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Sunday, July 7, 2019

Tiny change has big effects, reverses prediabetes in mice

Results highlight potential diabetes drug target and role for ceramides in sensing nutrition

A small chemical change — shifting the position of two hydrogen atoms — makes the difference between mice that are healthy and mice with insulin resistance and fatty liver, major risk factors for diabetes and heart disease. Making the change prevented the onset of these symptoms in mice fed a high-fat diet and reversed prediabetes in obese mice.
The scientists changed the trajectory of metabolic disease by deactivating an enzyme called dihydroceramide desaturase 1 (DES1). Doing so stopped the enzyme from removing the final hydrogens from a fatty lipid called ceramide, having an effect of lowering the total amount of ceramides in the body.
The finding highlights a role for ceramides in metabolic health and pinpoints DES1 as a “druggable” target that could be used to develop new therapies for metabolic disorders such as prediabetes, diabetes and heart disease — that affect the health of hundreds of millions of Americans. Scientists at University of Utah Health and Merck Research Laboratories led the research, published online in Science on July 4, 2019.
“We have identified a potential therapeutic strategy that is remarkably effective, and underscores how complex biological systems can be deeply affected by a subtle change in chemistry,” says Scott Summers, Ph.D., chair of Nutrition and Integrative Physiology at U of U Health, who was co-senior author on the study with David Kelley, M.D., formerly of Merck Research Laboratories.
“Our work shows that ceramides have an influential role in metabolic health,” says Summers. “We’re thinking of ceramides as the next cholesterol.”
This isn’t the first time that Summers’ group has found that lowering ceramides could reverse signs of diabetes and metabolic disease. However, techniques used in previous experiments caused severe side effects, showing the approach would not be suitable for therapeutic applications.
This time, rather than taking a sledgehammer to the problem, they developed a fine scalpel. They wondered whether making the smallest change possible and at a precise time and place might yield better results.
To lower ceramides, the investigators blocked the final step of ceramide synthesis in two ways. Summers’ group genetically engineered mice in which the gene coding for DES1 could be switched off during adulthood and deactivated the gene from tissues throughout the body, or alternatively from either liver or fat cells. Kelley’s group injected short hairpin RNA into the adult liver, a method that selectively lowered production of DES1 by destroying the RNA precursor.
The scientists tested the new approaches first by placing adult mice on a high-fat diet – one that resembled cookie dough with plenty of sugar and six times the fat of a normal rodent diet. The mice gained two times their body weight within three months. Along with obesity came a strain on their metabolic health. They developed insulin resistance and fat accumulated in the liver, both signs of metabolic disease.
Within weeks after lowering ceramides using either technique, there were significant changes. Mice remained obese but their metabolic health improved. Fat cleared from the liver and they responded to both insulin and glucose like a healthy, skinny mouse. In contrast to previous interventions, mice remained healthy during the two-month investigation. Long-term effects on health are currently under investigation.
“Their weight didn’t change but the way they handled nutrients did,” says Summers. “The mice were fat but they were happy and healthy.”
In another paradigm, lowering ceramides before putting the mice on a high-fat diet prevented weight gain and insulin resistance.
Although the impact of lowering ceramides in humans is still unknown, there is evidence that ceramides are linked to metabolic disease, says Summers. He points out that clinics are already performing ceramide screening tests to gauge an individual’s risk for developing heart disease.
Summers and Kelley are now developing drugs that inhibit DES1 with a goal of making new therapeutics. “This project provides substantial validation that this is a discreet and highly effective point of intervention,” says Kelley.
When the Good Goes Bad
If ceramides cause poor health, why do we have them in the first place? Summers’ group addressed the question by measuring how the lipid affects metabolism. They found that ceramides trigger a number of mechanisms that promote the storage of fat in cells. They also impair cells’ ability to use glucose, a type of sugar, as fuel.
The evidence for these effects includes activation of a molecular pathway, Akt/PKB, that inhibits both the ability of cells to synthesize sugars and to take them up from the bloodstream. At the same time, ceramides slow the turnover of fatty acids in part by causing cells in the liver to increase fatty acid storage and adipose tissue to burn less fat.
The shift in how cells use fuel is an advantage in the short-term, says Summers. This is because ceramides have another role in stiffening the cell membrane. Further, promoting fat storage increases production of ceramides. These data lead to a model suggesting that one benefit of ceramides is that they protect the cell. When food is plentiful and cells store lots of fat, the increase in ceramide levels strengthen the cells’ outer membrane, preventing ruptures.
“Serving in this role is usually good but it can potentially be bad,” explains Trevor Tippetts, a graduate student in Summers’ lab. He, U of U Health research assistant professor Bhagirath Chaurasia, Ph.D., and two of their Merck colleagues, Rafael Mayoral MoƱibas, Ph.D., and Jinqi Liu, Ph.D., share lead authorship.
Tippetts explains that problems arise in times of chronic overabundance, such as during obesity, when there are persistently high levels of ceramides. Summers’ team speculate that sustained impairment of metabolic homeostasis leads to insulin resistance and fatty liver disease.
The results hint at ceramide’s normal role. “We think that ceramides evolved to become a nutritional sensor,” says Chaurasia. He says that ceramides serve as a signal, helping the body to cope when the amount of fat that is coming into cells is exceeding its energetic needs, and its storage capacity.
These findings are leading to a deep understanding of how cells in the body assess nutrient status and adapt accordingly. “To me, that’s the really exciting result,” Chaurasia says.
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The research published as “Targeting a ceramide double bond improves insulin resistance and hepatic steatosis.” in Science online on July 4, 2019, and was carried out as a collaboration between investigators at University of Utah Health, Merck Research Laboratories, Baker IDI Heart and Diabetes Institute, University of Brunei Darussalam, and SCIEX.
Researchers received support from the National Institutes of Health, American Diabetes Association, Juvenile Diabetes Research Foundation, American Heart Association, and the Margolis Foundation.
Summers and Kelley are shareholders and consultants with Centaurus Therapeutics.

