It’s common for many people to fear the unknown, and exactly how artificial intelligence might transform the health care and medical experience is no exception. News Special: AI in Healthcare
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No, Robots Are Not Taking Over
People might be afraid, for example, that AI will remove all human interaction from health care in the future. Not true, say the experts. Doctors and other health care workers might fear the technology will replace their clinical judgment and experience. Also not true, experts say.
The AI robots are not taking over.
AI and machine learning remain technologies that add to human know-how. For example, AI can help track a patient over time better than a health care professional relying on memory alone, can speed up image analysis, and is very good at prediction.
But AI will never replace human intuition in medicine, experts say.
“AI is unemotional. It’s fast and very, very smart, but it does not have intuition,” says Naheed Kurji, board chair of the Alliance for Artificial Intelligence in Healthcare and CEO of Cyclica Inc.
Machine learning, a form of artificial intelligence where a computer learns over time as it gets more and more data, could sound threatening to a person who might not fully understand the technology. That’s why education and greater awareness are essential to ease any concerns about this growing technology.
“You need to have an understanding of human behavior and how to help people overcome their inherent fears of something new,” Kurji says.
All this new science needs to be explained to the public, and machine learning is certainly one that deserves explanation,” says Angeli Moeller, PhD, head of data and integrations generating insights at Roche in Berlin, and board vice chair for the Alliance for Artificial Intelligence in Healthcare.
“It’s useful to ground it in examples that the general population is familiar with and with technology that has grown,” she says. “On our smartphones, we benefit from a significant amount of machine learning – even if you just look at your Google search or your satellite navigation system.”
Moeller says it’s helpful to think of AI as an assistant to a doctor, nurse, a caregiver, or even a patient trying to understand more about a medical diagnosis, treatment plan, or prognosis.
Also, with big data comes big responsibility. “Health care industry accountability is important,” she says.
With than in mind, the Alliance for Artificial Intelligence in Healthcare was created in 2019 as a forum for industry players – drug companies, biotechnology firms, and database entities – to convene and address important AI questions. The group seeks to answer some fundamental questions, including: How do we ensure that we have ethical and appropriate use of artificial intelligence in health care? How do we make sure that that innovation gets to the patient as quickly as possible?
“If you think about your personal life, a decade ago, your car didn’t have autopilot modes where it drove itself,” says Sastry Chilukuri, co-CEO of Medidata and founder and president of Acorn AI. “You didn’t really have an iPhone – which is like a computer in your hand – much less like have an Apple Watch – which is like another minicomputer on your wrist pumping out all kinds of data.”
“Our world has dramatically changed over just like the last 15 years,” he says. “It’s very interesting, I think. It’s a good time to be alive.”
August 17, 2022, U.S. Centers for Disease Control and Prevention director Dr. Rochelle Walensky publicly admitted the agency’s COVID response “fell short,” and that an internal reorganization has been launched to improve response times and data sharing, and to make health guidance easier to understand. Six days before this announcement, the CDC published updated COVID guidance, now matching what “misinformation spreaders” have called for all along
The vow to revamp fails to take into account the real reasons why people no longer trust the CDC, namely their dishonesty, their suppression of science that doesn’t fit the Great Reset narrative, and their protection of Big Pharma at the expense of American lives, including children and pregnant women
Despite widespread failures and blatant corruption within the CDC, the National Institutes of Health and the Food and Drug Administration, the Health and Human Services’ (HHS) is asking for a bigger budget. Its proposed budget for 2023 is $1.7 TRILLION in mandatory spending and another $127.3 BILLION in discretionary spending
Of those budgeted trillions, the CDC will in 2023 receive about 1% of the HHS budget, or $10.6 billion — $2.3 billion more than its 2022 appropriation — and this includes “mandatory funding to establish a Vaccines for Adults program”
The CDC being wrong on everything about the pandemic — and taking two and a half years to admit even a fraction of it — is proof positive that centralizing health care decisions is a bad idea. Ideally, all that HHS money should be divided among the states. We’d be far better off with local community programs handling current HHS services — including pandemic response
After botching the COVID response in every possible and improbable way, the U.S. Centers for Disease Control and Prevention now wants more money — and more power.
August 17, 2022, CDC director Dr. Rochelle Walensky publicly admitted the agency’s COVID response “fell short,” and that an internal reorganization has been launched to improve response times and data sharing, and to make health guidance easier to understand.1,2
“My goal is a new, public health action-oriented culture at CDC that emphasizes accountability, collaboration, communication, and timeliness,” Walensky said in a statement.
The problem is that reorganization will not fix the foundational problem, which is that the CDC can’t seem to quit protecting Big Pharma the expense of public health. Americans have lost faith in the CDC for the simple reason that it’s been lying to us day in and day out for two and a half years.
They’ve flouted basic rules and regulations, they’ve redefined well-established medical terms to suit the chosen narrative, they’ve made recommendations without scientific support while telling us to “trust the science.” They’ve completely ignored massive, unprecedented safety signals for both the COVID jabs and remdesivir, flatly refusing to answer questions about the mounting deaths and injuries from these drugs.
They’ve refused to take into account other public health parameters such as suicides and alcoholism caused by lockdowns, and deaths due to lack of treatment for chronic conditions such as heart disease, diabetes and cancer. They’re also refusing to address what is clearly deteriorating immune function among the COVID-jabbed. The list goes on.
What’s more, she admitted her source for the “95% effective” claim was a CNN report (which in turn pulled its information directly from a Pfizer press release). That’s the level of data gathering for decision making we’re dealing with here.
I guess that’s what happens when the vast majority of CDC employees, including Walensky herself, work from home for years on end. Considering Walensky STILL works from home to this day,3,4 one also wonders how effective these supposed reorganization efforts can actually be.
The CDC is a wholly captured agency, beholden to Big Pharma, and as long as a single decision maker remains, they can reorganize and restructure to their hearts’ content. It won’t change a thing. As noted by The Defender,5 the CDC needs to be replaced with “a public health model that operates independently from Big Pharma.”
Health and Human Services to Get $1.7 Trillion
Failures and ineptitudes be damned, the Health and Human Services’ (HHS) proposed budget6 for 2023 is now an eye-popping $1.7 TRILLION in mandatory spending (up from 1.5 trillion in 20227), and another $127.3 BILLION in discretionary spending (down from 131.8 billion in 20228).
Operating divisions9 under the HHS include not only the CDC and the Food and Drug Administration, but also the National Institutes of Health (NIH), the Centers for Medicare & Medicaid and several others. In total, the HHS employs some 80,000 people.10
Of those budgeted trillions, the CDC will in 2023 receive 1% of the HHS budget or $10.6 billion11 — $2.3 billion more than its 2022 appropriation — and this includes “mandatory funding to establish a Vaccines for Adults program.”
Yet with all that supposed brain power and money, what exactly do they accomplish? I would argue “precious little,” and the CDC’s COVID response is a perfect example of how our taxpayer funds are being wasted on advice that range from bad to worse.
The problem with concentrated power is that it gets corrupted. After 69 years, it seems the HHS is finally entering its death throes, as corruption within many of its operating divisions is now shockingly blatant.
The same goes for the World Health Organization. Incidentally, its biannual budget for 2022-2023 of $6.7 billion12 is dwarfed by the HHS budget. Still, the WHO is now seeking to gain control over health decisions globally. I explain why this is such a horrendous and unworkable idea in “The WHO Is a Corrupt, Unhealthy Organization.”
Ideally, all that HHS money should be divided among the states. We’d be far better off with local community programs handling current HHS services — including pandemic response.
The CDC being wrong on everything about the pandemic, and taking two and a half years to admit even a fraction of it, is proof positive that centralizing health care decisions is a bad idea. Ideally, all that money should be divided among the states. We’d be far better off with local community programs handling current HHS services — including pandemic response.
CDC’s Botched Test Kits
The errors of the CDC are too numerous to recount in a single article, but let’s take a look at one of the doozies, namely its botched COVID test. As reported by HealthDay reporters Robert Preidt and Robin Foster, back in December 2021:13
“Along with being contaminated, there was also a basic design flaw in COVID-19 testing kits created by the U.S. Centers for Disease Control and Prevention early in the pandemic, a new agency review shows.
It was already known that the PCR kits were contaminated, but the CDC’s findings published Wednesday in the journal PLOS ONE14 are the first to note a design error that caused false positives.
When the CDC’s test kits were developed and distributed in the early weeks of the pandemic, there were no other authorized tests available … The agency started shipping the test kits to public health laboratories in early February 2020, but many labs soon told the CDC that the tests were producing inconclusive results.
The CDC acknowledged later that month that the kits were flawed, and U.S. Food and Drug Administration officials said in April that poor manufacturing practices had caused contamination of the kits …”
So, the tests had not just one but two problems. First, they were contaminated with synthetic fragments, sequences of genetic material from the virus that are used to ensure the test is working properly. These synthetic sequences are thought to have contaminated the kits during quality testing, as they were being manufactured in the same CDC lab where quality testing took place.
Secondly, the CDC failed to catch a serious design flaw. The test was designed to detect the presence of three specific genetic regions or sequences of the virus. The test kit included a set of primers that bound to and made copies of those regions (when they were present in the patient, indicating exposure to the virus), as well as probes that fluoresced to signal that copying was taking place.
To work properly, these primers and probes had to bind to the genetic sequences, but not to each other. Here, one of the probes had a tendency to bind to one of the primers, thereby triggering a fluorescent signal, suggesting a positive result. This is how the test ended up producing an unacceptable number of false positives.
Eventually, smaller private companies ended up providing most of the PCR tests — without encountering these contamination and design flaw problems. The fact that the PCR test cannot identify an active infection and were used to create a false “casedemic” is another story, which we’ve covered multiple times. Here too, the CDC displayed shocking dishonesty, alternatively hiding and manipulating data to make the pandemic out to be something it really wasn’t.
They also recommended mask wearing despite overwhelming scientific evidence showing masks don’t prevent the spread of viruses. Time and again, CDC leadership made public health decisions on what appears to have been nothing more than assumption, personal opinion or fear — and that’s if you’re kind enough to exclude the possibility of fraud and collusion to benefit Big Pharma and the globalist Great Reset agenda.
Did HHS Create the Problem?
As mentioned, the HHS runs the NIH and CDC, both of which are implicated in the creation of SARS-CoV-2. So, basically, the same circle of people who may have created the problem are also in charge of solving it and providing a cure.
We’ve already seen how “effective” they’ve been in that regard. They’ve devastated public health with useless lockdowns, mask mandates and social distancing, and killed an as-yet undetermined but extraordinarily high number of people with improper, dangerous and experimental treatments.
As noted in “Why the COVID Jab Should Be Banned for Pregnant Women,” the CDC to this day insists pregnant women get the COVID shot,15 despite trial data suggesting it may cause miscarriage in 8 out of 10 cases.16,17,18 Will reorganization eventually correct this murderous advice?
In an August 2, 2022, Current Affairs interview,19 professor Jeffrey Sachs, chair of The Lancet’s COVID-19 Commission, said he believes the U.S. government is preventing a thorough investigation into the origin of the pandemic, for the simple reason that the virus was the result of U.S. research. Indeed, there are patents spanning decades to suggest that’s true (see “Patents Prove SARS-CoV-2 Is a Manufactured Virus”).
If our very worst suspicions are true, then the U.S. government funded not only one bioweapon but two — the original SARS-CoV-2 and the gene transfer injections misrepresented as “COVID vaccines.” And the HHS divisions of the FDA and CDC went along with all of it, not even pausing at the possibility of killing or injuring 6-month-old infants and toddlers.
