Monthly Archives: October 2023

The AI-empowered patient is coming. Are doctors ready?

Posted on by • Posted in Artificial Intelligence, Artificial Intelligence Generative, ChatGPT, ChatGPT-4, Disruption, Disruptive Technology, Google’s Med-PaLM 2, Innovation, Physician • Leave a comment

https://www.linkedin.com/pulse/ai-empowered-patient-coming-doctors-ready-robert-pearl-m-d-/

Artificial intelligence (AI) has long been heralded as an emerging force in medicine. Since the early 2000s, promises of a technological transformation in healthcare have echoed through the halls of hospitals and at medical meetings.

But despite 20-plus years of hype, AI’s impact on medical practice and America’s health remains negligible (with minor exceptions in areas like radiological imaging and predictive analytics).

As such, it’s understandable that physicians and healthcare administrators are skeptical about the benefits that generative AI tools like ChatGPT will provide.

They shouldn’t be. This next generation of AI is unlike any technology that has come before. 

The launch of ChatGPT in late 2022 marked the dawn of a new era. This “large language model” developed by OpenAI first gained notoriety by helping users write better emails and term papers. Within months, a host of generative AI products sprang up from Google, Microsoft and Amazon and others. These tools are quickly becoming more than mere writing assistants.

In time, they will radically change healthcare, empower patients and redefine the doctor-patient relationship. To make sense of this bold vision for the future, this two-part article explores:

  1. The massive differences between generative AI and prior artificial intelligences
  2. How, for the first time in history, a technological innovation will democratize not just knowledge, but also clinical expertise, making medical prowess no longer the sole domain of healthcare professionals.

To understand why this time is different, it’s helpful to compare the limited power of the two earliest generations of AI against the near-limitless potential of the latest version.

Generation 1: Rules-Based Systems And The Dawn Of AI In Healthcare

The latter half of the 20th century ushered in the first generation of artificial intelligence, known as rule-based AI.

Programmed by computer engineers, this type of AI relies on a series of human-generated instructions (rules), enabling the technology to solve basic problems.

In many ways, the rule-based approach resembles a traditional medical-school pedagogy where medical students are taught hundreds of “algorithms” that help them translate a patient’s symptoms into a diagnosis.

These decision-making algorithms resemble a tree, beginning with a trunk (the patient’s chief complaint) and branching out from there. For example, if a patient complains of a severe cough, the doctor first assesses whether fever is present. If yes, the doctor moves to one set of questions and, if not, to a different set. Assuming the patient has been febrile (with fever), the next question is whether the patient’s sputum is normal or discolored. And once again, this leads to the next subdivision. Ultimately each end branch contains only a single diagnosis, which can range from bacterial, fungal or viral pneumonia to cancer, heart failure or a dozen other pulmonary diseases.

This first generation of AI could rapidly process data, sorting quickly through the entire branching tree. And in circumstances where the algorithm could accurately account for all possible outcomes, rule-based AI proved more efficient than doctors.

But patient problems are rarely so easy to analyze and categorize. Often, it’s difficult to separate one set of diseases from another at each branch point. As a result, this earliest form of AI wasn’t as accurate as doctors who combined medical science with their own intuition and experience. And because of its limitations, rule-based AI was rarely used in clinical practice.

Generation 2: Narrow AI And The Rise Of Specialized Systems

As the 21st century dawned, the second era of AI began. The introduction of neural networks, mimicking the human brain’s structure, paved the way for deep learning.

Narrow AI functioned very differently than its predecessors. Rather than researchers providing pre-defined rules, the second-gen system feasted on massive data sets, using them to discern patterns that the human mind, alone, could not.

In one example, researchers gave a narrow AI system thousands of mammograms, half showing malignant cancer and half benign. The model was able to quickly identify dozens of differences in the shape, density and shade of the radiological images, assigning impact factors to each that reflected the probability of malignancy. Importantly, this kind of AI wasn’t relying on heuristics (a few rules of thumb) the way humans do, but instead subtle variations between the malignant and normal exams that neither the radiologists nor software designers knew existed.

In contrast to rule-based AI, these narrow AI tools proved superior to the doctor’s intuition in terms of diagnostic accuracy. Still, narrow AI showed serious limitations. For one, each application is task specific. Meaning, a system trained to read mammograms can’t interpret brain scans or chest X-rays.

But the biggest limitation of narrow AI is that the system is only as good as the data it’s trained on. A glaring example of that weakness emerged when United Healthcare relied on narrow AI to identify its sickest patients and give them additional healthcare services.

