In the two years since Jeff Semenchuk took the reins in the newly created position of chief innovation officer for Blue Shield of California, the nonprofit health insurer with $20 billion in revenues has stepped up its investments in startup companies.
As one of California’s largest insurance providers with more than four million members, Blue Shield plays an outsized role in technology adoption among physicians, hospital networks and patients. With that in mind, and with the acceleration of entrepreneurial activity around the multitrillion health care market, Semenchuk was brought on board after serving as chief executive of Yaro (now Virgin Plus) and CIO of Hyatt Hotels and Citi Ventures.
Semenchuk said he sees Blue Shield as working to create a new health care system: “It’s not to perpetuate the health care system we have today.” Increasingly, startups have a role to play in that revisioning of health care services in America, according to Semenchuk.
“What I would say has happened over the last two years is that we have really focused on transformational innovation,” he added.
Investing in those transformational technologies involves taking cash directly from Blue Shield’s balance sheet for investments. The company doesn’t operate a corporate venture capital fund in the traditional sense, instead making strategic investments under the auspices of Semenchuk or Chief Financial Officer Robert Kolodgy.
Unless you’re an expert, there’s little difference between the Internet of Things (IoT) and the Internet of Everything (IoE). However, the latter term is broader, semantically. In this post, we’ll go into the details to explain why IoT software development companies use the term IoE comparatively rarely.
The term IoT was coined in 1999 to refer to machine-to-machine, or M2M, communication. IoE appeared a few years later, to describe interrelated elements of a whole system, including people. IoE entails not only M2M communication but also P2M (people-to-machine) and even P2P (people-to-people) communication.
To understand the differences between the three types of communication, let’s consider several examples. Say it got dark outside and you turned on a light in the office, then you sat and typed on a keyboard. This scenario provides P2M examples of IoE.
We are so used to these things that we don’t even realize they are part of a system. Another example: You make a Skype call to your colleague. That’s a simple human-to-human, or P2P, communication. An example of M2M communication, on the other hand, is the process of data exchange between your office temperature sensing devices and the HVAC mainframe.
You might think M2M communication, being technological, is the most progressive means of interaction. but IoE focuses on P2M and P2P interactions as the most valuable. According to aCisco analysis, as of 2022, 55% of connections will be of these two types.
IoE is now considered the next stage of IoT development. Maybe this is why there are so few IoT development companies offering IoE development services at the moment.Internet of Things solutions are now more common and widespread.
4 Main Elements of the IoE Concept
By thing, we mean an element of the system that participates in communication. A thing is an object capable of gathering information and sharing it with other elements of the system. The number of such connected devices, according to Cisco, will exceed 50 billion by 2020.
What are things? In the IoT, a thing could be any object, from a smart gadget to a building rig. In the IoE, that expands to include, say, a nurse, as well as an MRI machine and a “smart” eyedropper. Any element that has a built-in sensing system and is connected on a network can be a part of the IoE.
People play a central role in the IoE concept, as without them there would be no linking bridge, no intelligent connection. It is people who connect the Internet of Things, analyze the received data and make data-driven decisions based on the statistics. People are at the center of M2M, P2M, P2P communications. People can also become connected themselves, for example, nurses working together in a healthcare center.
In 2020, it’s projected that everyone using the internet will be receiving up to 1.7 MB of data per second.
As the amount of data available to us grows, management of all that information becomes more complicated. But it’s a crucial task because, without proper analysis, data is useless. Data is a constituent of both IoT and IoE. But it turns into beneficial insights only in the Internet of Everything. Otherwise, it’s just filling up memory storage.
Process is the component innate to IoE. This is how all the other elements — people, things, data — work together to provide a smart, viable system. When all the elements are properly interconnected, each element receives the needed data and transfers it on to the next receiver. The magic takes place through wired or wireless connections.
Another way to explain this is that IoT describes a network and things, while IoE describes a network, things, and also people, data, and process.
Where Is IoE Applied?
As to the market, we can say confidently that IoT is a technology of any industry. IoE technology is especially relevant to some of the most important fields, including (1) manufacturing, (2) retail, (3) information, (4) finance & insurance, (5) healthcare.
IoE technology has virtually unlimited possibilities. Here’s one example: More than 800 bicyclists die in traffic crashes around the world annually. What if there was a way to connect bike helmets with traffic lights, ambulances, and the hospital ecosystem in a single IoE. Would that increase the chances of survival for at least some of those cyclists?