Waging War on Pathogens Is a Failed Strategy
In an August 10, 2022, Brownstone Institute article, Aaron Vandiver, a wildlife conservationist, writer and former litigator, reviews why the global war on pathogens is a failed strategy that needs to end:20
“Bill Gates has called the global response to COVID-19 a ‘world war.’ His militaristic language has been echoed by Anthony Fauci and other architects of COVID-19 policy for the last two and half years … I believe that an ecological perspective reveals many of the flaws inherent in an aggressive high-tech attack on a pathogen…
To me, the ‘war’ on COVID-19 has been characterized by a destructive set of attitudes, beliefs, and behaviors that appear to be deeply engrained in our political and economic institutions, and which form a pattern that should be recognizable to conservationists and ecologists.
1. Aggressive intervention in complex natural processes using new, poorly understood technologies designed to achieve narrowly defined short-term goals, with disregard for the potential long-term ramifications;
2. Profiteering by private interests that own the technologies, enabled by government entities and ‘experts’ that have been financially captured by those interests;
3. Followed by a cascade of unintended consequences.”
In the remainder of the article,21 Vandiver goes into several aggressive and destructive COVID interventions in greater detail — and their consequences. I recommend reading through it.
Importantly, when we go to war against pathogens, we go to war against ourselves, because without pathogens we cannot exist. The key to health is balanced co-existence with bacteria, viruses and other pathogens, which exist by the trillions in and on our bodies.
The Twisted Logic Behind Gain-of-Function Research
Vandiver, like Sachs, also points out that gain-of-function research funded by the NIH appears to be the most logical and most heavily supported theory as to the origin of the pandemic, and that denial of the lab leak theory is underpinned by reckless scientists unwilling to recognize the risks inherent in their work.
“Most fail to realize that Fauci and other proponents of ‘gain of function’ have long shown reckless disregard for the risks of tampering with natural viruses, expressing a paranoid attitude toward nature that is the antithesis of respect for ecology,” Vandiver writes.
“Fauci and others claim that ‘Mother Nature Is the Ultimate Bioterrorist’ to justify their Frankenstein-like efforts to hunt down the most dangerous viruses that exist in wild nature, take them to labs like the one in Wuhan, and tinker around with them to make them more dangerous and deadly.
Their twisted logic seems to be that if they intentionally create superviruses, they can somehow anticipate and prepare for natural pandemics. Most objective observers, however, say that ‘gain of function’ is a military-industrial boondoggle that has no practical benefit whatsoever and dramatically increases the risk of pandemics …
It remains inconclusive whether ‘gain of function’ research actually caused the COVID-19 pandemic, but its potential to have done so is a vivid example of how powerful actors like Fauci use technological tools to interfere with natural processes, with disregard if not outright contempt for the long-term ecological consequences, thereby creating opportunities to exercise more power.”
In conclusion, Vandiver notes:22
“If we carefully analyze each aspect of ‘world war’ on COVID-19, we can see how each tactic and high-tech ‘weapon’ has harmed human health, destabilized civil society, and possibly disrupted the ecological balance between the human population and the virus, while enriching private interests and empowering financially captured government regulators.
The ‘war’ has been characterized the distinct pattern that I described at the beginning of this essay … This destructive pattern appears to be deeply ingrained in our institutions and in the outlook of our leaders. It largely defines our society’s dysfunctional relationship with the natural world.
An ecological perspective that keeps this pattern in mind, and takes into account all of the consequences of launching high-tech ‘wars’ on pathogens or any other part of our environment may help us avoid similar catastrophes in the future, or at least to recognize them.”
Death by Medicine
In “Are Medical Errors Still the Third Leading Cause of Death?” I review the now decades-long history of modern medicine being a leading cause of death, at times spinning up to take first place, and rarely dipping below fourth. Several studies and investigations over the years have placed medicine and medical errors as the third leading cause of death in the U.S.
The pandemic has revealed just how dangerous it is to listen to dog whistles like “trust the science.” Which science? The one Big Pharma concocts to make money or the one that double checks and investigates claims independently?
The CDC’s COVID policies were all wrong — consistently 180 degrees from helpful — and have only recently been updated23 to match what all of us “misinformation spreaders” have been saying for well over two years. That update was published August 11, just six days before Walensky announced the CDC’s reorganization plans.
I’m not buying the idea that the CDC suddenly realized it was going in the wrong direction. They knew it from the start, and they did it intentionally. I suspect they’re only now starting to course correct because mainstream media are losing its grip on the public.
Mainstream media were their cover for every obnoxious, unscientific recommendation, and without that brainwashing arm, the CDC has no way to turn but back. Like Dr. Anthony Fauci, they probably realize that the political tide is turning, people are fed up with the “1984” double-speak, and if Republicans take the House in November, the CDC could well be facing any number of investigations.
Senators Promise Investigations
August 23, 2022, two U.S. senators promised a “full-throated investigation” of Fauci’s and former NIH-chief Francis Collins’ potential roles in the origin of the pandemic, and issued a formal request for the HHS and NIH to preserve documents and communications.24
Leadership at the CDC and FDA also need to be investigated and questioned about the ins and outs of their decision making. Not that I think they’ll ever admit to “working for the devil,” meaning the Deep State cabal that is using COVID as a cover for a global takeover, but there needs to be a reckoning nonetheless.
Those willing to sacrifice the lives, futures and Constitutional rights of Americans on behalf of these transhumanist psychopaths need to be ruthlessly weeded out. And then, we need to implement new public health systems, perhaps new agencies, with powers that are more limited in scope and state-run rather than federal.
Never, ever, should an agency like the CDC be allowed to ban doctors from treating patients, for example, based on their own expertise and experience. What has happened during this pandemic, and is still happening, is a true crime against humanity. We must never forget how health officials, government officials, media and other influencers tried to foment hatred against the unvaccinated, and how they’ve been willing to discriminate to the point of death.
The CDC has now backtracked on discrimination, agreeing people should not be treated based on their vaccination status. But we remember the calls for “re-education camps” and no-fly lists. Backtracking is not going to erase the attempts to destroy the lives of those who refused to play their Russian roulette.
I, for one, would love to hear the CDC explain why they have ignored the blaring safety signal of nearly 1.3 MILLION reports of COVID jab injuries in the Vaccine Adverse Event Reporting System (VAERS).25 Wouldn’t you?
Less than two decades into the genomic era in health care, our knowledge about the connections between DNA and human health continues to explode, from the genetic underpinnings of disease to how to use genetics to identify what therapy is best suited to each person. Ultimately genomics will transform the way we understand our health and make most health decisions, from pregnancy to newborn health to childhood disorders, cancer treatment and more.
One of the most dramatic changes is in the world of so-called rare disease. In total, rare diseases are anything but rare. There are thousands of conditions that may individually impact relatively small groups of people but together impact more than 30 million people in the U.S. alone.1
Advances in genetic medicine have made a major contribution to improving diagnosis for these patients
While too many patients still endure a years-long “diagnostic odyssey,” the increasing availability of genetic testing offers hope. Genetic testing has the ability to diagnose thousands of rare conditions, accurately and quickly, and that capability is only growing, with hundreds of new gene-disease relationships identified each year.
Recently the industry has seen gene therapies and other targeted treatments enter the market, offering therapeutic options to patients who previously had none
Today, treatments for rare diseases account for $24 billion or roughly 20 percent of all prescription drug spending and is growing at 12 percent a year.2 But the role of genetics in rare disease treatment does not end at diagnosis, even one that provides a gateway to treatment. Each diagnostic test not only gives an answer to a patient, but also contributes to a treasure trove of data that improves diagnosis for the next patient and can transform how we understand and treat these conditions.
GeneDx – a diagnostic company founded in 2000 by two scientists from the National Institutes of Health (NIH) – has already seen this play out. To date the company has completed more than 300,000 clinical exome sequences, more than anyone else in the industry. Exome sequencing covers all 20,000 genes responsible for proteins in the body and provides the broadest look at the potential genetic causes of health conditions. Only sequencing a person’s entire genome is broader. That experience, coupled with a dataset that includes 2.1 million health descriptions (phenotypes), has allowed the company to accelerate discovery in ways that are impossible without the benefit of large datasets. For example, by plotting symptoms and age at testing across a large cohort, researchers can see the course of disease changing over time, with patients under age two showing very different symptoms than patients at age six. For physicians who see these types of cases infrequently, the conditions may look different when in fact they’re the same. Again and again, a symptomatic, hypothesis-based approach may miss what’s really going on.
Where medicine is heading, then, will begin not with the hypothesis but with the data
Through large datasets of both genomic and clinical information, we will be able to start by asking, “What do the genes say?” Then, “What do the symptoms likely mean?” And finally, “What treatment will help?” It upends diagnosis and treatment in ways that can dramatically improve care. For patients with rare diseases, many of which worsen over time, the sooner a diagnosis and an effective treatment is found, the better the outcome.
Likewise, drug development can be transformed. Large datasets of both genomic and clinical data can drastically speed every phase of the drug development cycle, from identifying novel targets to designing clinical trials to developing the kinds of complementary diagnostics needed to ensure the right patient receives the right therapy at the right time.
The most impactful datasets will be those that are large, deep and structured, combining both genetic and health information
Health systems, payers, diagnostic companies and biopharma companies all have a role to play in creating a framework that will generate the greatest benefit for patients. Improving access to testing, ensuring reimbursement, creating useable datasets, contributing to public data trusts and fostering cross-industry partnerships will all be part of building a new ecosystem to deliver genome-informed health care.
Millions of patients enduring rare diseases have for years had too few options. Given the potential to transform health care for the better, now’s the time to double-down on the transition to a data-driven approach to diagnosis and drug development.
As health care has shifted to increasingly rely on digital tools for patient care, digital inclusion has become critical to promoting health care equity. The recently enacted Infrastructure Investment and Jobs Act (IIJA) makes investments that could foster sustainable digital inclusion. Although the law isn’t focused on health care, it addresses long-standing drivers of digital health disparities, presents new opportunities for community-based digital inclusion, and could be a critical lever for improving access to care. We believe it’s important for health care organizations to understand the opportunities the law presents, to advocate for its effective and equitable implementation, and to take advantage of improvements to digital infrastructure (see table).
With digital health tools such as telehealth and patient portals becoming prominent components of care delivery, the barriers to digital inclusion have grown increasingly apparent.1 For example, more than 100 studies have revealed disparities in portal use based on age, race, socioeconomic status, English-language proficiency, and other factors.2 Digital inclusion refers to “the activities necessary to ensure that all individuals and communities, including the most disadvantaged, have access to and use of [digital tools].”3 Structural barriers to digital inclusion, such as digital redlining, have limited the reach of digital health tools. Digital redlining entails “discrimination by Internet service providers in the deployment, maintenance, or upgrade of infrastructure or delivery of services.”3 Communities affected by digital redlining are generally the same ones that already have poor health outcomes. Health care systems have, by necessity, implemented digital tools in communities affected by digital redlining to reach underserved patients and combat health disparities.
Beyond digital redlining, limited broadband infrastructure, high prices for broadband service, and lack of access to Internet-enabled devices have made deploying digital health tools difficult. At least 21 million people in the United States don’t have broadband access, which has constrained the use of telehealth. People living in areas with low broadband access are less likely to make use of video visits than people in areas with more widespread access. The digital health expansion fueled by the Covid-19 pandemic has also made evident the importance of digital literacy for empowering patients to engage with this new care model. Delivering equitable care to underserved patients requires an inclusive system that ensures access to affordable broadband, Internet-enabled devices, digital-literacy supports, and appropriately designed platforms.