Cancer urgent care clinics may slow growth of ER utilization

A cancer urgent care clinic in Texas reduced growth of emergency department utilization by more than half.


KEY TAKEAWAYS

For cancer patients, emergency department visits are associated with high hospitalization rates, disrupted treatment schedules, and high costs of care.
Oncology patients can be treated safely in the urgent care setting for several cancer-related symptoms such as nausea and pain.
Healthcare organizations considering adoption of cancer urgent care clinics should assess their oncology patient population to determine which services would be most be most effective and useful for patients in an urgent care setting.
Cancer urgent care clinics can reduce emergency department (ED) utilization, new research indicates.
In the first six months after a cancer diagnosis, adult patients generate a high volume of unplanned visits to emergency rooms for commonly expected complaints such as nausea and pain. After ED visits, many cancer patients are hospitalized, which exposes them hospitalization risks, disrupts treatment schedules, and increases cancer care expenditures.
The new research, which was published in the Journal of Oncology Practice, found cancer urgent care clinics are a promising strategy to lower cost of care, the researchers wrote.
“Given the outsized role of unplanned hospital care in the early expenditures of cancer treatment, this delivery innovation was highly effective and may be a promising strategy for organizations participating in the Oncology Care Model demonstration project and other risk-bearing contracts, such as accountable care organizations.”

IMPACT OF CANCER URGENT CARE CLINIC ON ER VISITS

The researchers examined the ED-visit impact of a cancer urgent care clinic opened at Dallas-based University of Texas Southwestern Medical Center in May 2012. The cancer urgent care clinic is available to patients during standard weekday business hours—8 a.m. to 4 p.m.
The study features an analysis of data collected from more than 33,000 adult patients. The research generated a key finding: the cancer urgent care clinic was associated with significantly reduced growth of ED service utilization.
Before the cancer urgent care clinic was opened, weekday ED visits were increasing at a rate of 0.43 visits per 1,000 patient-months. After the clinic was opened, the growth of ED visits was cut by more than half to 0.19 visits per 1,000 patient-months.
On weekends, when the cancer urgent care center was closed, there was no significant change in ED utilization.
“More than four years after implementation, the creation of a cancer urgent care clinic was associated with a significant reduction in ED visits made within the 180 days after a new cancer diagnosis. In contrast, the rate of ED visits made on weekends, when the UCC was closed, remained unchanged over the same period of time. This increases our confidence that the urgent care clinic was associated with a real reduction in ED visits when the clinic was open,” the researchers wrote.