Another example: Do you realize how much food goes to waste, say at large supermarkets, because food isn’t purchased by its best-before date? Some perishable products like fruit and vegetables are thrown away due to overstocks even before they get to the market. What happens if you find a way to connect your food stocks with the racks and forklifts of the supermarket in-stock control system using IoE?
There are endless variations on uses of IoE right now, and many of them are already becoming familiar in our “smart” homes.
In our industry, few would deny the value of IoE in improving our standard of living. Luckily, there’s a flourishing market of IoT development services. Who knows, maybe one day soon, you’ll be a “thing” in the IoE environment.
We are living through an unprecedented crisis. During the COVID-19 pandemic, healthcare workers have emerged as frontline heroes, working overtime to protect our communities from the spread of novel coronavirus. But they aren’t immune to the anxious, uncertain atmosphere the pandemic has fostered nor, indeed, the coronavirus itself.
We need to protect the first responders and hospital staff who put their wellbeing on the line to support their communities during a crisis. To my mind, that means using every tool at our disposal to the fullest — with AI chief among those at hand.
There’s little doubt that the current situation demands a creative solution. The United States has become the center of the global pandemic; as of April 16th, the US confirmed 644,188 cases and endured 28,579 deaths. Despite efforts to flatten the curve by ordering regional shut-downs and stay-at-home orders, hospitals across the county have been all but overwhelmed by incoming cases. The impact on provider morale has, according to reporting from NPR, been similarly problematic.
“Nearly a month into the declared pandemic, some health care workers say they’re exhausted and burning out from the stress of treating a stream of critically ill patients in an increasingly overstretched health care system,” NPR reporters Will Stone and Leila Fadel recently wrote. “Many are questioning how long they can risk their own health […] In many hospitals, the pandemic has transformed emergency rooms and upended protocols and precautions that workers previously took for granted.”
Hospitals are doing all they can to keep their caregivers safe and protected, but their resources are stretched far too thin. According to reports, some hospitals in high-infection areas like New York City can only afford to give healthcare workers one N95 mask every five days. Used masks are collected, disinfected, and returned on a cycle between uses. But some frontline workers worry that, given the highly contagious nature of the disease, they may not be adequately protected.
“It can be disheartening to have that feeling of uncertainty that you are not going to be protected,” Sophia Rago, an ER nurse based in St. Louis, told reporters for NPR.
We need to shield our frontline workers as much as possible. The obvious solution would be to increase stores of personal protective equipment (PPE) and N95 masks; however, given that we face a national shortfall and harsh state-to-state bidding wars over the gear, that fix seems unlikely. What we can do to at least lessen the risk of patient-to-provider transmission is to invest in AI-powered solutions that can automate some healthcare protocols and limit the need for close contact.
“Traditional processes — those that rely on people to function in the critical path of signal processing — are constrained by the rate at which we can train, organize, and deploy human labor. Moreover, traditional processes deliver decreasing returns as they scale,” a team of digital health researchers recently wrote in an article for the Harvard Business Review.
“Digital systems can be scaled up without such constraints, at virtually infinite rates. The only theoretical bottlenecks are computing power and storage capacity — and we have plenty of both. Digital systems can keep pace with exponential growth.”
These AI-powered, digitally-facilitated solutions generally fall into two broad categories: disease containment and patient management.
Assessing AI’s Ability to Limit Disease Transmission
When it comes to limiting disease spread, the aim is to use AI tools to allocate human resources better while still protecting patients and staff. Take the screening system that was recently deployed at Tampa General Hospital in Florida, for example. This AI framework was designed by the autonomous care startup Care.ai and intended to facilitate early identification and interception of infected people before they come into contact with others. According to a report from the Wall Street Journal, the Care.ai tool taps into entryway cameras and conducts a facial thermal scan. If the system flags any feverish symptoms such as sweat or discoloration, it can notify healthcare staff and prompt immediate intervention.
Other technology companies––Microsoft, for one––have rolled out similar remote diagnostic and alert tools in facilities across the globe. Their unique capabilities vary, but their purposes are the same: to prevent the spread of infection and provide support to overworked personnel.
As representatives for Microsoft shared in a recent press release, “[AI technology] not only improves the efficiency of epidemic prevention, but it also reduces the work burden of frontline personnel so that limited human resources can be used more effectively.”
In these resource-strapped time, the aid is undoubtedly needed.
