The
“maximum fair price” for a drug must not only be equitable to those with unmet medical needs
who may benefit from the use of the drug but also provide equitable returns on both public and
private sector investments.

Now, at last, thanks to the Inflation Reduction Act (IRA), the federal government will be allowed to negotiate a “maximum fair price” for drugs covered by Medicare Part D. This historical change, taking place in the face of intense industry opposition, incrementally reverses policies that have prohibited the government from engaging in price negotiations since Medicare Part D was first established in 2003. While only ten drugs will be subject to negotiation in the first year of the IRA and 90 over the first five years, negotiations are now ongoing.

The IRA creates a highly scripted process for identifying the drugs that will be subject to price negotiations each year and lists specific factors that may be considered in these negotiations, including the manufacturer’s research and development costs, the returns on these investments, federal financial support for discovery and development, and the extent to which the drug addresses unmet medical needs. However, the Act leaves unsettled how these factors will be weighed by the government in these negotiations.

It has been suggested that the government should negotiate for value-based pricing that would benchmark the Medicare Part D price measures of the health benefit provided to those using these drugs. This would be analogous to the approach currently used by most European countries for drug pricing. We believe this approach is inadequate and fails to provide the public with a return on the massive US government investments in biomedical research related to these drugs that enabled these products to be developed and commercialized in the first place.

Extensive research has demonstrated that the government plays a critical role as an early investor in innovation and that the risk embodied in these investments is not proportional to the public sector returns. For example, our previous studies show that government-funded biomedical research plays an essential role in enabling successful drug development and that investment by the US National Institutes of Health (NIH) in research related to drugs approved from 2010-2019 was comparable in scale to reported investment by industry.

In our new INET working paper, we extend these analyses to the ten drugs selected for Medicare price negotiation in the first year of the IRA. Our analysis reveals that the NIH spent $11.7 billion on basic or applied research related to the drugs selected for Medicare price negotiations, representing a median investment cost of $895.4 million per drug and, by making this research available to industry, saving industry a median of $1,485 million per drug. While data on industry investments in these ten drugs is not publicly available, this level of NIH investment is comparable to reported investment by industry in the drugs approved from 2010 to 2019.

Our analysis also assessed the health benefit (health value) provided to consumers through Medicare Part D spending on these drugs. This analysis involved a review of the published literature on the number of Quality-Adjusted Life Years (QALYs) accruing to individuals using these products and multiplying this measure of value by the number of Medicare Part D beneficiaries who received these products. This analysis showed that eight of the ten products selected for price negotiation (excluding products for diabetes) created a total health value of 650,940 QALYs or $67.7 billion based on estimates of US consumer’s willingness to pay (WTP) of $104,000 per QALY. Our analysis also showed that Medicare Part D spending on these eight drugs was $97.4 billion, resulting in a net negative residual health value (analogous to the consumer surplus) of -$29.6 billion before rebates. While manufacturer rebates on drug sales can be substantial (estimates range from 20-45% from 2017-2021), our conclusion was that there is a negative, or at best, a narrow, margin between the price currently being paid by Medicare Part D and the health value accruing to consumers from this spending.

While benchmarking the price paid by Medicare Part D to the health value created by this spending (i.e., value-based pricing) would rectify any deficit in the balance between price and value, it would not provide for a return on public investment in the discovery or development of these products. That return would require a surplus between the value received by the public and the price paid to producers. In this context, our new INET working paper argues that price negotiations under the IRA need to benchmark the margin between the price paid by Medicare Part D and the health value created – the residual (or net) health value - against the expected return on public sector investment in these products.

Consideration of investment costs, risks, and returns is central to the concept of a “fair price” that can be agreed upon by buyers and sellers. As such, the maximum fair price must be one that provides appropriate returns on both government and industry investment in drugs consistent with the scale and risk of these investments. Moreover, as argued by Lazonick, Mazzucato, and others, these negotiations should recognize the co-creating roles of the public and private sectors in innovation and assure that the returns on public sector investments are comparable to those investments made by the private sector. The empirical analysis of public sector investments and the health value created by the drugs selected for Medicare price negotiations provides a cost basis for such an assessment.

Political discourse is pretty depressing at the moment – so I thought I’d turn to food – which is, I fear, not much better. The first piece is what in the UK is called, a little more honestly, pre-formed ham and’, like Spam, is delivered, even for counter service, in a large can: The automatic... Read more

When Swiss cardiologist Thomas F. Lüscher attended an international symposium in Turin, Italy, last summer, he encountered an unusual “attendee:” Suzanne, Chat GPT’s medical “assistant.” Suzanne’s developers were eager to demonstrate to the specialists how well their medical chatbot worked, and they asked the cardiologists to test her. 