INNOVATIVE CANCER CARE STRATEGY

Cancer urgent care clinics are a relatively new and innovative approach to cancer care worthy of more widespread adoption and research, the lead author of the new study told HealthLeaders.
“What patients really want is a trusted resource and an alternative to the emergency department because—anywhere you go in the country—there are untold hours you could wait at an ED. Patients also may be worried about being exposed to the dangerous infections that people have in an emergency department. An urgent care center checks off a lot of boxes in terms of what cancer patients are looking for,” said Arthur Hong, MD, an assistant professor of internal medicine and clinical sciences at UT Southwestern.
Only a handful of healthcare organizations have reported adoption of cancer urgent care centers, including Parkland Hospital in Dallas and Yale New Haven Health in Connecticut, he said.
Hong said there are common cancer-related symptoms that can be treated safely in the urgent care setting, including pain, headache, nausea, vomiting, diarrhea, and constipation. The UT Southwestern cancer urgent care clinic offers services that address these symptoms such as administering intravenous fluids and medications, obtaining basic laboratory tests with rapid results, and conducting common imaging tests such as chest X-ray.
Before launching a cancer urgent care clinic, healthcare organizations should assess their cancer patient population to determine which services would be most be most effective and useful for patients in an urgent care setting, he said. “Each institution has the ability to look at its line of business and find the common reasons patients would use a cancer urgent care clinic.”