In filtering through the data, researchers later discovered the AI had made a fatal assumption. Patients who received less medical care were categorized as healthier than patients who received more. In doing so, the AI failed to recognize that less treatment is not always the result of better health. This can also be the result of implicit human bias.

Indeed, when researchers went back and reviewed the outcomes, they found Black patients were being significantly undertreated and were, therefore, underrepresented in the group selected for additional medical services.

Media headlines proclaimed, “Healthcare algorithm has racial bias,” but it wasn’t the algorithm that had discriminated against Black patients. It was the result of physicians providing Black patients with insufficient and inequitable treatment. In other words, the problem was the humans, not narrow AI.

Generation 3: The Future Is Generative

Throughout history, humankind has produced a few innovations (printing press, internet, iPhone) that transformed society by democratizing knowledge—making information easier to access for everyone, not just the wealthy elite.

Now, generative AI is poised to go one step further, giving every individual access to not only knowledge but, more importantly, expertise as well.

Already, the latest AI tools allow users to create a stunning work of art in the style of Rembrandt without ever having taken a painting class. With large language models, people can record a hit song, even if they’ve never played a musical instrument. Individuals can write computer code, producing sophisticated websites and apps, despite never having enrolled in an IT course.

Future generations of generative AI will do the same in medicine, allowing people who never attended medical school to diagnose diseases and create a treatment plan as well as any clinician.

Already, one generative AI tool (Google’s Med-PaLM 2) passed the physician licensing exam with an expert level score. Another generative AI toolset responded to patient questions with advice that bested doctors in both accuracy and empathy. These tools can now write medical notes that are indistinguishable from the entries that physicians create and match residents’ ability to make complex diagnoses on difficult cases.

Granted, current versions require physician oversight and are nowhere close to replacing doctors. But at their present rate of exponential growth, these applications are expected to become at least 30 times more powerful in the next five years. As a result, they will soon empower patients in ways that were unimaginable even a year ago.

Unlike their predecessors, these models are pre-trained on datasets that encompass the near-totality of publicly available information—pulling from medical textbooks, journal articles, open-source platforms and the internet. In the not-distant future, these tools will be securely connected to electronic health records in hospitals, as well as to patient monitoring devices in the home. As generative AI feeds on this wealth of data, its clinical acumen will skyrocket.

Within the next five to 10 years, medical expertise will no longer be the sole domain of trained clinicians. Future generations of ChatGPT and its peers will put medical expertise in the hands of all Americans, radically altering the relationship between doctors and patients.

Whether physicians embrace this development or resist is uncertain. What is clear is the opportunity for improvement in American medicine. Today, an estimated 400,000 people die annually from misdiagnoses, 250,000 from medical errors, and 1.7 million from mostly preventable chronic diseases and their complications.

In the next article, I’ll offer a blueprint for Americans as they grapple to redefine the doctor-patient relationship in the context of generative AI. To reverse the healthcare failures of today, the future of medicine will have to belong to the empowered patient and the tech-savvy physician. The combination will prove vastly superior to either alone.

As HLTH 2023 Convenes, Three Themes speak Volumes about Where U.S, Healthcare is Headed

Posted on by • Posted in Access to Capital, Health Policy, Health System, Hospital, Hospital Industry, Inflation, Price Transparency, Private Equity, Public Opinion, Public Perception, Systemness, Value-Based Healthcare • Leave a comment
As HLTH 2023 Convenes, Three Themes speak Volumes about Where U.S, Healthcare is Headed

In Las Vegas this week, 10,000 healthcare entrepreneurs, investors, purchasers and industry onlookers are gathered to celebrate the business of U.S. healthcare. It follows the inaugural Nashville Healthcare Sessions last month that drew a crowd to Music City touting “the premier healthcare conference set in the most relevant, exciting, and welcoming city in the south.“

Besides their locations and exceptional marketing, three notable themes are prominent that speak volumes about where this industry is:

1- The focus is systemness—integrated, connected, data-driven and scalable. Traditional divides that separate health and social services, hospitals and insurers, biotherapeutics and companion diagnostics are obsolete and access to private capital and swift execution vitals. And embedded in systemness is an expanded role of human resources that create workforces that are right-sized, diverse, AI-enabled and productive.
2-Technologies focused on end user value are gaining traction. Solutions that enable better, quicker, more accurate and affordable transactions with consumers are prominent. While traditional providers—hospitals, physicians, long-term care providers and public health programs– see HIT and AI investments as ways to make their work more efficient and satisfying, disruptors are focused on the untapped consumer market that’s dissatisfied with the status quo.
3-Access to smart capital is key. The venture capital and private equity markets in healthcare services are weathering corrections that have deflated returns and forced many to pullback or exit. The possibility of regulatory reforms involving greater transparency, carried interest restrictions and minimum hold periods means stronger funds with experienced operating partners and stable LP funding will be advantaged. In Vegas, they’ll be working the hallways to find tuck-ins for their platform bets and courting not-for-profit hospitals needing non-operating income to fund their growth and diversification efforts.

Those attending recognize the U.S. health industry faces unprecedented challenges:

  • Growing employer activism against lack of price transparency and inexplicably high unit costs for hospital care, prescription drugs, insurer overhead and mal-effect of consolidation in each sector.
  • Medical inflation that’s persistent but disproportionately absorbed by fewer and fewer employers and individuals who lack bargaining power.
  • Value-based purchasing activities that have failed to achieve desired cost containment goals.
  • Public dissatisfaction with the “system” and growing receptivity to alternatives.
  • Growing hostility in media coverage about hospitals, especially large not-for-profit hospitals, deemed to be profitable and wasteful.
  • Increased tension between providers (hospitals, medical groups) and insurers.
  • Increased regulation in states and court rulings that change (or have the potential to alter) how care is defined, provided, funded and legally authorized.

HLTH and Session attendees recognize the uncertainties of the political, economic and global markets in which healthcare operates. Israel will be front of mind to all as the fast-paced HLTH proceedings continue this week. 

The root causes of the system’s poor performance are understood and considered: they’re daunting. But that does not impede the willingness of private investors to make bets presuming the future of the U.S. healthcare is not a repeat of its past.

Contrary to pop culture, what happens in Vegas this week will not stay in Vegas: that’s the point. The health system is not working well. While some HLTH and Sessions attendees are no doubt focused on incremental innovations to improve the performance of their legacy organizations, others are looking beyond. And, if industries akin to healthcare like financial services and higher education are instructive, the latter are better prepared to respond than the former.

PS: Nearly 50 years to the day after the Yom Kippur War in 1973, Israel was again taken by surprise by a sudden attack. Unlike the series of clashes with Palestinian forces in Gaza over the past few years, this appears to be a full-scale conflict mounted by Hamas and its allies including Iran. 

Thousands are dead, more are injured and the health systems in both will be overwhelmed by the need. Health systems matter!

Failing to earn the consumer’s referral

Posted on by • Posted in Consumer Experience, Consumer Focus, Consumer Preferences, Consumer-Centric, Consumerism, Health System, Hospital, Hospital Industry, Patient Experience, Patient Referrals, Patient Satisfaction, Referral • Leave a comment

https://mailchi.mp/9fd97f114e7a/the-weekly-gist-october-6-2023?e=d1e747d2d8

There is a local urgent care chain that we frequented regularly when my kids were young and cycling through rounds of ear infections and strep throat. The experience was always solid, driven by online scheduling, efficient operations, and good customer service.

A few years ago, the clinics were bought by a local health system. We recently visited one for the first time post-acquisition, when my now teenage son needed to rule out a broken bone from a sports injury. This experience at the same urgent care left a very different impression.

In contrast to the “easy in, easy out” experience I expected, we sat in an exam room for hours, even though the place was not crowded. While this could be due to the staffing challenges pervasive across the industry, other elements of the acquisition left a different impression.

Gone was the advertised cash pricing (and I’m anticipating a higher bill once we get one). The new patient self-registration system was overly complex, built for a hospital, not an immediate care setting. 

The only signs of “systemness”? Multiple prompts to sign up for the health system’s MyChart patient portal (not interested, they have few facilities close by), and a printed referral to an employed orthopedic surgeon a forty-minute drive from home (with no guidance as to whether or when we should seek it, given that no bones were broken). 
 
A few days ago, a scheduler from the system called to book the appointment. With no inquiry as to whether my son’s pain had improved, the interaction felt like a business transaction, not clinical follow-up. I declined.

Just because a care site is acquired by a health system, that doesn’t mean that patients will feel any value from its being part of a system.

Right or wrong, my impression was that health system ownership has made for a worse experience: inefficient, more complicated, and possibly more expensive. 

