confirmed cases of COVID-19 in the US has skyrocketed to over a million, medical providers have a clear mandate: treat patients at home and keep them out of the hospital.

But to be effective, this will require more than simply swapping an office visit for FaceTime or Zoom. A coordinated, proactive, and team-based system can help patients get the care they need and keep communities safe. These elements are essential for an effective telemedicine response to COVID-19. And after the crisis has passed, I hope we continue to use telemedicine as the foundation for a new model of care, not simply as a shallow replacement for the doctor’s office.

Anish Mehta is a practicing physician and the Director of Clinical Affairs at Eden Health.

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A Vigilante in Statistical Badlands

By ANISH KOKA, MD

Something didn’t seem right to epidemiologist Eric Weinhandl when he glanced at an article published in the venerated Journal of the American Medical Association (JAMA) on a crisp fall evening in Minnesota. Eric is a smart guy – a native Minnesotan and a math major who fell in love with clinical quantitative database-driven research because he happened to work with a nephrologist early in his training. After finishing his doctorate in epidemiology, he cut his teeth working with the Chronic Disease Research Group, a division of the Hennepin Healthcare Research Institute that has held The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) contract for the United States Renal Data System Coordinating Center.  The research group Eric worked for from 2004-2015 essentially organized the data generated from almost every dialysis patient in the United States.  He didn’t just work with the data as an end-user, he helped maintain the largest, and most important database on chronic kidney disease in the United States. 

For all these reasons this particular study published in JAMA that sought to examine the association between dialysis facility ownership and access to kidney transplantation piqued Eric’s interest.  The provocative hypothesis is that for-profit dialysis centers are financially motivated to keep patients hooked to dialysis machines rather than refer them for kidney transplantation.  A number of observational trials have tracked better outcomes in not-for-profit settings, so the theory wasn’t implausible, but mulling over the results more carefully, Eric noticed how large the effect sizes reported in the paper were. Specifically,  the hazard ratios for for-profit vs. non-profit were 0.36 for being put on a waiting list, 0.5 for receiving a living donor kidney transplant, 0.44 for receiving a deceased donor kidney transplant.  This roughly translates to patients being one-half to one-third as likely to get referred for and ultimately receiving a transplant.  These are incredible numbers when you consider it can be major news when a study reports a hazard ratio of 0.9.  Part of the reason one doesn’t usually see hazard ratios that are this large is because that signals an effect size that’s so obvious to the naked eye that it doesn’t require a trial. There’s a reason there are no trials on the utility of cauterizing an artery to stop bleeding during surgery. 

But it really wasn’t the hazard ratios that first struck his eye.  What stuck out were the reported event rates in the study. 1.9 million incident end-stage kidney disease patients in 17 years made sense. The exclusion of 90,000 patients who were wait-listed or received a kidney transplant before ever getting on dialysis, and 250,000 patients for not having any dialysis facility information left ~1.5 million patients for the primary analysis.  The original paper listed 121,000 first wait-list events, 23,000 living donor transplants and ~50,000 deceased donor transplants.  But the United Network for Organ Sharing (UNOS), an organization that manages the US organ transplantation system, reported 280,000 transplants during the same period. 

The paper somehow was missing almost 210,000 transplants.

Eric proceeded to re-run a rough version of the paper’s analysis, and came up with numbers that were markedly different than the initial analysis. The analysis still favored the not-for-profit centers, but the hazard ratios were now 0.8 – 0.9.

Eric felt compelled to act, but he felt awkward.  He was, after all, an epidemiologist employed by a for-profit dialysis chain poking holes in a paper from academics that made for-profit dialysis companies look bad.  On the recommendation of an associate editor at JAMA, he began a correspondence with the senior author of the paper, who expressed confidence in her team’s analysis despite the basic and serious flaws that were obvious with regards to event rates.  The author did offer to place the code for the paper on an open-source platform for Eric to examine. 

It didn’t take long to figure out what the error was.  The USRDS database that Eric knew so well includes provider numbers – numeric values that map to CMS certification numbers that label the dialysis facilities.  The code merged the patient roster database which contained the certification number of the dialysis facility at initiation with the waitlist database that contained its own certification number of the hospital at which the patient was first listed.

The researchers used a commonly used statistical software to do their analysis called SAS. A particularly unique feature of SAS learned the hard way for many is that it doesn’t handle merges where columns may contain the same name. It doesn’t even throw up a warning when this happens.  It simply overwrites one column with the other. 

