Tag Archives: patients

#431907 The Future of Cancer Treatment Is ...

In an interview at Singularity University’s Exponential Medicine in San Diego, Richard Wender, chief cancer control officer at the American Cancer Society, discussed how technology has changed cancer care and treatment in recent years.
Just a few years ago, microscopes were the primary tool used in cancer diagnoses, but we’ve come a long way since.
“We still look at a microscope, we still look at what organ the cancer started in,” Wender said. “But increasingly we’re looking at the molecular signature. It’s not just the genomics, and it’s not just the genes. It’s also the cellular environment around that cancer. We’re now targeting our therapies to the mutations that are found in that particular cancer.”
Cancer treatments in the past have been largely reactionary, but they don’t need to be. Most cancer is genetic, which means that treatment can be preventative. This is one reason why newer cancer treatment techniques are searching for actionable targets in the specific gene before the cancer develops.

When asked how artificial intelligence and machine learning technologies are reshaping clinical trials, Wender acknowledged that how clinical trials have been run in the past won’t work moving forward.
“Our traditional ways of learning about cancer were by finding a particular cancer type and conducting a long clinical trial that took a number of years enrolling patients from around the country. That is not how we’re going to learn to treat individual patients in the future.”
Instead, Wender emphasized the need for gathering as much data as possible, and from as many individual patients as possible. This data should encompass clinical, pathological, and molecular data and should be gathered from a patient all the way through their final outcome. “Literally every person becomes a clinical trial of one,” Wender said.
For the best cancer treatment and diagnostics, Wender says the answer is to make the process collaborative by pulling in resources from organizations and companies that are both established and emerging.
It’s no surprise to hear that the best solutions come from pairing together uncommon partners to innovate.
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#431427 Why the Best Healthcare Hacks Are the ...

