While the Alphabet-owned company has offered plenty of self-driving rides before, they usually came with a human in the driver’s seat for safety. Members of the early rider program who’d signed nondisclosure agreements were able to try out fully driverless rides — but again, they had to sign NDAs first.
Today, the company said members of its more open Waymo One program in Phoenix will be able to go fully driverless, and to take friends and family with them. And over the next few weeks, the program will open up to even more passengers.
Since the start of the coronavirus pandemic, America’s roughly 640 billionaires have seen their fortunes soar by $845 billion in combined assets or 29% collectively, widening the already yawning gap between the very richest and the rest of the U.S.
Many of those billions were made by tech founders, including Mark Zuckerberg, Jeff Bezos, and Elon Musk, whose companies have soared in value and, in tandem, their net worth. In fact, so much has been made so fast and by so few relatively, that it’s easy to wonder if greater equality is now forever out of reach.
Giridharadas’s message at the time was largely that the generosity of the global elite is somewhat laughable — that many of the same players who say they want to help society are creating its most intractable problems. Think, for example of Bezos, whose company paid zero in federal tax in 2017 and 2018 and who is now on the cusp of opening a tuition-free preschool for underserved children called the Bezos Academy.
Given the aggressive escalation over the last six months of the same trends Giridharadas has tracked for years, we wondered how he views the current situation. Our chat has been edited for length and clarity.
TC: You have a weekly newsletter where you make the point that Jeff Bezos could give every one of Amazon’s 876,000 employees a ‘pandemic’ bonus of $105,000 and he would still have as much money as he did in March.
AG: There’s this way in which these crises are not merely things that rich and powerful survive. They’re things that they leverage and exploit, and it starts to raise the question of: are they even on the same team as us? Because when you have discussions about stimulus relief around what kind of policy responses you could have to something like the 2008 financial crisis or the pandemic, there’s initially some discussion and clamor for universal basic income, or substantial monthly checks for people, or even the French approach of nationalizing people salaries… and those things usually die. And they die thanks to corporate lobbyists and advocates of the rich and powerful, and are replaced by forms of relief that are upwardly redistributive that essentially exploit a crisis to transfer wealth and power to the top.
TC: Earlier in the 20th century, there was this perception that industry would contribute to solving a crisis with government. In this economy, we didn’t see a lot of the major tech companies, or a lot of the companies that were benefiting from this crisis, really sacrificing something to help the U.S. Do you see things that way?
AG: I think that’s right. I’m always wary of idealizing certain periods in the past, and I think there were a lot of problems in that time. But I think there’s no question that it was not as difficult back then, as it is today, to summon some kind of sense of common purpose and even the need to sacrifice values like profit seeking for other values.
I mean, after 9/11, President George W. Bush told us all to go shopping as the way to advance the common good. Donald Trump is now 18 levels of hell further down that path, not even telling us that we need to do anything for each other and [instead describing earlier this week] a pandemic that has killed 200,000 people as being something that doesn’t really affect most people.
So there’s just been a coarsening. And that kind of selfish trajectory of our culture, after 40 years of being told that what we do alone is better than what we do together, that what we do to create wealth is more important than what we do to advance shared goals — that quite dismal, dull message has had its consequences. And when you get a pandemic like this, and you suddenly need to be able to summon people to all socially distance at a minimum or, more ambitiously, pull for the common good or pay higher taxes or things that might even cost them a little bit, it’s very hard to do because the groundwork isn’t there.
TC: You’ve talked quite a bit over the years about “fake change.”
AG: Silicon Valley is the new Rome of our time, meaning a place in the world that ends up deciding how a lot of the rest of the world lives. No matter where you lived on the planet Earth, when the Roman Empire started to rise, it had plans for you one way or another, through your legal system, or your language, or culture, or something else. The Roman Empire was coming for you.
Silicon Valley is that for our time. It’s the new Rome [in] that you can’t live on planet Earth and be unaffected, directly or indirectly, by the decisions made in this relatively small patch of [of the world]. So the question then becomes, what does that new Rome want? And my impression of having reported on that world is that it’s an incredibly homogeneous world of people at the top of this new Rome. It’s white male dominated in a way that even other white male dominated sectors of the American economy are not . . . and it’s a lot of a certain kind of man who often is actually more obtuse about understanding human society and sociological dynamics and human beings than the average person.
