The following article is an exploration of health care’s path forward from the perspective of a fictional senior leader in the year 2030. It is a companion piece to "Dispatch no. 1: The population aged, as expected, but it was the aging of young people that changed health care in the most surprising ways."
My fellow health care leaders,
Like many of you, I am mostly hopeful about technology’s future place in society. As far as health care is concerned, better technology can yield the ultimate return—lives saved. But the gains are often uneven, and sometimes good technology can have unforeseen, even bad, consequences. I’ve made it the goal of my second dispatch—written in 2030—to help you anticipate and perhaps avoid those consequences.
What follows are five ways that technology’s benefits to health care across the 2020s were met by powerful countervailing impacts. If the story I’m telling you feels unfinished, that’s because it is. I’m at a waypoint in technology’s evolution, much as you are. My waypoint just happens to be a bit further up the road. You decide if you want to meet me here or follow a different path.
In 2030, providers interact with their patients less than ever before. Quite simply, their job is changing. For instance, “diagnosticians” primarily verify and interpret the judgment of AI-powered tools. High-end concierge PCPs serve patients almost exclusively via their digital twins (that is, fully digitized composites based on an individual’s detailed medical records, imaging, diagnostic analyses, and remote monitoring). And specialists like dermatologists manage practices that are often entirely virtual at this point.
Even among clinicians that still spend a lot of time in aggregate with patients—for example, virtual PCPs with 5,000-patient panels—AI’s involvement ensures that their individual interactions are exceptionally brief. Those interactions also tend to be less recurring, as demand for instant access—particularly among millennials and Generation Z—has superseded the traditional doctor-patient relationship.
Primary care providers, behavioral health specialists, and clinicians across a range of medical specialties no longer see, hear, or touch their patients as often as they once did. Their treatment decisions are based on observations made of digitized representations of their patients or algorithmic recommendations. A patient’s values, fears, preferences, and psychosocial needs are harder to grasp this way, and are not relied upon. This is a problem.
The medical profession has always sought balance between the objective truths of science and data with the variable truths of human emotion and values. Trust and empathy are as much for the patient, who needs them to develop commitment to health actions, as they are for the provider, who needs them to make informed, holistic, and sensitive treatment decisions. Yet we’ve built a reality in which there’s a surplus of clinical capability—clinicians and computers are increasingly redundant—and a deficit of empathy.
To their credit, digital health companies improved the ability of AI-powered tools to deliver seemingly human interactions to patients. The anthropomorphizing of health bots is impressive. In testing, many digital interactions are virtually indistinguishable from their human counterparts (no health bots have conclusively passed a Turing test, yet, though I think we’re close). But the personalization bots convey is cosmetic—the veneer of empathy—and ultimately does not impact the underlying guidance they deliver.
The impact of waning provider-patient empathy is most apparent in measures of adherence and loyalty. Low adherence to treatment plans is one of the most vexing challenges for digital health organizations and their payers today. And provider loyalty has become an increasingly quaint concept, generated largely through payer steerage rather than individual preference. Indeed, the most loyal patients are those who have resisted their own digitization, instead seeking out the remaining docs who promise an “old-school” hands-on approach to delivering care.
The consumer health tech marketplace has grown substantially across the decade. Wearables and implanted tech are now more sophisticated and considerably more mainstream than they were in the early 2020s. Ambient computing allows background devices—those not physically attached to people—to passively collect users’ health data. Single sign-on platforms (yes, Amazon Prime) tie together a person’s Internet of Things (IoT), offering instant access to comprehensive personal health information. And AI-powered assistants, enhanced by augmented reality, use that data to prompt behavioral changes in line with an individual’s health and wellness goals.
The health care industry’s ambition to transfer control over care management to patients has ostensibly succeeded, but patients’ control is often illusory. As individuals deepened their trust in devices, they pulled medical influence away from clinicians and handed it to technology companies. To be sure, tech-led personal wellness is convenient, and some patients can fairly credit personal devices and apps for knowing them better than any medical professionals. But the premium put on convenience has prevented many patients from examining the underlying incentives of the companies that seek to shape their behavior. It’s worth noting that many of those companies still use a business model that’s built upon advertising revenue and cross-promotion of their goods and services.