Behind the Apple Watch’s ECG ability

In February 2016, a small start-up company called AliveCor hired Frank Petterson and Simon Prakash, two Googlers with AI expertise, to transform their business of smartphone electrocardiograms (ECG). The company was struggling. They had developed the first smartphone app capable of single-lead ECG, and, by 2015, they were even able to display the ECG on an Apple Watch. The app had a “wow” factor but otherwise seemed to be of little practical value. The company faced an existential threat, despite extensive venture capital investment from Khosla Ventures and others.
But Petterson, Prakash, and their team of only three other AI talents had an ambitious, twofold mission. One objective was to develop an algorithm that would passively detect a heart-rhythm disorder, the other to determine the level of potassium in the blood, simply from the ECG captured by the watch. It wasn’t a crazy idea, given whom AliveCor had just hired. Petterson, AliveCor’s VP of engineering, is tall, blue-eyed, dark-haired with frontal balding, and, like most engineers, a bit introverted. At Google, he headed up YouTube Live, Gaming, and led engineering for Hangouts. He previously had won an Academy Award and nine feature film credits for his design and development software for movies including the
Transformers, Star Trek, the Harry Potter series, and Avatar. Prakash, the VP of products and design, is not as tall as Petterson, without an Academy Award, but is especially handsome, dark-haired, and brown-eyed, looking like he’s right out of a Hollywood movie set. His youthful appearance doesn’t jibe with a track record of twenty years of experience in product development, which included leading the Google Glass design project. He also worked at Apple for nine years, directly involved in the development of the first iPhone and iPad. That background might, in retrospect, be considered ironic.
Meanwhile, a team of more than twenty engineers and computer scientists at Apple, located just six miles away, had its sights set on diagnosing atrial fibrillation via their watch. They benefited from Apple’s seemingly unlimited resources and strong corporate support: the company’s chief operating officer, Jeff Williams, responsible for the Apple Watch development and release, had articulated a strong vision for it as an essential medical device of the future. There wasn’t any question about the importance and priority of this project when I had the chance to visit Apple as an advisor and review its progress. It seemed their goal would be a shoo-in.
The Apple goal certainly seemed more attainable on the face of it. Determining the level of potassium in the blood might not be something you would expect to be possible with a watch. But the era of deep learning, as we’ll review, has upended a lot of expectations.
The idea to do this didn’t come from AliveCor. At the Mayo Clinic, Paul Friedman and his colleagues were busy studying details of a part of an ECG known as the T wave and how it correlated with blood levels of potassium. In medicine, we’ve known for decades that tall T waves could signify high potassium levels and that a potassium level over 5.0 mEq/L is dangerous. People with kidney disease are at risk for developing these levels of potassium. The higher the blood level over 5, the greater the risk of sudden death due to heart arrhythmias, especially for patients with advanced kidney disease or those who undergo hemodialysis. Friedman’s findings were based on correlating the ECG and potassium levels in just twelve patients before, during, and after dialysis. They published their findings in an obscure heart electrophysiology journal in 2015; the paper’s subtitle was “Proof of Concept for a Novel ‘Blood-Less’ Blood Test.” They reported that with potassium level changes even in the normal range (3.5–5.0), differences as low as 0.2 mEq/L could be machine detected by the ECG, but not by a human-eye review of the tracing.
Friedman and his team were keen to pursue this idea with the new way of obtaining ECGs, via smartphones or smartwatches, and incorporate AI tools. Instead of approaching big companies such as Medtronic or Apple, they chose to approach AliveCor’s CEO, Vic Gundotra, in February 2016, just before Petterson and Prakash had joined. Gundotra is another former Google engineer who told me that he had joined AliveCor because he believed there were many signals waiting to be found in an ECG. Eventually, by year’s end, the Mayo Clinic and AliveCor ratified an agreement to move forward together.
The Mayo Clinic has a remarkable number of patients, which gave AliveCor a training set of more than 1.3 million twelve-lead ECGs gathered from more than twenty years of patients, along with corresponding blood potassium levels obtained within one to three hours of the ECG, for developing an algorithm. But when these data were analyzed it was a bust.
Here, the “ground truths,” the actual potassium (K+) blood levels, are plotted on the x-axis, while the algorithm-predicted values are on the y-axis. They’re all over the place. A true K+ value of nearly 7 was predicted to be 4.5; the error rate was unacceptable. The AliveCor team, having made multiple trips to Rochester, Minnesota, to work with the big dataset, many in the dead of winter, sank into what Gundotra called “three months in the valley of despair” as they tried to figure out what had gone wrong.
Petterson and Prakash and their team dissected the data. At first, they thought it was likely a postmortem autopsy, until they had an idea for a potential comeback. The Mayo Clinic had filtered its massive ECG database to provide only outpatients, which skewed the sample to healthier individuals and, as you would expect for people walking around, a fairly limited number with high potassium levels. What if all the patients who were hospitalized at the time were analyzed? Not only would this yield a higher proportion of people with high potassium levels, but the blood levels would have been taken closer to the time of the ECG.
They also thought that maybe all the key information was not in the T wave, as Friedman’s team had thought. So why not analyze the whole ECG signal and override the human assumption that all the useful information would have been encoded in the T wave? They asked the Mayo Clinic to come up with a better, broader dataset to work with. And Mayo came through. Now their algorithm could be tested with 2.8 million ECGs incorporating the whole ECG pattern instead of just the T wave with 4.28 million potassium levels. And what happened?
asdf
The receiver operating characteristic (ROC) curves of true versus false positive rates, with examples of worthless, good, and excellent plotted. Source: Wikipedia (2018)Eureka! The error rate dropped to 1 percent, and the receiver operating characteristic (ROC) curve, a measure of predictive accuracy where 1.0 is perfect, rose from 0.63 at the time of the scatterplot to 0.86. We’ll be referring to ROC curves a lot throughout the book, since they are considered one of the best ways to show (underscoring one, and to point out the method has been sharply criticized and there are ongoing efforts to develop better performance metrics) and quantify accuracy—plotting the true positive rate against the false positive rate (Figure 4.2). The value denoting accuracy is the area under the curve, whereby 1.0 is perfect, 0.50 is the diagonal line “worthless,” the equivalent of a coin toss. The area of 0.63 that AliveCor initially obtained is deemed poor. Generally, 0.80–.90 is considered good, 0.70–.80 fair. They further prospectively validated their algorithm in forty dialysis patients with simultaneous ECGs and potassium levels. AliveCor now had the data and algorithm to present to the FDA to get clearance to market the algorithm for detecting high potassium levels on a smartwatch.
There were vital lessons in AliveCor’s experience for anyone seeking to apply AI to medicine. When I asked Petterson what he learned, he said, “Don’t filter the data too early. . . . I was at Google. Vic was at Google. Simon was at Google. We have learned this lesson before, but sometimes you have to learn the lesson multiple times. Machine learning tends to work best if you give it enough data and the rawest data you can. Because if you have enough of it, then it should be able to filter out the noise by itself.”
“In medicine, you tend not to have enough. This is not search queries. There’s not a billion of them coming in every minute. . . . When you have a dataset of a million entries in medicine, it’s a giant dataset. And so, the order or magnitude that Google works at is not just a thousand times bigger but a million times bigger.” Filtering the data so that a person can manually annotate it is a terrible idea. Most AI applications in medicine don’t recognize that, but, he told me, “That’s kind of a seismic shift that I think needs to come to this industry.”
Excerpted from Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Copyright © 2019 by Eric Topol. Available from Basic Books.