Maybe they didn’t spend a lot of time negotiating human dynamics at sleepovers, which is fine. But when you end up with a new Rome and it’s hyper dominated by people of one race and one gender, many of whom are disproportionately socially unintelligent, running the platforms through which most human sociality now occurs — democratic discourse, family community, so on and so forth — we all start to live in a world created by people who are just quite limited. They are smart at the thing they’re smart at and they’ve become in charge of a lot of how the world works. And there’s simply not up to the task. And we see evidence of that every day.
TC: Are you speaking about empathy?
AG: Empathy is absolutely one of [the factors]. The ability to understand the more amorphous, non technological, non quantifiable things . . . it’s so interesting, because it’s people who are clearly very smart in a certain area but just honestly do not understand democratic theory. There’s just so much work that’s been done — deep, complicated thinking going back to Plato and Aristotle, but also modern sociological work, including why a safety net and welfare is complicated. And there’s a certain kind of personality type that I have found very dominant in Silicon Valley, where it’s these men who just don’t really have a lens for that.
They’re often geniuses. It’s a certain kind of particular personality type where you care a lot about one thing and you go deep on that one thing, and it’s probably the same personality type that Beethoven had. It’s a great thing, actually. It’s just not great for governing us, and what these people are doing is privately governing us, and they have no humility about the limitations of their worldview
TC: We’re talking largely about social media here. Is it reasonable to expect some kind of government action. Do you think that’s naive?
AG: It’s absolutely essential that the tech industry be brought into the same kind of sensible regulatory regime. I mean, you have kids, I have kids. If you’ve ever read the side of their car seats or any of the other products in their lives, you understand how much regulation there is for our benefit. . . I often say that the government at its best is like a lawyer for all of us. The government is like ‘Why don’t we check out these car seats for you and create some rules around them and then you can just buy a car seat and not have to wonder whether it’s the kind that protects your child or crumbles?’ That’s what the government does for all kinds of things.
TC: You’ve talked about billionaires who don’t want to pay taxes yet don’t hesitate to make a donation because they have control over where their money is spent and they get their name on a building, and it’s true. Many companies whose founders also consider themselves philanthropists, like Salesforce and Netflix, paid no federal tax in 2018, which amounts to billions of dollars lost. If you had to prioritize between taking antitrust action or closing the tax loopholes, what would you choose?
AG: They’re both important. But I think I would prioritize taxation.
One way to think about it is this pre distribution and redistribution. The monopoly issue in a way is pre distribution, which is how much money you get to make in the first place. If you get to be a monopoly because we don’t enforce antitrust laws, you’re going to end up making pre tax a lot more money than you would otherwise have made if you had to compete in an actual free market.
Once you’ve made that money, the tax question comes up. So both are important, but I think you can’t overestimate the extent to which the tax thing is just totally foundational. If you look at the report that the 400 richest families in America pay a lower effective tax rate than the bottom half of families, it’s appalling.
We live in a complicated world. A lot of different things have been going on, including just in the last few months. But if you have to really summarize the drift and the shift of the last 40 years, it’s been a war on taxation. And it’s been a massive redistribution of wealth from the bottom to the top of American life through taxation. Since the ’80s, the top 1% has gained $21 trillion of wealth, and the bottom half of Americans have lost $900 billion of wealth on average — and much of that was prosecuted through the tax code.
Awkward! Above, Giridharadas shaking hands with Amazon founder Jeff Bezos at a Wired event in 2018.
Ring built its entire business on reinventing the doorbell – and now it’s taking a similar approach to the humble home security camera, with the Ring Always Home Cam, set to be available sometime next year. You might not guess from its name, but this security camera is actually mobile: It’s a drone that flies autonomously throughout your home, to provide you with the view you want of whatever room you want, without having to have video cameras installed in multiple locations throughout your house.
The Always Home Cam is a diminutive drone that can be scheduled to fly preset paths, which you lay out as a user. The drone can’t actually be manually flown, and it begins recording only once its in flight (the camera lens is actually physically blocked while it’s docked) – both features the company says will help ensure it operates strictly with privacy in mind. Always Home Cam is also designed intentionally to produce an audible hum while in use, to alert anyone present that it’s actually moving around and recording.
As you’d expect, the Always Home Cam doesn’t have the exposed rotors you’d see on a drone designed for use in outdoor open spaces. It has a plastic border and grills that enclose those for safety. It’s also small, at 5″x 7″x7″, which is useful for safety of both people and household objects.
