Category: Uncategorized

Hundreds of millions of patients are affected by chronic diseases globally, often suffering from several of them. Despite such a huge opportunity, the coverage of chronic conditions with digital solutions is still below the acceptable level. Today, more than twenty common chronic conditions have its own specificity and are currently at its digital development stage, yet all of them face the same challenges in the digital health market, according to a recent Research2Guidance report.

1.  Lack of Consumer Capital

All chronic conditions, apart from digital diabetes, still lack the consumer focus in digital offerings. The main goal of the consumer-oriented approach is to provide and promote a solution that empowers patients to better manage their disease.

2.  Low add value

The existing chronic disease apps often create little added value for end users. Most of the currently available solutions offer either basic educational advice or simple tracking based on manual or automated data input.

3.  Missing business models for consumers and payers

In the digital health market, payer organizations are now pushing digital services to their member base; however, in the majority of chronic conditions, the reimbursement of digital services is still an issue. Apart from the digital diabetes market, consumer bundle subscription offers or payer models (implemented on the pay-per-member-per-month basis) hardly exist. Digital solution providers should pay more attention to creating reimbursement-oriented packages, which can potentially bundle services, digital content, medical products, and accessories.

4.  Insufficient cost-saving evidence for payers

There are many clinical studies across all major chronic conditions that have generated evidence to support efficiency and efficacy of digital solutions in assisting chronic disease patients. What is currently missing is the link to cost savings, which hinders a larger engagement of payer organizations.

5.  Unfavorable demographic and behavioral characteristics of patient populations

Patient population characteristics can have significant implications for digitalizing disease management. Solution providers have to find appropriate strategies to increase patient control despite these demographic and behavioral barriers. The development of caregiver solutions is one of the possible responses to this problem. Currently, nearly all of the available solutions are designed for patients or doctors, whereas the role of caregivers in patient management is still underestimated.

6.  Non-regular use of digital solutions

Chronic conditions are rather different in terms of usage regularity. In conditions with a limited necessity of regular measurements, the creation of passive monitoring solutions, such as wearable-based measurements, could be one of the means to ensure a constant use of digital solutions.

7. Low connectivity level

Although the use of digital apps in managing chronic conditions is increasing, it is not accompanied by significant growth in the use of connected medical devices, such as blood glucose meters, spirometers, blood pressure meters, etc.

8. Missing services for comorbidities

All chronic conditions have numerous comorbidities, which affect patients no less than diagnosed chronic disease. In many cases, patients suffer from several closely-related chronic conditions at a time. However, solution providers currently focus on primary chronic diseases, offering no services for such conditions as anxiety, depression, obesity, etc. As a result, many chronic condition offerings remain single use case solutions, which do not fully address patients’ needs. Weight-loss, hypertension, and diabetes solution providers are already expanding their primary use cases to other conditions, whereas respiratory conditions still lag behind them

9. Low communication support between medical professionals and patients

Communication between healthcare professionals and patients is seen as the most valuable feature within digital health solutions to drive user engagement and retention. Current digital respiratory solutions offer population management features for HCPs or PDF report generation but do not support direct HCP-patient interaction via chat, email or video call.

10. Dependency on slow regulatory processes

Many digital disease management solutions are treated as medical devices under local regulations, if they include regulated components, such as devices and/or medical products. To avoid extensively long time-to-market, services and/or connected devices can be split into regulated (such as a bolus calculator in diabetes solutions) and a non-regulated (such as behavior change features) components.

Research2Guidance states these barriers is the prerequisite for the success of digital health solutions designed for managing chronic conditions. The market has already seen several successful cases when digital solution providers went beyond the obstacles that were perceived as “natural”.

Source:
https://hitconsultant.net/2019/04/15/barriers-digitalization-chronic-conditions/#.XMQB5ujHxPY

There’s no doubt that artificial intelligence (AI) is a transformative technology – perhaps even the most transformative technology available today. But if you think the transformative nature of AI is limited to global tech giants and blue-chip companies, think again. AI is ultimately going to transform every business, in every industry. 

Perhaps you read that last sentence and thought to yourself, well, not my business. My retail business [or HR consultancy, B2B service provider, fashion design business, disaster relief charity, football club or whatever] has nothing to do with AI. I repeat, think again. Even if you can’t yet imagine how AI will impact your organisation, trust that, in the not-too-distant future, it most definitely will.  

That’s why every company needs an AI strategy.  