AI’s Applications for Diagnostics and Patient Management
Fighting a pandemic is a task that requires speed. Now more than ever, providers must be able to accurately and quickly identify infected patients so that they can trace and hopefully contain the viral spread. But doing so isn’t an easy order.
To borrow a quote from Forbes contributor Wendy Singer, “Analyzing test results nowadays requires skilled technicians and a lot of precious time, as much as a few days. But in our current reality, healthcare systems need to analyze thousands of results instantly, and to expose as few lab workers as possible to the virus.”
We don’t have that kind of time––and we can’t put our lab workers at undue risk. Thankfully, cutting-edge AI technologies may provide a solution. With AI, hospitals can automate some steps of the testing process, cutting down on the time and effort needed to process test results. These capabilities aren’t just hypothetical; in the weeks since the start of the pandemic, the health tech startup Diagnostics.ai has provided laboratories in the US and UK with a diagnostic tool that streamlines the testing process by automating DNA analysis.
However, the applications of AI diagnostics aren’t limited to testing alone. Some have also used artificial intelligence to support population management in overstretched hospitals. One Israeli medical-device developer, EarlySense, recently developed an AI-powered sensor that can identify which patients will most likely face complications like sepsis and respiratory failure within six to eight hours. This can give a hospital the information it needs to best allocate limited resources and staff attention.
No AI innovation — no matter how brilliant or helpful — will fix our resources shortfall. There is no question that healthcare providers need more PPE and support, or that they need it immediately. However, the benefits that AI provides to screen and patient management efforts are evident. It seems reasonable that we at least consider the weight the deployment of such tools could remove from our exhausted front-liners’ shoulders.
The COVID-19 pandemic has exposed weaknesses around the US healthcare system and resulted in a large amount of uncertainty around the healthcare and health insurance costs for the pandemic, says GlobalData, a leading data and analytics company.
As the US does not provide universal healthcare to its citizens, and with more than 30,000,000 citizens who have recently become unemployed, many have lost their employer-provided healthcare and are facing tough economic decisions about covering their healthcare costs out-of-pocket. Additionally, health insurers are facing uncertainty about the costs of the pandemic and the rates they will need to charge.
Johanna Swanson, Product Manager at GlobalData, comments: “In the US, most citizens receive health insurance through their employer, but the high levels of unemployment have resulted in significant numbers of people losing their coverage. Those unemployed can continue to receive health insurance by using coverage from the Consolidated Omnibus Budget Reconciliation Act (COBRA) or applying for Medicaid, but COBRA coverage can be costly as employers are often paying an average of nearly 82% of the cost of their employees’ health insurance.”
The US Government is considering helping employers cover the cost of COBRA, so future rounds of financial support measures could include this assistance. Also, states that did not expand Medicaid under the Affordable Care Act may reconsider due to COVID-19 in order to receive more funding for their unemployed population.
Swanson adds: “Healthcare providers and insurers face uncertainty around the costs and rates that will be caused by the pandemic. It remains unclear what the true cost of the pandemic will be for the health insurance industry. It is expected to reach billions of dollars, but estimates vary widely.
“Health insurers need to submit 2021 rates for approval in June 2020. These rates must be based on expected future costs, which has proven challenging due to the current level of uncertainty. Many insurers have indicated that they will not increase rates for 2021, but it remains to be seen how they will mitigate the high costs of COVID-19. Additionally, healthcare companies may be facing uncertainty about the rules and regulations on obtaining federal pandemic relief funds. This leaves a large amount of uncertainty around the costs for the COVID-19 pandemic.”
Data is one of the best tools we have for fighting the Covid-19 outbreak, but right now health data — like consumer data — is held in silos in many different institutions and companies. And while third parties can track, trade, and negotiate that data, the people who create it and who have the biggest stake in it, are often cut out of the deal. Their virtual self doesn’t belong to them, which creates problems of access, security, privacy, monetization, and advocacy.
Blockchain can be used to solve these issues, by putting individuals in control of their data, which would be encrypted and and stored in a distributed network that no entity owned. Putting people in control of their data, and their health data in particular, would allow them to control who has access to it, and what they’re allowed to do with it. It would also allow secure sharing of data for critical public health purposes, such as contract tracing, without compromising privacy. It’s time that we reclaim our data as an asset that we create, and which we should both control and benefit from. Healthcare data is a perfect place to start.
