Tag Archives: predictive

#432271 Your Shopping Experience Is on the Verge ...

Exponential technologies (AI, VR, 3D printing, and networks) are radically reshaping traditional retail.

E-commerce giants (Amazon, Walmart, Alibaba) are digitizing the retail industry, riding the exponential growth of computation.

Many brick-and-mortar stores have already gone bankrupt, or migrated their operations online.

Massive change is occurring in this arena.

For those “real-life stores” that survive, an evolution is taking place from a product-centric mentality to an experience-based business model by leveraging AI, VR/AR, and 3D printing.

Let’s dive in.

E-Commerce Trends
Last year, 3.8 billion people were connected online. By 2024, thanks to 5G, stratospheric and space-based satellites, we will grow to 8 billion people online, each with megabit to gigabit connection speeds.

These 4.2 billion new digital consumers will begin buying things online, a potential bonanza for the e-commerce world.

At the same time, entrepreneurs seeking to service these four-billion-plus new consumers can now skip the costly steps of procuring retail space and hiring sales clerks.

Today, thanks to global connectivity, contract production, and turnkey pack-and-ship logistics, an entrepreneur can go from an idea to building and scaling a multimillion-dollar business from anywhere in the world in record time.

And while e-commerce sales have been exploding (growing from $34 billion in Q1 2009 to $115 billion in Q3 2017), e-commerce only accounted for about 10 percent of total retail sales in 2017.

In 2016, global online sales totaled $1.8 trillion. Remarkably, this $1.8 trillion was spent by only 1.5 billion people — a mere 20 percent of Earth’s global population that year.

There’s plenty more room for digital disruption.

AI and the Retail Experience
For the business owner, AI will demonetize e-commerce operations with automated customer service, ultra-accurate supply chain modeling, marketing content generation, and advertising.

In the case of customer service, imagine an AI that is trained by every customer interaction, learns how to answer any consumer question perfectly, and offers feedback to product designers and company owners as a result.

Facebook’s handover protocol allows live customer service representatives and language-learning bots to work within the same Facebook Messenger conversation.

Taking it one step further, imagine an AI that is empathic to a consumer’s frustration, that can take any amount of abuse and come back with a smile every time. As one example, meet Ava. “Ava is a virtual customer service agent, to bring a whole new level of personalization and brand experience to that customer experience on a day-to-day basis,” says Greg Cross, CEO of Ava’s creator, an Austrian company called Soul Machines.

Predictive modeling and machine learning are also optimizing product ordering and the supply chain process. For example, Skubana, a platform for online sellers, leverages data analytics to provide entrepreneurs constant product performance feedback and maintain optimal warehouse stock levels.

Blockchain is set to follow suit in the retail space. ShipChain and Ambrosus plan to introduce transparency and trust into shipping and production, further reducing costs for entrepreneurs and consumers.

Meanwhile, for consumers, personal shopping assistants are shifting the psychology of the standard shopping experience.

Amazon’s Alexa marks an important user interface moment in this regard.

Alexa is in her infancy with voice search and vocal controls for smart homes. Already, Amazon’s Alexa users, on average, spent more on Amazon.com when purchasing than standard Amazon Prime customers — $1,700 versus $1,400.

As I’ve discussed in previous posts, the future combination of virtual reality shopping, coupled with a personalized, AI-enabled fashion advisor will make finding, selecting, and ordering products fast and painless for consumers.

But let’s take it one step further.

Imagine a future in which your personal AI shopper knows your desires better than you do. Possible? I think so. After all, our future AIs will follow us, watch us, and observe our interactions — including how long we glance at objects, our facial expressions, and much more.

In this future, shopping might be as easy as saying, “Buy me a new outfit for Saturday night’s dinner party,” followed by a surprise-and-delight moment in which the outfit that arrives is perfect.

In this future world of AI-enabled shopping, one of the most disruptive implications is that advertising is now dead.

In a world where an AI is buying my stuff, and I’m no longer in the decision loop, why would a big brand ever waste money on a Super Bowl advertisement?