Technology has the potential to solve some of our most intractable healthcare problems. In fact, it’s already doing so, with inventions getting us closer to a medical Tricorder, and progress toward 3D printed organs, and AIs that can do point-of-care diagnosis.
No doubt these applications of cutting-edge tech will continue to push the needle on progress in medicine, diagnosis, and treatment. But what if some of the healthcare hacks we need most aren’t high-tech at all?
According to Dr. Darshak Sanghavi, this is exactly the case. In a talk at Singularity University’s Exponential Medicine last week, Sanghavi told the audience, “We often think in extremely complex ways, but I think a lot of the improvements in health at scale can be done in an analog way.”
Sanghavi is the chief medical officer and senior vice president of translation at OptumLabs, and was previously director of preventive and population health at the Center for Medicare and Medicaid Innovation, where he oversaw the development of large pilot programs aimed at improving healthcare costs and quality.
“How can we improve health at scale, not for only a small number of people, but for entire populations?” Sanghavi asked. With programs that benefit a small group of people, he explained, what tends to happen is that the average health of a population improves, but the disparities across the group worsen.
“My mantra became, ‘The denominator is everybody,’” he said. He shared details of some low-tech but crucial fixes he believes could vastly benefit the US healthcare system.
1. Regulatory Hacking
Healthcare regulations are ultimately what drive many aspects of patient care, for better or worse. Worse because the mind-boggling complexity of regulations (exhibit A: the Affordable Care Act is reportedly about 20,000 pages long) can make it hard for people to get the care they need at a cost they can afford, but better because, as Sanghavi explained, tweaking these regulations in the right way can result in across-the-board improvements in a given population’s health.
An adjustment to Medicare hospitalization rules makes for a relevant example. The code was updated to state that if people who left the hospital were re-admitted within 30 days, that hospital had to pay a penalty. The result was hospitals taking more care to ensure patients were released not only in good health, but also with a solid understanding of what they had to do to take care of themselves going forward. “Here, arguably the writing of a few lines of regulatory code resulted in a remarkable decrease in 30-day re-admissions, and the savings of several billion dollars,” Sanghavi said.
2. Long-Term Focus
It’s easy to focus on healthcare hacks that have immediate, visible results—but what about fixes whose benefits take years to manifest? How can we motivate hospitals, regulators, and doctors to take action when they know they won’t see changes anytime soon?
“I call this the reality TV problem,” Sanghavi said. “Reality shows don’t really care about who’s the most talented recording artist—they care about getting the most viewers. That is exactly how we think about health care.”
Sanghavi’s team wanted to address this problem for heart attacks. They found they could reliably determine someone’s 10-year risk of having a heart attack based on a simple risk profile. Rather than monitoring patients’ cholesterol, blood pressure, weight, and other individual factors, the team took the average 10-year risk across entire provider panels, then made providers responsible for controlling those populations.
“Every percentage point you lower that risk, by hook or by crook, you get some people to stop smoking, you get some people on cholesterol medication. It’s patient-centered decision-making, and the provider then makes money. This is the world’s first predictive analytic model, at scale, that’s actually being paid for at scale,” he said.
3. Aligned Incentives
If hospitals are held accountable for the health of the communities they’re based in, those hospitals need to have the right incentives to follow through. “Hospitals have to spend money on community benefit, but linking that benefit to a meaningful population health metric can catalyze significant improvements,” Sanghavi said.
Darshak Sanghavi speaking at Singularity University’s 2017 Exponential Medicine Summit in San Diego, CA.
He used smoking cessation as an example. His team designed a program where hospitals were given a score (determined by the Centers for Disease Control and Prevention) based on the smoking rate in the counties where they’re located, then given monetary incentives to improve their score. Improving their score, in turn, resulted in better health for their communities, which meant fewer patients to treat for smoking-related health problems.
4. Social Determinants of Health
Social determinants of health include factors like housing, income, family, and food security. The answer to getting people to pay attention to these factors at scale, and creating aligned incentives, Sanghavi said, is “Very simple. We just have to measure it to start with, and measure it universally.”
His team was behind a $157 million pilot program called Accountable Health Communities that went live this year. The program requires all Medicare and Medicaid beneficiaries get screened for various social determinants of health. With all that data being collected, analysts can pinpoint local trends, then target funds to address the underlying problem, whether it’s job training, drug use, or nutritional education. “You’re then free to invest the dollars where they’re needed…this is how we can improve health at scale, with very simple changes in the incentive structures that are created,” he said.
5. ‘Securitizing’ Public Health
Sanghavi’s final point tied back to his discussion of aligning incentives. As misguided as it may seem, the reality is that financial incentives can make a huge difference in healthcare outcomes, from both a patient and a provider perspective.
Sanghavi’s team did an experiment in which they created outcome benchmarks for three major health problems that exist across geographically diverse areas: smoking, adolescent pregnancy, and binge drinking. The team proposed measuring the baseline of these issues then creating what they called a social impact bond. If communities were able to lower their frequency of these conditions by a given percent within a stated period of time, they’d get paid for it.
“What that did was essentially say, ‘you have a buyer for this outcome if you can achieve it,’” Sanghavi said. “And you can try to get there in any way you like.” The program is currently in CMS clearance.
AI and Robots Not Required
Using robots to perform surgery and artificial intelligence to diagnose disease will undoubtedly benefit doctors and patients around the US and the world. But Sanghavi’s talk made it clear that our healthcare system needs much more than this, and that improving population health on a large scale is really a low-tech project—one involving more regulatory and financial innovation than technological innovation.
“The things that get measured are the things that get changed,” he said. “If we choose the right outcomes to predict long-term benefit, and we pay for those outcomes, that’s the way to make progress.”
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#431189 Researchers Develop New Tech to Predict ...