Big data is perhaps the most powerful asset we have in solving big problems these days. We need it to track and trace infection, manage healthcare talent and medical supply chains, and plan for our economic futures.
But how can we balance data and privacy? Legislation and regulation of big data such as the European Union’s General Data Protection Regulation (GDPR) and California’s Consumer Privacy Act are partial measures at best. Regulators and pundits have focused so much on the demand side of the data equation — that is, on the use or sale of private citizens’ data in corporate applications like Facebook,Google, and Uber without the individuals’ awareness — that they’ve failed to look at the supply side of data: where data originates, who creates it, who really owns it, and who gets to capture it in the first place.
The answer is you do. All these data are a subset of your digital identity — the “virtual you,” created by your data contrail across the Internet. That’s how most corporations and institutions view you. As Carlos Moreira, CEO of WISeKey, said, “That identity is now yours, but the data that comes from its interaction in the world is owned by someone else.”
It’s time we started taking our personal data as seriously as the top tech firms do. We need to understand its real value to us in all aspects of our lives. Blockchain technology can help us do that, enabling us to use our data proactively and improve our well-being. And while there are many areas where taking control of our data might improve our lives, there is one particularly promising place to start: healthcare data.
Why should we care about our health data?
“Imagine if General Motors did not pay for its steel, rubber, or glass — its inputs,” economist Robert J. Shapiro once said. “That’s what it’s like for the big Internet companies. It’s a sweet deal.” It’s also a real conundrum for business leaders who want as much data as they can get for their enterprise, yet truly value privacy and individual freedom. Consider the tradeoffs we’re making as individuals:
We can’t use our own data to plan our lives and long-term healthcare: our treatment plans, the pharmaceuticals and medical supplies we use, our insurance or Medicare supplements, or how we use our health savings accounts. All these data about us reside in other people’s silos — in the separate databases of myriad healthcare providers, pharmacies, insurance companies, and local, state, and national agencies — which we can’t access but third parties like the American Medical Collection Agency (AMCA) can, and often without our knowledge.
We enjoy none of the rewards of this data usage, yet bear most of the risk and responsibility for its clean up if it’s lost or abused. In 2019, AMCA was hacked, and the hackers made off with the personal data of some 5 million people whose lab tests were handled by AMCA’s clients Quest Diagnostics, LabCorp, BioReference Lab, and others. None of these clients have to deal with the tsunami of fraud alerts and bespoke phishing scams aimed at patients. Yet, unlike Alectra, Amazon, or Tesco, these parties aren’t using our data to improve our healthcare outcomes or cut our costs. To us, this is data malpractice.
We can’t monetize or manage these data assets for ourselves, family, or heirs — think of Henrietta Lacks, whose cancer cells revolutionized the development of cancer treatment without her knowledge— resulting in a bifurcation of reputation, wealth, and all its discontents. Those who lack access to the Internet altogether may not have data profiles or privacy problems per se, but they often don’t have official identity cards, home addresses, or bank accounts either, and so they can’t participate in the global economy. These aren’t people without papers. These are people without data.
Our privacy is at risk all the time, as is our family’s. The Chinese government used mass surveillance to gain some measure of control over the spread of Covid-19, tracking data about who specifically was infected, where they lived, when they were infected, when they recovered, how were they infected, whether they sheltered in place, what temperature they had when they went outside, and who else they contacted. Privacy is the foundation of freedom, and while sometimes — perhaps in a pandemic — we may choose to trade on this privacy for the social good, the trouble is that once the crisis is over, we have no way to reclaim or mask our data.
We can’t develop or contribute to the proposed health policies of elected officials, we can’t effectively advocate for the changes our family needs, and we can’t collectively bargain with other patients or powers of attorney to lower costs or improve delivery — yet every other party in the system can do all these with our data, not just negotiating coverage and rates with governments but lobbying them for industry-favorable regulations. The Pharmaceutical Research and Manufacturers of America alone spent a record $27.5 million on lobbying in 2018, with individual companies supplementing these efforts to the tune of $194.3 million.
How patient control over health records could expedite data for treatments
Pioneers like Canada’s University Health Network (UHN) have come up with a win-win solution using blockchain technology, a software that operates as a shared ledger distributed across computer devices connected to a communications network. What sets this type of ledger apart from the interfaces to conventional databases or health record repositories is a) its decentralization, which means we can control transactions involving our data peer to peer, and b) its immutability, in that no one else can alter or undo those transactions behind the scenes or without a majority of the network’s approval.