I spoke to Ring founder and CEO Jamie Siminoff about why they decided to create such an ambitious, unorthodox home security camera – especially given their track record of relatively down-to-Earth, tech-enabled versions of tried-and-tested home hardware like doorbells and floodlights. He said that it actually came out of user feedback – something he still personally pays close attention to, even now that Ring is part of the larger corporate apparatus of Amazon . Siminoff said that a lot of the feedback he was seeing was from customers who wished they’d either been home or been able to see when some specific thing happened at a specific place in their house, or that they wanted a camera for particular room, but only for certain times – and then a different camera in a different room for others.
“It’s not practical to have a camera at every angle in every room of the home,” he said. “Even if you had unlimited resources, I think it’s still not practical. What I love about the Always Home Cam is that it really does solve this problem of being one cam for all – it allows you to now see every angle of the home, in every part of the home.”
Drones are also not Ring’s main business, and yet the Always Home Cam will be available at the relatively low price of $249 when it becomes available, despite the technical challenges of creating a small aircraft able to operate indoors safely, and fully autonomously. I asked Siminoff how Ring was able to achieve that price point in a category that’s outside its core expertise, with a design developed fully in-house.
“As the technology has kind of aged, a lot of these parts come down in price,” he said. “There’s also a lot of price compression happening because auto manufacturers are using a lot of these parts now at higher volumes, because to have an autonomous drone, you need some similar things to autonomous cars. Obviously, it’s not the same exact parts, but so all of those costs have been coming down, and we were able to go with a fresh perspective to it. But I also challenged the team when we came up with this, that this has to be affordable.”
The Ring Always Home Cam will also work with Ring’s existing suite of products, including Ring Alarm, to automatically fly a pre-set path when an alarm is triggered. You’re able then to stream the video live to your mobile device via the Ring app. In many ways, it does seem like a natural extension of the Ring ecosystem of products and services, but at the same time, it also seems like something out of science fiction. I asked Siminoff if he thinks consumers are ready to take this kind of technology seriously as something that’s part of their daily lives.
“I think it is sort of something that is, in some ways, way out there,” he acknowledged. “What I love about it, though, is that it’s what happens when you just take the constraints away of this linear thinking. I love that we are doing stuff from really looking at the need backwards, and then what technology exists, and ask what can we build? It’s really exciting for me to be able to do somethin,g and put our stamp on something that is an industry first.”
According to the Bloomberg report Trump said, “I have given the deal my blessing,” as he left the White House for a campaign rally in North Carolina on Saturday.
“I approved the deal in concept,” Trump reportedly said.
The spinout of TikTok’s U.S. operations from its parent company Bytedance was something that Trump administration had demanded on the grounds that the company’s data handling policies and popularity in the U.S. posed a national security threat.
That said, the U.S. has been looking to curtail the operations of several Chinese technology companies on the grounds that they pose security threats to the U.S. Indeed, the Presidential order that demanded TikTok’s spinout also called for the discontinuation of the U.S. operations of the messaging service WeChat, which is owned by Tencent — one of China’s largest technology companies. And the U.S. government has also put a target on the telecommunications and networking technology developer, Huawei.
With the TikTok deal set to be approved, a new company called TikTok Global will be created as part of the deal, according to statements from Treasury Secretary, Steven Mnuchin, earlier this week.
Bloomberg reported that Trump said the new company would be headquartered in Texas, would hire as many as 25,000 people and would contribute $5 billion toward U.S. education.
The bulk of TikTok’s U.S. operations are now in Los Angeles.
As the Trump Administration continues its push to disrupt the operations of Chinese tech companies in the U.S., strange bedfellows are uniting to voice opposition to the deal.
“This order violates the First Amendment rights of people in the United States by restricting their ability to communicate and conduct important transactions on the two social media platforms,” said Hina Shamsi, director of the American Civil Liberties Union’s National Security Project, in a statement on Friday.
All of this could be exceptionally bad for U.S. technology businesses, as Instgram’s chief, Adam Mosseri pointed out in a series of Friday tweets.
“A US ban of TikTok would be meaningful step in the direction of a more fragmented nationalized internet, which would be bad for US tech companies which have benefited greatly from the ability to operate across borders,” Mosseri wrote.
RealPage, a publicly traded full-service property management technology firm with over 12,200 clients worldwide, today announced that it has acquired Stratis IoT, a startup that provides IoT services to the real estate industry, with a focus on access and energy management tools.
“RealPage aims to become a leading provider in the burgeoning rental property automation market, and thereby create significant opportunity for operators to increase rents, improve sustainability, add operational efficiencies, reduce operating costs and enhance the customer experience for the company’s approximately 19 million units throughout the United States,” said RealPage CEO Steve Winn. “The smart building technology also provides a launching pad for expanded international operations, thanks to Stratis’ existing international presence.”
