Like many countries, China has a health care problem. Changing demographics and lifestyles mean demand for health care is outstripping growth in medical resources and its cost is rising faster than the insurance premium.
With 250 million people over the age of 60, the world’s most populous country is ageing. Diseases associated with more affluent societies, such as cardiovascular conditions and diabetes, are on the rise. China has 400 million chronic disease patients whose treatment costs 70% of total health care resources. And there is a shortage of medical professionals—China needs an additional 700,000 general practitioners and 10 million nursing staff. In 2019, the country spent 6 trillion RMB ($928 billion) on health care, a figure that’s expected to reach 16 trillion RMB in 2030.
But an uneven distribution of resources and their inefficient use mean the cost of providing health care services is unnecessarily high: China’s top hospitals are overwhelmed with patients, but many of them have mild conditions and don’t need to be in a health care facility at all. Of all hospitalized patients, 23% are in top tier hospitals, which account for only 0.3% of the total number of hospitals. And patient data is fragmented among thousands of local clinics and hospitals, making diagnosis, treatment, and effective public health policy implementation more complicated. This inefficient structure leads to wildly disparate service levels and costs in health care, which makes it difficult for insurers to provide standardized coverage.
The government acknowledges these challenges and the need to reform the health care system. President Xi Jinping has put public health at the core of the country’s policy-making programme, emphasizing health in government policy-making agenda. The national goal of “Healthy China 2030” focuses on disease prevention and a comprehensive overhaul of the health care system.
The big question, though, is how to connect all the stakeholders in China’s sprawling health care system in a way that reduces costs, improves public health outcomes and makes health care more insurable? For Ping An, trying to impact one segment or another is not the answer. The solution must involve a whole ecosystem involving the government, patients, providers, payors, and technology in dynamic interaction which enables all to function to their full potential.
It is no surprise that the efficiencies offered by digitalization hold the potential to transform the health care provision in China. But given the scale, complexity, and importance of the challenge, Ping An believes that improving outcomes for all stakeholders has to go further than bringing more health care delivery online or connecting different data sources.
Instead, there needs to be a transformation that achieves “horizontal” and “vertical” integration. Payors, providers, and patients need to be more connected to improve efficiency, and payors also need to be able to communicate their needs to providers, helping to determine the cost and level of health care services. Only a high level of integration can ensure that a health care ecosystem will be sustainable. That is why Ping An’s health care ecosystem strategy—and the role of its 12 distinct entities in the sector—are built on this holistic online and offline approach.
Technology is at the heart of this strategy. Building an effective digital infrastructure for better health care in China involves leveraging a high level of professionalism developed working in the Chinese health care market, as well as substantial investment in cloud, artificial intelligence (AI) and data management systems: fields in which Ping An is a recognized leader. Underlining its commitment to innovation, Ping An invests 1% of its annual revenue in research and development for healthtech and fintech technologies.
Making it work
This leadership in technology is the key to Ping An’s ecosystem strategy—and to better outcomes for all participants, including the company itself. Reimbursements paid by private commercial health care insurers only account for 6% of China’s health care spending at the moment. That means insurers, in this capacity alone, can exert little influence over the cost and service level of the health care provision. As a result, insurers’ potential to contribute to China’s health care system is limited.
Ping An’s technology and services change this equation. The value of Ping An’s technology to the government in monitoring and improving public health—not to mention its benefits to medical professionals—allows the company to access public health care institutions. That means Ping An can help institutions to improve operations, manage costs, and deliver better and more affordable service to patients, making health care more insurable.
Built on a vast database of diseases, medical products, treatments, medical resources and patient information, Ping An Smart Healthcare is at the heart of this “vertical integration.” It provides tools to manage public health care, empower providers, and improve medical resource accessibility and patients’ disease outcomes.