It is one of the top 10 deadliest diseases in the United States, and it cannot be cured or prevented. But new studies are finding ways to diagnose Alzheimer’s disease in its earliest stages, while some of the latest research says technologies like artificial intelligence can detect dementia years before the first symptoms occur.
These advances, in turn, will help bolster clinical trials seeking a cure or therapies to slow or prevent the disease. Catching Alzheimer’s disease or other forms of dementia early in their progression can help ease symptoms in some cases.
“Often neurodegeneration is diagnosed late when massive brain damage has already occurred,” says professor Francis L Martin at the University of Central Lancashire in the UK, in an email to Singularity Hub. “As we know more about the molecular basis of the disease, there is the possibility of clinical interventions that might slow or halt the progress of the disease, i.e., before brain damage. Extending cognitive ability for even a number of years would have huge benefit.”
Blood Diamond
Martin is the principal investigator on a project that has developed a technique to analyze blood samples to diagnose Alzheimer’s disease and distinguish between other forms of dementia.
The researchers used sensor-based technology with a diamond core to analyze about 550 blood samples. They identified specific chemical bonds within the blood after passing light through the diamond core and recording its interaction with the sample. The results were then compared against blood samples from cases of Alzheimer’s disease and other neurodegenerative diseases, along with those from healthy individuals.
“From a small drop of blood, we derive a fingerprint spectrum. That fingerprint spectrum contains numerical data, which can be inputted into a computational algorithm we have developed,” Martin explains. “This algorithm is validated for prediction of unknown samples. From this we determine sensitivity and specificity. Although not perfect, my clinical colleagues reliably tell me our results are far better than anything else they have seen.”
Martin says the breakthrough is the result of more than 10 years developing sensor-based technologies for routine screening, monitoring, or diagnosing neurodegenerative diseases and cancers.
“My vision was to develop something low-cost that could be readily applied in a typical clinical setting to handle thousands of samples potentially per day or per week,” he says, adding that the technology also has applications in environmental science and food security.
The new test can also distinguish accurately between Alzheimer’s disease and other forms of neurodegeneration, such as Lewy body dementia, which is one of the most common causes of dementia after Alzheimer’s.
“To this point, other than at post-mortem, there has been no single approach towards classifying these pathologies,” Martin notes. “MRI scanning is often used but is labor-intensive, costly, difficult to apply to dementia patients, and not a routine point-of-care test.”
Crystal Ball
Canadian researchers at McGill University believe they can predict Alzheimer’s disease up to two years before its onset using big data and artificial intelligence. They developed an algorithm capable of recognizing the signatures of dementia using a single amyloid PET scan of the brain of patients at risk of developing the disease.
Alzheimer’s is caused by the accumulation of two proteins—amyloid beta and tau. The latest research suggests that amyloid beta leads to the buildup of tau, which is responsible for damaging nerve cells and connections between cells called synapses.
The work was recently published in the journal Neurobiology of Aging.
“Despite the availability of biomarkers capable of identifying the proteins causative of Alzheimer’s disease in living individuals, the current technologies cannot predict whether carriers of AD pathology in the brain will progress to dementia,” Sulantha Mathotaarachchi, lead author on the paper and an expert in artificial neural networks, tells Singularity Hub by email.
The algorithm, trained on a population with amnestic mild cognitive impairment observed over 24 months, proved accurate 84.5 percent of the time. Mathotaarachchi says the algorithm can be trained on different populations for different observational periods, meaning the system can grow more comprehensive with more data.
“The more biomarkers we incorporate, the more accurate the prediction could be,” Mathotaarachchi adds. “However, right now, acquiring [the] required amount of training data is the biggest challenge. … In Alzheimer’s disease, it is known that the amyloid protein deposition occurs decades before symptoms onset.”
Unfortunately, the same process occurs in normal aging as well. “The challenge is to identify the abnormal patterns of deposition that lead to the disease later on,” he says
One of the key goals of the project is to improve the research in Alzheimer’s disease by ensuring those patients with the highest probability to develop dementia are enrolled in clinical trials. That will increase the efficiency of clinical programs, according to Mathotaarachchi.
“One of the most important outcomes from our study was the pilot, online, real-time prediction tool,” he says. “This can be used as a framework for patient screening before recruiting for clinical trials. … If a disease-modifying therapy becomes available for patients, a predictive tool might have clinical applications as well, by providing to the physician information regarding clinical progression.”
Pixel by Pixel Prediction
Private industry is also working toward improving science’s predictive powers when it comes to detecting dementia early. One startup called Darmiyan out of San Francisco claims its proprietary software can pick up signals before the onset of Alzheimer’s disease by up to 15 years.
Darmiyan didn’t respond to a request for comment for this article. Venture Beat reported that the company’s MRI-analyzing software “detects cell abnormalities at a microscopic level to reveal what a standard MRI scan cannot” and that the “software measures and highlights subtle microscopic changes in the brain tissue represented in every pixel of the MRI image long before any symptoms arise.”
Darmiyan claims to have a 90 percent accuracy rate and says its software has been vetted by top academic institutions like New York University, Rockefeller University, and Stanford, according to Venture Beat. The startup is awaiting FDA approval to proceed further but is reportedly working with pharmaceutical companies like Amgen, Johnson & Johnson, and Pfizer on pilot programs.
“Our technology enables smarter drug selection in preclinical animal studies, better patient selection for clinical trials, and much better drug-effect monitoring,” Darmiyan cofounder and CEO Padideh Kamali-Zare told Venture Beat.
Conclusions
An estimated 5.5 million Americans have Alzheimer’s, and one in 10 people over age 65 have been diagnosed with the disease. By mid-century, the number of Alzheimer’s patients could rise to 16 million. Health care costs in 2017 alone are estimated to be $259 billion, and by 2050 the annual price tag could be more than $1 trillion.
In sum, it’s a disease that cripples people and the economy.
Researchers are always after more data as they look to improve outcomes, with the hope of one day developing a cure or preventing the onset of neurodegeneration altogether. If interested in seeing this medical research progress, you can help by signing up on the Brain Health Registry to improve the quality of clinical trials.
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#431022 Robots and AI Will Take Over These 3 ...