In this new digital care environment, health care organizations are playing an important role in addressing digital divides. Organizations are developing dashboards to better measure digital disparities and guide systemwide solutions. Some are attempting to narrow access and affordability gaps by purchasing and offering patients tools to support broadband access (e.g., Wi-Fi hotspots) and Internet-enabled devices. The Federal Communications Commission (FCC) Covid-19 Telehealth Program provided financial support for devices and organizational telehealth infrastructure, though resources were limited. Organizations have also reviewed their existing platforms and translated content into multiple languages. Digital navigators, who had previously been deployed outside health care, emerged as new care team members to help patients use digital tools. Navigators can be costly and resource-intensive for health care organizations, however, which limits the sustainability of navigator programs.
The IIJA takes much of the responsibility for building digital infrastructure away from individual health care organizations and makes digital inclusion a public concern. The law includes $65 billion for digital-inclusion initiatives. It earmarks $42.5 billion for investment in broadband infrastructure by means of state deployment grants, promotes broadband affordability by providing $14.2 billion for $30-per-month subsidies for Internet costs for underserved people, and allocates $2.8 billion for the creation of digital-literacy programs. It also includes funds to support connectivity in tribal communities and broadband deployment in rural areas.
Although health care organizations have begun screening for digital needs, resources for addressing identified needs have been limited or underused. For example, the Covid-related Emergency Broadband Benefit, which provides subsidies for Internet and device purchases, hasn’t been widely adopted.4 Moving forward, health care organizations, particularly those in underserved communities, could not only refer people to digital-inclusion programs, but also serve as active stakeholders in targeted outreach initiatives.
In addition to providing funding, the IIJA could address health disparities stemming from digital exclusion. The law charges the FCC with adopting rules to prevent “digital discrimination of access based on income level, race, ethnicity, color, religion, or national origin.” These policies are an attempt to overcome structural barriers to digital inclusion, such as redlining. Health care organizations will have to actively support antidiscrimination efforts to ensure that telecommunication companies are held to policies requiring equitable implementation. Organizations could also partner with community groups and nonprofit organizations, such as the National Digital Inclusion Alliance, to advocate for equitable broadband deployment so that digital health tools could be harnessed to promote health care equity.
The IIJA also emphasizes a community-based approach to digital inclusion by establishing state-based grant mechanisms that encourage collaboration. Digital health initiatives that are integrated into community-based programs can be more sustainable than piecemeal digital-literacy programs. For example, health care organizations could work with groups offering classes for English-language learners or with libraries, which have long been essential to digital inclusion, to codevelop digital-literacy training programs for marginalized populations. In addition, the IIJA supports connectivity for community anchor institutions (e.g., schools and libraries), which have become integral to equitable care delivery. By capitalizing on new policies, health care organizations could become a critical part of an environment of community-based players working toward digital inclusion. In this way, digital inclusion could be framed as a social determinant of health, and supporting digital inclusion could have positive effects on other social determinants, including education, employment, civic engagement, and housing.5
Although passage of the IIJA represents an exciting step toward digital inclusion, we believe health care organizations must address additional barriers to create an inclusive system. First, it’s unclear whether the new funding will be adequate to close access gaps, given the FCC’s unreliable broadband data. Organizations could collect information on which patients have broadband access to guide allocation of infrastructure-building resources. Second, the IIJA considers adequate broadband speeds to be at least 100 megabits per second for downloads and 20 megabits per second for uploads. Digital health care implementation teams should be aware of bandwidth limitations, which could exclude patients with slower Internet speeds. Third, the law doesn’t address changes that are needed to the design of digital health platforms (e.g., language translation) or to make workflows more inclusive (e.g., integration of interpreters into telehealth visits). Fourth, it will be critical to evaluate the effects of digital tools to avoid exacerbating disparities. Finally, organizations should continue to provide multimodal care options, since access to digital tools may vary, and such tools may not be appropriate for or preferred by all patients.
The future of digital health care relies not only on digital inclusion but also on the extension of policies enacted during the Covid-19 public health emergency that align with value-based care and equity. Harnessing community anchor sites will require the permanent removal of geographic restrictions and originating-site restrictions, which depend on a patient’s location during a telehealth visit. Simplification of interstate licensing laws for clinicians would also enable digital tools to increase access to care for marginalized populations. In addition, reimbursement parity among various forms of telehealth, including audio-only visits, would ensure that patients without full digital access could still benefit from remote care, including mental health services. A combination of progressive digital-inclusion efforts and digital health policies could lay the foundation for technology-powered health care equity.
Patients will benefit from digital tools only if health care systems advocate for digital-inclusion policies. As health care increasingly moves to a “digital first” approach, digital inclusion is becoming intertwined with health care equity. Health care organizations should therefore engage with digital-policy initiatives, including the IIJA, and providers should be prepared to capitalize on the opportunities afforded by such policies.
The best route for artificial intelligence to support care delivery is through a stepwise approach — aiding physician decision-making first, before complete automation.
Justin G. Norden, MD, MBA, MPhil and
Nirav R. Shah, MD, MPH
March 7, 2022
Summary
Health care is often frustratingly slow to embrace technology; electronic medical records were a striking example. While artificial intelligence (AI) is clearly a part of the future of medical care, due to the understandably conservative nature of medicine, AI’s potential will be first realized elsewhere. One field in particular that has captured the attention of many is autonomous vehicles (AVs). Large and small tech companies are actively involved in AV development, which has attracted billions of dollars of investments and many of the sharpest minds from around the globe. While the effort has yet to reach the end goal of vehicles that are fully autonomous in all circumstances (vehicles known as level 5 systems, the highest on a 0 to 5 scale), today there are widely deployed driver augmentation AI systems that are saving lives (level 1 and level 2 systems). When most people think of AI, whether vehicles or health care, they think of fully replacing the driver or fully bypassing the doctor. While there are many good reasons to completely replace the driver for transportation, this thinking is counterproductive in health care. We must learn from the challenges and many steps in deploying “fully autonomous” AI in other fields. Moreover, we must recognize that in health care there are distinct advantages of augmentation over complete automation.
Today, the algorithms most widely deployed in health systems are rule-based systems or decision trees that are set to trigger alerts when certain conditions are met. For example, sepsis alert systems notify care teams based on specified values of labs and vital signs. These systems, based on clinical trials, use a simple rule-based decision tree on vital signs entered into the electronic medical record (EMR) to automatically flag patients who might be at risk. While these systems are not perfect, and have recently come under scrutiny, they do flag many of the highest-risk patients.1 We see these rule-based systems deployed widely across a few areas of our health care systems today: in clinical decision-support systems in the EMR, in triage from online symptom checkers, and in our medical billing systems. While we are in the early days of deploying artificial intelligence (AI) in health care, many rule-based systems are already deployed. (Brief explanations of AI categories and terms used in this article are provided in Table 1.)
Table 1.
Artificial Intelligence Categories and Terminology
Evolution of AI Categories
Term
Explanation
Examples
Rule-based Systems
Translating human knowledge into predetermined actions
Sepsis alerts for flagging an abnormal set of vitals
Supervised Learning
A set of algorithms that builds predictive models from data with known outcomes
A radiologist groups 1,000 chest X-rays into those with lesions and those without, then a supervised learning algorithm is trained on this labeled data to determine a pattern to sort these chest X-rays automatically into these groups
Unsupervised Learning
Sets of algorithms that identify patterns in data without predetermined labels or known outcomes
Using large gene expression data sets, we have discovered 10 breast cancer subtypes that correspond to different patient outcomes and treatment responses
Reinforcement Learning
These are AI algorithms that train toward a certain goal using an iterative approach of trial and error, updating their models dynamically toward the more successful outcomes
Type of AI used to train AlphaGO through iterative self-play, ultimately outperforming the world championship GO player
AI Terminology
Term
Explanation
Examples
Synthetic Data
This is data created artificially by algorithms that is meant to mimic real-world data
1. People train fall-detection algorithms using artificially generated falls; the synthetic fall data is made by moving stick figure models through human poses that portray how people may fall 2. Use cases today in health care are to create digital twins for control arms in clinical trials instead of recruiting patients who will receive placebo
Computer Vision and Deep Learning
Types of AI techniques that use multilayer artificial neural networks trained off large data sets (deep learning) to find information in images or videos
Hospitals today are using computer-vision algorithms to find strokes in head-computed tomography scans (CTs)
Natural Language Processing (NLP)
These algorithms work to extract or “understand” useful information from human speech or text
Physicians are using NLP tools to transcribe speech to write patient encounter notes
Source: The authors
How do rule-based systems fit in the AI deployment time line across industries? They were deployed in the 1960s, early in the development of AI.2,3 These were the first types of AI systems, designed to automate tasks by taking “knowledge” from a human and programming that into a machine. Initially, there was a lot of excitement that we could create intelligent systems by simply programming all of the facts and rules that we know. As the work progressed, however, people soon realized we could only solve simple problems with these systems. For more complex problems, the number of rules needed grew unmanageably large. While the field of AI moved on to more advanced systems to handle these more complex problems, for the most part, AI in health care progressed little beyond rule-based systems. But now there’s reason for hope.
Supervised Learning in Health Care
In the 1990s, AI researchers moved from rule-based systems toward data-driven approaches. In these systems, rather than prescribing exactly how the system should act with human-derived insights, algorithms were trained from historical data to derive patterns and insights. This allowed computers to derive insights directly from the data and make connections that humans might not have previously known.
The first step in data-driven approaches were supervised learning–based methods. Such systems relied on historical data with input and output labels. With clear targets for what task we wanted the algorithm to complete, computers could work to create their own rules for how to split the groups apart. One prominent example of supervised learning happened in the field of computer vision using deep learning on labeled images. Deep learning is a type of AI technique that uses multilayer artificial neural networks to find patterns in large data sets. These deep-learning networks showed how computers could be excellent at image detection and approach human performance.4
While the field of AI moved on to more advanced systems to handle these more complex problems, for the most part, AI in health care progressed little beyond rule-based systems. But now there’s reason for hope.
In health care, computer vision and deep-learning AI systems are some of the first next-generation AI systems to be studied and deployed in academic settings. In February 2022, we did a PubMed search of papers published in 2010 with the keyword search “deep learning” and found 107 papers identified. For 2021, this search returns 14,685 citations. These papers are most prominent in the fields of radiology and pathology — where there are large, curated data sets with clean labels. Deep-learning computer vision algorithms are now being deployed in hospitals, used in triaging head-computed tomography (CT) scans (Viz.ai), and predicting the risk of deterioration for Covid-19 patients based on chest X-rays.5 The most success is achieved when AI tools are carefully integrated to augment existing workflows.
Notably, companies such as Viz.ai are augmenting the health system using AI rather than trying to replace doctors completely. For example, Viz.ai specifically focused on the problem of trying to reduce the time to initiate interventional care for a stroke by detecting large vessel occlusions on CT scans and alerting teams immediately. Research has been published showing that these integrated algorithms were associated with reduced time to treatment and ultimately better patient outcomes.6 With this level of evidence, Viz.ai created the first AI algorithm to be reimbursed by the Centers for Medicare & Medicaid using the New Technology Add-on Payment (NTAP) designation,7 and Viz.ai is currently deployed across hundreds of health systems. Viz.ai tackled a very clear problem using AI and built their solution into a workflow that could be used immediately. There is a graveyard full of health care computer-vision companies that promised much but failed to make any impact on our health care system. By focusing on augmentation rather than replacement, Viz.ai is having a large impact today.
Deep learning is also being applied to problems outside of computer vision in the AI community, with impressive results. When large amounts of data are available, these systems perform well. But deep learning systems are sometimes criticized for being a black box, i.e., they do not give insight into the nature of what goes into a prediction. While it may be dangerous to rely on a system like this to independently determine a course of treatment, we can still use these highly predictive systems to augment clinician judgment. For example, such approaches are used to predict early sepsis, leveraging data from thousands of sources.8,9 Flagging a deteriorating patient sooner can make a big difference and, despite false positives, these systems can help save lives. Finally, explainable AI is an area of active research that works to show why an algorithm is flagging something and to give humans insight into the data that led to the result.