Blood Biomarkers Predict Concussion Recovery

Blood-based inflammation markers predicted recovery time in high school and college football players who had concussions, a small prospective study showed.
Circulating levels of interleukin-6 (IL-6) and interleukin-1 receptor antagonist (IL-1RA) were elevated 6 hours after concussion, and IL-6 levels predicted the number of days concussion symptoms persisted, reported Timothy Meier, PhD, of the Medical College of Wisconsin in Milwaukee, and co-authors in Neurology.
“Despite the large number of concussions that occur every year during sports, we still don’t have great tools to identify athletes who may be at risk for having prolonged symptoms,” Meier told MedPage Today.
While most concussion biomarker research has focused on identifying blood markers fairly specific to brain injury, other aspects of the neurometabolic cascade hold promise, he added: “These results suggest that acute levels of general markers of inflammation may ultimately prove to be prognostic markers for sport-related concussion, though additional research is needed.”
The findings also highlight IL-6 and IL-1 pathways as possible targets for treatment development, consistent with recent research into recombinant human IL-1RA to treat severe traumatic brain injury.
“What we see in concussion is that most people completely recover, but some go on to have problems,” noted Michael Alosco, PhD, of Boston University, who was not involved with the study. “These inflammatory markers actually might be very important for identifying who does go on to have prolonged problems or delayed recovery,” he told MedPage Today.
But the markers are nonspecific; they can be elevated in other types of injuries, Alosco pointed out. “If you’re focusing on one or two biomarkers, your diagnostic accuracy goes down,” he observed. “What you really need is a panel of biomarkers that can start to tease apart different causes.”
In this study, Meier and colleagues looked at 41 high school and college football players with concussions and 43 matched control athletes. They assessed athletes before injury, at approximately 6 hours and 24 to 48 hours post-concussion, and at about 8, 15, and 45 days after injury. Team physicians or certified athletic trainers identified and diagnosed concussions, which were defined by the CDC’s HEADS UP initiative.
Athletes were male and had an average age of 18. At pre-injury (baseline) and follow-up visits, researchers collected serum levels of IL-6, IL-1β, IL-10, tumor necrosis factor, C-reactive protein, interferon-γ, and IL-1RA, and obtained Sport Concussion Assessment Tool, 3rd edition (SCAT3) symptom severity scores.
Concussed athletes had symptoms for an average of about 9 days and had SCAT3 scores that were significantly higher at the 6- and 24- to 48-hour visits.
Relative to baseline and all other visits, IL-6 and IL-1RA were significantly elevated in concussed athletes at 6 hours post-concussion. Before injury, IL-6 levels were 0.44 pg/mL in the concussion group and 0.40 pg/mL in controls; at 6 hours, they were 1.01 pg/mL in the concussion group and 0.39 pg/mL in controls. The area under receiver operating characteristic curve (AUC) for discriminating concussed athletes from controls at the 6-hour visit was 0.79 (95% CI 0.65-0.92) for IL-6 and 0.79 (95% CI 0.67-0.90) for IL-1RA.
Elevated IL-6 levels at 6 hours post-concussion were significantly associated with longer symptom recovery (HR for symptom recovery 0.61, 95% CI 0.38-0.96, P=0.031). IL-1RA levels, however, were not tied to recovery time. All injured athletes with elevated IL-6 or IL-1RA were symptom-free by the 15-day visit.
The researchers “looked out to 45 days, so this study is really acute,” Alosco said. “It will be interesting to see further follow-up: can these markers predict who’s going to develop midlife or later-life problems? In subconcussive impacts and in concussion, a very small percentage of people go on to develop symptoms that last for up to a year. Can these markers identify those people, too?”
The study has several limitations, Meier and co-authors noted. The sample size may not be large enough to determine the accuracy, precision, specificity, and sensitivity of inflammatory markers as clinical biomarkers of concussion. The study assessed only male high school and college athletes, and the findings might not apply to other groups.
Last Updated July 03, 2019
The study was supported by the Department of Defense, the National Institute of Neurological Disorders and Stroke, the National Institute of General Medical Sciences, the National Institute of Mental Health, and the National Center for Advancing Translational Sciences.
The authors reported no relevant disclosures.