For example, Ping An Smart Healthcare’s intelligent image analysis system enables doctors to shorten diagnosis times from 15 minutes to 15 seconds. The integrated data analysis package, AskBob, aims to be the “Bloomberg for doctors.” Already, AskBob is used by as many as 710,000 doctors, covers around 3,000 diseases, and its AI capabilities in diagnosing and treating cardiovascular disease are comparable to that of human doctors. In a competition at the Great Wall International Congress of Cardiology last year, AskBob scored 97.7 points compared to 93.9 points for a team of doctors from top tier hospitals.
In collaboration with China’s National Clinical Research Centre for Metabolic Diseases, Ping An Smart Healthcare has developed an advanced type 2 diabetes management tool powered by its underlying AI technology and database resources. The tool has been deployed in the center and more than 600 hospitals nationwide, serving more than 100,000 patients and delivering a 30% improvement in patients’ compliance rate.
Across the spectrum
Ping An Good Doctor and Smart Healthcare work together to create a robust product and service cost model, driving synergies by drawing providers and social health insurers into the system. If Ping An Smart Healthcare is the vertical thread connecting government and medical institutions with the delivery of health care to patients, Ping An Good Doctor is crucial to the group’s efforts to connect patients, providers and payors through the “horizontal” axis of its ecosystem strategy. Ping An Good Doctor provides online consultations with AI-assisted medical teams and integrates seamlessly with offline medical services within the ecosystem. Users can search for basic information for free, with consultations and treatments available at a cost.
The adoption of telemedicine soared during the pandemic. By 30 December 2020, Ping An Good Doctor had 373 million total users, with a monthly average of 72.6 million users, and some 903,000 daily enquiries. Ping An Good Doctor has an in-house medical team of more 2,200 members and a network of more than 20,000 domestic medical experts and 300 renowned doctors across China.
But while these numbers are impressive and should continue to grow, currently only 3% of all medical consultations are conducted online in China—a smaller proportion than in the United States. To be truly transformative, the ecosystem must address the substantial proportion of consultations that still take place offline.
The ecosystem’s network of offline health care providers is therefore highly important. Ping An Good Doctor partners with 151,000 pharmacies, 49,000 clinics, more than 3,700 hospitals, and over 2,000 medical examination centers to provide services such as hospital referrals, appointments, and inpatient arrangements. Ping An Good Doctor also works with 1,000 prominent international doctors and the world’s top ten hospitals to ensure handy and accurate medical services for users.
Paying the bill
The benefits of this ecosystem strategy to government, patients, and providers are clear. But covering the cost of improved health care in China involves payors—and that’s where the opportunity lies for Ping An to commercialize its strategy. Insurance products are integrated throughout the Ping An health care ecosystem. Ping An HealthKonnect provides payors such as social health insurers and companies with anti-fraud and health care resource management models that reduce overtreatment, fraud, and abuse. The group’s recent Ping An Doctor Home proposition, which offers private family medical services online, includes up to 1 million RMB of insurance coverage against any injury or loss of time or property caused by the platform.
In total, the Ping An Group already provides health care services to 210 million individual financial services customers and four million corporate clients. However, transformation of China’s health care system is a long-term investment into Ping An’s own future: a healthier health care system allows the company to acquire new financial services customers as well as to retain and grow spending with the group by existing financial services customers.
The facts reinforce this business case. In recent years, 15% to 20% of Ping An’s new financial services customers have come from this growing health care ecosystem. Financial services customers who also have health care services use, on average, three Ping An financial services products, whereas a financial services customer without health care services uses only two of the group’s products. The average assets under management (AUM) of customers using both financial and health care services is $10,000, versus $5,600 for those who only use financial services.
Lessons for the world
Confucius said that “when it is obvious that the goals cannot be reached, don’t adjust the goals; adjust the action steps.” Ping An’s ecosystem strategy reflects a willingness to think differently about how to solve a longstanding problem, deliver on the “Healthy China 2030” goal, and may offer a useful example to other countries.
Some of the unique characteristics of China’s system, especially an open approach towards data-sharing and the role of government in health care, unquestionably help to make Ping An’s ecosystem strategy viable. However, the health care challenges China faces are not unique. Indeed, data fragmentation, inefficiency, high cost, and a shortage of medical professionals afflict health care systems around the world. Undoubtedly, all governments would want to use technology to better monitor and protect public health, especially since the outbreak of covid-19.