Like any business transformation, if you want to get the most out of AI, it all starts with strategy. Your AI strategy will help you to focus on your core business objectives and prioritise ways that AI can help deliver those business goals. 

In general, there are two ways businesses are using AI to drive success: 

• Creating intelligent products and services 
• Designing intelligent business processes  

Let’s look at these two uses in a little more detail.  

Intelligent products and services  

AI is, at heart, about making machines smarter, so that they can think and act like humans (or even better). We need only look at the popularity of devices like smart phones, smart fitness trackers and smart thermostats to see how consumers wholeheartedly embrace products and services that can make their life easier, smarter, more streamlined, more connected.  

Source: https://www.forbes.com/sites/bernardmarr/2019/03/21/why-every-company-needs-an-artificial-intelligence-ai-strategy-for-2019/#2a0311c868ea

In theory, that is of hundreds of AI start-ups examined over the past few years, ‘very few companies are building unambiguously labour-replacing technologies’

Microsoft has unveiled the results of a survey of business leaders on the topic of artificial intelligence (AI). The findings are surprising: German and Russian entrepreneurs and executives appear to come out ahead of those from the US and other advanced European economies when it comes to adopting the technology.

Mostly, however, this and several other studies confirm a frustrating problem: the AI hype is making it impossible to figure out how much businesses really need it and are using it.

The 800 respondents in the study came from seven countries — the US, Germany, France, the UK, Italy, the Netherlands and Switzerland. It’s not a globe-spanning data-set and it doesn’t include the potential AI leader, China, nor one of the leaders in AI research, Canada. But the study’s scope is respectable. It shows that the US isn’t among the leaders of the AI race, though a 2018 study by Capgemini Consulting, for example, puts it out ahead and Russia far behind.

The problem with this survey — and a similar one by McKinsey — is that when people say they are using AI in their business, they may not all mean the same thing; they may not even be describing uses that fall under the rather broad definition of AI; and they may simply be boasting because the technology is fashionable.

In a new report, “The State of AI: Divergence, 2019,” the UK venture capital fund MMC Ventures claims that “one in seven large companies has adopted AI; in 24 months, two thirds of large companies will have live AI initiatives. In 2019, AI ‘crosses the chasm’ from early adopters to the early majority.”

Read more: https://www.businesslive.co.za/bd/opinion/2019-03-10-business-leaders-love-ai-in-theory-that-is/

Hebbian Learning

In 1949, Canadian psychologist Donald Hebb proposed the theory of Hebbian learning to explain how a learning task is transformed into a long-term memory. In this way, healthy habits become automatically retained after their continual repetition.

Learning and memory are a consequence of how our brain cells (neurons) communicate with each other. When we learn, neurons communicate through molecular transmissions which hop across synapses producing a memory circuit. Known as long-term potentiation(LTP), the more often a learning task is repeated, the more often transmission continues and the stronger a memory circuit becomes. It is this unique ability of neurons to create and strengthen synaptic connections by repeated activation that leads to Hebbian learning.

Memory and the hippocampus

Understanding the brain requires investigation through different approaches and from a variety of specialities. The field of cognitive neuroscience initially developed through a small number of pioneers. Their experimental designs and observations led to the foundation for how we understand learning and memory today.

Donald Hebb’s contributions at McGill University remain the driving force to explain memory. Under his supervision, neuropsychologist Brenda Milner studied a patient with impaired memory following a lobectomy. Further studies with neurosurgeon Wilder Penfield enabled Milner to expand her study of memory and learning in patients following brain surgery.

Read more: https://theconversation.com/a-memory-pill-cognitive-neurosciences-contributions-to-the-study-of-memory-109707

IN BRIEF

A team of engineering graduates has won the prestigious James Dyson Award for their cheap and portable device that can detect melanoma, a form of skin cancer. The device could potentially save thousands of lives, as skin cancer is the most common type worldwide.

MEET SKAN

Detecting skin cancer early isn’t easy. Currently, it’s done through visual inspections or biopsies, but some doctors may not pick up on the disease using the former, while some patients may not be able to afford the latter. As such, a team of graduates from McMaster University in Canada set out to develop an inexpensive skin cancer detector, and their innovative work has earned them the prestigious international James Dyson Award.

Cancer affects the metabolic rate of skin cells, with cancerous cells heating up faster than their healthy counterparts following a shock of cold temperature.

To make identifying these cells easier, the McMaster University team — Michael Takla, Rotimi Fadiya, Prateek Mathur, and Shivad Bhavsar — built a skin cancer detector with 16 thermistors that can track the rate of temperature increase following a cold shock from an ice pack.