“Technological homeopathy,” a term you may come to know, is creating less efficient use of the traditional care delivery system. Today, we see high levels of unnecessary utilization—and delayed utilization—of health care services because of inaccurate or misinterpreted guidance from digital personal health assistants.
Technological homeopathy has also contributed to a new and somewhat paradoxical digital divide. Individuals who have resisted turning to the IoT as a replacement for traditional access points—instead considering it as a useful supplement—indicate better health outcomes and more predictable utilization of the health care system.
For their part, even clinicians who view technology as a great enabler of their own work are frustrated by the interactions they have with tech-empowered patients. Many of these patients feel emboldened to disagree with or debate medical professionals about their health status and treatment needs. As a result, clinicians increasingly discourage the use of consumer health technology and resist engaging with the data produced by that technology.
It may be hard for you to believe, but social media giants gained control over most misinformation ahead of the 2024 election cycle. They did so through a combination of governmental action and self-regulation. Today, in 2030, the major social media platforms are unremarkable—if pervasive—features of daily life. One colleague of mine likens them to “operating systems” for our routine professional, commercial, and social interactions.
But as the social media giants were tamed, others filled the void. Smaller, anonymous, identity-driven networks cropped up and spread quickly. Dissociation from broader society became a defining characteristic of these club-like environments, which perpetuate a cycle of digital hyperconnectivity and distrust. Today, physical and behavioral health conditions—including loneliness, anxiety, depression, and sleep disorders—are widespread among users of these new platforms. To make matters worse, those users’ deep immersion in artificial public spaces renders them frustratingly difficult for the health care community to reach.
And new social media platforms aren’t the only forces blurring lines between real public spaces and artificial ones. Advancements in augmented reality, natural language processing, voice recognition, and holographic technology are changing the ways people experience digital life. We no longer need to interact directly with hardware—screens, keyboards, touchpads—to access digital environments. New entry points are seamlessly integrated into the physical spaces around us. In some cases, they are integrated into our persons.
This movement toward “disappearing hardware” has been heralded as a public health victory. Yet it masks insidious challenges to physical and behavioral health. The incentives among technology companies to keep users engaged in their content ecosystems remain as strong as ever. If anything, the negative mental health impacts of digital isolation are more pronounced because individuals have a harder time separating digital experiences from analog ones.
The U.S. Food and Drug Administration (FDA) has approved over 60 new cell and gene therapies (CGTs) since 2021. Now, in 2030, we’re averaging upwards of 20 new approvals each year9. While the number of patients receiving CGTs remains small in comparison with the total U.S. population and overall disease incidence, the impact of CGTs on health care financing is profound. Single-dose treatments can cost $500K to over $4M.
The hope is that the price of CGTs will be tempered by reducing a patient’s total cost of care over his or her lifetime. Many players in our industry hoped that commercial value-based payment models or installment plans would reduce the economic toll that CGTs can inflict upon payers. But significant obstacles remain even in 2030. We lack a robust infrastructure for collecting and analyzing data on treatments whose efficacy manifests across an entire lifetime. And the transient nature of health insurance at the individual level makes it hard for payers and purchasers to define “value” for these treatments.
Self-insured employers have been particularly disadvantaged, given the major financial hit that even a single treatment episode could deliver. Larger corporations are sometimes able to cover CGTs by spreading risk through stop-loss coverage or reinsurance, but even these tools are increasingly expensive and restrictive on account of growing utilization of ultra-expensive drugs. Many employers opt not to cover gene therapies altogether.