Unsupervised Learning Systems in Health Care
The next major shift in AI was toward unsupervised learning systems. For example, by analyzing thousands of gene signatures in women with breast cancer, researchers found 10 distinct subtypes of the disease — all of which respond differently to treatment and have different survival expectations.10 This empiric approach allowed unsupervised learning systems to identify novel phenotypes — new knowledge that is now used in clinical settings.
Another success of unsupervised learning techniques is in producing synthetic data, which is created artificially by algorithms and is meant to mimic real-world data. These techniques have been popularized by deepfakes or models that can produce a high-quality image, video, or audio of anyone, doing just about anything. One health care application for synthetic data is to create a digital twin, which is a virtual representation that serves as the counterpart to a physical entity or system. In health care, people are actively exploring digital twins as control arms for clinical trials. For example, as we typically know how a group of Alzheimer’s patients will progress in a control group, maybe instead of large cohorts being left untreated, we can use digital twins to power these trials. Companies such as Unlearn.AI are attempting this now and working with the U.S. Food and Drug Administration to get new treatments on the market faster.11 In cardiology, a field with lots of data to work with, people are using digital twins for researching more personalized cardiac care based on a unique cardiopulmonary model.12
Advanced AI Techniques
A final large category of AI techniques is reinforcement learning, an AI that trains itself toward a certain goal and updates its own model dynamically, as it does better or worse. This type of system is behind the breakthrough of AlphaGO (developed by DeepMind, a part of Alphabet) that eventually became the champion of the board game Go, after playing against itself to improve its own performance.13 In health care, reinforcement learning is currently confined to academia, and it has been challenging to create a strong feedback loop with messy health data.14 While reinforcement-learning AI models may not be deployed in the real world for some time, iterative quality improvement efforts that aim to build a learning health system are first steps in this direction.15
One health care application for synthetic data is to create a digital twin, which is a virtual representation that serves as the counterpart to a physical entity or system. In health care, people are actively exploring digital twins as control arms for clinical trials.
Finally, natural language processing (NLP) algorithms work to extract or “understand” useful information from human speech or text. Notably, NLP is not a distinct type of AI like supervised learning, unsupervised learning, and reinforcement learning; rather, it is a specific focus incorporating all these techniques to solve a specific problem. It is worth calling out because of rapid advances made over the past few years that have resulted in wide deployment across technology systems. Improved models such as GPT3 (an advanced NLP system developed by OpenAI) show just how advanced automated text generation has become, and how AI writing can be indistinguishable from that of humans.16
Today, these systems are not widely deployed in health care, but many health tech start-ups are starting to sell promising chatbot solutions to handle administrative workflows. Companies such as Memora Health, Health Note, and Nuance are deploying NLP chatbot systems for care navigation, triage, check-in, automated note-writing, content, and more. Such NLP systems are focused on augmentation of and integration with the health care system, not bypassing it completely for full automation. The most notorious example of aiming for too much with NLP in health care is IBM’s Watson Health, where billions of dollars were invested, but outcomes did not meet expectations.17,18
Learning from AI in Autonomous Vehicles
Autonomous vehicles (AVs) use rule-based systems to understand and follow the laws of the road, supervised learning systems to train their computer vision systems, unsupervised methods to find features that are most important in a particular scene, and reinforcement learning techniques to follow a goal and find the right driving path through an intersection.
Yet self-driving car companies are largely split on how to eventually solve the problem of reaching fully autonomous driving, considered a level 5 system on a scale of levels from 0 (fully operated by a human) to 5 (fully operated by the vehicle) (Table 2).19
Table 2.
Levels of Automation for Vehicles
Scroll table to see more
Level 0
Level 1
Level 2
Level 3
Level 4
Level 5
Concept
No driving automation
Advanced driver-assistance system, assists human with some tasks
Advanced driver-assistance system, full human attention required
Automated driving system, for some circumstances
Automated driving system, for certain circumstances
Full driving automation, for all circumstances, humans as passengers
Explanation
The human driver does all the driving.
An advanced driver-assistance system (ADAS) on the vehicle can sometimes assist the human driver with either steering or braking/accelerating, but not both simultaneously.
An advanced driver-assistance system (ADAS) on the vehicle can control both steering and braking/accelerating simultaneously under some circumstances. The human driver must continue to pay full attention (monitor the driving environment) at all times and perform the rest of the driving task.
An automated driving system (ADS) on the vehicle can perform all aspects of the driving task under some circumstances. In those circumstances, the human driver must be ready to take back control at any time when the ADS requests the human driver to do so. In all other circumstances, the human driver performs the driving task.
An automated driving system (ADS) on the vehicle can perform all driving tasks and monitor the driving environment — essentially, do all the driving — in certain circumstances. The human need not pay attention in those circumstances.
An automated driving system (ADS) on the vehicle can do all the driving in all circumstances. The human occupants are just passengers and need never be involved in driving.
The first camp (Tesla and traditional automakers) is focused on building simple AV systems that help with steering, braking, and acceleration, that first augment the human driver (level 2 systems) but leave the driver responsible. The second camp (Waymo, Cruise, Zoox), is focused on high automation and fully autonomous systems (level 4 and level 5, respectively) that remove any responsibility of the driver and take full control, in most or all circumstances, respectively.
Getting to 100% automation is the goal for AVs, because the advantages of taking the driver completely out of the equation are clear and compelling. Not so in health care. In fact, quite the contrary, as the doctor-patient relationship is critical to outcomes.
Notably, level 2 systems like Tesla’s autopilot have driven more than 3 billion miles on the road, with crash rates lower than human drivers. However, there have also been catastrophic failures due to system issues and humans overestimating the autonomous system’s performance. Meanwhile, the fully autonomous systems have yet to be broadly deployed (e.g., Waymo is operating in a small area around Phoenix, Arizona; Cruise is operating in San Francisco, California) and may still be many years away from widespread use.20 While there is a large reward in both cost and safety by completely removing the human driver, the final step is the hardest. Systems today perform at around human performance. However, getting to meaningful deployment means they will have to be 1,000 times better — and this is significantly more challenging.21,22
Getting to 100% automation is the goal for AVs, because the advantages of taking the driver completely out of the equation are clear and compelling. Not so in health care. In fact, quite the contrary, as the doctor-patient relationship is critical to outcomes. Moreover, augmentation systems are far easier to develop and deploy and can be ready to use in years, not decades. Even without full automation, we can catch errors, aid diagnosis, and save time. Finally, full automation brings a host of barriers in terms of acceptance by patients, physicians, and regulators. So, in health care, when it comes to AI, less is actually more.
Justin G. Norden, MD, MBA, MPhilPartner, GSR Ventures, Palo Alto, California, USA
Nirav R. Shah, MD, MPHSenior Scholar, Stanford University, Stanford, California, USA; Chief Medical Officer, Sharecare, Atlanta, Georgia, USA
The climate crisis and the Covid-19 pandemic are revealing limits to the economic, environmental, and social resources and systems on which society depends. These dual crises are driving increased demand for transparent, equitable, and sustainable enterprise and, consequently, a significant change in business strategy and operations. Corporations of all kinds are undertaking a new form of accounting that not only captures financial performance, but also measures efforts to mitigate the destructive impacts that business operations have on the environment and society. Many corporations, such as The Coca-Cola Company, now publish regular reports using standardized frameworks to communicate progress in sustainability initiatives. In contrast, the U.S. health care delivery system — a financial behemoth that generates substantial adverse environmental and social impacts — has yet to engage in this important practice. In this article, the authors discuss the numerous benefits to the U.S. health care sector from mandated participation in cutting-edge sustainability management and accounting.
Introduction
Coca-Cola is a globally distributed brand with products that contribute to the obesity epidemic, the plastic pollution crisis, and water scarcity in vulnerable communities, all of which harm human health.1-3 Yet The Coca-Cola Company does something that almost no U.S. hospital or health care system does — it systematically measures, manages, mitigates, and regularly publicly discloses verified data on the negative environmental and societal consequences of its business operations.
The company performed its first assessment of the environmental impact of its products, raw materials, and fuel use in 1969 and since 2005, has regularly published sustainability reports in alignment with the Global Reporting Initiative (GRI), the most commonly used sustainability reporting framework worldwide. The Coca-Cola Company’s 2020 report discloses quantitative progress toward mitigating greenhouse gas (GHG) emissions, plastic waste, the sugar content of its products, and water scarcity4 (Figure 1, Figure 2). The company also communicates how their sustainability efforts specifically help to achieve the 17 United Nations Sustainable Development Goals (UNSDGs) released in 2015 to define progress by governments, businesses, civil society, and the general public toward a healthy global community (Figure 3).
The Coca-Cola Company is not unique among large corporations. More than 90% of Standard & Poor’s 500 Companies annually publish sustainability reports, as do many private and nongovernmental entities.5 Finding detailed information on sustainability programs for hundreds of companies is as easy as a Google search.
The same cannot be said of U.S. health care organizations, in which there has been little engagement or disclosure utilizing currently accepted best practices in sustainability. This article reviews the current state of sustainability accounting and reporting and explores why the U.S. health care sector must rapidly adopt this common business practice.
Corporate Sustainability Is Well Established
The rapid and widening uptake of corporate sustainability practices and the associated reporting stems from three related factors: (1) global environmental megatrends such as climate change and loss of biodiversity and ecosystems that are forcing companies to address greater disruptions, scarcity, and higher costs; (2) rising interest among investors and lenders increasingly concerned about the potential financial impacts from environmental and social risks; and (3) increasing social pressure from corporate stakeholders such as consumers, investors, employees, regulators, policy-makers, lawmakers, governments, and communities.6
Beyond the idea that addressing environmental and social externalities from health care is the “right thing to do,” growing evidence indicates that these efforts have significant and wide-ranging positive impacts on financial and business performance that can provide substantial cost savings, reduce risks, and improve numerous measures of corporate performance.
Large asset owners and managers (e.g., BlackRock) are increasingly committing to investing in more sustainable companies through initiatives such as the United Nations’ Principles for Responsible Investment. In 2019, the Business Roundtable called for radical change when it released a new Statement on the Purpose of a Corporation. The organization, representing 200 of the largest U.S. corporations, reworked the definition of corporate purpose, which was traditionally understood to be returning profit to shareholders. The new statement declared that “companies must serve not only their shareholders, but also deliver value to their customers, invest in employees, deal fairly with suppliers, and support the communities in which they operate.” In line with this cultural shift, corporations are increasingly committing to science-based climate targets or to net-zero climate emissions and impact.7
Science-based targets encompass a set of goals that a business develops to provide itself with a clear route to reducing GHG emissions. The goals are considered “science based” if they are developed in line with the scale of reductions required to keep global warming to less than 2°C above preindustrial levels.
Net zero is a balance between the amount of GHG produced and the amount removed from the atmosphere. Net zero is achieved when the amount added is no more than the amount taken away.
Commitment to sustainable enterprise is not limited to publicly traded companies. Many private companies, nongovernmental organizations (NGOs), and public sector entities now seek to demonstrate good corporate citizenship via environmental and social disclosures. Examples include Cargill, the largest American privately held company; NGOs such as the World Bank and Oxfam; and public sector entities such as the University of Michigan, the City of Chicago, and Amtrak.
These disclosures are commonly termed Corporate Social Responsibility (CSR) or Environment, Social, and Governance (ESG) reporting. Companies create these reports using frameworks promulgated by organizations such as GRI or the Sustainability Accounting Standards Board that provide guidance on how to organize, collect, track, and publish sustainability information. CSR and ESG are based on the Triple Bottom Line — People, Planet, Profit — an accounting concept that seeks to quantitatively measure the environmental and social costs, in addition to the financial costs, of business operations (Figure 4).