An Italian cardiology professor told the chatbot about the case of a 27-year-old patient who was taken to his clinic in unstable condition. The patient had a massive fever and drastically increased inflammation markers. Without hesitation, Suzanne diagnosed adult-onset Still’s disease. “I almost fell off my chair because she was right,” Lüscher remembers. “This is a very rare autoinflammatory disease that even seasoned cardiologists don’t always consider.”

Lüscher — director of research, education and development and consultant cardiologist at the Royal Brompton & Harefield Hospital Trust and Imperial College London and director of the Center for Molecular Cardiology at the University of Zürich, Switzerland — is convinced that artificial intelligence is making cardiovascular medicine more accurate and effective. “AI is not only the future, but it is already here,” he says. “AI and machine learning are particularly accurate in image analysis, and imaging plays an outsize role in cardiology. AI is able to see what we don’t see. That’s impressive.” 

The cardiology team at Royal Brompton Hospital in London uses AI to speed up calculations based on MRIs. Credit: Andriy Blokhin

At the Royal Brompton Hospital in London, for instance, his team relies on AI to calculate the volume of heart chambers in MRIs, an indication of heart health. “If you calculate this manually, you need about half an hour,” Lüscher says. “AI does it in a second.” 

Few patients are aware of how significantly AI is already determining their health care. The Washington Post tracks the start of the boom of artificial intelligence in health care to 2018. That’s when the Food and Drug Administration approved the IDx-DR, the first independent AI-based diagnostic tool, which is used to screen for diabetic retinopathy. Today, according to the Post, the FDA has approved nearly 700 artificial intelligence and machine learning-enabled medical devices.

The Mayo Clinic in Rochester, Minnesota, is considered the worldwide leader in implementing AI for cardiovascular care, not least because it can train its algorithms with the (anonymized) data of more than seven million electrocardiograms (ECG). “Every time a patient undergoes an ECG, various algorithms that are based on AI show us on the screen which diagnoses to consider and which further tests are recommended,” says Francisco Lopez-Jimenez, director of the Mayo Clinic’s Cardiovascular Health Clinic. “The AI takes into account all the factors known about the patient, whether his potassium is high, etc. For example, we have an AI-based program that calculates the biological age of a person. If the person in front of me is [calculated to have a biological age] 10 years older than his birth age, I can probe further. Are there stressors that burden him?”

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Examples where AI makes a sizable difference at the Mayo Clinic include screening ECGs to detect specific heart diseases, such as ventricular dysfunction or atrial fibrillation, earlier and more reliably than the human eye. These conditions are best treated early, but without AI, the symptoms are largely invisible in ECGs until later, when they have already progressed further.

Currently, the AI tools are mainly developed and used at elite clinics with advanced research departments, such as the Mayo Clinic or the clinics associated with Oxford University. “From the moment we enter a patient’s data into our system, AI helps guide the patient’s path in the clinic, screens for diseases and assists with interpreting test results,” says Charalambos Antioniades, British Heart Foundation chair of cardiovascular medicine and cardiologist at Oxford University. “When the patient undergoes an ECG or CT scan, the AI diagnosis is automatically shown on the screen.”

Antioniades’ team at the University of Oxford’s Radcliffe Department of Medicine analyzed data from over 250,000 patients who underwent cardiac CT scans in eight British hospitals. “Eighty-two percent of the patients who presented with chest pain had CT scans that came back as completely normal and were sent home because doctors saw no indication for a heart disease,” Antioniades says. “Yet two-thirds of them had an increased risk to suffer a heart attack within the next 10 years.” In a world-first pilot, his team developed an AI tool that detects inflammatory changes in the fatty tissues surrounding the arteries. These changes are not visible to the human eye. But after training on thousands of CT scans, AI learned to detect them and predict the risk of heart attacks. “We had a phase where specialists read the scans and we compared their diagnosis with the AI’s,” Antioniades explains. “AI was always right.” These results led to doctors changing the treatment plans for hundreds of patients. “The key is that we can treat the inflammatory changes early and prevent heart attacks,” according to Antioniades. 