In 2018, UHN launched a patient control-and-consent platform to enhance the patient experience and to facilitate clinical research using patient data. Designed after workshops with different stakeholder groups and developed in partnership with IBM, the platform leverages blockchain not simply to secure and consolidate patient data across the network, but also to obtain and record patient consent before any information is shared with researchers. When patients consent, the software automatically encrypts and records details of the consent transaction on the shared ledger. The platform also records which parties accessed the data, at what time, and for what purpose.
This kind of functionality can be expanded to uses such as contact tracing. Imagine a scenario where the UHN solution is interconnected to healthcare facilities across Canada, so that every Canadian patient had an opportunity to share personal data, including location over time. With such “a platform for reporting, tracking, and notifying that is global in nature and respects privacy,” said Brian Magierski of the Care Chain collaboration, we can “identify new cases rapidly and verify those who have immunity.” To that effect, the start-up Workwolf has invited the Canadian government to use its proprietary blockchain for tracking Covid-19 cases, immunity or resistance, and test results. And Vital Chain is turning clinically certified results into blockchain-based health and safety credentials for employees to prove their fitness for returning to work.
If we applied these capabilities at a global scale, we could capture a single, comprehensive account of global incidence rates and outcomes that was verified and secure. That’s what the start-up Hacera is trying to do. With the support of IBM, Microsoft, Oracle, the Linux Foundation, and others, it launched MiPasa, an initiative to integrate, aggregate, and share information at a global scale from multiple verified sources — from the Center for Disease Control or the World Health Organization, but also hard-to-get data from local public health agencies, licensed private facilities, and even individuals — all without personal identifiers. MiPasa onboards data providers through Hacera’s Unbounded network, a decentralized blockchain powered by Hyperledger Fabric, and then streams data using the IBM Blockchain platform and IBM Cloud. Hacera has developed a tutorial for coders to build applications on top of the platform. This kind of value creation is the gigantic incentive needed to rally numerous institutions so that we can trace people’s exposure to infected individuals, reduce transmissions, save lives, and put more people back to work.
Finding a Covid-19 vaccine is a top priority. To accelerate discovery, the blockchain start-up Shivom is working on a global project to collect and share virus host data in response to a call for action from the European Union’s Innovative Medicines Initiative. Shivom scientists formed a global Multi-Omics Data Hub Consortium comprised of universities, medical centers, and companies, many of which have expertise in AI and blockchain, all for combatting coronavirus infections. The consortium’s data hub is based on part of Shivom’s blockchain-based precision medicine platform. Founded by Dr. Axel Schumacher, Shivom’s platform uses blockchain not only to manage patient consent dynamically but also to share genomic data and data analysis securely and privately with third parties anywhere, without providing access to raw genomic data. Dr. Schumacher said that researchers “can run algorithms over the data that provide summary statistics to the data sets. No individual, de-identifying data can be obtained without the explicit consent of the patient.”
Transitioning to this self-sovereign future
To realize this future, we need to address the real problem: that you don’t own your virtual self. Each of us needs a self-sovereign and inalienable digital identity that is neither bestowed nor revocable by any central administrator and is enforceable in any context, in person and online, anywhere in the world. Until blockchain, we didn’t have the technological means to assert such sovereignty. Now the technical groundwork has been laid. Organizations are looking at how to deploy it in public key infrastructure, how to separate identification and verification from transactions, and how to expand the use of smart contracts, zero-knowledge proofs, homomorphic encryption, and secure multiparty computation.
Imagine having a digital identity that you stored in your digital wallet on a blockchain. Your wallet collects and protects all your biological, financial, and geospatial data throughout the day, and you decide how you want to use it. Your medical records are central to this identity. Your body generates health data. You, not big companies or governments, have a heart rate and a body temperature. When clinicians measure you or take tests of various kinds, they’re providing a service; the results are your asset, deriving from your body. You should control it.
What we’re shooting for is a wholesale shift in how we define and assign ownership of data assets and how we establish, manage, and protect our identities in a digital world. Change those rules, and we end up changing everything.
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The Covid-19 pandemic presents the U.S. health care system with a mind-boggling array of challenges. One of the most urgent is coping with a simultaneous glut and dearth of information. Between tracking outbreaks, staying abreast of the latest information on effective treatments and vaccine development, keeping tabs on how each patient is doing, and recognizing and documenting a seemingly endless stream of weird new symptoms, the entire medical community is being chronically overwhelmed.