Since almost all transplants in the United States take place at not-for-profit centers, the waitlist database consists almost entirely of not-for-profit facilities. When this database was combined with the larger end stage kidney database, the merged database ended up enriched with the non-profit facilities associated with the waitlisted patients.  Ultimately, too many wait-list events and transplants end up being mapped to not-for-profit hospitals, and other events that should be mapped to a dialysis facility, regardless of profit status, are excluded. The result is a paper with exaggerated hazard ratios in favor of not-for-profit facilities. 

Six months later JAMA retracted and republished the paper with corrected numbers.  The results still suggested a statistically significant benefit in favor of non-profits, but the size of the effect was much attenuated with an absolute cumulative 2.6% lower rate of referrals and kidney transplantation at for-profit dialysis centers over 5 years.   These conclusions are fragile for a number of reasons.  This study, for instance, attributes a patient’s waitlist/transplant outcome to the very last dialysis facility the patient was associated with.  In epidemiology speak, this means the causal inference authors are trying to draw between for-profit status and good transplant outcomes is subject to time-varying confounding. As an example, if one is seeking an association between testosterone levels and risk of a heart attack, using the last testosterone level available would be a poor way of doing this study because testosterone levels are known to vary over time. The same applies to dialysis facilities. Patients change dialysis facilities, and facilities may change their profit status if ownership changes. Eric did his own sensitivity analysis (personal communication), and this time changed the profit status indicator to the dialysis facility on record after 3 months of dialysis as opposed to the last dialysis facility – the difference in outcomes seen is even further attenuated.  The choice of the last dialysis facility prior to waitlisting is particularly interesting given that most waitlist activity happens in the first 2 years of getting on dialysis.   Clinical reality would suggest that what would be of most interest to physicians and patients alike would be the discovery of dialysis facilities that are not aggressive in referring patients for transplant early after patients initiate dialysis.  But beyond the somewhat esoteric discussions of how much exposure and when exposure occurs to dialysis facilities, waitlist and transplantation rates are arguably most dependent on a number of factors that have little to do with the dialysis facility.  In reality transplant rates vary widely by factors like geography and the number of locally available transplant centers that are well outside of the dialysis facility’s control.  In summary, the republished effect sizes are so small as to be considered remarkably uncertain when all the other confounding factors in an observation analysis are considered. 

The original editorial that ran with the first (now retracted paper) noted:

Assuming the findings of these studies …  are valid and unbiased, it might be reasonable to infer that for-profit dialysis organizations have systematically and disproportionately focused their resource investments to prioritize the delivery of dialysis services while paying less attention to ensuring patients receive transplants.

While the qualifier here about “assuming the findings of the study were valid” is appreciated, the fundamental rotting core of the problem is that authors of the editorial have no business writing an editorial about a topic they clearly have no domain expertise in. The authors and journal would have everyone believe that the error is confined to a technical problem related to database merges.  The real problem, of course, runs far deeper.

The authors, peer reviewers, and editorial writers for one of the most prestigious journals in the world didn’t notice that the original manuscript was missing almost 200,000 transplants that took place during the study period.  This is akin to MSNBC’s Brian Williams and New York Times editorial board member Mara Gay nodding and smilling as they discussed a tweet that complained Michael Bloomberg wasted $500million on campaign advertising, when he could have given a $1million check to every one of 327 million Americans.  

MSNBC’s Brian Williams reads a tweet: "Bloomberg spent $500 million on ads. U.S. Population, 327 million. He could have given each American $1 million"

NYT Editorial Board Member Mara Gay: “It’s an incredible way of putting it. It’s true. It’s disturbing”

It's $1.53 per person pic.twitter.com/dIiwCESgh8

— Ryan Saavedra (@RealSaavedra) March 6, 2020

https://platform.twitter.com/widgets.js

One may excuse journalists in 2020 for being unplugged from math and reality, but the bar should be a little higher for reviewers and editorial writers of the top scientific journals in the land.  Part of the problem may be that the researchers and the editors (we don’t know who the reviewers were) aren’t nephrologists and certainly aren’t epidemiologists with a deep understanding of kidney disease. Instead, what we have are public health dilettantes and population health scientists who specialize in number crunching, but have a poor understanding of the data they are analyzing. 

It would be of little consequence if this group of academics was confined to discussing their results at cocktail parties, but this particular group of outcomes researchers help guide federal government policy on reimbursement. It’s like giving a 12 year old keys to a Ferrari.  What we’re supposed to get is an intelligent policy path paved by evidence, but the trouble is that this isn’t a dispassionate objective group seeking truth wherever it may lie.  Rather, their forte is data crunching and writing papers to confirm strong biases held within the public health community at large.