Nothing about the visit gave me confidence that there was a benefit to following up with an affiliated provider. The health system had failed to earn our referral.

Systems buy assets like urgent care to create entry points that will generate downstream demand and hopefully build loyalty to the brand. But capturing that must start with delivering an excellent experience in every encounter, not merely changing the name on the building. 

Health “insurtechs” struggling to stay relevant

Posted on by • Posted in Accountable Care Organization (ACO), Disruption, Disruptive Technology, Financial Viability, Financially Distressed, Health Insurance Industry, Initial Public Offering (IPO), Innovation, Insurtechs, Medical Loss Ratio (MLR), Medicare Advantage, Operating Expenses, Operating Income, Operating Margin, Operating Performance • Leave a comment

https://mailchi.mp/9fd97f114e7a/the-weekly-gist-october-6-2023?e=d1e747d2d8

“Insurtechs” Clover Health, Oscar Health, and Bright Health all went public in the midst of the hot equity market of 2021. Investors were excited by the fast growth of these health insurer startups, and their potential to revolutionize an industry dominated by a few large players.

However, the hype has dissipated as financial performance has deteriorated. After growing at all costs during a period of low interest rates, changing market conditions directed investors to demand a pivot to profitability, which the companies have struggled to deliver—two years later, none of the three has turned a profit. 

Oscar and Bright have cut back their market presence significantly, while Clover has mostly carried on while sustaining high losses. In the last two years, only Oscar has posted a medical loss ratio in line with other major payers, who meanwhile are reporting expectation-beating profits. While Oscar has shown signs of righting the ship since the appointment of former Aetna CEO Mark Bertolini, 

the future of these small insurers remains uncertain. As their losses mount and they exit markets, they may become less desirable as acquisition targets for large payers.

How US is failing to keep its citizens alive into old age

Posted on by • Posted in Access & Quality of Healthcare, Accessible Care, Affordability, Chronic Care Management, Chronic Conditions, Chronic Diseases, Health Equity, Health Inequity, Health Insurance Coverage Adequacy, Health Policy, Health Predictors, Mortality, Poverty, Social Determinants of Health, Systemic Issues • Leave a comment

https://mailchi.mp/9fd97f114e7a/the-weekly-gist-october-6-2023?e=d1e747d2d8

Published this week in the Washington Post, this unsparing article packages a year of investigative reporting into a thorough accounting of why US life expectancy is undergoing a rapid decline. 

After peaking in 2014, US life expectancy has declined each subsequent year, trending far worse than peer countries. In a quarter of US counties, working-age Americans are dying at the highest rates in 40 years, reversing decades of progress. While deaths from firearms and opioids play a role, chronic diseases remain our nation’s greatest killer, erasing more than double the years of life as all overdoses, homicides, suicides, and car accidents combined.

The drivers of this trend are too numerous to list, but experts suggest targeting “the causes of the causes”, namely social factors, as the death rate gap between the rich and poor has grown almost 15x faster than the income gap since 1980. 

The Gist: This reporting is a sobering reminder of the responsibilities—and failures—borne by our nation’s healthcare system. 

The massive death toll of chronic disease in this country is not an indictment of the care Americans receive, but of the care and other resources they cannot access or afford. 

While it’s not the mandate of health systems to reduce systemic issues like poverty, there is no solution to the problem without health systems playing a key role in increasing access to care, while convening community resources in service of these larger goals.

CBO Report finds CMMI hasn’t saved Medicare any money

Posted on by • Posted in ACA, Accountable Care Organization (ACO), Centers for Medicare and Medicaid Innovation (CMMI), Congressional Budget Office (CBO), Health Policy, High Prices, Medicare, Utilization Trends • Leave a comment

https://mailchi.mp/9fd97f114e7a/the-weekly-gist-october-6-2023?e=d1e747d2d8

Late last week, the Congressional Budget Office (CBO) released its analysis of the Center for Medicare and Medicaid Innovation (CMMI)’s spending outlays, revealing that in its first decade of operations it produced a $5.4B net increase in federal spending instead of a projected $2.8B reduction. 

Moreover, CBO revised its CMMI projection for 2021-2030 from a $77.5B net spending reduction to a $1.3B increase, predicting CMMI may only begin to generate annual savings in 2031. CBO says its updated projections largely reflect revised expectations on CMMI’s ability to identify and scale models that actually reduce Medicare spending.

CMMI was created by the Affordable Care Act (ACA) in 2010 to test new payment models and other initiatives for reducing the federal government’s healthcare costs, but of the nearly 50 models it has run, only four have become permanent programs.