Sorting through large amounts of information and finding the nuggets that apply to a particular patient’s situation is something that computers ought to be good at. But we still have problems of knowing what data is important and what is the right treatment and prevention plan for each patient.
During the Obama administration, the federal government supplied billions of dollars — and providers kicked in billions more — to speed the adoption of electronic health records. But even though up to 96% of hospitals and 86% of physician offices have adopted them, we still don’t have EHRs that can rise to the information challenges that clinicians face every day, let alone those posed by Covid-19.
Providers still encounter continual frustration on many levels: user interfaces and usability issues, the quality of the data entered, the limited ability of the data to support discovery and interoperability among systems, just to name a few. These limitations have compounded the ability of clinicians to deliver care during the Covid-19 crisis.
An overhaul of the electronic health record is overdue. It must go beyond fixing the user interface or improving interoperability. It must address the fundamental problems exposed by the pandemic. The overhaul must also support the ability of providers to adopt the new value-based-care business model of health care — one that rewards providers for outcomes rather than the volume of services and that shifts their focus from reactive sick care to the proactive management of health.
To address these needs, the electronic health record must transition from an emphasis on a person’s medical record to an emphasis on a person’s plan for health and from a focus on supporting clinical transactions to a focus on delivering information to the provider and the patient.
From the Record to the Plan
A redesign of the EHR is essential, but what should it look like? EHRs are reasonably good at the “record” part — keeping track of what happened to the patient — but they must evolve to address the “health” part by helping providers plan for what they want to happen. EHRs could become tools for making those plans and keeping them on track if we design them with that goal in mind.
Intermountain Healthcare, Virginia Mason, and Kaiser Permanente are pioneers in adopting the new health care business model. Their experiences point the way to the next generation of EHRs.
What would a “plan-centric” EHR system look like? It would include:
A library of care plans that covers a wide range of situations. Variations in patient circumstances and preferences would dictate variations in the plans. A patient with well-managed diabetes would have a different plan from one who is still struggling for control. A patient who lived alone would have a different plan from one who lived with a large, supportive family.
Algorithms to form a patient’s master plan. Patients hardly ever have just one clear, manageable issue. A master plan would combine appropriate algorithms for treating, say, a patient’s asthma, arthritis, depression, and weight reduction, automatically resolving conflicts and redundancies.
Care team support. Each team member — the patient’s primary care provider, specialists, nurse practitioners, pharmacists, case managers and the patient — would see both the master plan and their own to-do list. Team members could assign tasks to one another.
The ability to traverse care settings, geographies, and different EHRs. The plan would need to travel seamlessly with the patient. Providers would have interoperable systems that could integrate a patient’s plan regardless of its origin.
Decision support and workflow logic.The system must remind team members of upcoming and overdue activities, suggest changes in the plan when patient conditions and care needs change, and route messages to the appropriate team member regarding new test results or patient events.
Analytics for both individual patients and populations. The system must be able to assess how well the plan is achieving its goals, both for the individual patient and for the larger population that may be under the provider’s care. It should be able to apply lessons learned in treating one patient to other patients.
Imagine a plan-centric EHR ready to deal with Covid-19, incorporating the latest evidence-based treatments into each patient’s care plan based on their current status and underlying health conditions, and then feeding back data on how each patient responded in order to improve the plan for the next patient. Such capabilities could transform outcomes and save many lives.
From Transaction-Oriented to Intelligence-Oriented
As befits systems with origins in billing, the design focus of EHRs has been transactional: documenting a visit, retrieving a lab result, sending a prescription to the pharmacy. This focus is not all bad: It has reduced some types of errors and made it easier to generate work lists and logic to help ensure that the clinical order is complete.
However, exquisite transaction support is not enough to address the challenges that afflict care delivery: failure to follow the evidence, brittle operational and clinical processes, and the near impossibility of keeping up with advances in medicine. EHRs can compound these limitations by being very difficult to update.
We must reimagine the EHR not as a document but as a system that supports the generation and tracking of multiple documents, events, and processes. It must surround each transaction and clinical process with intelligence to ensure clinical appropriateness and sound execution.
It should help ensure that care follows the evidence, identify treatment options that result from the dazzling pace of medical discovery, and alert providers that care processes have deviated from acceptable levels of performance. This intelligence must detect acts of commission (the choice of an outdated treatment approach) and omission (a patient has failed to keep an appointment to see a specialist).