Stratis is currently installed in about 380,000 homes in the U.S., Japan, UK and several countries in Europe and Latin America. Both Stratis and RealPage target a wide range of the real estate industry, ranging from multifamily units to student housing, vacation homes and commercial real estate.
Image Credits: Stratis
Traditionally, the real estate market wasn’t always the first to adopt modern technologies. That’s quickly changing now, though, in part because of the promise of IoT, which isn’t just a boon to renters looking for modern solutions in their apartments but also represents the possibility of significant cost savings for the industry. RealPage argues that smart technology can generate a revenue lift of $55 per unit, for example, and that’s the kind of saving (and higher revenues) that will push even legacy B2B platforms to modernize.
One area where Stratis stands out is its ability to integrate with a wide variety of third-party solutions.
“Holistic building-wide access and utility management and control are integral to building optimization and the resident experience, which have become increasingly intertwined,” said Stratis IoT CEO Felicite Moorman. “RealPage and Stratis IoT combine two industry-leading, best-in-class platforms to create a powerhouse of control and single-app resident experience for multifamily, student housing, and beyond.”
The two companies did not disclose the price of the acquisition. It’s worth noting that RealPage isn’t a stranger to making acquisitions to bring its technology up to speed. A year ago, the company acquired Hipercept, for example, a firm that provided data services and data analytics to the institutional real estate market. Then, in December, it also acquired Buildium, a SaaS property management solution with over 2 million units under management. In 2019, the company said planned to spend just over $100 million on acquisitions.
Amit Garg and Sanjay Rao have spent the bulk of their professional lives developing technology, founding startups and investing in startups at places like Google and Microsoft, HealthIQ, and Norwest Venture Partners.
Over their decade-long friendship the two men discussed working together on a venture fund, but the time was never right — until now. Since last August, the two men have been raising capital for their inaugural fund, Tau Ventures.
The name, like the two partners, is a bit wonky. Tau is two times pi and Garg and Rao chose it as the name for the partnership because it symbolizes their analytical approach to very early stage investing.
It’s a strange thing to launch a venture fund in a pandemic, but for Garg and Rao, the opportunity to provide very early stage investment capital into startups working on machine learning applications in healthcare, automation and business was too good to pass up.
Meanwhile, Rao, a Palo Alto, Calif. native, MIT alum, Microsoft product manager and founder of the Accelerate Labs accelerator in Palo Alto, Calif., said that it was important to give back to entrepreneurs after decades in the Valley honing skills as an operator.
Both Rao and Garg acknowledge that there are a number of funds that have emerged focused on machine learning including Basis Set Ventures, SignalFire, Two Sigma Ventures, but these investors lack the direct company building experience that the two new investors have.
Garg, for instance, has actually built a hospital in India and has a deep background in healthcare. As an investor, he’s already seen an exit through his investment in Nutonomy, and both men have a deep understanding of the enterprise market — especially around security.
So far, the company has made three investments automation, another three in enterprise software, and five in healthcare.
The firm currently has $17 million in capital under management raised from institutional investors like the law firm Wilson Sonsini and a number of undisclosed family offices and individuals, according to Garg.
Much of that capital was committed after the pandemic hit, Garg said. “We started August 29th… and did the final close May 29th.”
The idea was to close the fund and start putting capital to work — especially in an environment where other investors were burdened with sorting out their existing portfolios, and not able to put capital to work as quickly.
“Our last investment was done entirely over Zoom and Google Meet,” said Rao.
That virtual environment extends to the firm’s shareholder meetings and conferences, some of which have attracted over 1,000 attendees, according to the partners.
For the past decade Apple has tried to make the iPhone one of the most secure devices on the market. By locking down its software, Apple keeps its two billion iPhone owners safe. But security researchers say that makes it impossible to look under the hood to figure out what happened when things go wrong.
Once the company that claimed its computers don’t get viruses, Apple has in recent years begun to embrace security researchers and hackers in a way it hadn’t before.
Last year at the Black Hat security conference, Apple’s head of security Ivan Krstic told a crowd of security researchers that it would give its most-trusted researchers a “special” iPhone with unprecedented access to the the device’s underbelly, making it easier to find and report security vulnerabilities that Apple can fix in what it called the iOS Security Research Device program.
Starting today, the company will start loaning these special research iPhones to skilled and vetted researchers that meet the program’s eligibility.