It’s well known that schools of public health have a certain politics attached to them that overwhelmingly favor a progressive ideology.  The bias, in this case, is an outgrowth of a strongly held public health belief that health care as a for-profit enterprise is a problem to be rectified.  It’s difficult to see where ideology ends and science starts in this arena. The public health community at this point has generated mountains of evidence for the superiority of a not-for-profit system in delivering health.  Unfortunately much of the evidence is generated by those with a strong pre-existing bias on the matter.  Every research project becomes an exercise in confirming that bias.  It’s normally hard to make this out, but the JAMA retraction does provide a nice window into how “science” in this case is far from value-neutral. 

Despite the fact that re-running the analysis resulted in much smaller absolute differences that are likely to disappear if slightly different methodologic paths are taken, basically the same editorial ran with the republished article.  If anything, the authors of the editorial attempt to make the small absolute difference seen even more meaningful by translating the fragile 2.5% difference over 5 years into the potential for 45,000 fewer waitlists over the 16 year study period.  This is standard operating procedure.  Take a large database.  Plumb it for a politically correct conclusion.  When an analytic path inevitably leads to a small but statistically significant conclusion, apply the small difference across the large denominator to emphasize the importance of the small finding. 

As an example, take another JAMA paper: A  large retrospective study of hospitalists by gender demonstrated patients treated by female physicians had a statistically significant 0.43% difference in mortality, the head of the Harvard School of Public health could have noted that the methodology was a bit crude.  There are no patients that are only taken care of by male or female physicians, so the study assigned physician gender based on the gender that took care of the patient during a hospitalization most of the time.  Since men and women physicians are now relatively equally split, most of the time translated to a particular physician being responsible, on average, is 51.1% of the total time ‘spent’ with the patient during a particular hospitalization. So an extra 1.1% of time spent with patients by female physicians is enough to generate a difference in mortality. Never mind that the teams of people like nurses, respiratory therapists, techs, and medical residents with varying gender mixes take care of patients as well in hospitals.  Even if this was somehow true how does one possibly apply this to the individual physician? Should we embark next on studies to generalize the effect of Hindus on patient care, and then with some small result in hand, make generalizations about the care of all Hindu physicians?

If any opinion is to be rendered on the basis of this particular study comparing male and female physicians, it should channel the difficulty of coming to any hard conclusions because of the limitations of the dataset and the small effects found.  Instead, we have the senior physician on the study write an opinion piece that suggests 32,000 fewer deaths per year are to be gained by making male physicians more like their female counterparts in some unknown way, and that the outcome gap makes the longstanding and controversial physician gender pay gap even more “unconscionable”.

It is little surprise then that the retracted and republished dialysis study runs with essentially the same editorial with the same conclusions regarding for-profit status without any reference to how the results materially changed.  A passing reference in the correction notice to a senior epidemiologist who works at a for-profit dialysis enterprise is made and is a testament to how academia functions.  Dominance in the academic hierarchy is frequently established not by the bubbling of the best ideas and evidence to the top, but by discrediting those with opinions that don’t come from the academic cabal.  Eric Weinhandl, the study authors are quick to point out, worked for a for-profit dialysis organization. 

So what?

I would argue that it was precisely because he was in this role that he chose to delve deeper into the original paper.  The perceived conflicts here are a distraction.  The key ingredients in this mess are large dollops of bias combined with a frightening lack of domain expertise in every step of the making of this sour-tasting stew.  

This isn’t to malign bias in research.  After all, bias is what allows intelligent hypotheses to be created.  The fact that there are health services researchers that think the for-profit enterprise is a bad model in healthcare is a good thing.  The issue is that the overwhelming majority of the public health community engaged in generating evidence believes this.  This leads to an echo chamber with no opponents that would allow the sharpening of arguments.  The problem isn’t that Eric Weinhandl was working for a for-profit organization, it is that there are no Eric Weinhandl’s that appear to be part of the academic tapestry. Combine this with poor methodological analysis that is, in part, derived from a shallow understanding of subject matter studied and we find ourselves with poor science and garbage results.  Sadly, there is no reason to think this isn’t a systemic problem that infects the entirety of the public health research enterprise. 

The schools of public health are production factories for graduates with an understanding of the tools one can use to analyze data.  This doesn’t mean they understand the limitations of the data or the disease they happen to be investigating.  What they do end up doing has been artfully described by statistician Andrew Gelman as taking the garden of forking paths: when researchers embark on projects with strong pre-existing biases and consciously or unconsciously choose analytic paths that confirm their biases. 