CSR is a self-regulating business model that helps a company describe how it is going to be socially accountable — to itself, its stakeholders, and the public — for its impacts on all aspects of society, including economic, social, and environmental. CSR presents the qualitative ideal of aspirations and goals toward a corporate culture of sustainability.
ESG is the quantitative framework to measure and track outcomes that operationalize sustainability. Environmental criteria consider how a company performs as a steward of nature. Social criteria examine how it manages relationships with employees, suppliers, customers, and the communities in which it operates. Governance deals with a company’s leadership, ethics, regulatory compliance, executive pay, audits, internal controls, and shareholder rights.
Some individual health systems have demonstrated exemplary leadership in sustainable operations, though they do not provide consistent disclosures.
ESG accounting provides assurance to stakeholders that negative environmental and social externalities are acknowledged as by-products of business activity; the corporate entity takes responsibility for these negative consequences; and efforts to measure, manage, and mitigate are part of routine business operations and are transparently communicated to all stakeholders. Most companies use third-party accounting firms to verify the accuracy of their ESG data.8
Table 1 provides a few general examples of ESG categories, metrics, key performance indicators (KPIs), and links to the UNSDGs potentially relevant for health care organizations.
Table 1.
Selected Examples of ESG Categories Relevant to Health Care Organizations
ESG Categories
Environmental (Planet)
Social (People)
Governance (Profit)
Waste and PollutionEnergy EfficiencyNatural ResourcesClimate Change
Diversity, Equity, InclusionSafety & Well-BeingQuality of CareHuman Rights & Labor StandardsPrivacy & Data Security
Example metrics and their mapping to UNSDGs for health care organizations
Total annual GHG emissions, per employee and patient encounter (UNSDG 13: Climate Action)Total annual energy consumed, percentage of grid electricity, percent renewable (UNSDG 7 & 13: Affordable and Clean Energy, Climate Action)Total annual waste by weight, type, and disposition (e.g., on-site incineration, landfill, treatment/storage/disposal facility) (UNSDG 12: Responsible Consumption and Production)Description of climate risk policy/practices (e.g., infrastructure resiliency projects, compliance with CMS Emergency Preparedness Rule) (UNSDG 13: Climate Action)
Number of hours/percentage of employees trained in antiracism (UNSDG 10: Reduced Inequalities)Percentage of employees leaving per total full-time employeesNumber of hours and expenses of safety training per employee (UNSDG 8: Decent Work and Economic Growth)Average Hospital Value-Based Purchasing total performance score, across all facilities (UNSDG 3: Good Health and Well-Being)Excess readmission ratio per hospital and payment adjustments as part of HARPPatient satisfaction score (UNSDG 3: Good Health and Well-Being)Percent total spend on community benefit and local/minority procurement (UNSDG 11: Sustainable Cities and Communities)
Percentage of board diversity, DEI policies (UNSDG 5 & 10: Gender Equality & Reduced Inequalities)Total annual cost of fees/fines associated with Medicare and Medicaid fraud under False Claims Act (UNSDG 16: Peace, Justice and Strong Institutions)Number of ESG KPIs in annual reportExecutive pay: ratio and link to ESG performance
ESG = Environment, Social, and Governance, UNSDG = United Nations Sustainable Development Goals, GHG = greenhouse gas, DEI = diversity, equity, and inclusion, CMS = U.S. Centers for Medicare & Medicaid Services, KPI = key performance indicator, HARP = Health and Recovery Plan. Source: The authors, created with information from: T1. Sepetis A. Sustainable finance in sustainable health care system. Open J Business Manage 2020;8:262-281. T2. Sustainability Accounting Standards Board. Health Care Delivery Sustainability Accounting Standard. 2018. Accessed December 13, 2021. https://www.sasb.org/wp-content/uploads/2018/11/Health_Care_Delivery_Standard_2018.pdf
Moving to Mandated ESG Disclosure
Despite broad participation in sustainability reporting by a wide variety of organizations, it remains a predominantly voluntary construct in the United States. However, the U.S. Securities and Exchange Commission (SEC), which regulates and enforces financial disclosures for listed companies, is considering potential ESG reporting requirements. This step follows recent European Union (EU) rules on financial disclosure of ESG topics.9
If the EU model is adopted here, publicly listed companies in the United States would be required to disclose ESG metrics, such as annual production of GHGs, air and water pollutants, and material waste; up-to-date risk assessments related to infrastructure damage from natural disasters; projections of potential climate risks that might affect company financial performance (e.g., stranded assets and supply chain disruptions); and clear corporate policies related to climate change (e.g., governance). SEC rules may also require companies to include data on social progress, such as human rights; workforce health, safety, and well-being; and diversity, equity, and inclusion (DEI).
While several hurdles exist to full accounting of ESG issues in financial statements — primarily related to the creation of accounting protocols, standards, and metrics that are uniform and widely accepted — the overall intent is to protect shareholders from these areas of growing financial risk and to create more comparable information to evaluate company performance and risk mitigation.
Action by the SEC would signal to all businesses, publicly held, private, and nonprofit, that economic enterprise must account for environmental and social impacts and demonstrate good governance. Lack of ESG action by nonprofits and public sector entities could limit access to capital markets, philanthropy, insurance coverage, affect business partnerships and influence bond ratings. (See Value Drivers for ESG: The Financial Benefits of Sustainability Management and Reporting for Health Care Organizations for further discussion.)
Current Sustainability Efforts in the U.S. Health Care Delivery Sector: Alone Together
Health care delivery is the largest source of U.S. jobs, represents 17.7% of gross domestic product, and results in $3.8 trillion in annual expenditures.10 The U.S. health care sector also generates substantial waste and pollution, including 8.5% of total U.S. GHG emissions and similar fractions of toxic air emissions, and is associated with health damages resulting in 388,000 disability-adjusted life years annually.11 But as Wall Street moves quickly ahead with ESG, the U.S. health care delivery sector lags far behind in terms of sustainability management and disclosure.12 Despite the sector’s inherent commitment to improving health, there are large gaps in information and little consistency or comparability in environmental sustainability management and disclosure, and there is almost no accounting or reporting of social and governance performance.
Unmeasured and unmanaged, climate risks to health care operations are substantial and, for some locations, perhaps existential.
At the leading edge, a few large U.S. health systems have published CSR reports consistent with current best practices using established frameworks (e.g., GRI) and disclosure platforms that include ESG reporting. Reports from Cleveland Clinic (see Appendix) and Dignity Health, now part of CommonSpirit Health, are excellent examples that communicate quantitative information about corporate efforts to meet environmental (Figure 5) and social goals (see full discussion in Social Capital: ESG to Enhance the Health Care Workforce) and ensure ethical governance, (see Dignity Health Sustainability Report, pages 15-17).
Some hospitals and health systems have joined fee-based membership organizations that offer proprietary guidance on environmental sustainability operations, collect some facility-level environmental data, and provide green awards. The criteria for such awards are often unclear, unverified or undisclosed. For example, in 2018, Becker’s Hospital Review named the 68 greenest hospitals in America, although GHG emission information, the most basic measure of sustainable operations, was available for only seven of the awardees.13
Other hospitals have sustainability committees or employ a sustainability director or manager; however, according to the American Hospital Association, none have a Chief Sustainability Officer with direct access to the C-suite.14 Many hospitals have bottom-up recycling or waste-reduction programs driven by “champions” or “green teams” that rely heavily on passionate individuals to rally coworkers, as noted in the American Hospital Association’s Sustainability Roadmap.15 Despite uncertainty regarding the environmental footprint of health care products, health systems may select “sustainable,” “green,” or “environmentally preferable/friendly” purchasing options from vendors and group purchasing organizations. The lack of standards and oversight can foster greenwashing, the process of conveying a false or misleading impression about environmentally preferable characteristics of products or performance to gain a marketing advantage.
Some hospitals, at state or local urging, participate in voluntary emission-reduction programs. In New York City, for example, several health systems participate to some degree in the Mayor’s Carbon Challenge, which aims to significantly reduce carbon emissions from buildings. Many hospitals participate in the Environmental Protection Agency Energy Star Portfolio Manager benchmarking program and the American Society for Health Care Engineering Energy to Care program, which provide guidance on energy-saving measures and help energy managers track reductions in consumption, although few share these data publicly. Numerous case studies and analyses have demonstrated significant cost savings from improved environmental management in health care facilities.16-18
Some individual health systems have demonstrated exemplary leadership in sustainable operations, although they do not provide consistent disclosures. Among them is Kaiser Permanente, which eliminated its 800,000-ton carbon footprint through energy efficiency and purchased carbon offsets, reaching carbon neutrality in 2020, the first U.S. health system to do so.19
Spaulding Rehabilitation Hospital in Boston, MA, was built to withstand sea level rise, with a first floor above the flood zone and critical infrastructure placed on higher floors. This facility can remain fully operational during extreme storms and floods.20
While most hospitals do not provide quantitative information regarding sustainable operations, some declare commitments, goals, and/or achievements in marketing materials and/or sporadically and informally (e.g., press release or website) disclose progress toward a wide variety of environmental sustainability initiatives (Table 2).
Table 2.
Selected Sustainability Initiatives Undertaken by U.S. Health Care Delivery Organizations
Energy use efficiencyWater use efficiencyWaste reduction and recycling programsReduced red bag wasteCleaner energy purchasesHVAC setbacksLED lighting retrofitBuilding retrofitting or recommissioningReduced purchase of single-use productsReprocessing medical devicesEnvironmentally preferable product purchasingLocal and organic food sourcingReduced meat and dairy procurement
Greenspace creationSustainable grounds managementFacility-grown produceReduced consumption of inhaled anesthetic gases, and elimination of desfluraneClean-fuel fleet vehiclesEncouragement of rideshares, public, and active transportationProvision of charging stations for electric vehiclesLEED constructionClimate-resilient constructionReduced use of harmful cleaning chemicalsFossil fuel divestment commitmentsCarbon reduction commitments
HVAC = heating, ventilating, and air conditioning, LED = light-emitting diode, LEED = Leadership in Energy and Environmental Design. Source: The authors
Working groups and academics have explored pathways and roadmaps for decarbonizing the U.S. health care sector and moving it toward sustainable operations.21,22 Some call for action linked to the U.S. Centers for Medicare and Medicaid Services (CMS) reimbursement.11,23 Recently, as part of broader efforts to address climate-related health impacts, the Department of Health and Human Services (HHS) announced that new regulations to reduce health care emissions are likely24,25 and that it is working with the National Academy of Medicine Action Collaborative on Decarbonizing the U.S. Health Sector to identify pathways to operationalize sector-wide reductions. These efforts could be modeled on the existing Federal Government Sustainability Program, which has tracked and reduced emissions and waste in federal buildings, including Veterans Administration Medical Facilities, since 2007.
VBC, SDOH programs, and ESG share a similar ethos and can be envisioned as concentric circles centered around the patient.
Nonetheless, there is no sector-wide push from academic or industry leaders, government, financial backers, regulators, lawmakers, or payors for mandated ESG participation by health care organizations that mirrors the sustainability revolution underway on Wall Street. A lack of clear guidance results in fragmented activity with scattered examples of environmental performance, mostly without verified supporting data. Practically speaking, this means that pollution, emissions, and waste are most likely unaccounted for and unmanaged by U.S. health care organizations.
Reasons for lack of engagement in ESG by the U.S. health care delivery sector have not been systematically explored, but barriers to participation may include: (1) the misperception that sustainability programs are costly or burdensome, especially for hospitals with slim operating margins; this bias persists, even though sustainability programs can provide substantial cost savings/avoidance and improve a wide variety of measures of corporate performance (see full discussion in Value Drivers for ESG: The Financial Benefits of Sustainability Management and Reporting for Health Care Organizations); (2) the rapid pace of health system mergers and acquisitions can complicate efforts to undertake ESG accounting; (3) hospital executives tend to be recruited from within the health care industry and may be unfamiliar with or lack exposure to cutting-edge business practices emerging in other economic sectors; and (4) the sentiment that the enormous societal benefit of providing care exempts health care organizations from the perceived burden of reducing pollution, enhancing social infrastructure, or demonstrating good governance.