We’re no stranger to robotics in the medical field. Robot-assisted surgery is becoming more and more common. Many training programs are starting to include robotic and virtual reality scenarios to provide hands-on training for students without putting patients at risk.
With all of these advances in medical robotics, three niches stand out above the rest: surgery, medical imaging, and drug discovery. How have robotics already begun to exert their influence on these practices, and how will they change them for good?
Robot-Assisted Surgery
Robot-assisted surgery was first documented in 1985, when it was used for a neurosurgical biopsy. This led to the use of robotics in a number of similar surgeries, both laparoscopic and traditional operations. The FDA didn’t approve robotic surgery tools until 2000, when the da Vinci Surgery system hit the market.
The robot-assisted surgery market is expected to grow steadily into 2023 and potentially beyond. The only thing that might stand in the way of this growth is the cost of the equipment. The initial investment may prevent small practices from purchasing the necessary devices.
Medical Imaging
The key to successful medical imaging isn’t the equipment itself. It’s being able to interpret the information in the images. Medical images are some of the most information-dense pieces of data in the medical field and can reveal so much more than a basic visual inspection can.
Robotics and, more specifically, artificial intelligence programs like IBM Watson can help interpret these images more efficiently and accurately. By allowing an AI or basic machine learning program to study the medical images, researchers can find patterns and make more accurate diagnoses than ever before.
Drug Discovery
Drug discovery is a long and often tedious process that includes years of testing and assessment. Artificial intelligence, machine learning and predictive algorithms could help speed up this system.
Imagine if researchers could input the kind of medicine they’re trying to make and the kind of symptoms they’re trying to treat into a computer and let it do the rest. With robotics, that may someday be possible.