The Gist: This critical report confirms what many in the healthcare world already believed: the ACA’s value-based care initiatives have largely struggled to reduce Medicare spending. 

There are plenty of policy factors to blame, including the lack of mandatory participation for providers and conflicting incentives across care models, but one factor left out of the CBO report is CMMI’s disproportionate emphasis on accountable care organizations (ACOs) to produce meaningful cost savings, even as years of data proved otherwise. 

ACOs are designed to reduce spending primarily through utilization management, but research has shown that prices, not utilization, are responsible for the US’s high medical spend relative to other countries.

While CMMI’s mission is still laudable and important, the center must make good on its 2021 “strategic refresh” if it hopes to continue receiving Congressional support.

Kaiser Permanente healthcare workers initiate record strike

Posted on by • Posted in Employee Benefits, Employee Compensation, Employee Expectations, Employee Raise, Health System, Hospital, Hospital Industry, Kaiser Permanente, Labor Spend Management, Labor Strike, Labor Union, Operating Expenses, Operating Margin • Leave a comment

https://mailchi.mp/9fd97f114e7a/the-weekly-gist-october-6-2023?e=d1e747d2d8

On Wednesday, 75K Kaiser Permanente (KP) healthcare workers in five states and Washington, DC walked off the job as part of the largest healthcare strike in US history.

The striking workers are a diverse group, based mostly in California, that includes support staff, X-ray technicians, medical assistants, and pharmacy workers. They will continue their work stoppage until Saturday morning, though union leadership is threatening an even larger strike in November if a new contract agreement is not reached by then.

Their employment contract expired on September 30th, and while negotiations have progressed on issues like shift-payment differentials and employee training investments, union leaders and KP executives remain at odds over key wage increase demands, with the unions asking for a $25 national minimum wage, and KP proposing $21.

The company has sought to minimize disruptions to patient care during the strike, bringing in temporary labor to keep critical infrastructure open, but has told its members to expect some non-urgent procedures to be rescheduled, some clinic and pharmacy operating hours to be reduced, and call center wait times to be lengthy. 

The Gist: Kaiser Permanente has enjoyed solid relations with its unions for decades, making this strike a significant break from precedent, fueled by post-pandemic burnout and staffing shortages. 

While KP is keeping all essential services open, care disruptions are inevitable with around one third of its total workforce on strike. 

The stakes of these labor negotiations extend far beyond just KP and its employees, as union success could inspire other unionized healthcare workers to adopt similar tactics and demands. (Case in point: Employees at eleven Tenet Healthcare facilities in California represented by SEIU-UHW, one of the unions representing striking KP workers, just voted to authorize their own strike.)

While happening alongside high-profile strikes in other industries, labor unrest is a troubling trend for health systems, whose margins remain well below historical levels amid persistently high labor and supply expenses.

Tightening the Rules Around Short-Term Health Insurance Plans Won’t Lead to More People Going Without Insurance

Posted on by • Posted in ACA, Health Insurance Coverage Adequacy, Health Policy, Out of Pocket Expenses, Preexisting Conditions, Short Term Health Insurance, Skinny Health Plans, Underinsured, Uninsured • Leave a comment
https://www.commonwealthfund.org/blog/2023/tightening-rules-around-short-term-health-insurance-plans-wont-lead-more-people-going?utm_campaign=Achieving%20Universal%20Coverage&utm_medium=email&_hsmi=277181558&_hsenc=p2ANqtz–ufaBTL0WjY2jmyaMR5rMHK2CXJeRoV0TJdWU7NpyNnRfxoN9jXrKrarEMBbia0VnU80H3epvGGw9ZlaL12EzzgnMkjA&utm_source=alert

Short-term, limited-duration insurance (STLDI) plans are exempt from the Affordable Care Act’s (ACA) essential benefit coverage requirements and from prohibitions on medical underwriting.

This means that consumers with preexisting conditions can be denied coverage and anyone who purchases such a plan may lack coverage for key services.

In August 2018, under the Trump administration, the U.S. Department of Health and Human Services revised the definition of short-term plans to include coverage with an initial term of less than 12 months that could be renewed for up to 36 months. While the purported goal of this change was to increase coverage and reduce uninsured rates, our analysis indicates that it did not accomplish this: coverage did not increase and the uninsured rate did not drop.