The dematerialization, demonetization, and democratization of personalized shopping has only just begun.

The In-Store Experience: Experiential Retailing
In 2017, over 6,700 brick-and-mortar retail stores closed their doors, surpassing the former record year for store closures set in 2008 during the financial crisis. Regardless, business is still booming.

As shoppers seek the convenience of online shopping, brick-and-mortar stores are tapping into the power of the experience economy.

Rather than focusing on the practicality of the products they buy, consumers are instead seeking out the experience of going shopping.

The Internet of Things, artificial intelligence, and computation are exponentially improving the in-person consumer experience.

As AI dominates curated online shopping, AI and data analytics tools are also empowering real-life store owners to optimize staffing, marketing strategies, customer relationship management, and inventory logistics.

In the short term,retail store locations will serve as the next big user interface for production 3D printing (custom 3D printed clothes at the Ministry of Supply), virtual and augmented reality (DIY skills clinics), and the Internet of Things (checkout-less shopping).

In the long term,we’ll see how our desire for enhanced productivity and seamless consumption balances with our preference for enjoyable real-life consumer experiences — all of which will be driven by exponential technologies.

One thing is certain: the nominal shopping experience is on the verge of a major transformation.

The convergence of exponential technologies has already revamped how and where we shop, how we use our time, and how much we pay.

Twenty years ago, Amazon showed us how the web could offer each of us the long tail of available reading material, and since then, the world of e-commerce has exploded.

And yet we still haven’t experienced the cost savings coming our way from drone delivery, the Internet of Things, tokenized ecosystems, the impact of truly powerful AI, or even the other major applications for 3D printing and AR/VR.

Perhaps nothing will be more transformed than today’s $20 trillion retail sector.

Hold on, stay tuned, and get your AI-enabled cryptocurrency ready.

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#432009 How Swarm Intelligence Is Making Simple ...

As a group, simple creatures following simple rules can display a surprising amount of complexity, efficiency, and even creativity. Known as swarm intelligence, this trait is found throughout nature, but researchers have recently begun using it to transform various fields such as robotics, data mining, medicine, and blockchains.

Ants, for example, can only perform a limited range of functions, but an ant colony can build bridges, create superhighways of food and information, wage war, and enslave other ant species—all of which are beyond the comprehension of any single ant. Likewise, schools of fish, flocks of birds, beehives, and other species exhibit behavior indicative of planning by a higher intelligence that doesn’t actually exist.

It happens by a process called stigmergy. Simply put, a small change by a group member causes other members to behave differently, leading to a new pattern of behavior.

When an ant finds a food source, it marks the path with pheromones. This attracts other ants to that path, leads them to the food source, and prompts them to mark the same path with more pheromones. Over time, the most efficient route will become the superhighway, as the faster and easier a path is, the more ants will reach the food and the more pheromones will be on the path. Thus, it looks as if a more intelligent being chose the best path, but it emerged from the tiny, simple changes made by individuals.

So what does this mean for humans? Well, a lot. In the past few decades, researchers have developed numerous algorithms and metaheuristics, such as ant colony optimization and particle swarm optimization, and they are rapidly being adopted.

Swarm Robotics
A swarm of robots would work on the same principles as an ant colony: each member has a simple set of rules to follow, leading to self-organization and self-sufficiency.

For example, researchers at Georgia Robotics and InTelligent Systems (GRITS) created a small swarm of simple robots that can spell and play piano. The robots cannot communicate, but based solely on the position of surrounding robots, they are able to use their specially-created algorithm to determine the optimal path to complete their task.

This is also immensely useful for drone swarms.

Last February, Ehang, an aviation company out of China, created a swarm of a thousand drones that not only lit the sky with colorful, intricate displays, but demonstrated the ability to improvise and troubleshoot errors entirely autonomously.

Further, just recently, the University of Cambridge and Koc University unveiled their idea for what they call the Energy Neutral Internet of Drones. Amazingly, this drone swarm would take initiative to share information or energy with other drones that did not receive a communication or are running low on energy.