This isn’t a perfect solution yet—these systems require massive amounts of data before they can start making decisions or predictions. By feeding data into the cloud where these programs can access it, researchers can take the first steps towards setting up a functional database.
Another benefit of these AI programs is that they might see connections humans would never have thought of. People can make those leaps, but the chances are much lower, and it takes much longer if it happens at all. Simply put, we’re not capable of processing the sheer amount of data that computers can process.
This isn’t a field where we’re worrying about robots stealing jobs.
Quite the opposite, in fact—we want robots to become commonly-used tools that can help improve patient care and surgical outcomes.
A human surgeon might have intuition, but they’ll never have the steadiness that a pair of robotic hands can provide or the data-processing capabilities of a machine learning algorithm. If we let them, these tools could change the way we look at medicine.
Image Credit: Intuitive Surgical Continue reading

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#430649 Robotherapy for children with autism

New Robotherapy for children with autism could reduce patient supervision by therapists.
05.07.2017
Autism treatments and therapies routinely make headlines. With robot enhanced therapies on the rise, often overlooked though, is the mental stress and physical toll the procedures take on therapists. As autism treatments can be taxing on both patient and therapists, few realize the stress and workload of those working with autistic patients.
It is against this backdrop, that researchers from the Vrije Universiteit Brussel are pioneering a new technology to aid behavioural therapy, and one with a very deliberate aspect: they are using robots to boost the basic social learning skills of children with ASD and while doing so, they hope to make the therapists’ job substantially easier.
A study, just published in PALADYN – Journal of Behavioural Robotics examines the use of social robots as tools in clinical situations by addressing the challenge of increasing robot autonomy.
The growing deployment of robot-assisted therapies in recent decades means children with Autism Spectrum Disorder (ASD) can develop and nurture social behaviour and cognitive skills. Learning skills that hold out in real life is the first and foremost goal of all autism therapies, including the Robot-Assisted Therapy (RAT), with effectiveness always considered a key concern. However, this time round the scientists have set off on the additional mission to take the load off the human therapists by letting parts of the intervention be taken over by the supervised yet autonomous robots.
The researchers developed a complete system of robot-enhanced therapy (RET) for children with ASD. The therapy works by teaching behaviours during repeated sessions of interactive games. Since the individuals with ASD tend to be more responsive to feedback coming from an interaction with technology, robots are often used for this therapy. In this approach, the social robot acts as a mediator and typically remains remote-controlled by a human operator. The technique, called Wizard of Oz, requires the robot to be operated by an additional person and the robot is not recording the performance during the therapy. In order to reduce operator workload, authors introduced a system with a supervised autonomous robot – which is able to understand the psychological disposition of the child and use it to select actions appropriate to the current state of the interaction.
Admittedly, robots with supervised autonomy can substantially benefit behavioural therapy for children with ASD – diminishing the therapist workload on the one hand, and achieving more objective measurements of therapy outcomes on the other. Yet, complex as it is, this therapy requires a multidisciplinary approach, as RET provides mixed effectiveness for primary tasks: the turn-taking, joint attention and imitation task comparing to Standard Human Treatment (SHT).
Results are likely to prompt a further development of the robot assisted therapy with increasing robot’s autonomy. With many outstanding conceptual and technical issues yet to tackle –it is definitely the ethical questions that pose one of the major challenges as far as the potential and maximal degree of robot autonomy is concerned.
The article is fully available in open access to read, download and share on De Gruyter Online.
Research was conducted as a part of DREAM (Development of Robot-Enhanced therapy for children with Autism spectrum disorders) project.
DOI: 10.1515/pjbr-2017-0002
Image credit: P.G. Esteban
About the Journal: PALADYN – Journal of Behavioural Robotics is a fully peer-reviewed, electronic-only journal that publishes original, high-quality research on topics broadly related to neuronally and psychologically inspired robots and other behaving autonomous systems.
About De Gruyter Open: De Gruyter Open is a leading publisher of Open Access academic content. Publishing in all major disciplines, De Gruyter Open is home to more than 500 scholarly journals and over 100 books. The company is part of the De Gruyter Group (www.degruyter.com) and a member of the Association of Learned and Professional Society Publishers (ALPSP). De Gruyter Open’s book and journal programs have been endorsed by the international research community and some of the world’s top scientists, including Nobel laureates. The company’s mission is to make the very best in academic content freely available to scholars and lay readers alike.
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