The thermistors are simply placed on the potentially cancerous area of skin, and the device produces a heat map that can be used to determine the presence of melanoma

“By using widely available and inexpensive components, the sKan allows for melanoma skin cancer detection to be readily accessible to the many” award founder James Dyson said in a statement announcing the win. “It’s a very clever device with the potential to save lives around the world.”

In addition to winning the Dyson Award for their skin cancer detector, the team also received a cash prize of approximately $40,000 to advance their research. They received $10,000 at the the Forge’s Student Start-up Pitch competition in March.

DIAGNOSING SKIN CANCER

According to Mathur, the team was inspired to create sKan after realizing technology hadn’t had the same impact on skin cancer diagnosis as it had on other medical fields.

“We found research that used the thermal properties of cancerous skin tissue as a means of detecting melanoma. However, this was done using expensive lab equipment,” he said in a McMaster University news release. “We set out to apply the research and invent a way of performing the same assessment using a more cost-effective solution.”

Going forward, the sKan team hopes to create a more advanced prototype that will allow them to begin pre-clinical testing.

As reported by The Guardian, nearly 39 people are diagnosed with skin cancer every day in the U.K., and the American Cancer Society (ACS) estimates 87,110 new cases of melanoma will be diagnosed in the U.S. 2017, with 9,730 people dying from the condition. Early detection is key to cancer survival, so if sKan succeeds, it could significantly reduce that number.

“Our aspirations have become a reality,” said Mathur. “Skin cancers are the most common form of cancer worldwide, and the potential to positively impact the lives of those affected is both humbling and motivating.”

Source: Futurism

A New Quantum Physics?

During the mid- to late-twentieth century, quantum physicists picked apart the unified theory of physics that Einstein’s theory of relativity offered. The physics of the large was governed by gravity, but only quantum physics could describe observations of the small. Since then, a theoretical tug-o-war between gravity and the other three fundamental forces has continued as physicists try to extend gravity or quantum physics to subsume the other as more fundamental.

Recent measurements from the Large Hadron Collider show a discrepancy with Standard Model predictions that may hint at entirely new realms of the universe underlying what’s described by quantum physics. Although repeated tests are required to confirm these anomalies, a confirmation would signify a turning point in our most fundamental description of particle physics to date.

Quantum physicists found in a recent study that mesons don’t decay into kaon and muon particles often enough, according to the Standard Model predictions of frequency. The authors agree that enhancing the power of the Large Hadron Collider (LHC) will reveal a new kind of particle responsible for this discrepancy. Although errors in data or theory may have caused the discrepancy, instead of a new particle, an improved LHC would prove a boon for several projects on the cutting edge of physics.

The Standard Model

The Standard Model is a well-established fundamental theory of quantum physics that describes three of the four fundamental forces believed to govern our physical reality. Quantum particles occur in two basic types, quarks and leptons. Quarks bind together in different combinations to build particles like protons and neutrons. We’re familiar with protons, neutrons, and electrons because they’re the building blocks of atoms.

The “lepton family” features heavier versions of the electron — like the muon — and the quarks can coalesce into hundreds of other composite particles. Two of these, the Bottom and Kaon mesons, were culprits in this quantum mystery. The Bottom meson (B) decays to a Kaon meson (K) accompanied by a muon (mu-) and anti-muon (mu ) particle.

The Anomaly

They found a 2.5 sigma variance, or 1 in 80 probability, “which means that, in the absence of unexpected effects, i.e. new physics, a distribution more deviant than observed would be produced about 1.25 percent of the time,” Professor Spencer Klein, senior scientist at Lawrence Berkeley National Laboratory, told Futurism. Klein was not involved in the study.

This means the frequency of mesons decaying into strange quarks during the LHC proton-collision tests fell a little below the expected frequency. “The tension here is that, with a 2.5 sigma [or standard deviation from the normal decay rate], either the data is off by a little bit, the theory is off by a little bit, or it’s a hint of something beyond the standard model,” Klein said. “I would say, naïvely, one of the first two is correct.”

To Klein, this variance is inevitable considering the high volume of data run by computers for LHC operations. “With Petabyte-(10¹⁵ bytes)-sized datasets from the LHC, and with modern computers, we can make a very large number of measurements of different quantities,” Klein said. “The LHC has produced many hundreds of results. Statistically, some of them are expected to show 2.5 sigma fluctuations.” Klein noted that particle physicists usually wait for a 5-sigma fluctuation before crying wolf — corresponding to roughly a 1-in-3.5-million fluctuation in data.