Militaries all of the world are utilizing this as well.

Last year, the US Department of Defense announced it had successfully tested a swarm of miniature drones that could carry out complex missions cheaper and more efficiently. They claimed, “The micro-drones demonstrated advanced swarm behaviors such as collective decision-making, adaptive formation flying, and self-healing.”

Some experts estimate at least 30 nations are actively developing drone swarms—and even submersible drones—for military missions, including intelligence gathering, missile defense, precision missile strikes, and enhanced communication.

NASA also plans on deploying swarms of tiny spacecraft for space exploration, and the medical community is looking into using swarms of nanobots for precision delivery of drugs, microsurgery, targeting toxins, and biological sensors.

What If Humans Are the Ants?
The strength of any blockchain comes from the size and diversity of the community supporting it. Cryptocurrencies like Bitcoin, Ethereum, and Litecoin are driven by the people using, investing in, and, most importantly, mining them so their blockchains can function. Without an active community, or swarm, their blockchains wither away.

When viewed from a great height, a blockchain performs eerily like an ant colony in that it will naturally find the most efficient way to move vast amounts of information.

Miners compete with each other to perform the complex calculations necessary to add another block, for which the winner is rewarded with the blockchain’s native currency and agreed-upon fees. Of course, the miner with the more powerful computers is more likely to win the reward, thereby empowering the winner’s ability to mine and receive even more rewards. Over time, fewer and fewer miners are going to exist, as the winners are able to more efficiently shoulder more of the workload, in much the same way that ants build superhighways.

Further, a company called Unanimous AI has developed algorithms that allow humans to collectively make predictions. So far, the AI algorithms and their human participants have made some astoundingly accurate predictions, such as the first four winning horses of the Kentucky Derby, the Oscar winners, the Stanley Cup winners, and others. The more people involved in the swarm, the greater their predictive power will be.

To be clear, this is not a prediction based on group consensus. Rather, the swarm of humans uses software to input their opinions in real time, thus making micro-changes to the rest of the swarm and the inputs of other members.

Studies show that swarm intelligence consistently outperforms individuals and crowds working without the algorithms. While this is only the tip of the iceberg, some have suggested swarm intelligence can revolutionize how doctors diagnose a patient or how products are marketed to consumers. It might even be an essential step in truly creating AI.

While swarm intelligence is an essential part of many species’ success, it’s only a matter of time before humans harness its effectiveness as well.

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#431836 Do Our Brains Use Deep Learning to Make ...