Operational Drivers for ESG in Health Care: Keeping the Doors Open
As critical as it is to reduce emissions, health care organizations also face the need to adapt all operations and systems to ensure doors remain safely open as the climate crisis worsens. The most recent Intergovernmental Panel on Climate Change report confirms that climate-related disasters are certain to escalate near term, leading to more hurricanes, floods, extreme precipitation events, wildfires, droughts, extreme heat and cold, infectious disease spread, political conflict, and forced migration.26 Hospitals provide life-supporting services in times of need and often are anchor institutions within their communities. When hospitals are damaged or unable to safeguard health care workers, their capacity to deliver care diminishes, compounding harms beyond employees to patients and their families and the communities they serve.27
Extreme weather events and the Covid-19 pandemic have stretched U.S. health systems beyond capacity and brought the industry to an inflection point — how can care delivery systems operate safely and reliably in the face of converging and escalating threats, a portion of which is caused by their own activities?28 This is the exact challenge large corporations such as The Coca-Cola Company are confronting and a primary impetus for the rapid rise of ESG.
In 2015, the Financial Stability Board, an international body that monitors global financial systems, created the Task Force on Climate-Related Financial Disclosures (TCFD) to determine ESG metrics that best capture the financial, social, and physical infrastructure risks from growing climate instability. These metrics, in addition to current disaster-planning requirements (e.g., local/state regulations, CMS disaster-preparedness rules, and HHS Health Care Readiness Programs), would help hospitals recognize the totality of the risks they face as the climate crisis worsens.
Unmeasured and unmanaged, climate risks to health care operations are substantial and, for some locations, perhaps existential. Adapting physical and social health care infrastructure to climate change will require hyperlocal climate risk assessment (e.g., emergency management mapping inclusive of sea level rise) and a deep knowledge of the unique needs related to care delivery. ESG and TCFD climate-risk disclosures provide a framework around which health care organizations can strategically anticipate and manage threats from a rapidly destabilizing climate.
Mission Drivers for ESG: Getting to “Triple Aim” via the “Triple Bottom Line”
The mission of health care, the “why” — improve health — is nearly identical to the “what” — deliver care. Few economic endeavors can claim such a close alignment between product and purpose. The health care mission encompasses a triple aim: improving health outcomes and quality through improved patient experience, advancing population health, and reducing costs.29 A number of industry drivers for meeting the triple aim are shifting the care delivery landscape. Chief among these is value-based care (VBC), which seeks savings from improved health outcomes.
The current predominant payment model, fee-for-service, leads to overconsumption of health care and increased waste, costs, and pollution without necessarily improving health outcomes.30 Health care overuse and lack of preventive care resulting in poor health outcomes leads not only to higher direct health care costs, but also to indirect costs related to environmental and social damage,31 costs not captured in traditional accounting. ESG would provide the tools to expose the hidden or “true” costs associated with overuse, waste, pollution, and poor health outcomes, better aligning with VBC.
The Covid-19 pandemic sheds new light on the importance of enhancing social infrastructure and investing in health care workforce safety and well-being.
Parallel to and intertwined with the VBC trend is an increased emphasis on the social determinants of health (SDOH). Up to 80% of health outcomes are determined by social, behavioral, and environmental factors.32 This recognition has prompted health care spending totaling more than $2.5 billion in recent years toward addressing these factors: community programs related to housing, food insecurity, transportation, employment, local purchasing, and education.32 Such programs cement the broader role of health care organizations in society, help them meet their mission, and further the creation of healthy, sustainable communities.
VBC, SDOH programs, and ESG share a similar ethos and can be envisioned as concentric circles centered around the patient. ESG actions make up the outermost loop to close in meeting the health care mission and are consistent with the emergent concept of planetary health care that acknowledges the crucial links among ecological change, human health, and our ability to thrive.31
Health care organizations have a duty to do no harm, or at least less harm, to the ecosystems on which we depend. ESG gives a more complete accounting of costs and benefits that can inform a wider range of strategic actions and accountability to avert harms and enhance population well-being, consistent with the planetary care framework (Figure 6).
Social Capital: ESG to Enhance the Health Care Workforce
Over time, the definition of sustainable enterprise has evolved from a primary focus on the environment to a broader consideration that also encompasses impacts on social systems and human capital, particularly workforces. The Covid-19 pandemic sheds new light on the importance of enhancing social infrastructure and investing in health care workforce safety and well-being. Covid-19 death rates in health care workers have been significantly higher among lower-paid workers of color who provided hands-on front-line care and critical support services.33 Safe worksites and DEI are core features of the “S” in the ESG framework. The Coca-Cola Company, like many large companies, discloses progress toward creating equitable, safe, and healthy workplaces and communities (Figure 7). Dignity Health has done the same (Figure 8).
ESG normalizes the concept that health care workers are human capital to be strengthened rather than cost centers to be driven down.
Workforce safety is more than just ensuring adequate personal protective equipment during a pandemic. Health systems must systematically analyze, account for, and fully disclose benchmarked actions that enhance the safety and well-being of workers, especially given the racial disparities in the health care workforce. The industry must make long-term investments that foster well-being and holistic resiliency. Newer ESG frameworks emphasize action and accounting on leading indicators of workforce well-being, such as preventive and health promotion programs, rather than on lagging indicators, such as simple accounting of injuries and death. In addition to direct and indirect cost savings (Table 3), health care workforce engagement and well-being are linked to better patient outcomes.34
Table 3.
ESG Value to Health Care Organizations
Value chain
Performance benefit
Direct/stewardshipT1
Reduced costs from energy use and emissions intensity; water use; air treatment; and solid waste managementReduced health insurance costsReduced forbearance costs (e.g., payment defaults related to inequality/financial stress)Reduced costs from catastrophic events and recovery (e.g., infrastructure repairs, lost research/academic output, reduced workforce productivity/capacity, lost revenue due to closures/treatment delays, reduced demand for care due to migration)Reduced legal risks/costs
Capital access/costT2
Respond to pressure for ESG from wide range of investorsAlpha strategies (outperform the market)Beta strategies (lower risk, diversify)Improved corporate financial performanceMeet demand for good stewardship from donors, funders, municipal bond issuers, capital markets, philanthropy, contractors, business partnerships, local/state/federal governmentsImproved bond ratingsImproved due diligence for mergers/acquisition
Intangible/reputationT3
Enhanced brand/reputational value (80% stock market value linked to ESG)Enhanced confidence/loyalty from patients, employees (current/potential), academic researchers, students, trainees, regulators, policy makers, communities, business partners and suppliersCompetitive advantage in multihealth system settingReduced risks from lower revenue, higher cost of employment (recruitment/retention), higher cost of goods and services, and reduced access to critical permits and operating constraintsReduced risk to board from activists (e.g., link ESG performance to executive pay)Reduced risk of stigmatization
RegulationT4
Enhanced regulatory compliance (current/future)Reduced risk of fees/fines related to noncomplianceStronger management of risks/opportunities (e.g., routine stakeholder feedback)SEC ESG regulation and disclosure could apply to both profit and nonprofit health care organizations
ESG normalizes the concept that health care workers are human capital to be strengthened rather than cost centers to be driven down. Millennials — now the largest portion of the U.S. workforce — place greater emphasis on environmental and social values when making financial, educational, and employment decisions.35 They are highly sensitized to authenticity and likely to reject employment or educational opportunities with institutions that do not align with these values. ESG would give health care organizations a wider lens and more formal mechanisms by which to demonstrate strong ESG values that would maximize the recruitment, retention, and overall well-being of medical undergraduate and graduate trainees, high-value researchers, skilled staff, and allied health care workers.
Value Drivers for ESG: The Financial Benefits of Sustainability Management and Reporting for Health Care Organizations
Beyond the idea that addressing environmental and social externalities from health care is the “right thing to do,” growing evidence indicates that these efforts have significant and wide-ranging positive impacts on financial and business performance that can provide substantial cost savings, reduce risks, and improve numerous measures of corporate performance. Climate change–related business risks and costs are increasing. Climate-driven severe weather events disrupt supply chains, physical infrastructure, and workforce and business operations; increase health risks (e.g., heat and infectious disease); and increase the costs associated with recovery management. Extreme events and climate-related disasters that cost at least $1 billion dollars have risen steadily over the past 20 years.36
Hospital financing is complex, and accurate national financial accounting and disclosures are difficult to find. ESG could create greater transparency in hospital financial disclosures. The U.S. Internal Revenue Service requires nonprofit hospitals to demonstrate benefit by assessing community health needs every 3 years to maintain their tax-exempt status. ESG provides a framework for health care organizations to demonstrate their benefit to communities more broadly and holistically. ESG disclosures would appeal to a broad range of health care stakeholders, thereby enhancing community acceptance and facilitating licenses to operate, meeting local and state regulations related to public need and safety, staffing, and operational requirements. In addition to providing a tax advantage, ESG could benefit the health care organization value chain through four broad categories: direct/stewardship (cost savings), capital access/cost, intangible/reputation, and regulation (Table 3).
Integrating ESG into Health Care Operations
The U.S. health care sector must measure, manage, mitigate, and transparently disclose the negative environmental and social externalities associated with health care delivery and must prepare for rapidly escalating climate-related threats. The National Health Service (NHS) in England provides valuable inspiration — the first national health system to commit to carbon net zero. The NHS has tracked, managed, and reduced health care GHG emissions since a parliamentary mandate with the Climate Act of 2008 (Figure 9).37
To achieve a similar outcome in the United States, all health care organizations must participate, and all relevant ESG data must be nonproprietary and assured by credible accounting firms. All hospitals, nonprofit, profit, and local/state-controlled, should, like The Coca-Cola Company, communicate ESG progress through annual publications. The industry’s current limited engagement in sustainability management and disclosure suggests that voluntary efforts will not meaningfully move the needle.12 Pathways and mechanisms to ensure participation and to operationalize ESG in the health care sector are outlined in Table 4. To avoid undue financial or administrative burden in operationalizing ESG, federally funded technical support, expert sustainability consulting and accounting services, and financial advising should be uniform and freely available to all health care organizations regardless of tax status.
Table 4.
Key Elements to Operationalize ESG in the U.S. Health Care Sector
Mandate participation for all U.S. health care organizations receiving federal funding through HHS.Link ESG performance to CMS Conditions of Participation and reimbursements.Build off existing reporting frameworks (e.g., GRI, SASB, and TCFD) to create core health care ESG metrics for:Environmental impacts (reconciled with health outcomes), climate risks (e.g., climate modeling to determine hyperlocal risks to supply chains, physical and social infrastructure, and community resilience)Social impacts (e.g., workforce well-being, fair wages, and DEI)Governance (e.g., board diversity, ethics, link ESG performance to executive pay)Require third-party assurance of ESG measures through auditing that evaluates processes, systems, and data.Map ESG actions to the UNSDGs.Require public disclosure in a format consistent with currently available disclosure frameworks.Collaborate with public and private sector ESG and sustainability experts to bring best-practice sustainability science into hospital operations and care delivery.Fund academic research into sustainability science and health services research, including quality and safety. (Funding: Agency for Healthcare Research and Quality).Establish a national health care sustainability task force for oversight of all programs housed at HHS.Create a national data center, funded by and housed at HHS and tasked to:Capture facility- and system-level ESG data through the American Hospital Association/EPA Energy Star program for operations/infrastructure and CMS for clinical operations and outcomes.Develop baselines, benchmarks, KPIs, and best practices in ESG metric creation.Create and publish annually a Sustainability Scorecard for every facility and health system.Build sustainability science into allied health professional education.Encourage incorporation of ESG metrics into national health system rankings (e.g., U.S. News & World Report).