In July 2023, the Biden administration issued a notice to limit the initial duration of short-term plans to three months, with an option to renew for one additional month. This change was intended to ensure that people purchasing insurance coverage have meaningful protection and to preserve the preexisting condition protections in the ACA. 

Critics feared and some cost estimates suggested that tightening the STLDI rules could leave many without any coverage at all.

In 2019, the Congressional Budget Office (CBO), using its forecast model (data were not yet available), estimated that 1.5 million people would purchase short-term plans and that 500,000 would gain coverage (relative to being uninsured). Our analysis suggests that these forecasts substantially overstated the effects of the rule change; far fewer people enrolled in STLDI plans and the enrollment that did occur was from people moving off marketplace coverage.

There is no evidence that the number of uninsured people declined because these plans became available.

Using data from the American Community Survey and marketplace enrollment from the Centers for Medicare and Medicaid Services (CMS), we assessed whether the loosening of STLDI regulations (under the Trump administration) led to increased enrollment in off-marketplace nongroup coverage in states that permitted sales compared to those that did not. Plans sold off the marketplace include STLDI as well as ACA-compliant plans, grandfathered coverage, health care sharing ministries, and fixed indemnity plans. Next, we looked to see whether the Trump-era regulations increased nongroup insurance coverage altogether (including marketplace coverage) in these states. Finally, we looked to see whether the broader availability of STLDI was associated with lower uninsured rates. We examined coverage patterns for adults ages 26 to 64 and then focused on young men ages 26 to 35, who may be most sensitive to the presence of regulations similar to those in the ACA because they are less likely to have preexisting conditions or to seek comprehensive coverage.

In 2017, 2.6 million adults ages 26 to 64, about 1.6 percent of that population, purchased private nongroup insurance outside the marketplace. By 2020, about 270,000 more people were enrolled in off-marketplace nongroup plans, across all states, than had been in 2017. There was a larger increase in off-marketplace nongroup enrollment among all adults and among young adults (we cannot separate young men in the CMS data) in states that permitted the sale of STLDI coverage, compared to those that prohibited it. This is consistent with the evidence of growth in sales of these plans. Across all states, about 160,000 more young adults, ages 26 to 34, held off-marketplace nongroup coverage in 2020 than in 2017.

The ACS data show that off-marketplace plans largely substituted for marketplace plans in states that permitted the sale of STLDI. Patterns of enrollment in nongroup plans overall were very similar in states with and without STLDI plans available for purchase over this period. While nongroup coverage was consistently more popular in states with no restrictions, between 2017 and 2020 enrollment in nongroup plans declined slightly more in states where STLDI plans were available for purchase than in those where they were not. The same pattern of marginally greater declines held for young men (and young adults) in states where STLDI plans were available.

Nongroup coverage was slightly higher in states where STLDI plans were available for sale, but the overall uninsured rate is much higher in these states, primarily because many did not expand Medicaid eligibility.

The gap in uninsured rates between states with STLDI plans available and those in which they were not available widened through 2018, narrowed slightly in 2019, and rose again in 2020. Patterns among young men were similar.

The lack of reliable information on STLDI plans and the small size of the market make it difficult to draw strong inferences about how changes in regulations affected participation. Nonetheless, by comparing states where the 2018 regulatory changes took effect and those where they did not, we are able to rule out any notable effects. A modest number of people — no more than one-fifth of the 1.5 million the CBO projected — are likely to have enrolled in STLDI plans that became available after the Trump administration’s regulatory change. This enrollment mainly appears to have displaced marketplace coverage.

There is no evidence that the broader availability of STLDI plans had any meaningful effect on nongroup coverage in general or on uninsurance, either in the full population or among young men.

This suggests that the Biden administration’s proposed tightening of STLDI is unlikely to have substantial negative effects on nongroup coverage or uninsurance. Instead, limiting STLDI will likely strengthen the health insurance marketplaces that offer reliable, comprehensive nongroup coverage.

Misinformation About Health Is Nothing New

Posted on by • Posted in Disinformation, False, False Information, False Narrative, Lying, Misinformation, Mislead, Misleading, Trust, Truth • Leave a comment
Misinformation About Health Is Nothing New

Misinformation. A recent and major problem facing us all, and one that is pervasive in many realms including medicine and healthcare, which are, of course, favorite realms around here. But is all this stuff recent? Is misinformation a new phenomenon in the world of medicine and health, or does it have a history?

The answer to that, thanks in part to funding from the National Institute for Healthcare Management, is the topic of this week’s Healthcare Triage.