The first time Dr. Blake Richards heard about deep learning, he was convinced that he wasn’t just looking at a technique that would revolutionize artificial intelligence. He also knew he was looking at something fundamental about the human brain.
That was the early 2000s, and Richards was taking a course with Dr. Geoff Hinton at the University of Toronto. Hinton, a pioneer architect of the algorithm that would later take the world by storm, was offering an introductory course on his learning method inspired by the human brain.
The key words here are “inspired by.” Despite Richards’ conviction, the odds were stacked against him. The human brain, as it happens, seems to lack a critical function that’s programmed into deep learning algorithms. On the surface, the algorithms were violating basic biological facts already proven by neuroscientists.
But what if, superficial differences aside, deep learning and the brain are actually compatible?
Now, in a new study published in eLife, Richards, working with DeepMind, proposed a new algorithm based on the biological structure of neurons in the neocortex. Also known as the cortex, this outermost region of the brain is home to higher cognitive functions such as reasoning, prediction, and flexible thought.
The team networked their artificial neurons together into a multi-layered network and challenged it with a classic computer vision task—identifying hand-written numbers.
The new algorithm performed well. But the kicker is that it analyzed the learning examples in a way that’s characteristic of deep learning algorithms, even though it was completely based on the brain’s fundamental biology.
“Deep learning is possible in a biological framework,” concludes the team.
Because the model is only a computer simulation at this point, Richards hopes to pass the baton to experimental neuroscientists, who could actively test whether the algorithm operates in an actual brain.
If so, the data could then be passed back to computer scientists to work out the next generation of massively parallel and low-energy algorithms to power our machines.
It’s a first step towards merging the two fields back into a “virtuous circle” of discovery and innovation.
The blame game
While you’ve probably heard of deep learning’s recent wins against humans in the game of Go, you might not know the nitty-gritty behind the algorithm’s operations.
In a nutshell, deep learning relies on an artificial neural network with virtual “neurons.” Like a towering skyscraper, the network is structured into hierarchies: lower-level neurons process aspects of an input—for example, a horizontal or vertical stroke that eventually forms the number four—whereas higher-level neurons extract more abstract aspects of the number four.
To teach the network, you give it examples of what you’re looking for. The signal propagates forward in the network (like climbing up a building), where each neuron works to fish out something fundamental about the number four.
Like children trying to learn a skill the first time, initially the network doesn’t do so well. It spits out what it thinks a universal number four should look like—think a Picasso-esque rendition.
But here’s where the learning occurs: the algorithm compares the output with the ideal output, and computes the difference between the two (dubbed “error”). This error is then “backpropagated” throughout the entire network, telling each neuron: hey, this is how far off you were, so try adjusting your computation closer to the ideal.
Millions of examples and tweakings later, the network inches closer to the desired output and becomes highly proficient at the trained task.
This error signal is crucial for learning. Without efficient “backprop,” the network doesn’t know which of its neurons are off kilter. By assigning blame, the AI can better itself.
The brain does this too. How? We have no clue.
Biological No-Go
What’s clear, though, is that the deep learning solution doesn’t work.
Backprop is a pretty needy function. It requires a very specific infrastructure for it to work as expected.
For one, each neuron in the network has to receive the error feedback. But in the brain, neurons are only connected to a few downstream partners (if that). For backprop to work in the brain, early-level neurons need to be able to receive information from billions of connections in their downstream circuits—a biological impossibility.
And while certain deep learning algorithms adapt a more local form of backprop— essentially between neurons—it requires their connection forwards and backwards to be symmetric. This hardly ever occurs in the brain’s synapses.
More recent algorithms adapt a slightly different strategy, in that they implement a separate feedback pathway that helps the neurons to figure out errors locally. While it’s more biologically plausible, the brain doesn’t have a separate computational network dedicated to the blame game.
What it does have are neurons with intricate structures, unlike the uniform “balls” that are currently applied in deep learning.
Branching Networks
The team took inspiration from pyramidal cells that populate the human cortex.
“Most of these neurons are shaped like trees, with ‘roots’ deep in the brain and ‘branches’ close to the surface,” says Richards. “What’s interesting is that these roots receive a different set of inputs than the branches that are way up at the top of the tree.”
This is an illustration of a multi-compartment neural network model for deep learning. Left: Reconstruction of pyramidal neurons from mouse primary visual cortex. Right: Illustration of simplified pyramidal neuron models. Image Credit: CIFAR
Curiously, the structure of neurons often turn out be “just right” for efficiently cracking a computational problem. Take the processing of sensations: the bottoms of pyramidal neurons are right smack where they need to be to receive sensory input, whereas the tops are conveniently placed to transmit feedback errors.
Could this intricate structure be evolution’s solution to channeling the error signal?
The team set up a multi-layered neural network based on previous algorithms. But rather than having uniform neurons, they gave those in middle layers—sandwiched between the input and output—compartments, just like real neurons.
When trained with hand-written digits, the algorithm performed much better than a single-layered network, despite lacking a way to perform classical backprop. The cell-like structure itself was sufficient to assign error: the error signals at one end of the neuron are naturally kept separate from input at the other end.
Then, at the right moment, the neuron brings both sources of information together to find the best solution.
There’s some biological evidence for this: neuroscientists have long known that the neuron’s input branches perform local computations, which can be integrated with signals that propagate backwards from the so-called output branch.
However, we don’t yet know if this is the brain’s way of dealing blame—a question that Richards urges neuroscientists to test out.
What’s more, the network parsed the problem in a way eerily similar to traditional deep learning algorithms: it took advantage of its multi-layered structure to extract progressively more abstract “ideas” about each number.
“[This is] the hallmark of deep learning,” the authors explain.
The Deep Learning Brain
Without doubt, there will be more twists and turns to the story as computer scientists incorporate more biological details into AI algorithms.
One aspect that Richards and team are already eyeing is a top-down predictive function, in which signals from higher levels directly influence how lower levels respond to input.
Feedback from upper levels doesn’t just provide error signals; it could also be nudging lower processing neurons towards a “better” activity pattern in real-time, says Richards.
The network doesn’t yet outperform other non-biologically derived (but “brain-inspired”) deep networks. But that’s not the point.
“Deep learning has had a huge impact on AI, but, to date, its impact on neuroscience has been limited,” the authors say.
Now neuroscientists have a lead they could experimentally test: that the structure of neurons underlie nature’s own deep learning algorithm.
“What we might see in the next decade or so is a real virtuous cycle of research between neuroscience and AI, where neuroscience discoveries help us to develop new AI and AI can help us interpret and understand our experimental data in neuroscience,” says Richards.
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#431599 8 Ways AI Will Transform Our Cities by ...