These research iPhones will come with specific, custom-built iOS software with features that ordinary iPhones don’t have, like SSH access and a root shell to run custom commands with the highest access to the software, and debugging tools that make it easier for security researchers to run their code and better understand what’s going on under the surface.
Apple told TechCrunch it wants the program to be more of a collaboration rather than shipping out a device and calling it a day. Hackers in the research device program will also have access to extensive documentation and a dedicated forum with Apple engineers to answer questions and get feedback.
These research devices are not new per se, but have never before been made directly available to researchers. Some researchers are known to have sought out these internal, so-called “dev-fused” devices that have found their way onto underground marketplaces to test their exploits. Those out of luck had to rely on “jailbreaking” an ordinary iPhone first to get access to the device’s internals. But these jailbreaks are rarely available for the most recent iPhones, making it more difficult for hackers to know if the vulnerabilities they find can be exploited or have been fixed.
By giving its best hackers effectively an up-to-date and pre-jailbroken iPhone with some of its normal security restrictions removed, Apple wants to make it easier for trusted security researchers and hackers to find vulnerabilities deep inside the software that haven’t been found before.
But as much as these research phones are more open to hackers, Apple said that the devices don’t pose a risk to the security of any other iPhone if they are lost or stolen.
The new program is a huge leap for the company that only a year ago opened its once-private bug bounty program to everyone, a move seen as long overdue and far later than most other tech companies. For a time, some well-known hackers would publish their bug findings online without first alerting Apple — which hackers call a “zero-day” as they give no time for companies to patch — out of frustration with Apple’s once-restrictive bug bounty terms.
Now under its bounty program, Apple asks hackers to privately submit bugs and security issues for its engineers to fix, to help make its iPhones stronger to protect against nation-state attacks and jailbreaks. In return, hackers get paid on a sliding scale based on the severity of their vulnerability.
Apple said the research device program will run parallel to its bug bounty program. Hackers in the program can still file security bug reports with Apple and receive payouts of up to $1 million — and up to a 50% bonus on top of that for the most serious vulnerabilities found in the company’s pre-release software.
The new program shows Apple is less cautious and more embracing of the hacker community than it once was — even if it’s better late than never.
Facebook, Google, Microsoft, Twitter, and even China-headquartered TikTok said last week they would no longer respond to demands for user data from Hong Kong law enforcement — read: Chinese authorities — citing the new unilaterally imposed Beijing national security law. Critics say the law, ratified on June 30, effectively kills China’s “one country, two systems” policy allowing Hong Kong to maintain its freedoms and some autonomy after the British handed over control of the city-state back to Beijing in 1997.
Noticeably absent from the list of tech giants pulling cooperation was Apple, which said it was still “assessing the new law.” What’s left to assess remains unclear, given the new powers explicitly allow warrantless searches of data, intercept and restrict internet data, and censor information online, things that Apple has historically opposed if not in so many words.
Facebook, Google and Twitter can live without China. They already do — both Facebook and Twitter are banned on the mainland, and Google pulled out after it accused Beijing of cyberattacks. But Apple cannot. China is at the heart of its iPhone and Mac manufacturing pipeline, and accounts for over 16% of its revenue — some $9 billion last quarter alone. Pulling out of China would be catastrophic for Apple’s finances and market position.
The move by Silicon Valley to cut off Hong Kong authorities from their vast pools of data may be a largely symbolic move, given any overseas data demands are first screened by the Justice Department in a laborious and frequently lengthy legal process. But by holding out, Apple is also sending its own message: Its ardent commitment to human rights — privacy and free speech — stops at the border of Hong Kong.
Here’s what else is in this week’s Decrypted.
THE BIG PICTURE
Police used Twitter-backed Dataminr to snoop on protests
Remote patient monitoring is a subset of telehealth that involves the collection, transmission, evaluation, and communication of patient health data from electronic devices. These devices include wearable sensors, implanted equipment, and handheld instruments. During the pandemic, such monitoring programs have proven valuable. But special measures and conditions made that possible. By encouraging regulators to make permanent the temporary measures introduced during the pandemic and by following six guidelines to integrate these programs into health care, providers realize their tremendous promise.
By making the collection of valuable patient data feasible outside of the clinic, remote monitoring can facilitate care for conditions ranging from chronic diseases to recovery from acute episodes of care. For years, it has been touted as one of the most promising opportunities for health care in the digital age. But the pandemic has underscored its value. Indeed, policy changes introduced during the pandemic due to the riskiness of in-person patient visits have created conditions ripe for its adoption. We urge regulators to extend these changes beyond the pandemic and for health care leaders to take advantage of this window of opportunity to develop, test, and improve remote-patient-monitoring programs.
