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Supreme Court: Government Owes ACA Insurers $12 Billion 

Health insurers that took on the risk of covering millions of previously uninsured Americans must be paid the $12 billion they are owed under a program set up under the Aff ordable Care Act (ACA), the US Supreme Court…ruled on April 27.1

In an 8-to-1 ruling, the justices reversed a lower court decision that supported Congress’ suspension of full payments due for 2014 to 2016, during the early years of enrollment on the exchanges. The ruling will mean a significant 1-time cash infusion from the government to companies like Humana and Centene.

Conservative Justice Samuel Alito was the only dissenter, arguing that the court’s decision amounts to a bailout for insurance companies. The payment will cover the losses insurers sustained in the early years of the ACA, which President Barack Obama signed in 2010.2 Insurers had argued that if the lower court ruling stood, it would let the government continue to withhold money that had been promised in the ACA in language that served as a contract.

Before the exchanges began covering millions of uninsured Americans on January 1, 2014, insurers had to be enticed to enter a market that would include consumers who had lacked insurance for years or had never had it at all. The early years of the ACA brought a spike of claims among some groups, as chronic conditions were finally diagnosed and patients received long-delayed surgeries.3

To off set these losses, the ACA promised payments through “risk corridors,” which would protect the insurers from heavy losses and hold down premiums. The…statute “set a formula for calculating payments under the program: If an insurance plan loses a certain amount of money, the Federal Government ‘shall pay’ the plan; if the plan makes a certain amount of money, the plan ‘shall pay’ the Government,” according to the court’s opinion, written by Justice Sonia Sotomayor.1

“Health insurance providers kept their commitments while incurring substantial losses.…[The] decision, as the Supreme Court observes, reflects ‘a principle as old as the Nation itself: The Government should honor its obligations,’” said Matt Eyles, president and CEO of America’s Health Insurance Plans, quoting Sotomayor’s opinion.4

While the program was scheduled to last for 3 years, Congress passed a provision modifying HHS’s spending bills from 2015 to 2017, limiting payments under the program. This happened after Democrats lost control of Congress in November 2014, and Republicans prevented HHS from using any funds outside the risk corridor program to repay insurers.

As a result of Congress’ actions, many community-based insurance funds went bankrupt, and many commercial insurers raised premiums or left markets to attempt to make up for the monies not paid under the program.“[The] Supreme Court decision will help inject much-needed stability in the market, at an especially uncertain time. The added support will help our plans better maintain aff ordable premiums and continue to focus on improving the health of the communities they serve,” said Ceci Connolly, president and CEO of the Alliance of Community Health Plans, in a statement.5

Sotomayor emphasized the importance of holding a government accountable to its obligations: “Alexander Hamilton stressed this insight as a cornerstone of fiscal policy. ‘States,’ he wrote, ‘who observe their engagements…are respected and trusted: while the reverse is the fate of those…who pursue an opposite conduct.’…Centuries later, this Court’s case law still concurs.”


1. Maine Community Health Options v. the United States. No. 18-1023. Argued December 10, 2019—Decided April 27, 2020. Supreme Court of the United States.

2. Patient Protection and Affordable Care Act, HR 3590, 111th Congress, 2nd Sess (2010). Accessed May 4, 2020.

3. Cox C, Semanskee A, Claxton G, Levitt L. Explaining health care reform: risk adjustment, reinsurance, and risk corridors. Kaiser Family Foundation. Published August 17, 2016. Accessed May 4,

4. AHIP issues statement on Supreme Court decision regarding 2014-2016 risk corridor payments. News release. America’s Health Insurance Plans. April 27, 2020. Accessed May 4, 2020.


Artificial Intelligence, Deep Learning Combine as Powerful Oncological Tool

Artificial intelligence (AI) and deep learning (DL) are beginning to make a major impact in cancer care, but numerous challenges remain before the full potential of the new technologies can be realized, according to a new study. Writing in Cancer Communications, authors from China’s Tianjin Medical University and Tsinghua University say AI is increasingly being used to help understand tumor pathology. However, the investigators also concede that some pathologists, clinicians, and patients remain skeptical about the technology, as do regulators and payers.

Corresponding author Xiangchun Li, PhD, and colleagues say AI pathological analysis has grown in recent decades to become superior to human expertise

and even to machine learning. DL, a newer technology, is based on hard data rather than subjective factors and is highly accurate, Li said, outperforming

older methods.