Each country will make its own decisions on how patient data can be gathered, aggregated and shared, and the role of government in health care will of course vary in every country. But it is clear that achieving better outcomes for patients, providers, payors, and governments—as Ping An’s health care ecosystem strategy aims to do in China—must in some way harness the power of data and AI to create efficiencies and standardize the cost and level of medical services.
This content was produced by Ping An. It was not written by MIT Technology Review’s editorial staff.
AI and data fuel innovation in clinical trials and beyond
Laurel: So mentioning the pandemic, it really has shown us how critical and fraught the race is to provide new treatments and vaccines to patients. Could you explain what evidence generation is and then how it fits into drug development?
Arnaub: Sure. So as a concept, generating evidence in drug development is nothing new. It’s the art of putting together data and analyses that successfully demonstrate the safety and the efficacy and the value of your product to a bunch of different stakeholders, regulators, payers, providers, and ultimately, and most importantly, patients. And to date, I’d say evidence generation consists of not only the trial readout itself, but there are now different types of studies that pharmaceutical or medical device companies conduct, and these could be studies like literature reviews or observational data studies or analyses that demonstrate the burden of illness or even treatment patterns. And if you look at how most companies are designed, clinical development teams focus on designing a protocol, executing the trial, and they’re responsible for a successful readout in the trial. And most of that work happens within clinical dev. But as a drug gets closer to launch, health economics, outcomes research, epidemiology teams are the ones that are helping paint what is the value and how do we understand the disease more effectively?
So I think we’re at a pretty interesting inflection point in the industry right now. Generating evidence is a multi-year activity, both during the trial and in many cases long after the trial. And we saw this as especially true for vaccine trials, but also for oncology or other therapeutic areas. In covid, the vaccine companies put together their evidence packages in record time, and it was an incredible effort. And now I think what’s happening is the FDA’s navigating a tricky balance where they want to promote the innovation that we were talking about, the advancements of new therapies to patients. They’ve built in vehicles to expedite therapies such as accelerated approvals, but we need confirmatory trials or long-term follow up to really understand the evidence and to understand the safety and the efficacy of these drugs. And that’s why that concept that we’re talking about today is so important, is how do we do this more expeditiously?
Laurel: It’s certainly important when you’re talking about something that is life-saving innovations, but as you mentioned earlier, with the coming together of both the rapid pace of technology innovation as well as the data being generated and reviewed, we’re at a special inflection point here. So, how has data and evidence generation evolved in the last couple years, and then how different would this ability to create a vaccine and all the evidence packets now be possible five or 10 years ago?
Arnaub: It’s important to set the distinction here between clinical trial data and what’s called real-world data. The randomized controlled trial is, and has remained, the gold standard for evidence generation and submission. And we know within clinical trials, we have a really tightly controlled set of parameters and a focus on a subset of patients. And there’s a lot of specificity and granularity in what’s being captured. There’s a regular interval of assessment, but we also know the trial environment is not necessarily representative of how patients end up performing in the real world. And that term, “real world,” is kind of a wild west of a bunch of different things. It’s claims data or billing records from insurance companies. It’s electronic medical records that emerge out of providers and hospital systems and labs, and even increasingly new forms of data that you might see from devices or even patient-reported data. And RWD, or real-world data, is a large and diverse set of different sources that can capture patient performance as patients go in and out of different healthcare systems and environments.
Ten years ago, when I was first working in this space, the term “real-world data” didn’t even exist. It was like a swear word, and it was basically one that was created in recent years by the pharmaceutical and the regulatory sectors. So, I think what we’re seeing now, the other important piece or dimension is that the regulatory agencies, through very important pieces of legislation like the 21st Century Cures Act, have jump-started and propelled how real-world data can be used and incorporated to augment our understanding of treatments and of disease. So, there’s a lot of momentum here. Real-world data is used in 85%, 90% of FDA-approved new drug applications. So, this is a world we have to navigate.