What is remote patient monitoring? While “telehealth” broadly refers to all health care activities that are conducted through telecommunications technology, remote patient monitoring is a subset that involves the collection, transmission, evaluation, and communication of patient health data from electronic devices. These devices include wearable sensors, implanted equipment, and handheld instruments.
We define remote patient monitoring as the set of activities that meet four key criteria: (1) data on patients is collected remotely (e.g., in a home setting without oversight from a health care provider); (2) the data collected is transmitted to a health care provider in a different location; (3) the data is evaluated and care providers are notified, as needed; and (4) care providers communicate relevant data-driven insights and interventions to patients.
Remote Monitoring During the Pandemic
By making it possible to virtually perform medical activities that have traditionally been conducted in person, remote monitoring technologies have played a significant role in patient care during the Covid-19 pandemic. For example, providers such as Mount Sinai Health System in New York City, University Hospitals in Cleveland, Ohio, St. Luke’s University Health Network in Bethlehem, Pennsylvania, and Providence St. Joseph Health in Renton, Washington, started programs during the Covid-19 pandemic in order to monitor vital sign and symptom data and assess the status of coronavirus patients. Other hospitals, such as Mayo Clinic in Rochester, Minnesota, are working to set up remote patient monitoring programs for non-Covid-19 patients (e.g., as those with congestive heart failure).
New policies have recognized the importance of remote patient monitoring in this context. The U.S. Centers for Medicare and Medicaid Services expanded Medicare coverage for remote patient monitoring to include patients with acute conditions and new patients as well as existing patients. Moreover, the U.S. Food and Drug Administration issued a new policy allowing certain devices (FDA-approved non-invasive devices used to monitor vital signs) to be used in remote settings. Nonetheless, these changes remain temporary: They have only been authorized for the duration of the Covid-19 public health emergency. We hope that additional policies will be enacted to ensure that these programs can serve a variety of patients and conditions beyond the context of Covid-19.
Guidelines for Development and Implementation
These guidelines are drawn from our own experience managing remote-patient-monitoring programs, including one created specifically to care for Covid-19 patients, and research on the drivers of clinical success of established programs.
The technology must be easy for both patients and clinicians to adopt and continue using. It is essential to provide both patients and clinicians with intuitive equipment and user interfaces as well as resources for trouble-shooting when needed. Clinicians should be able to easily explain the equipment to patients, and it should be easy for patients to set up and use. The patient data generated by remote monitoring should also be simple to monitor and analyze.
This need is illustrated by a trial that studied remote monitoring of patients with congestive heart failure. In this trial, study physicians could not collect data for 12 out of 66 enrolled patients because these patients were unable to properly operate the mobile-phone-based monitoring device to begin data transmission.
The tools should be incorporated into clinician workflows. Given the high burden of administrative work that clinicians already face, it is imperative to introduce remote tools that blend seamlessly into their work processes. In some cases, this may require redesigning processes in order to ensure that remote monitoring is appropriately integrated into an organization’s practices.
For example, the administrators of a diabetes management program established at Massachusetts General Hospital found that they needed to modify the existing workflow for managing patients with diabetes in order to readily identify which patients required laboratory testing. Subsequently, the program built an application that remotely monitored diabetic patients and helped coordinate responsibilities for following up with patients about laboratory testing. This redesigned workflow improved efficiency by making it easier for nurse managers to remind patients about laboratory testing.
Sources of sustainable funding must be identified and tapped. This is especially critical at a time when hospitals are struggling financially due to the huge amount of revenue they have lost from pandemic-related cancellations and delays in performing surgeries and imaging.
Reimbursement for remote-patient-monitoring programs is challenging to navigate given that individual activities eligible for reimbursement — such as device set-up, patient education, interpretation of data, and follow-up patient conversations — are reimbursed separately. Nonetheless, reimbursement for such programs has improved with the advent of risk-based models of reimbursement such as Medicare Advantage plans and accountable care organizations, which offer providers increased flexibility in allocating capital to remote monitoring programs.
Many remote-patient-monitoring programs may have to rely on other sources of funding besides reimbursement, especially to fulfill upfront capital needs. In some instances, these sources of funding may be from the provider system’s operating budget. Internal innovation grants also may support programs. For instance, a diabetes remote monitoring program at Su Clinica Familiar, a federally qualified health center, was funded through a grant by the University of Texas System. Regardless of the nature of funding, we believe it is essential to identify a committed source of capital before establishing a remote-patient-monitoring program.