The British National Health Service (NHS) has approved the AI tool, and it is now used in five public hospitals. “We hope that it will soon be used everywhere because it can help prevent thousands of heart attacks every year,” Antioniades says. A startup at Oxford University offers a service that enables other clinics to send their CT scans in for analysis with Oxford’s AI tool.

AI can use electrocardiograms to detect certain heart conditions much earlier than would otherwise be possible. Credit: Chaikom / Shutterstock

Similarly, physician-scientists at the Smidt Heart Institute and the Division of Artificial Intelligence in Medicine at Cedars-Sinai Medical Center in Los Angeles use AI to analyze echograms. They created an algorithm that can effectively identify and distinguish between two life-threatening heart conditions that are easy to overlook: hypertrophic cardiomyopathy and cardiac amyloidosis. “These two heart conditions are challenging for even expert cardiologists to accurately identify, and so patients often go on for years to decades before receiving a correct diagnosis,” David Ouyang, cardiologist at the Smidt Heart Institute, said in a press release. “This is a machine-beats-man situation. AI makes the sonographer work faster and more efficiently, and it doesn’t change the patient experience. It’s a triple win.”

However, using artificial intelligence in clinical settings has disadvantages, too. “Suzanne has no empathy,” Lüscher says about his experience with Chat GPT. “Her responses have to be verified by a doctor. She even says that after every diagnosis, and has to, for legal reasons.”

Also, an algorithm is only as accurate as the information with which it was trained. Lüscher and his team cured an AI tool of a massive deficit: Women’s risk for heart attacks wasn’t reliably evaluated because the AI had mainly been fed with data from male patients. “For women, heart attacks are more often fatal than for men,” Lüscher says. “Women also usually come to the clinic later. All these factors have implications.” Therefore, his team developed a more realistic AI prognosis that improves the treatment of female patients. “We adapted it with machine learning and it now works for women and men,” Lüscher explains. “You have to make sure the cohorts are large enough and have been evaluated independently so that the algorithms work for different groups of patients and in different countries.” His team made the improved algorithm available online so other hospitals can use it too. 

Another issue is trust. “Many doctors and patients don’t trust AI because it’s basically a black box,” says Lopez-Jimenez at the Mayo Clinic. “For example, when someone asks, ‘Why does AI recognize atrial fibrillation though the ECG looks completely normal?’ The short answer is: We don’t know either. Only the computer knows the answers.”

He tells his colleagues and patients that the reliability of AI tools currently lies at 75 to 93 percent, depending on the specific diagnosis. “Compare that with a mammogram that detects breast tumors with an accuracy of 85 percent,” Lopez-Jimenez says. “But because it’s AI, people expect 100 percent. That simply does not exist in medicine.”

And of course, another challenge is that few people have the resources and good fortune to become patients at the world’s most renowned clinics with state-of-the-art technology. “One of my main goals is to make this technology available to millions,” Lopez-Jimenez says. He mentions that Mayo is trying out high-tech stethoscopes to interpret heart signals with AI. “The idea is that a doctor in the Global South can use it to diagnose cardiac insufficiency,” Lopez-Jimenez explains. “It is already being tested in Nigeria, the country with the highest rate of genetic cardiac insufficiency in Africa. The results are impressively accurate.” 

The Mayo Clinic is also working with doctors in Brazil to diagnose Chagas disease with the help of AI reliably and early. “New technology is always more expensive at the beginning,” Lopez-Jimenez cautions, “but in a few years, AI will be everywhere and it will make diagnostics cheaper and more accurate.”

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And the Children’s National Hospital in Washington developed a portable AI device that is currently being tested to screen children in Uganda for rheumatic heart disease, which kills about 400,000 people a year worldwide. The new tool reportedly has an accuracy of 90 percent. 

Both Lopez-Jimenez and Lüscher are confident that AI tools will continue to improve. “One advantage is that a computer can analyze images at 6 a.m. just as systematically as after midnight,” Lüscher points out. “A computer doesn’t get tired or have a bad day, whereas sometimes radiologists overlook significant symptoms. AI learns something and never forgets it.”

Antoniades even believes that in the not-too-far future, patients won’t need to see a doctor anymore because computers can analyze their symptoms better than humans. 

Lüscher is unconvinced. “A computer might analyze symptoms better,” he says, “but remember, computers don’t have empathy.” If you were diagnosed with a heart disease, you’d probably still rather hear it from a seasoned physician than a chatbot.

The post The Smart Heart: How AI Is Sharpening Cardiovascular Medicine appeared first on Reasons to be Cheerful.

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