The EHR must provide the ability for clinicians to easily analyze patient data to discover new treatments, uncover safety issues, and identify unusual clinical findings. Such capabilities would have enabled, for example, the much faster discovery of blood clotting in Covid-19 patients.
Intelligence can be leveraged to help address clinician concerns with EHR usability. Logic that presents the physician with data and potential actions tailored to reflect the patient’s conditions, the physician’s preferences, and the medical evidence can save the physician time and improve the quality of care.
Many of these intelligence and plan capabilities are present to some degree in today’s EHR. However, the old fee-for-service business model has not rewarded their refinement and extensive use.
Some providers — including Kaiser Permanente, Geisinger, Intermountain Healthcare, and UPMC — are using their EHRs in this way, but these organizations share a key characteristic: They insure a significant percentage of their patients as well as providing their care, and therefore their financial incentives are more like those of payers. They have already embraced the new value-based-care business model that the rest of the industry is moving toward.
Achieving the Intelligent, Plan-Centric IT Foundation
We will always need medical record documentation and transaction capabilities. An accurate, comprehensive health record is critical to the delivery of care and is also a required legal document.
How can we preserve these functions of the EHR while migrating it to the new intelligent, plan-centric design?
One major obstacle to fixing the EHR problem is that the health care industry is in the middle of a transition to the new business model. But the change is happening so gradually — in fits and starts, depending on the payer and the political environment — that it’s difficult for providers and EHR vendors alike to gauge the appropriate moment for a system redesign. Consequently, providers will have to juggle two opposing business models for an unknown period of time, and their information technology portfolio will have to support both.
Exasperated users might support the idea of tossing out what we have and starting over from scratch, but that’s not going to happen for a number of reasons: the stupefyingly large cost, the enormous development and implementation time, the disruption of operations, and the potential danger to patients during the transition.
Health care should take a lesson from banking. Instead of rewriting legacy systems, the banking community modified current systems, added complementary applications, and “wrapped” legacy systems with newer technologies and capabilities. To transform the EHR from a (quasi) document into the new design, we need a full array of complementary applications that “wrap around” appropriately modified EHRs and provide significant care-plan and intelligence support. Providers can make these investments as needed to match the pace and address the specific needs of their migration to value-based care and measure the return on investment as they go. These applications and capabilities might include the following:
Population health management. Providers will be accountable for the health and health care of populations of people with common health conditions such as diabetes and asthma. Population-health-management systems combine data from diverse sources (EHRs, claims, patient-monitoring devices, census, and other demographic databases that can track social determinants of health). The population-health-management systems “surround” the EHR so that the provider can view the plan from the record and the population health management system can send alerts and messages to the EHR inbox.
Health information exchanges. Connecting a wide variety of health care organizations in a region or state, the HIE enables them to exchange data about a patient. For example, when a patient presents at an emergency room, the care team can use the exchange to retrieve patient data from other care settings and get a complete clinical picture of the patient. Some HIEs have developed applications that measure regional care quality and costs, portals that enable patients to see their aggregated clinical data, and alert systems that tell a provider when one of its patients has been seen elsewhere.
Patient-health-management applications. These enable consumers to aggregate their health data, view their health status, track their appointments and prescription refills, converse with their care team, participate in care communities, view and alter their shared care plan, and research health issues.
Big data analytics systems. These aggregate very large amounts of health and health care data to compare the effectiveness of treatments, identify medication and device safety problems, facilitate medical discovery, and analyze shifting patterns of patient characteristics and diseases. Artificial intelligence can be used to support automatic correction of data inconsistencies and extraction of data from images, sound, and free text: for example, going through free text and pulling out quality measures or problems that were not previously in a patient’s problem list.
Many health systems have begun to adopt the strategy of surrounding their EHRs with the next generation of intelligent, plan-centric capabilities. As the health care business model evolves, organizations such as Mass General Brigham (formerly Partners HealthCare), Memorial Hermann, Geisinger, CommonSpirit Health, and Cedars Sinai are vigorously implementing population health, big data analytics, and patient-health-management applications.
Health care delivery is in the early stages of an extraordinary change. This change is being driven by the relentless movement to the value-based care model and the problems exposed by the Covid-19 crisis. This ongoing transformation is paving the way for a new EHR design: a platform that fuses the current EHR with complementary systems, capabilities, and technologies.