ESG = Environment, Social, and Governance, HHS = Department of Health and Human Services, CMS = U.S. Centers for Medicare & Medicaid Services, GRI = Global Reporting Initiative, SASB = Sustainability Accounting Standards Board, TCFD = Task Force on Climate-Related Financial Disclosures, DEI = diversity, equity, and inclusion, UNSDG = United Nations Sustainable Development Goals, EPA = Environmental Protection Agency, KPI = key performance indicator. Source: The authors
Conclusions
Kaiser Permanente, Cleveland Clinic, and many other hospitals and health systems have demonstrated leadership in environmentally sustainable operations and inspire possibilities on a larger scale. Yet, without standardized metrics, outside assurance, and transparent disclosure, there is no way to verify the true impact or value of efforts, develop best practices to inform sector-wide actions, or ensure accountability. Voluntary efforts will inevitably wane and fall short of well-intentioned promises. A unified, sector-wide “all-in” mandate that incentivizes specific ESG actions and disclosures is the only way to rapidly operationalize and ensure enduring progress.
Organizational systems already exist in most hospitals that can capture ESG measures. For instance, many hospitals routinely convene environment of care committees for which measures of sustainable operations would make a natural fit. Similarly, human resource departments capture data that can illuminate ways to enhance workforce resiliency. Annual reports, already published by many hospitals and health systems, could be expanded to communicate to stakeholders environmental and social progress and policies for good governance. Creating uniformity in these disclosures will help track sustainability progress sector wide and reduce greenwashing.
Some might argue that more regulatory reporting is unfairly burdensome to health care institutions, especially because sustainability measures in general are evolving and greenwashing abounds. Metrics are an area of intense debate, especially when it comes to accounting for carbon embodied in the supply chain and developing measures that facilitate comparability within and among industries. Health care delivery has additional unique, but not insurmountable, sustainability challenges. Reconciling negative externalities to quantum of health delivered will be the most challenging, given that some environmental and social harms are simply unavoidable.
The cost savings and performance improvements associated with ESG accounting should alone compel action.
But the answer is not less engagement, rather more. The cost savings and performance improvements associated with ESG accounting should alone compel action. Engaging the health care sector’s enormous intellectual and financial capital could greatly contribute to sustainability science, particularly as it relates to direct and indirect impacts on ecosystems and human health — arguably the only metrics that matter. Consider the role medical academia could play in the emerging sustainability science that seeks to more fully understand and quantify positive restorative actions — so-called “handprints” — to offset harms from footprints.38 ESG would help move health care action upstream to disease prevention and health promotion, thereby reducing disease burden, improving health outcomes, and lowering health care costs and related environmental emissions and pollution and would realign health care delivery with its mission — the restoration and protection of human health.
Integrating a sustainability program into enterprise operations begins with a thorough analysis of all stakeholders within the sphere of corporate influence. This not only engages stakeholders, but also gives them a voice; consider the positive impact this would have on the health care workforce, which has faced the dual crises of the pandemic and extreme events. Meeting the challenge presented by the growing climate crisis requires accounting for and strengthening both social and physical infrastructure. Stakeholder analysis gives rich insight into operational strengths, weaknesses, risks, opportunities, and emerging trends.
Social and environmental data can inform the creation of strategic priorities; pinpoint areas in need of change; set baselines, benchmarks, and performance goals; and mark progress toward continual improvement. Good governance would ensure commitments are kept.
While many large corporations such as The Coca-Cola Company are working to create a more sustainable enterprise, no sector of society alone can stem the tide of converging threats. The climate crisis is here; the pandemic persists; new threats loom.39 In 2019, the United States spent more on health care than did any other high-income country in the world, up to a third of which is waste40 — only to rank last in measures of health outcomes.41 Compounding this failure, the true costs associated with wasted care (pollution, extreme events, and social instability) are unmeasured and unmanaged. The U.S. health care sector can no longer afford not to act on sustainability. It is time for bold, collective action. Health care organizations must strive to be at least as good as a sugared beverage company when it comes to protecting people and the planet.
The Omicron variant of SARS-CoV-2 has renewed attention on Covid-19 antigen test kits. They are in high demand across the United States, and many Americans are having trouble getting them, prompting President Biden to announce that the White House will buy and ship 500 million rapid tests for free beginning in January.
The untold backstory here is that many people are growing accustomed to simultaneously serving as doctor and patient. Before the pandemic struck, the idea that millions of people would administer tests on themselves at home to determine whether they had a life-threatening illness seemed difficult to imagine. Today, it’s part of daily life.
While home-based antigen tests have made a questionable epidemiological contribution to tracking and confronting the pandemic, the real value they present goes far beyond Covid-19. If people trust their results, the ultimate outcome of the U.S. government investing an expected $50 billion to subsidize at-home testing may spark movement on many fronts that radically decentralizes health care and changes what Americans expect from and how they access care.
The current discussion around antigen testing is naturally focused on the availability, cost, and efficacy of tests. Trust is another big issue. The rush for Food and Drug Administration approval may have backfired, undermining trust in the tests for some and creating a false sense of security for others.Related: Scientists try to pinpoint why rapid Covid tests are missing some cases
Still, as Americans come to value rapid home testing for Covid-19, I believe this will usher in a new era of health care in which people feel comfortable testing for a variety of conditions at home because of their positive experiences during the pandemic.
Just a few years ago, trying to engineer a shift like this would have seemed like a far-off dream requiring years of education, billions of dollars, and a dramatic change in mindset. It is astounding that this transformation appears to be taking place with relative ease.
Antigen tests are just the start. The public is increasingly familiar with — and has come to expect — tools that allow access to health care on their own terms, such as at-home tests for everything from strep throat to food sensitivities, FaceTime calls with their physicians, and prescription medicines delivered to their doors. That expectation is not going away anytime soon. The dilemma that health tech companies now face is finding ways to take advantage of the positive aspects of this trend while guarding against the challenges created in its wake.
My company, Healthy.io is, for example, working on applying this approach to chronic kidney disease. We call it “health care at the speed of life” because it means people will be able to take lifesaving medical tests in their own homes, when it is most convenient for them, without having to find their way to a lab or clinic or doctor’s office
This is consistent with the key lessons of the U.S.’s massive experiment with at-home antigen testing. Specifically, everyone should have access to key tests, not just those who can afford upfront costs. Otherwise, the new system will only reinforce the shortcomings of the one it replaces.
At-home tests must adhere to high clinical-grade standards. To have any diagnostic value, patients and practitioners alike need to believe that the tests are as effective and as reliable as those used in medical offices.
For the at-home testing revolution to truly benefit patients, it must also tackle systemic issues confronting the health care system. As Covid-19 home testing has shown, if a test exists but is too expensive for the people who need it most, it will simply reinforce existing inequities in health care.
The pandemic has forced Americans to address their health care needs in new and innovative ways. To their credit, they’ve done so with gusto and determination. We now have a golden opportunity to rewrite the rules of the game in health care. That must be done today, and in the right way, or it will be lost tomorrow.
Artificial intelligence (AI) is gaining high visibility in the realm of health care innovation. Broadly defined, AI is a field of computer science that aims to mimic human intelligence with computer systems.1 This mimicry is accomplished through iterative, complex pattern matching, generally at a speed and scale that exceed human capability. Proponents suggest, often enthusiastically, that AI will revolutionize health care for patients and populations. However, key questions must be answered to translate its promise into action.
What Are the Right Tasks for AI in Health Care?
At its core, AI is a tool. Like all tools, it is better deployed for some tasks than for others. In particular, AI is best used when the primary task is identifying clinically useful patterns in large, high-dimensional data sets. Ideal data sets for AI also have accepted criterion standards that allow AI algorithms to “learn” within the data. For example, BRCA1 is a known genetic sequence linked to breast cancer, and AI algorithms can use that as “the source for truth” criterion when specifying models to predict breast cancer. With appropriate data, AI algorithms can identify subtle and complex associations that are unavailable with traditional analytic approaches, such as multiple small changes on a chest computed tomographic image that collectively indicate pneumonia. Such algorithms can be reliably trained to analyze these complex objects and process the data, images, or both at a high speed and scale. Early AI successes have been concentrated in image-intensive specialties, such as radiology, pathology, ophthalmology, and cardiology.2,3
However, many core tasks in health care, such as clinical risk prediction, diagnostics, and therapeutics, are more challenging for AI applications. For many clinical syndromes, such as heart failure or delirium, there is a lack of consensus about criterion standards on which to train AI algorithms. In addition, many AI techniques center on data classification rather than a probabilistic analytic approach; this focus may make AI output less suited to clinical questions that require probabilities to support clinical decision making.4 Moreover, AI-identified associations between patient characteristics and treatment outcomes are only correlations, not causative relationships. As such, results from these analyses are not appropriate for direct translation to clinical action, but rather serve as hypothesis generators for clinical trials and other techniques that directly assess cause-and-effect relationships.
What Are the Right Data for AI?
AI is most likely to succeed when used with high-quality data sources on which to “learn” and classify data in relation to outcomes. However, most clinical data, whether from electronic health records (EHRs) or medical billing claims, remain ill-defined and largely insufficient for effective exploitation by AI techniques. For example, EHR data on demographics, clinical conditions, and treatment plans are generally of low dimensionality and are recorded in limited, broad categorizations (eg, diabetes) that omit specificity (eg, duration, severity, and pathophysiologic mechanism). A potential approach to improving the dimensionality of clinical data sets could use natural language processing to analyze unstructured data, such as clinician notes. However, many natural language processing techniques are crude and the necessary amount of specificity is often absent from the clinical record.
Clinical data are also limited by potentially biased sampling. Because EHR data are collected during health care delivery (eg, clinic visits, hospitalizations), these data oversample sicker populations. Similarly, billing data overcapture conditions and treatments that are well-compensated under current payment mechanisms. A potential approach to overcome this issue may involve wearable sensors and other “quantified self” approaches to data collection outside of the health care system. However, many such efforts are also biased because they oversample the healthy, wealthy, and well. These biases can result in AI-generated analyses that produce flawed associations and insights that will likely fail to generalize beyond the population in which they are generated.5
What Is the Right Evidence Standard for AI?
Innovations in medications and medical devices are required to undergo extensive evaluation, often including randomized clinical trials and postmarketing surveillance, to validate clinical effectiveness and safety. If AI is to directly influence and improve clinical care delivery, then an analogous evidence standard is needed to demonstrate improved outcomes and a lack of unintended consequences. The evidence standard for AI tasks is currently ill-defined but likely should be proportionate to the task at hand. For example, validating the accuracy of AI-enabled imaging applications against current quality standards for traditional imaging is likely sufficient for clinical use. However, as AI applications move to prediction, diagnosis, and treatment, the standard for proof should be significantly higher.1 To this end, the US Food and Drug Administration is actively considering how best to regulate AI-fueled innovations in care delivery, attempting to strike a reasonable balance between innovation, safety, and efficacy.
Using AI in clinical care will need to meet particularly high standards to satisfy clinicians and patients. Even if the AI approach has demonstrated improvements over other approaches, it is not (and never will be) perfect, and mistakes, no matter how infrequent, will drive significant, negative perceptions. An instructive example can be seen with another AI-fueled innovation: driverless cars. Although these vehicles are, on average, safer than human drivers, a pedestrian death due to a driverless car error caused great alarm. A clinical mistake made by an AI-enabled process would have a significant chilling effect. Thus, ensuring the appropriate level of oversight and regulation is a critical step in introducing AI into the clinical arena.