For their part, state Medicaid programs vary greatly in their utilization management and reimbursement policies for CGTs. Some programs severely constrain utilization by enforcing eligibility criteria that are narrower than the FDA label. Others make coverage decisions based on individual medical necessity, leaving room for clinical judgment to be unevenly applied. And provider reimbursement is equally varied. Some states with healthier Medicaid budgets have created carveouts in their bundled payments for expensive CGTs, while other states have been unwilling or unable to do so.
As more CGTs hit the market, and inconsistent access became more apparent, the government gradually expanded Medicare eligibility to include individual conditions for which a CGT is the most effective therapy, regardless of a beneficiary’s age. While Medicare has become a de facto public option for a growing share of the U.S. population, taxpayers are as averse as ever to new or increased taxes to fund the program. The government’s long-term bet is that high up-front costs for CGTs will be recouped through savings over time. But the near-term financing challenges have forced additional cost-control measures, including heavy payment cuts to providers.
Cybersecurity threats are now, in 2030, even more problematic than before (yes, it was possible). Ransomware attacks zeroed in on hospital-based delivery organizations due to their critical role in society and relatively vulnerable IT. As attacks became more potent and sophisticated, the cost to defend against them rose. Some providers mitigated risk through cybersecurity insurance, but that is cost-prohibitive for many organizations and doesn’t prevent operational interruptions. Around 2025, ransom itself became a line item in the average hospital budget. Legislative efforts to ban such payments have largely failed.
The increased financial burdens on hospitals gave way to further consolidation of the provider marketplace. Vulnerable independent hospitals and small health systems sought protection in so-called “right to survive” deals that never met with more than perfunctory FTC scrutiny. And while large health systems were able to protect their hospital assets from operational disruptions, that protection carried a hefty cost that was ultimately passed along to payers.
The government was willing to let consolidation run its course, but it was far less willing to turn a blind eye toward increased price growth. Federal legislation created a national all-payer rate-setting framework, not unlike the state-level system used by Maryland since the late 1970s. When price controls went into force, hospital-based provider organizations effectively became public utilities.
The combination of substantial regulation, persistently poor margin performance, and heightened risk of cyber-attack constrained access to capital for hospitals, particularly not-for-profits. After cybersecurity upgrades, much of a hospital’s available capital is typically allocated to systems maintenance, break-fix projects, and post-Covid era safety upgrades. This leaves little room for major forward-looking construction and reconstruction projects. The unsurprising effect is that the average age of plant for U.S. hospitals continues its decades-long rise.
The once-predicted “smart hospital” has not become a prominent feature—let alone a mainstay—of the delivery ecosystem. Even relatively well-funded hospitals found themselves unable or unwilling to incorporate the requisite technologies, given the heighted exposure they create to security breaches. For much of the traditional provider marketplace, interoperability requirements have been approached as a compliance mandate rather than a strategic opportunity to become tech-differentiated.
Hospitals maintained adequate standards of quality and experience, but they still ceded ground to competitors. Alternative players gained traction by offering a higher level of patient experience, one that permits greater interaction between popular consumer health technology and provider-owned IT platforms. Hospital-led provider organizations have also lost favor with clinicians as employers of choice, particularly among newer professionals whose training and expectations are aligned with technologically enabled work environments. Understaffing has become an endemic and self-perpetuating challenge for many community health systems.
Perhaps the most significant impact has been the uptick in closures and divestitures by over-bedded health systems and rural community hospital operators. While facility closures have been offset in part by the commoditization of retail medicine and expansion of virtual care, reduced acute care capacity is disproportionately impacting individuals who rely upon public insurance programs and those who live in lower-density markets. The downstream effects of cybersecurity threats seem likely to include significant new barriers to access and worse health outcomes among rural and lower socioeconomic groups for some time to come.
Alas, I must conclude. But fellow leaders—I’ll end this dispatch with the same message I left you with last time. If you have read this far, please know that you are not too late. You may be alarmed, but do not be dismayed. This future is not (yet) written in stone. You can help our industry make different choices; find a better path forward. And if you do, what I’m describing will exist as nothing more than the shadow of a future that you purposefully avoided.
Until next time,
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