How do we keep the rigor of the clinical trial and tell the entire story, and then how do we bring in the real-world data to kind of complete that picture? It’s a problem we’ve been focusing on for the last two years, and we’ve even built a solution around this during covid called Medidata Link that actually ties together patient-level data in the clinical trial to all the non-trial data that exists in the world for the individual patient. And as you can imagine, the reason this made a lot of sense during covid, and we actually started this with a covid vaccine manufacturer, was so that we could study long-term outcomes, so that we could tie together that trial data to what we’re seeing post-trial. And does the vaccine make sense over the long term? Is it safe? Is it efficacious? And this is, I think, something that’s going to emerge and has been a big part of our evolution over the last couple years in terms of how we collect data.
Laurel: That collecting data story is certainly part of maybe the challenges in generating this high-quality evidence. What are some other gaps in the industry that you have seen?
Arnaub: I think the elephant in the room for development in the pharmaceutical industry is that despite all the data and all of the advances in analytics, the probability of technical success, or regulatory success as it’s called for drugs, moving forward is still really low. The overall likelihood of approval from phase one consistently sits under 10% for a number of different therapeutic areas. It’s sub 5% in cardiovascular, it’s a little bit over 5% in oncology and neurology, and I think what underlies these failures is a lack of data to demonstrate efficacy. It’s where a lot of companies submit or include what the regulatory bodies call a flawed study design, an inappropriate statistical endpoint, or in many cases, trials are underpowered, meaning the sample size was too small to reject the null hypothesis. So what that means is you’re grappling with a number of key decisions if you look at just the trial itself and some of the gaps where data should be more involved and more influential in decision making.
So, when you’re designing a trial, you’re evaluating, “What are my primary and my secondary endpoints? What inclusion or exclusion criteria do I select? What’s my comparator? What’s my use of a biomarker? And then how do I understand outcomes? How do I understand the mechanism of action?” It’s a myriad of different choices and a permutation of different decisions that have to be made in parallel, all of this data and information coming from the real world; we talked about the momentum in how valuable an electronic health record could be. But the gap here, the problem is, how is the data collected? How do you verify where it came from? Can it be trusted?
So, while volume is good, the gaps actually contribute and there’s a significant chance of bias in a variety of different areas. Selection bias, meaning there’s differences in the types of patients who you select for treatment. There’s performance bias, detection, a number of issues with the data itself. So, I think what we’re trying to navigate here is how can you do this in a robust way where you’re putting these data sets together, addressing some of those key issues around drug failure that I was referencing earlier? Our personal approach has been using a curated historical clinical trial data set that sits on our platform and use that to contextualize what we’re seeing in the real world and to better understand how patients are responding to therapy. And that should, in theory, and what we’ve seen with our work, is help clinical development teams use a novel way to use data to design a trial protocol, or to improve some of the statistical analysis work that they do.
Power beaming comes of age
The global need for power to provide ubiquitous connectivity through 5G, 6G, and smart infrastructure is rising. This report explains the prospects of power beaming; its economic, human, and environmental implications; and the challenges of making the technology reliable, effective, wide-ranging, and secure.
The following are the report’s key findings:
Lasers and microwaves offer distinct approaches to power beaming, each with benefits and drawbacks. While microwave-based power beaming has a more established track record thanks to lower cost of equipment, laser-based approaches are showing promise, backed by an increasing flurry of successful trials and pilots. Laser-based beaming has high-impact prospects for powering equipment in remote sites, the low-earth orbit economy, electric transportation, and underwater applications. Lasers’ chief advantage is the narrow concentration of beams, which enables smaller trans- mission and receiver installations. On the other hand, their disadvantage is the disturbance caused by atmospheric conditions and human interruption, although there are ongoing efforts to tackle these deficits.
Power beaming could quicken energy decarbonization, boost internet connectivity, and enable post-disaster response. Climate change is spurring investment in power beaming, which can support more radical approaches to energy transition. Due to solar energy’s continuous availability, beaming it directly from space to Earth offers superior conversion compared to land-based solar panels when averaged over time. Electric transportation—from trains to planes or drones—benefits from power beaming by avoiding the disruption and costs caused by cabling, wiring, or recharge landings.