 

These latest anomalous observations do not exist in a vacuum. “The interesting aspect of the two taken in combination is how aligned they are with other anomalous measurements of processes involving B mesons that had been made in previous years,” Dr. Tevong You, co-author of the study and junior research fellow in theoretical physics at Gonville and Caius College, University of Cambridge, told Futurism. “These independent measurements were less clean but more significant. Altogether, the chance of measuring these different things and having them all deviate from the Standard Model in a consistent way is closer to 1 in 16000 probability, or 4 sigma,” Tevong said.

Extending the Standard Model

Barring statistical or theoretical errors, Tevong suspects that the anomalies mask the presence of entirely new particles, called leptoquarks or Z prime particles. Inside bottom mesons, quantum excitations of new particles could be interfering with normal decay frequency. In the study, researchers conclude that an upgraded LHC could confirm the existence of new particles, making a major update to the Standard Model in the process.

“It would be revolutionary for our fundamental understanding of the universe,” said Tevong. “For particle physics […] it would mean that we are peeling back another layer of Nature and continuing on a journey of discovering the most elementary building blocks. This would have implications for cosmology, since it relies on our fundamental theories for understanding the early universe,” he added. “The interplay between cosmology and particle physics has been very fruitful in the past. As for dark matter, if it emerges from the same new physics sector in which the Zprime or leptoquark is embedded, then we may also find signs of it when we explore this new sector.”

The Power to Know

So far, scientists at the LHC have only observed ghosts and anomalies hinting at particles that exist at higher energy levels. To prove their existence, physicists “need to confirm the indirect signs […], and that means being patient while the LHCb experiment gathers more data on B decays to make a more precise measurement,” Tevong said. “We will also get an independent confirmation by another experiment, Belle II, that should be coming online in the next few years. After all that, if the measurement of B decays still disagrees with the predictions of the Standard Model, then we can be confident that something beyond the Standard Model must be responsible, and that would point towards leptoquarks or Zprime particles as the explanation,” he added.

To establish their existence, physicists would then aim to produce the particles in colliders the same way Bottom mesons or Higgs bosons are produced, and watch them decay. “We need to be able to see a leptoquark or Zprime pop out of LHC collisions,” Tevong said. “The fact that we haven’t seen any such exotic particles at the LHC (so far) means that they may be too heavy, and more energy will be required to produce them. That is what we estimated in our paper: the feasibility of directly discovering leptoquarks or Zprime particles at future colliders with higher energy.”

Quantum Leap for the LHC

Seeking out new particles in the LHC isn’t a waiting game. The likelihood of observing new phenomena is directly proportional to how many new particles pop up in collisions. “The more the particle appears the higher the chances of spotting it amongst many other background events taking place during those collisions,” Tevong explained. For the purposes of finding new particles, he likens it to searching for a needle in a haystack; it’s easier to find a needle if the haystack is filled with them, as opposed to one. “The rate of production depends on the particle’s mass and couplings: heavier particles require more energy to produce,” he said.

This is why Tevong and co-authors B.C. Allanach and Ben Gripaios recommend either extending the LHC loop’s length, thus reducing the amount of magnetic power needed to accelerate particles, or replacing the current magnets with stronger ones.

According to Tevong, the CERN laboratory is slated to keep running the LHC in present configuration until mid-2030s. Afterwards, they might upgrade the LHC’s magnets, roughly doubling its strength. In addition to souped-up magnets, the tunnel could see an enlargement from present 27 to 100 km (17 to 62 miles). “The combined effect […] would give about seven times more energy than the LHC,” Tevong said. “The timescale for completion would be at least in the 2040s, though it is still too early to make any meaningful projections.”

If the leptoquark or Z prime anomalies are confirmed, the Standard Model has to change, Tevong reiterates. “It is very likely that it has to change at energy scales directly accessible to the next generation of colliders, which would guarantee us answers,” he added. While noting that there’s no telling if dark matter has anything to do with the physics behind Zprimes or leptoquarks, the best we can do is seek “as many anomalous measurements as possible, whether at colliders, smaller particle physics experiments, dark matter searches, or cosmological and astrophysical observations,” he said. “Then the dream is that we may be able to form connections between various anomalies that can be linked by a single, elegant theory.”

Source: Futurism