How will AI shape the average North American city by 2030? A panel of experts assembled as part of a century-long study into the impact of AI thinks its effects will be profound.
The One Hundred Year Study on Artificial Intelligence is the brainchild of Eric Horvitz, technical fellow and a managing director at Microsoft Research.
Every five years a panel of experts will assess the current state of AI and its future directions. The first panel, comprised of experts in AI, law, political science, policy, and economics, was launched last fall and decided to frame their report around the impact AI will have on the average American city. Here’s how they think it will affect eight key domains of city life in the next fifteen years.
1. Transportation
The speed of the transition to AI-guided transport may catch the public by surprise. Self-driving vehicles will be widely adopted by 2020, and it won’t just be cars — driverless delivery trucks, autonomous delivery drones, and personal robots will also be commonplace.
Uber-style “cars as a service” are likely to replace car ownership, which may displace public transport or see it transition towards similar on-demand approaches. Commutes will become a time to relax or work productively, encouraging people to live further from home, which could combine with reduced need for parking to drastically change the face of modern cities.
Mountains of data from increasing numbers of sensors will allow administrators to model individuals’ movements, preferences, and goals, which could have major impact on the design city infrastructure.
Humans won’t be out of the loop, though. Algorithms that allow machines to learn from human input and coordinate with them will be crucial to ensuring autonomous transport operates smoothly. Getting this right will be key as this will be the public’s first experience with physically embodied AI systems and will strongly influence public perception.
2. Home and Service Robots
Robots that do things like deliver packages and clean offices will become much more common in the next 15 years. Mobile chipmakers are already squeezing the power of last century’s supercomputers into systems-on-a-chip, drastically boosting robots’ on-board computing capacity.
Cloud-connected robots will be able to share data to accelerate learning. Low-cost 3D sensors like Microsoft’s Kinect will speed the development of perceptual technology, while advances in speech comprehension will enhance robots’ interactions with humans. Robot arms in research labs today are likely to evolve into consumer devices around 2025.
But the cost and complexity of reliable hardware and the difficulty of implementing perceptual algorithms in the real world mean general-purpose robots are still some way off. Robots are likely to remain constrained to narrow commercial applications for the foreseeable future.
3. Healthcare
AI’s impact on healthcare in the next 15 years will depend more on regulation than technology. The most transformative possibilities of AI in healthcare require access to data, but the FDA has failed to find solutions to the difficult problem of balancing privacy and access to data. Implementation of electronic health records has also been poor.
If these hurdles can be cleared, AI could automate the legwork of diagnostics by mining patient records and the scientific literature. This kind of digital assistant could allow doctors to focus on the human dimensions of care while using their intuition and experience to guide the process.
At the population level, data from patient records, wearables, mobile apps, and personal genome sequencing will make personalized medicine a reality. While fully automated radiology is unlikely, access to huge datasets of medical imaging will enable training of machine learning algorithms that can “triage” or check scans, reducing the workload of doctors.
Intelligent walkers, wheelchairs, and exoskeletons will help keep the elderly active while smart home technology will be able to support and monitor them to keep them independent. Robots may begin to enter hospitals carrying out simple tasks like delivering goods to the right room or doing sutures once the needle is correctly placed, but these tasks will only be semi-automated and will require collaboration between humans and robots.
4. Education
The line between the classroom and individual learning will be blurred by 2030. Massive open online courses (MOOCs) will interact with intelligent tutors and other AI technologies to allow personalized education at scale. Computer-based learning won’t replace the classroom, but online tools will help students learn at their own pace using techniques that work for them.