Dedicate sufficient non-physician staff to operate the program. A key reason this is necessary is that busy physicians will have difficulty carving out additional time to administer a program and sift through data. For example, Ochsner Medical Center in New Orleans developed a digital hypertension program staffed by pharmacists, who monitored 6,000 high-risk patients’ blood pressure readings remotely and followed up with patients via text and email. This program resulted in a significant increase in the proportion of patients who met their blood pressure goals.
As demonstrated by this example, it’s critical that staff in these roles are matched with the nature of the work. For instance, the complex tasks involved in hypertension-medication management might require a pharmacist or nurse as opposed to a patient navigator without clinical expertise.
Focus on digital health equity. Patients may appear to be better candidates for remote monitoring if they are younger, technologically savvy, or are fluent English-speakers. However, access to technology may be limited by poverty, and numerous other socio-demographic factors may influence engagement and participation in remote monitoring programs. At a time when the Covid-19 pandemic has disproportionately affected minority populations, care providers should go the extra mile to ensure that underserved patients not only have access to programs, but are also provided education and support needed to make them successful .
Start with an initial pilot and expand after demonstrated successes. Even in a pandemic setting in which time is of the essence, it is essential to demonstrate that remote patient monitoring initiatives improve clinical outcomes. Not all programs have demonstrated success, so the use of pilots can help avoid expensive mistakes. One successful program that scaled gradually is the Hospital of the University of Pennsylvania’s remote postpartum hypertension monitoring program. This program expanded from a small pilot to a larger clinical trial to the entire academic medical center based on evidence that it decreased admissions and costs associated with postpartum hypertension.
Covid-19 has created an opportunity to accelerate the adoption of remote patient monitoring as our health care system struggles to care for patients outside of the physical walls of a clinic or hospital. We encourage leaders to act decisively in establishing new programs by following best-in-class examples and guidelines. We believe that leaders who do so will spur a paradigm shift in how patient care is delivered that lasts far beyond the current crisis.
Faced with the Covid-19 pandemic, the healthcare delivery infrastructure in much of the United States has faced the equivalent of an impending hurricane but without a national weather service to warn us where and when it will hit, and how hard. To build a forecasting model that works at the local level, the Beth Israel Deaconess Medical Center relied on an embedded research group, the Center for Healthcare Delivery Science, that reports to the CMO and is dedicated to applying rigorous research methods to study healthcare delivery questions
The Covid-19 pandemic created an unprecedented strain on healthcare systems across the globe. Beyond the clinical, financial, and emotional impact of this crisis, the logistical implications have been daunting, with crippled supply chains, diminished capacity for elective procedures and outpatient care, and a vulnerable labor force. Among the most challenging aspects of the pandemic has been predicting its spread. The healthcare delivery infrastructure in much of the United States has faced the equivalent of an impending hurricane but without a national weather service to warn us where and when it will hit, and how hard.
To build a forecasting model that works at the local level – within a hospital’s service area, for example — the Beth Israel Deaconess Medical Center (BIDMC), relied on an embedded research group, the Center for Healthcare Delivery Science, that reports to the CMO and is dedicated to applying rigorous research methods to study healthcare delivery questions. We used a series of methods derived from epidemiology, machine learning, and causal inference, to take a locally focused approach to predicting the timing and magnitude of Covid-19 clinical demands for our hospital. This forecasting serves as an example of a new opportunity in healthcare operations that is particularly useful in times of extreme uncertainty.
In early February, as the U.S. was grappling with the rapid spread of SARS-COV-2, the virus that causes Covid-19, the healthcare community in Boston began to brace for the months ahead. Later that month, participants in a biotechnology conference and other residents returning from overseas travel were diagnosed with the new disease.
It was the start of a public health emergency. To understand how to respond, our hospital needed a Covid-warning system, just as coastal towns need hurricane warning systems. Our hospital is an academic medical center with over 670 licensed beds, of which 77 are intensive care beds. We knew it was hurricane season, but when would the storm arrive, and how hard would it hit? We were uncertain about what lay ahead.
Hurricane season — but where is the storm?
Lesson 1: National forecasting models broke down when predicting hospital capacity for Covid-19 patients because no local variables were included.