Beaming could also transfer power from remote renewables sites such as offshore wind farms. Other areas where power beaming could revolutionize energy solutions include refueling space missions and satellites, 5G provision, and post-disaster humanitarian response in remote regions or areas where networks have collapsed due to extreme weather events, whose frequency will be increased by climate change. In the short term, as efficiencies continue to improve, power beaming has the capacity to reduce the number of wasted batteries, especially in low-power, across-the- room applications.
Public engagement and education are crucial to support the uptake of power beaming. Lasers and microwaves may conjure images of death rays and unanticipated health risks. Public backlash against 5G shows the importance of education and information about the safety of new, “invisible” technologies. Based on decades of research, power beaming via both microwaves and lasers has been shown to be safe. The public is comfortable living amidst invisible forces like wi-fi and wireless data transfer; power beaming is simply the newest chapter.
Commercial investment in power beaming remains muted due to a combination of historical skepticism and uncertain time horizons. While private investment in futuristic sectors like nuclear fusion energy and satellites booms, the power-beaming sector has received relatively little investment and venture capital relative to the scale of the opportunity. Experts believe this is partly a “first-mover” problem as capital allocators await signs of momentum. It may be a hangover of past decisions to abandon beaming due to high costs and impracticality, even though such reticence was based on earlier technologies that have now been surpassed. Power beaming also tends to fall between two R&D comfort zones for large corporations: it does not deliver short-term financial gain, but it is also not long term enough to justify a steady financing stream.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.
The porcelain challenge didn’t need to be real to get views
“I’ve dabbled in the past with trying to make fake news that is transparent about being fake but spreads nonetheless,” Durfee said. (He once, with a surprising amount of success, got a false rumor started that longtime YouTuber Hank Green had been arrested as a teenager for trying to steal a lemur from a zoo.)
On Sunday, Durfee and his friends watched as #PorcelainChallenge gained traction, and they celebrated when it generated its first media headline (“TikTok’s porcelain challenge is not real but it’s not something to joke about either”). A steady parade of other headlines, some more credulous than others, followed.
But reflex-dependent viral content has a short life span. When Durfee and I chatted three days after he posted his first video about the porcelain challenge, he already could tell that it wasn’t going to catch as widely as he’d hoped. RIP.
Nevertheless, viral moments can be reanimated with just the slightest touch of attention, becoming an undead trend ambling through Facebook news feeds and panicked parent groups. Stripping away their original context can only make them more powerful. And dubious claims about viral teen challenges are often these sorts of zombies—sometimes giving them a second life that’s much bigger (and arguably more dangerous) than the first.
For every “cinnamon challenge” (a real early-2010s viral challenge that made the YouTube rounds and put participants at risk for some nasty health complications), there are even more dumb ideas on the internet that do not trend until someone with a large audience of parents freaks out about them.
Just a couple of weeks ago, for instance, the US Food and Drug Administration issued a warning about boiling chicken in NyQuil, prompting a panic over a craze that would endanger Gen Z lives in the name of views. Instead, as Buzzfeed News reported, the warning itself was the most viral thing about NyQuil chicken, spiking interest in a “trend” that was not trending.
And in 2018, there was the “condom challenge,” which gained widespread media coverage as the latest life-threatening thing teens were doing online for attention—“uncovered” because a local news station sat in on a presentation at a Texas school on the dangers teens face. In reality, the condom challenge had a few minor blips of interest online in 2007 and 2013, but videos of people actually trying to snort a condom up their nose were sparse. In each case, the fear of teens flocking en masse to take part in a dangerous challenge did more to amplify it to a much larger audience than the challenge was able to do on its own.
The porcelain challenge has all the elements of future zombie content. Its catchy name stands out like a bite on the arm. The posts and videos seeded across social media by Durfee’s followers—and the secondary audience coming across the work of those Durfee deputized—are plausible and context-free.