Ideally, public policy research would be a self-correcting enterprise where ideological diversity combined with subject matter expertise allowed for robust critique and analysis.  When this doesn’t happen, the conclusions and accompanying editorials are written before the data is even analyzed.  The research, then, is just for show.

Anish Koka (@anish_koka) is a for-profit cardiologist in Philadelphia and co-host of a healthcare-focused podcast, The Accad & Koka report.  He still maintains he’s a nice guy.

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The New Normal is Still Unknown, on Earth as it is in Healthcare

By HANS DUVEFELT, MD

From the vantage point of our self-quarantined shrunken universes, we cannot see even the immediate future, let alone what our personal and professional lives will look like some years from now.

Factories are closed, luxury department stores are in bankruptcy, hospitals have stopped performing elective procedures and patients are having their heart attacks at home, unattended by medical professionals. New York office workers may continue to work from home while skyscrapers stand empty and city tax revenues evaporate.

Quarantined and furloughed families are planting gardens and cooking at home. Affluent families are doing their own house cleaning and older retirees are turning their future planning away from aggregated senior housing and assisted living facilities.

In healthcare, procedure performing providers who were at the pinnacle of the pecking order sit idle while previously less-valued cognitive clinicians are continuing to serve their patients remotely, bringing in revenues that prop up hospitals and group practices.

The social experiment we stumbled into has already demonstrated the value of self-sufficiency and it has started to shake the notion that our society can continue to rely on the medical system to fix just about any medical problem we might develop. Self-care and prevention seem like more powerful pieces of our individual plans for our future wellbeing.

I believe this is an unstoppable trend and I hope it will make us a healthier people.

Thinking back very briefly at influenza, our old scourge, didn’t we rely so completely on the vaccine, which is at most 50% effective in preventing the disease, that we did less than we could of commonsensical hand washing and social distancing?

Didn’t we, because of America’s denial of the wisdom of staying home when you’re sick, as it is manifested in the lack of sick days for many workers and the much celebrated “work ethic” of our society, allow ourselves to spread even less dangerous but economically impactful diseases unnecessarily?

Didn’t we, for far too long, ignore the indisputable fact that the majority of chronic illnesses that burdened our pre-pandemic health care system are in fact preventable and reversible?

Didn’t we, as citizens and as a society, ignore personal responsibility for and stewardship of the health of our human bodies in our thinking of health and disease?

Imagine how our newfound sense of self-reliance can create a new view of preventive and restorative healthcare.

I have said it before, but consider the pathological notion that more diabetes, more cancer, more autoimmune diseases means more drug sales and pharmaceutical profits, more hospital procedures and more money processed and profited from by insurance companies.

Imagine a society with less chronic disease, more able-bodied workers, a pharmaceutical industry more ready for new viruses and more focused on advanced disease prevention, correcting genetic and non-lifestyle triggered diseases.

Imagine a working public health system, capable of disease tracking and empowered to demand a better environment and better social circumstances for all Americans.

Imagine doctors with more time to help patients avoid ill health and less need to continually escalate pharmaceutical treatment for steadily advancing lifestyle-related diseases.

Who would pay for all this?

Well, I’m just a Country Doctor who used to think Sweden’s health care system wasn’t efficient enough. I have since realized that neither is the United States’.

It is time to reimagine what kind of healthcare system we really need.

Hans Duvefelt is a Swedish-born rural Family Physician in Maine.

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Post-Pandemic Solutions: A Public Option for Universal Healthcare

By ROSEMARIE DAY

As the coronavirus pandemic overtook the tail end of the Democratic primary season, attention rapidly shifted from examining the nuances of the differences between the candidates’ healthcare platforms to simply demanding a response to the pandemic. Beyond addressing the immediate crisis, however, lie many questions about the weaknesses of our current healthcare system, and how we will address them in the long run.  These questions should be at the forefront of voters’ minds as we head into the election this fall. 

One of the major weaknesses in our system is that we do not have universal healthcare. Importantly, virtually all of the Democratic candidates called for making healthcare a right in the U.S. This is a key first step toward universal healthcare.  Their approaches to achieving this varied, however. Bernie Sanders and Elizabeth Warren called for “Medicare for All,” but most of the other candidates, including Joe Biden, have pushed for some kind of public option. The public option has faced criticism that it will simply maintain the status quo. This criticism inspired me to write this blog, because a large-scale public option program could actually help to reshape the US healthcare system and result in improvements in access to care in this country, ultimately getting us to universal healthcare.