AI-enabled education systems will learn individuals’ preferences, but by aggregating this data they’ll also accelerate education research and the development of new tools. Online teaching will increasingly widen educational access, making learning lifelong, enabling people to retrain, and increasing access to top-quality education in developing countries.
Sophisticated virtual reality will allow students to immerse themselves in historical and fictional worlds or explore environments and scientific objects difficult to engage with in the real world. Digital reading devices will become much smarter too, linking to supplementary information and translating between languages.
5. Low-Resource Communities
In contrast to the dystopian visions of sci-fi, by 2030 AI will help improve life for the poorest members of society. Predictive analytics will let government agencies better allocate limited resources by helping them forecast environmental hazards or building code violations. AI planning could help distribute excess food from restaurants to food banks and shelters before it spoils.
Investment in these areas is under-funded though, so how quickly these capabilities will appear is uncertain. There are fears valueless machine learning could inadvertently discriminate by correlating things with race or gender, or surrogate factors like zip codes. But AI programs are easier to hold accountable than humans, so they’re more likely to help weed out discrimination.
6. Public Safety and Security
By 2030 cities are likely to rely heavily on AI technologies to detect and predict crime. Automatic processing of CCTV and drone footage will make it possible to rapidly spot anomalous behavior. This will not only allow law enforcement to react quickly but also forecast when and where crimes will be committed. Fears that bias and error could lead to people being unduly targeted are justified, but well-thought-out systems could actually counteract human bias and highlight police malpractice.
Techniques like speech and gait analysis could help interrogators and security guards detect suspicious behavior. Contrary to concerns about overly pervasive law enforcement, AI is likely to make policing more targeted and therefore less overbearing.
7. Employment and Workplace
The effects of AI will be felt most profoundly in the workplace. By 2030 AI will be encroaching on skilled professionals like lawyers, financial advisers, and radiologists. As it becomes capable of taking on more roles, organizations will be able to scale rapidly with relatively small workforces.
AI is more likely to replace tasks rather than jobs in the near term, and it will also create new jobs and markets, even if it’s hard to imagine what those will be right now. While it may reduce incomes and job prospects, increasing automation will also lower the cost of goods and services, effectively making everyone richer.
These structural shifts in the economy will require political rather than purely economic responses to ensure these riches are shared. In the short run, this may include resources being pumped into education and re-training, but longer term may require a far more comprehensive social safety net or radical approaches like a guaranteed basic income.
8. Entertainment
Entertainment in 2030 will be interactive, personalized, and immeasurably more engaging than today. Breakthroughs in sensors and hardware will see virtual reality, haptics and companion robots increasingly enter the home. Users will be able to interact with entertainment systems conversationally, and they will show emotion, empathy, and the ability to adapt to environmental cues like the time of day.
Social networks already allow personalized entertainment channels, but the reams of data being collected on usage patterns and preferences will allow media providers to personalize entertainment to unprecedented levels. There are concerns this could endow media conglomerates with unprecedented control over people’s online experiences and the ideas to which they are exposed.
But advances in AI will also make creating your own entertainment far easier and more engaging, whether by helping to compose music or choreograph dances using an avatar. Democratizing the production of high-quality entertainment makes it nearly impossible to predict how highly fluid human tastes for entertainment will develop.
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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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