In addition to demonstrating its clinical effectiveness, evaluation of the cost-effectiveness of AI is also important. Huge investments into AI are being made with promised efficiencies and assumed cost reductions in return, similar to robotic surgery. However, it is unclear that AI techniques, with their attendant needs for data storage, data curation, model maintenance and updating, and data visualization, will significantly reduce costs. These tools and related needs may simply replace current costs with different, and potentially higher, costs.
What Are the Right Approaches for Integrating AI Into Clinical Care?
Even after the correct tasks, data, and evidence for AI are addressed, realization of its potential will not occur without effective integration into clinical care. To do so requires that clinicians develop a facility with interpreting and integrating AI-supported insights in their clinical care. In many ways, this need is identical to the integration of more traditional clinical decision support that has been a part of medicine for the past several decades. However, use of deep learning and other analytic approaches in AI adds an additional challenge. Because these techniques, by definition, generate insights via unobservable methods, clinicians cannot apply the face validity available in more traditional clinical decision tools (eg, integer-based scores to calculate stroke risk among patients with atrial fibrillation). This “black box” nature of AI may thus impede the uptake of these tools into practice.
AI techniques also threaten to add to the amount of information that clinical teams must assimilate to deliver care. While AI can potentially introduce efficiencies to processes, including risk prediction and treatment selection, history suggests that most forms of clinical decision support add to, rather than replace, the information clinicians need to process. As a result, there is a risk that integrating AI into clinical workflow could significantly increase the cognitive load facing clinical teams and lead to higher stress, lower efficiency, and poorer clinical care.
Ideally, with appropriate integration of AI into clinical workflow, AI can define clinical patterns and insights beyond current human capabilities and free clinicians from some of the burden of integrating the vast and growing amounts of health data and knowledge into clinical workflow and practice. Clinicians can then focus on placing these insights into clinical context for their patients and return to their core (and fundamentally human) task of attending to patient needs and values in achieving their optimal health.6 This combination of AI and human intelligence, or augmented intelligence, is likely the most powerful approach to achieving this fundamental mission of health care.
A Balanced View of AI
AI is a promising tool for health care, and efforts should continue to bring innovations such as AI to clinical care delivery. However, inconsistent data quality, limited evidence supporting the clinical efficacy of AI, and lack of clarity about the effective integration of AI into clinical workflow are significant issues that threaten its application. Whether AI will ultimately improve quality of care at reasonable cost remains an unanswered, but critical, question. Without the difficult work needed to address these issues, the medical community risks falling prey to the hype of AI and missing the realization of its potential.
Corresponding Author: Thomas M. Maddox, MD, MSc, Cardiovascular Division, Washington University School of Medicine/BJC Healthcare, Campus Box 8086, 660 S Euclid, St Louis, MO 63110 (tmaddox@wustl.edu).
Conflict of Interest Disclosures: Dr Maddox reports employment at the Washington University School of Medicine as both a staff cardiologist and the director of the BJC HealthCare/Washington University School of Medicine Healthcare Innovation Lab; grant funding from the National Center for Advancing Translational Sciences that supports building a national data center for digital health informatics innovation; and consultation for Creative Educational Concepts. Dr Rumsfeld reports employment at the American College of Cardiology as the chief innovation officer. Dr Payne reports employment at the Washington University School of Medicine as the director of the Institute for Informatics; grant funding from the National Institutes of Health, National Center for Advancing Translational Sciences, National Cancer Institute, Agency for Healthcare Research and Quality, AcademyHealth, Pfizer, and the Hairy Cell Leukemia Foundation; academic consulting at Case Western Reserve University, Cleveland Clinic, Columbia University, Stonybrook University, University of Kentucky, West Virginia University, Indiana University, The Ohio State University, Geisinger Commonwealth School of Medicine; international partnerships at Soochow University (China), Fudan University (China), Clinica Alemana (Chile), Universidad de Chile (Chile); consulting for American Medical Informatics Association (AMIA), National Academy of Medicine, Geisinger Health System; editorial board membership for JAMIA, JAMIA Open, Joanna Briggs Institute, Generating Evidence & Methods to improve patient outcomes, BioMed Central Medical Informatics and Decision Making; and corporate relationships with Signet Accel Inc, Aver Inc, and Cultivation Capital.
Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402-2410. doi:10.1001/jama.2016.17216ArticlePubMedGoogle ScholarCrossref
Harrell F. Is medicine mesmerized by machine learning? Statistical Thinking website. http://fharrell.com/post/medml/. Published February 1, 2018. Accessed October 26, 2018.
ARTIFICIAL INTELLIGENCE USED to mean something. Now, everything has AI. That app that delivers you late-night egg rolls? AI. The chatbot that pops up when you’re buying new kicks? AI. Tweets, stories, posts in your feed, the search results you return, even the people you swipe right or left; artificial intelligence had an invisible hand in what (and who) you see on the internet.
But in the walled-off world of health care, with its HIPAA laws and privacy hot buttons, AI is only just beginning to change the way doctors see, diagnose, treat, and monitor patients. The potential to save lives and money is tremendous; one report estimates big data-crunching algorithms could save medicine and pharma up to $100 billion a year, as a result of AI-assisted efficiencies in clinical trials, research, and decision-making in the doctor’s office. Which is why tech titans like IBM, Microsoft, Google, and Apple are spinning up their own AI health care pet projects. And why every health-focused startup pitching Silicon Valley VCs throws in a “machine learning” or “deep neural net” for good measure.
These algorithms get better the more data they see. And health data is practically hemorrhaging out of mobile devices, wearables, and electronic medical files. But their siloed storage systems don’t make it easy to share that data with each other, let alone with an artificial intelligence. Until that changes, AI won’t be curing the world of, well, probably anything.
Which is not to say AI in health care is all hype. Sure, Watson turned out to be less cancer-crushing computer prodigy and more very expensive electrical bill. But 2017 wasn’t all flops. In fact, this year saw artificial intelligence begin demonstrating real concrete usefulness inside exam rooms and out.
In the doctor’s office, AI is already helping dermatologists tell cancerous growths from harmless spots, diagnose rare genetic conditions using facial recognition algorithms, and lending an assist in reading X-rays and other medical images. Soon, it will be detecting signs of diabetes-related eye disease in India. But image classification isn’t the only thing it’s getting good at; AI can also mine text data. That kind of tech undergirds a platform that gives any primary care doc access to the expertise of specialists from all over the world. No more waiting six months for that referral you can’t really afford anyway. And after you get that diagnosis, you can now take home an AI-equipped robot to help you stick to your treatment plan. It nags, but it looks cute while it’s doing it.
Health care-focused AI has also seeped into virtual care, as medicine experiments with ways to offer preventive care and between-visit support via the omnipresent smartphone. Your phone no longer just tells you how to sleep better, eat healthier, exercise more, and keep a quiet mind. Now, AI can pick up patterns in the way you talk and text, to detect the first signs of depression and suicide risk. And it can help you deal with that stuff too. Amiable chatbots trained on cognitive behavioral therapy concepts are now helping people who can’t find time or money for a proper shrink. For veterans struggling with PTSD, researchers designed a human therapist avatar with a mind built by machine learning. Both approaches take advantage of the fact that people open up better to machines than other humans—the algorithms don’t judge.
Of course, as machine learning powers more and more medical device software, it’s made regulating them a whole lot trickier. This year the US Food and Drug Administration even had to create an entirely new digital health task force just to tackle it. How exactly do you regulate software that is always learning and evolving, constantly changing on the fly? What happens in a zero code world, where AI writes and rewrites its own instructions? Instead of trying to keep up with that radically different pace, the agency is piloting a new course—that certifies trusted companies with good track records, as opposed to individual software packages.
Still, those regulations will only control AI-informed devices, diagnostics, and treatments. The technology is seeping into the practice of medicine at every level, not just at the stage of final device approval. It’s now baked into the way biomedical researchers sift through tsunamis of geneticdata and pharma firmsdiscover new drugs. It’s how public health officials predict the next epidemic, and keep track of opioid hot spots. And it’s increasingly how doctors and scientists try to make sense their data-drenched realities. As AI opens those new avenues of understanding and treating human disease, it’s important to remember that algorithms, like people, are imperfect. They’re only as good as the data they see and the biases they carry.
No matter how many black box neural networks start finding their way into the health care system, medicine is still fundamentally a human endeavor. And people don’t always do what’s best for them, even on a doctor’s orders. Which means the biggest challenge in health care isn’t about changing people’s bodies, but about changing people’s minds. And that’s not the kind of intelligence computers are good at. AI won’t be replacing MDs, anytime soon. But it is coming for their fax machines.
The words You have cancer strike fear in the minds of nearly every patient who has the disease. When it happens, we want the best doctors in the best health care organization to give us the best chance of achieving the outcomes of care personally important to us as individuals, at a price we can afford. As consumers, we traditionally seek value in many spheres. We compare prices and features on TVs, computers, cars — almost everything we bring into our lives. Consumers understand that value is the best quality at the lowest price. But when we become patients, we sometimes stop behaving like consumers. It is unusual to choose our doctor based on how well he or she treats a particular condition, and we rarely have a full picture of what the costs of care will be.
Lately, there has been a lot of talk about value in health care. A value proposition for health care arose from the work of Harvard Business School Professor Michael Porter, who advocates that value for patients should be the overarching principle for fixing our broken system. So what do doctors and patients need to know about value in health care? It is the balance between the health outcomes patients want and the cost to achieve them. Over the past ten years, doctors and hospitals in increasing numbers are trying to assess their value in treating specific problems. They are beginning to measure outcomes of care more regularly and are taking a hard look at health care costs.
High value in health care is a great outcome that matters to the patient at the lowest possible cost. It is neither a lower-cost treatment that has a worse outcome nor a great outcome that is not affordable. It is critically important that doctors and patients understand that outcomes of care are about more than basic questions such as, Will the patient survive an operation? or How long will a patient live with cancer? Outcomes also involve the patient experience — questions such as, Will there be pain with treatment? How long will the patient miss work?, and What will the impact on the family be?In a value-based system, both doctors and patients should know what outcomes to expect, and demand that outcomes be measured and publicly reported. When outcomes of care are reported, doctors can improve their results, and patients can make meaningful choices about where to receive care.
Costs of care delivery — the other part of the value equation — also need to be measured and made transparent to doctors and patients. Cancer is the leading cause of personal bankruptcy, yet most doctors do not know how much the care they outline will cost the patient, and most patients do not know how much their care will cost. It is time that patients know this information and that providers be transparent about their costs.
In addition, in a value-based system how doctors and hospitals get paid will change. Reimbursements will likely evolve to replace fee-for-service payment largely with bundled payments, which will require doctors to be sure they do only what is necessary to get a good health outcome — but not more. Bundled pricing does not mean that doctors will do less because they are getting a fixed amount of money; just the opposite, since bundled payments will be tied to getting the best outcomes. Doctors will be paid more for good outcomes and less for worse outcomes — something that happens in most industries, but has never been true in health care.
In a true value-based system, volume matters. Doctors will see a lot of patients with the same problem and become expert in treating those problems. Right now, too many clinicians try to treat too many different problems in which they may not be expert. As value-based care progresses, not all hospitals will treat everything. In Europe, there is a movement to certify breast cancer centers, where only those that see a lot of patients are certified.
A true value-based health care system controls costs through efficiency, eliminating things that do not need to be done and doing only those things that improve outcomes. Less health care does not mean worse care, often it means better care. Good health outcomes cost less. Lower cost in health care is good for patient health and for our economy. Fixing our health care system cannot be done simply by government fiat, by health care providers, by administrators, by patients, or by any party acting alone — but only with all parties focusing in tandem on value for the patient.
ARYAN'S BLOG 