Why more patients are getting hit with unexpected medical bills

Hardly a week goes by without another story in the media covering a family somewhere in America dealing with an outrageous medical bill. Yet, in more and more cases, these families don’t have junk insurance, or lack coverage altogether. Indeed, they have what Americans would consider decent coverage, either through their employer or an Affordable Care Act marketplace. They also followed, or so they thought, the rules of their insurance policy requiring them to seek care inside their provider network. Yet, they are slapped with surprise bills, and often threatened by bankruptcy.
What gives?
In my view as a health care policy researcher, the increasing occurrence of surprise medical bills is not an accident. Rather, it is a reflection of a larger trend in the American health care system. There’s been a massive wave of consolidation in the health care business to gain greater bargaining clout. These surprise bills are a byproduct of the wrangling between two sets of players – insurers and care providers – a battle of giants that often leaves patients holding the bill.
Recent efforts at the federal level to provide protections to patients are long overdue. Yet, it is unclear whether patients will see any tangible outcomes, as insurers and providers are fiercely protecting their interests. Even if successful, these protections would likely only alleviate the most glaring problems of surprise bills untouched.

What’s going on here?

Patients in emergency rooms are receiving surprise bills more often than in years past. Monkey Business Images/Shutterstock.com
The story is nearly always the same. A patient, often in an emergency, receives care, as required by his or her insurance coverage, in a hospital that was part of their provider networks.
Patients usually assume that all doctors participating in their treatment in the facility are also covered in their network. However, while their primary providers may be part of their network, ancillary physicians with little or no contact with the patient, such as anesthesiologists and radiologists, may not be. And, in many hospitals, the very doctor who takes care of you in an emergency – the ER doctor – may not have any insurance contracts whatsoever.
Patients may only realize their miscalculation when it’s too late – when “surprise” bills start arriving in the mail, often outrageously high, a few week later. Not even members of Congress are immune from the practice, as Rep. Katie Porter, D-Calif., experienced when she received a US$2,800 out-of-pocket bill after an appendectomy.
The results for patients are often devastating. While the full extent of the problem is unclear, studies have shown, that about 20% of inpatient emergency department cases result in surprise bills. Insurance companies will usually pay a part of the bill, but physicians then send the remainder directly to the patients.
The sums are often horrendous and bear little correspondence to the cost of the care provided: $229,000 for spinal fusion surgery$117,000 for neck surgery, or $250,000 for back surgery. These are bills after insurance companies paid for part of the bill. And of course, the threat of being turned over to a collection agency looms largely over patients’ heads, with medical debt typically listed as the primary reason for being contacted by a collection agency.