What is a public option?

A public option is a health insurance plan (or plans) sold by the government and available to all Americans, regardless of income, age, or other personal characteristics. It competes with private insurance, rather than supplanting it. The public option idea has been kicking around for some time. It was originally included in the Affordable Care Act but was dropped due to strong opposition from Republicans, moderate Democrats, and health care interest groups.

Would a public option constrain runaway costs?

A public option is likely to challenge the prices of the private insurance plans against which it competes. Chances are the public option would be built off of Medicare or Medicaid’s lean administrative infrastructure, and would therefore have lower administrative costs (Medicare’s administrative costs would be about 6% versus the 12-20% average for private insurance). It also wouldn’t need to turn a profit to satisfy shareholders and could tie its rates to the lower ones offered through existing public plans. Lower prices would lead to higher market share, putting further pressure to reduce overall healthcare spending.

Has any state implemented a public option?

A few states have tried. It wasn’t until 2019 that Washington state became the first to ultimately pass a bill. Washington state’s bill, which was not priced as aggressively as some would have liked, is still projected to save consumers 5-10% on their premiums. Industry watchers, myself included, eagerly await the effects of this program when it goes live in 2021.

Would a public option achieve universal coverage?

It depends on how it’s implemented. If a public option plan is made available as just another option on the state and federal exchanges, requiring people to opt in and pay premiums, then we would see more moderate increases in coverage. However, if the plan instead automatically enrolls people, with the ability to opt out for private coverage, then universal coverage would be a true possibility. Most of the candidates’ public option proposals suggested auto-enrolling people below a certain income, which would make a big dent in the number of uninsured in the country by negating the need for them to go through the process of applying for coverage.

Would insurers have a role in the public option?

They certainly could. Washington state’s public option will be offered through insurers, similar to the private managed care organizations that cover roughly two-thirds of Medicaid enrollees and a third of Medicare enrollees in the country. A federal public option could be implemented more quickly and easily if done through private insurers like Washington state is doing, but expanding Medicare’s capacity to cover these additional lives would probably result in lower administrative costs.

But wouldn’t a single-payer system like other countries have ultimately work better?

There is a big misconception that the healthcare systems in Germany, the United Kingdom, Canada, Australia, Denmark, France and other countries are all single-payer systems. Most are not. Most are more similar to a public option system than they are to Bernie Sanders’ Medicare for All proposal. None of these countries have a true single-payer system and none of them outlawed private insurance, as Medicare for All proposes to do.  All of the aforementioned countries feature a health system in which most people are covered by a public plan but have the option to pay for private insurance that gives them additional services.

Who would oppose a public option?

The idea of a public option is now widely embraced by the Democratic party—a far cry from just ten years ago when conservative and centrist members helped remove it from the Affordable Care Act. Republicans and many health industry interest groups oppose it, and passage would require both a Democratic supermajority and a large expense of political capital. Although the road is difficult, it is far less difficult than Medicare for All, which is supported by fewer than a third of Democrats in the House.

The public option could be a viable path to affordable health care

The public option is a more incremental approach to universal health care than Medicare for All, but could still be a bold step, particularly if it has an auto-enrollment component. Rather than enacting abrupt change by a government fiat that eliminates private insurance, the public option works by tipping the economic scales, changing the system through a more affordable consumer option. This gradual approach would be far less disruptive to hospitals and clinicians whose cost structures cannot accommodate a sudden, dramatic drop in revenue, and allow them to slowly adapt as more and more consumers move towards the public option. It also wouldn’t force Americans, most of whom have indicated that they like their insurance, to drop it in favor of a government plan.

The fact that a public option, thought unpassable a decade ago, was enacted in Washington state and was supported by the most centrist of the Democratic Presidential candidates, says a lot about how far this idea has come. A public option would be a big deal. It wouldn’t be as big of a change as Medicare for All, but it is more politically and economically realistic. We shouldn’t underestimate the public option’s potential to make some fundamental improvements to the US’s broken healthcare system, constraining runaway costs and covering millions of currently un-and under-insured Americans. And this is exactly what we need to have in place before the next pandemic.

I am grateful to Niko Lehman-White for his contributions to this piece.

Rosemarie Day is the Founder & CEO of Day Health Strategies and author of “Marching Toward Coverage:  How Women Can Lead the Fight for Universal Healthcare” (Beacon Press, 2020).  Twitter:  @Rosemarie_Day1

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