“The application of AI in pathology helps to overcome the limitations of subjective visual assessment from pathologists and [to] integrate multiple measurements for precision tumor treatment,” Li and colleagues wrote. The authors go on to highlight several ways they believe AI and DL can improve cancer care. Among the most important is in tumor diagnosis. DL can help distinguish tumors from other types of lesions, they wrote, and also distinguish between malignant and benign tumors.

Models have also been developed that Li and colleagues believe can already or will soon be able to help clinicians subtype, grade, and stage cancers. The technologies also have the potential to detect biomarkers and genetic changes in tumors, the authors explained.

Turning to challenges, the investigators say the algorithms underpinning AI and DL technologies need to be validated on larger scales and adapted as new data become available.

“Building comprehensive quality control and standardization tools, data share and validation with multi³institutional data can increase the generalizability

and robustness of the AI algorithms,” they wrote. “In addition, AI algorithms need to be continually validated and corrected by the diagnosis of expert pathologists.”

Another problem is that the images used by these systems tend to be massive files; their size can make storing and sharing them difficult, given existing information technology infrastructure. With advances in the “foreseeable future,” though, the problem will abate, brought about “by improvements in information technology such as universal adoption of 5G,” Li and colleagues said.

Unsolvable by technology itself are the negative sentiments about the entire field held by some medical professionals and patients. When physicians do not fully understand how AI and DL technology work, it can be difficult to gain their confi dence. Improvements must be made so the output of such technologies is easily interpreted by clinicians and standardized among systems.

However, if and when all clinicians are on board, payers will not necessarily reimburse hospitals for the use of the technology, Li and colleagues said. “At present, there are no dedicated procedure codes for the use of AI in digital pathology with diagnostic or prognostic intent,” the authors wrote. “AI based tools probably need to be approved by [the] FDA before they get the new procedure codes and are reimbursable.”

In summary, Li and coauthors said the technology is proving meaningful in cancer care, but its proponents will need to work carefully to provide evidence gleaned from rigorous scientific studies and,then,to create the clinician comfort that will be necessary to deploy the tools widely. “People will have more confi dence in AI algorithms after they are validated using multi‚center data and have increased interpretability,” they say.


Jiang Y, Yang M, Wang S, et al. Emerging role of deep learning‚based artificial intelligence in tumor pathology.‡Cancer

Commun (Lond). 2020;40(4):154-166. doi:10.1002/cac2.12012

Report Describes Patient With MM, COVID-19 Treated Successfully With Tocilizumab

Older patients are already at a higher risk of severe manifestations if they contract coronavirus disease 2019 (COVID-19), but a new study explores what happened when a 60-year-old became ill with the disease while suffering from multiple myeloma (MM).

The case of the patient in Wuhan, China, was described in the current issue of Blood Advances. He was first diagnosed with symptomatic MM in 2015, when

a bone marrow aspirate showed 17.1% clonal plasma cells and radiography showed multiple osteolytic bone lesions in frontal and temporal bone.

“His kidney biopsy confi rmed amyloidosis; laboratory testing also showed proteinuria,” said corresponding and senior author Chengchang Zheng, PhD,

of the University of Science and Technology of China. “The patient received 2 cycles of induction chemotherapy consisting of bortezomib, thalidomide,

and‡dexamethasone, and his symptoms completely disappeared.”

The patient subsequently refused bortezomib-based treatment, Zheng said, and was therefore given thalidomide as a maintenance therapy.

Then, on February 1, 2020, the patient visited a Wuhan hospital after experiencing chest tightness with no fever or cough. A CT scan showed multiple ground-glass opacities and pneumatoceles in both subpleural spaces. Doctors prescribed intravenous moxifl oxacin and ordered nasopharyngeal tests for SARSCoV-2, the virus that causes COVID-19. When the test came back positive, the patient was prescribed the antiviral umifenovir, 200 mg taken orally 3 times daily.

Two weeks later, on February 16, the patient was diagnosed with severe disease after experiencing shortness of breath and decreased arterial saturation of around 93% at rest. He was admitted to his hospital’s cancer center.

Zheng wrote that the immune incompetence of patients with malignancies not only puts them at higher risk of contracting COVID-19, but increases the potential for treatment diffi culties. He also noted that the patient’s symptoms in this case diff ered somewhat from the most common symptoms of COVID-19–fever and cough–indicating that symptoms may be atypical in patients with comorbidities.

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