The larger picture: Battle of the giants

The distinguishing characteristic of the U.S. health care system is its high cost: Americans simply pay more for health care than their counterparts in the developed world.
Given the dual threats of high costs and large uncertainties, Americans have long relied on insurance arrangements when it comes to financing their health care needs. As a result, patients today are trapped between two massive bureaucracies intent on maximizing their income: providers and insurers.
For decades, Americans and public payers have, by-and-large, accepted accelerating health care costs. Yet, with costs around 18% of GDP and exerting intense pressure of public and private budgets, the health care sector has drawn greater scrutiny.
As a result, pressure to contain costs have begun to emerge, leading to intensifying conflict between the two entities. More recently, these developments have triggered significant and increasing consolidation efforts on both sides.
A CVS sign in Ridgeland, Miss. Rogelio V.. Solis/AP Photo
Health insurers are seeking mergers with other insurers. Recent examples include Centene buying WellCare but also Cigna’s ill-fated attempt to merge with Anthem and Aetna’s and Humana’s aborted consolidation attempt.
Insurers are also attempting to expand beyond their traditional role into the direct provision of services, including Aetna’s efforts to team up with drugstore chain CVS, and Cigna merging with Express Scripts. Insurers are trying to get greater market power, not only versus other insurers, but perhaps even more prominently, against other stakeholders in the health care sector.
Both sides have sought to capitalize on their increasing clout.
Many providers have sought to utilize their new market powers to gain concessions from insurers in the form of larger reimbursements for patient care. Insurers, for their part, have resisted these demands where possible. Most importantly, they have started to deliberately exclude certain high-cost providers from their networks. These range from prominent hospitals like Cedars-Sinai to rural specialists like endocrinologists.
In this struggle, certain providers – those you do not have a choice in selecting, such as emergency room doctors, anesthesiologists and radiologists – hold a more pivotal role in the provision of health care. To maximize their profits, they often have deliberately chosen not to contract with any insurers.
As insurers reduce the number of providers in their networks, patients benefit from lower premiums. However, with fewer providers to see for treatment patients are, of course, more likely to obtain treatment out-of-network.
As insurers and providers push back on paying bills for care, patients usually end up holding the bill.

Is Congress fixing the mess?

Surprise bills are nothing new. However, patients caught between two massive competing bureaucracies offers a compelling narrative for policymakers. The issue of surprise medical bills has been so outrageous that Democrats and Republicans in Washington, D.C., and across state legislatures, have started to work together to offer patients with some sort of protection.
So far, some 20 states have established various forms of consumer protections. These protections differ significantly, and often are rather limited. Moreover, these protections are significantly limited by the reach of state regulators, leaving many Americans largely unprotected.
The federal government is starting to the jump on the bandwagon. The recent proposal by Sens. Patty Murray and Lamar Alexander serves just as the last example.
And yet, surprise bills are merely the tip of the iceberg of what ails the American health care system. Current proposals leave other issues, such as inadequate networks and inaccurate provider directories largely untouched.
Ultimately, I think we need more comprehensive solutions that address the excessive costs of the broken U.S. health care system. Everything else, while crucial to individual patients, fails to offer substantial systemwide improvements.

The demographics of Medicare for All fans

Medicare for All enthusiasts tend to be young and white, according to a poll by the right-leaning group One Nation.