Achieving the intelligent, plan-centric health care platform will require a level of industry cooperation that is unlike, and in some ways antithetical to, the way we’ve always done things. The pandemic has shown us health care collaboration at its best. In that respect, the response to the pandemic mirrors the new business model that we are trying to build.
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Hims & Hers, the startup focused on providing access to elective treatments for things like hair loss, skin care and erectile disfunction and online telemedicine services, is expanding its services to include a Spanish language option, the company said.
After Mexico, the U.S. has the second-largest Spanish speaking population in the world, with an estimated 41 million U.S. residents speaking Spanish at home. The population also prefers to receive healthcare information and frequent facilities that offer resources in Spanish.
Now, with a shortage looming in primary care physicians for rural areas and inner cities and a sky-high rate of Hispanics living without any form of healthcare coverage (roughly 15.1%, according to data provided by the company), Hims & Hers is pitching its telemedicine offering as an option.
“Language, cost, and location should not be barriers to receiving quality care, which is why we are launching a Spanish offering on our telemedicine platform,” the company said in a statement.
The company’s $39 primary care consultations at its Hims and its Hers websites will be in Spanish. That will include everything from communications like the patient intake form and instructions to prepare for an online consultation along with a connection to Spanish-speaking healthcare provider.
“The reason we created Hims & Hers was to break down barriers and provide more people with access to quality and convenient care,” the company’s co-founder and chief executive, Andrew Dudum, said in a statement. “As a telemedicine company, we recognize the need and understand the importance of serving the Spanish-speaking population. We hope those seeking access to care in Spanish find our platform to be a welcoming, inclusive, quality experience.”
The company’s pitch is that data scientists, clinical doctors, academics and pharmaceutical companies can all log in to the virtual lab that Owkin calls the Owkin Studio.
In that virtual environment, all parties can access anonymized data sets and models exclusively to refine their own research and development and studies to ensure that the most cutting-edge insights into novel biomarkers, mechanisms of action and predictive models inform the work that all of the relevant parties are doing.
The ultimate goal, the company said, is to improve patient outcomes.
In its quest to get more companies and institutions to open up and share information — with the promise that the information can’t be extracted or used in a way that isn’t allowed by the owners of the data — Owkin is replicating work that other companies are pursuing in fields ranging from healthcare to financial services and beyond.
The Israeli company Qedit has developed similar technologies for the financial services industry, and Sympatic, a recent graduate from one of the recent batches of Techstars companies, is working on a similar technology for the healthcare industry.
Owkin makes money by enabling remote access to the data sets for pharmaceutical companies and licensing the models developed by universities to those companies. It’s a way for the company to entice researchers to join the platform and provide another revenue stream for research institutions who have seen their funding decline over the last 40 years.
“We have a huge loop of academic universities that have access to the data and are developing algorithms and we share data,” said the company’s chief executive Dr. Thomas Clozel. “At the end what it helps is developing better drugs.”
Declines in federal funding for scientific research since the 1980s (Image courtesy of The Conversation)
The investment from Owkin’s new and existing investors takes the company to $55 million in total capital raised through the extension of its Series A round. In all, the round totaled $52 million, Clozel said.
“We are exactly where we need to be because it’s about privacy and privacy is more important than ever before,” said Clozel.
The COVID-19 epidemic has emphasized the need for closer collaboration among different corporations and research institutions, and that has also increased demand for the company’s technology. “It touches everything… We have access to the right data sets and centers to build the best models for COVID,” said Clozel. “We’re lucky to have the right traction before the COVID happens and we have the right research that has been done.”
In fact, the company has launched the Covid-19 Open AI Consortium (COAI), and is using its platform to advance collaborative research and accelerate clinical development of effective treatments for patients infected with the coronavirus, the company said. All of its findings will be shared with the global medical and scientific communities.
The initial focus on the research is on cardiovascular complications in COVID-19 patients in collaboration with CAPACITY, an international registry working with over 50 centers worldwide, the company said. Other areas of research will include patient outcomes and triage, and the prediction and characterization of immune response, according to Owkin.
“Since we first backed Owkin in 2017, we have been sharing its vision to apply AI to fighting one of the most dreadful diseases on earth: cancer,” said Jacky Abitbol, a partner at Cathay Innovation. “Owkin has risen to become a leader in digital health, we are proud to grow our investment in the company to fuel its ambition to pioneer AI for medical research, while preserving patient-privacy and data security.”
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