Our institution turned first to national models. The most widely used national model applied curve-fitting methods (which draw a best-fit curve on a series of data points) on earlier Covid-19 data from other countries to predict future developments in the United States. National models did not consider local hospital decision-making or local-level socioeconomic factors which dramatically impact key variables like population density, pre-existing health status, and reliance on public transportation. For example, social media data showed many student-dense neighborhoods in Boston emptying after colleges canceled in-person classes at the beginning of March, which meant fewer people were in Boston to contract the virus. Another critical variable in hospital capacity forecasting, the rate of hospitalization for people with Covid-19, varied as the weeks went on, even though national models held this variable constant. For example, early on our hospital was choosing to admit rather than send home many SARS-COV-2 positive patients, even with mild infections, because the clinical trajectory of the disease was so uncertain. Thus we needed a dynamic hyper-local model.
Building our storm alert system
Lesson 2: Local infection modeling required a range of different research methods, and the trust and commitment of operational leaders who recognized the value of the work.
The hospital turned to our research center to achieve these goals. The center, which is embedded in the hospital and reports to the Chief Medical Officer (Dr. Weiss), brought applied machine learning and epidemiological approaches to construct a hyper-local alert system.
To demonstrate the feasibility of forecasting local hospital-capacity needs for managing Covid-19 patients, we built a preliminary SIR model (a traditional epidemiological framework that models the number of Susceptible, Infected and Recovered people in a population), which was integrated into our institution’s incident command structure, an ad hoc team created with members of the hospital and disaster management leadership to respond to the pandemic. However, the accuracy of SIR models depends on the accuracy of estimates of disease characteristics such as incubation time, infectious period, and transmissibility, variables that are still not well understood. Therefore, we turned to machine learning approaches, harnessing real-time data from our electronic medical record to determine these variables directly from real patients. We also gathered Covid-patient census data from multiple hospitals simultaneously, using a common machine-learning technique called multi-task learning to capitalize on limited data. These methods allowed us to estimate when the demand for hospital capacity to treat Covid-19 patients would peak and plateau — predicting the timing to within five days of the true peak and more accurately modeling the slope of the peak and decline than national models did.
Had leadership relied on national models, they would have expected a sharper peak and decline, and a peak two weeks earlier than the actual peak. Our modeling affected key decisions, including the need to bolster personal protective equipment (PPE) supplies; to gauge the necessity of even urgent procedures, and postpone them if necessary in order assure we had the capacity to absorb the peak; and to establish staffing schedules that continued farther into the future than those originally planned.
Predicting the next hurricane
Lesson 3: Effective modeling in confusing times may require rapidly developing new methods for predicting the next storm.
Hospitals now face a difficult challenge. We need to open our doors to the patients without Covid-19 who didn’t seek care or whose care was deferred. But how do we make sure to have enough protective equipment for safely bringing back outpatient procedures? And when can nurses who had been redeployed to our ICUs return to the floors and interventional areas such as the endoscopy suite and cardiac catheterization lab? Complicating these questions is whether we will see another rise in infections with changes in state-wide policies, reopening of schools and businesses, or a coming influenza season.
In this new phase, we now need to develop methods for understanding how people will move within a community (going to school and visiting stores, for instance) and how much they will interact with one another and, therefore, affect the risk of infection over time. To this end, we constructed a risk index for local businesses by comparing pre-pandemic traffic to traffic as they reopen, and whether they are indoors or partly or entirely outdoors. Businesses where visitors are densely packed in indoor spaces, especially for longer periods, have a higher risk index — meaning they are more likely to be the site of infection spread. Using our risk index, we created and validated a model for identifying such potential “super-spreader” businesses in our service area. This analysis is part of another body of research that will undergo peer review and publication and, therefore, its results are provisional. Meanwhile, we can use our work with businesses to further inform our forecasting model by examining traffic in business locations we have identified as high-risk and assessing whether incorporating these data improves the ability of our model to predict the demand on hospital capacity.
Integrating rigorous research methods into hospital operations
Lesson 4: Given the profound future uncertainty in healthcare, small investments in trusted internal research groups that can answer operational questions with new methods can yield substantial returns.
Our institution made a prescient investment in creating an embedded and trusted research group made up of clinicians, economists, and epidemiologists studying healthcare operations. The team has brought specialized machine learning methods and expertise in extracting conclusions from messy data to quickly and accurately solve emerging real-world problems — capabilities that traditional business analytics groups are less likely to have. Other organizations can similarly unite the rigor and flexibility of methodological experts with the need to rapidly answer operational questions in dynamic and even chaotic environments.
The authors would like to thank Manu Tandon, Venkat Jegadeesan, Lawrence Markson, Tenzin Dechen, Karla Pollick and Joseph Wright for their valuable contributions to this work.
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