One Nation survey of 1,211 registered likely “non-hard partisan” voters in Alabama, Colorado, Florida, Georgia, Iowa, Kentucky, Maine, Michigan, North Carolina, Ohio and Wisconsin conducted April 13-17, 2019; margin of error ±2.8 percentage points; Chart: Axios Visuals
Why it matters: Politically, it means embracing Medicare for All may not be very helpful for Democrats hoping to make inroads with older and minority voters.

27 healthcare-related blockchain developments in 2019

While healthcare organizations vary with blockchain implementation, the decentralized ledger technology has made waves in 2019.
Below is a roundup of the blockchain developments in healthcare this year.
1. Solve.Care, a global healthcare blockchain platform, has partnered with Cyber Physical Chain, a company that builds new infrastructure for “internet of things.”
2. A new blockchain platform is being designed to help improve the reimbursement processing, linking payments to patient outcomes.
3. The National Cancer Institute approved a program June 21 to create a blockchain-based system that allows users to share clinical data.
4. Microsoft was the latest technology company to join the Hyperledger community for open source blockchain projects, the company announced in a blog post June 18.
5. IBM launched an upgraded version of its enterprise blockchain platform on June 18.
6. On June 13, IBM, KPMG, Merck and Walmart announced that they were selected by the FDA to evaluate the use of blockchain for identifying, tracking and tracing prescription drugs.
7. Microsoft plans to integrate new blockchain and artificial intelligence features to its Power Platform, the company announced June 10.
8. Blockchain developer Solve.Care plans to release a network for diabetic patients in partnership with Boehringer Ingelheim Pharmaceuticals and the Arizona Care Network.
9. Dell Medical School at The University of Texas at Austin in partnership with the Austin Blockchain Collective will explore blockchain’s potential in healthcare.
10. Walmart was the latest company to join the MediLedger consortium, a collaborative working to build a blockchain that will be designed to track and verify prescription drugs.
11. Salesforce released its first blockchain solution May 29 that is built on the Hyperledger Sawtooth platform.
12. IBM has partnered with Samsung SDS to strengthen an existing blockchain-based hyperledger while also improving other blockchain ecosystems.
13. Solve.Care, a blockchain-focused startup, is in the final stage of developing its Book-a-Ride card, a tool that will allow customers to schedule transportation to health-related appointments.
14. Syniverse and IBM teamed up to develop an open-source blockchain network that aims to validate wholesale billing and charging processes.
15. To reduce costs, errors and inefficiencies, Pfizer and other pharmaceutical companies have joined a blockchain project led by technology company Chronicled.
16. Companies looking to develop and grow blockchain networks can now do so using Amazon Managed Blockchain, which Amazon Web Services launched on the East Coast on April 30.
17. Indianapolis-based Indiana University Health and Raleigh, N.C.-based WakeMed Health & Hospitals are collaborating with blockchain and pharmacy companies to better track prescriptions.
18. Healthcare technology solutions provider Nasco teamed up with Express Scripts and three other providers to form a blockchain coalition, known as Coalesce Health Alliance.
19. Health data companies Bitfury and Longenesis developed a blockchain-based consent management solution that is designed to streamline data collection for medical research.
20. Startup Healthereum has created a blockchain-based mobile app that rewards patients with digital tokens for showing up to their appointments.
21. Pharmaceutical companies such as Genentech and AmerisourceBergen Corp. are testing different blockchains to determine whether the technology can track counterfeit drugs.
22. Startup CoverUS has developed a blockchain-supported platform that allows patients to sell their medical information to providers.
23. Researchers from UC San Francisco proposed using a blockchain-powered system to share medical data.
24. Cigna and Norfolk, Va.-based Sentara Healthcare joined a collaborative blockchain project designed to improve transparency and interoperability in the healthcare space.
25. Kalibrate Blockchain rolled out a new program for hospitals in various markets that allows them to license its mobile app
26. HSBlox launched its CuraBlox solution, which leverages blockchain technology to improve bundled-payments.
27. Aetna, Anthem, Health Care Service Corp., PNC Bank and IBM unveiled a new collaborative blockchain project Jan. 24 designed to improve transparency and interoperability in healthcare.