Healthtech’s Past Informs the Playbook for Its AI Future

For those who have long worked in healthcare, the buzz around AI may sound familiar. The promises of revolutionary efficiency and improved outcomes echo the past—the move to digitization, the emergence of the commercial Internet, and the shift to a mobile and wireless world. 

While AI represents a uniquely powerful leap forward, by examining the patterns of healthtech’s past transformations and their impacts on medical businesses, we can create a smarter playbook for an AI-powered future and better determine which products and services are worth the investment.

Healthtech is entering its fourth major leap with the rise of AI. As leaders and innovators rush to explore AI’s potential, a critical question emerges: What can we learn from the past to better understand AI, and how can we best determine which products and services are worth investing in?

Leap 1: The Dawn of Digital Records (circa 1960s-1990s)

The advent of microprocessors laid the groundwork for early digital tools, from advanced imaging to the first electronic health records (EHRs). Initially designed for academic medical centers, EHRs promised to make patient data more accessible and portable than paper charts.

However, the reality was fraught with challenges. Early EHRs were notoriously difficult to use and required massive upfront investment. Most critically, interoperability among the multiple “best-of-breed” software solutions that a practice might deploy with an EHR was rudimentary. The most common framework for connecting systems was HL7, which, like faxing, is another archaic technology product of the 1980s that persists to this day in many practices. Moreover, software point solutions rarely had standards for shared data, which created immense frustration for users, as pathways for data would constantly break. 

This era taught a fundamental truth: technology is only as effective as its adoption by frontline staff, and that requires holistic solutions or systems that can actually talk to each other.

Lesson Learned: Today’s AI solutions should seamlessly embed into existing practice management and EHR systems, not create new, isolated workflows that often require time-consuming or manual workarounds. An intuitive design that eases burdens rather than adding them is essential for driving adoption. True value emerges from seamless integration. 

Leap 2: Surviving the Dot-Com Hype (circa late 1990s – 2000s)

Later in the 90s, the internet offered new avenues for information sharing and communication, promising to revolutionize patient access and administrative efficiency. For several years, this potential fueled a massive investment boom, with venture capital flowing into any company with a “.com” in its name. But the hype quickly outpaced reality. Many early companies were more focused on style over substance, and they struggled with security and scalability, eroding trust and leading to the infamous dot-com bust of the early 2000s. While they promised transformation with innovative user interfaces, many vendors failed to deliver tangible operational value. 

This era taught a crucial lesson: an engaging user experience might attract customers, but only a robust and reliable operational backbone can build a sustainable business. 

The companies that survived, like Google, Netflix, and Amazon, did so by focusing on long-term value over short-term buzz. In healthcare, this lesson materialized in the form of early patient portals. Though developed during an era of internet skepticism, they delivered real utility—like like results sharing and easy access to messaging providers—that has made them indispensable today.

Lesson Learned: Hype is temporary, but utility is permanent. For AI tools to thrive long-term, they must be grounded in strong, fundamental value propositions, not just exciting technology. Provider organizations must meticulously vet AI partners, selecting those with proven efficacy, secure infrastructure, and a clear vision for solving real-world healthcare challenges.

Leap 3: The Mobile Era and Untethered Healthcare (circa late 2000s – 2020s)

The smartphone put a supercomputer in every pocket and fundamentally transformed how patients engage with their own health. This leap enabled “anytime, anywhere” healthcare, from mobile apps that track fitness to platforms that guide post-operative rehabilitation. The telehealth innovations of this era proved critical during the COVID-19 pandemic. 

Success in the mobile era was driven by patient-focused experiences, from how patients identify relevant health insights and connect with providers to how they share and store their health data.

In ophthalmology, some mobile apps allow patients to perform self-tests for visual acuity, with results securely transmitted to their physician. In orthopedics, mobile apps are being used to provide patients with video-guided post-operative rehabilitation exercises in efforts to improve outcomes. As of early 2025, 71% of smartphone users have downloaded a health app, proving the demand for accessible, user-centric tools.

In effect, healthcare was no longer confined to the exam room or doctor’s office visit – it became ‘untethered’, more patient-centric, and more accessible. However, the proliferation of mobile apps also amplified challenges around data privacy and integration. As personal health information began to move so freely, this paved the way for novel forms of illicit activity, including identity theft and ransomware attacks. 

Lesson Learned: The user experience is paramount for both patients and providers. New technology must alleviate administrative burdens and complement existing workflows, not disrupt them. As healthcare continues untethered, it’s even more critical to make certain that your technology provider is focused on security and stability.

Our Next Leap: The Advent of GAI, Agentic AI & the pursuit of IAG (2023 – today)

This fourth leap is not just an incremental improvement; it’s a paradigm shift. It’s defined by the advent of Generative AI (GAI), capable of drafting clinical notes and summarizing patient histories in seconds. Up next will be Agentic AI, designed to reason and autonomously execute complex multi-step tasks, like managing the entire prior authorization lifecycle from data retrieval to submission. This trajectory points toward the long-term horizon of Artificial General Intelligence (AGI), a future where AI could possess comprehensive, human-like cognitive abilities. 

There is little doubt that AI offers promising solutions for diagnostics, workflow automation, revenue cycle optimization, personalized medicine, and so much more. It could help address challenges with rising patient demand, staffing shortages, and administrative burdens such as prior authorizations and coding, freeing clinical staff to focus on patient care. But its success is not guaranteed. 

To realize this potential, we must apply the lessons of the past three eras:

• Integrate seamlessly: AI must work within existing systems, not against them.
• Deliver real value: It must go beyond the hype, solve concrete problems, and demonstrate tangible, sustainable ROI.
• Prioritize the user experience: It must be intuitive and beneficial for the providers and patients who use it every day.

A common thread runs through every technological era: software, like medicine, is fundamentally a people business. Success depends not just on the technology itself, but on choosing the right partner—one who earns trust by demonstrating a deep commitment to understanding the realities of a medical practice’s workflows.

The path forward requires software vendors to commit to a deep understanding of healthcare workflows and to unequivocally develop responsible, integrated, and genuinely beneficial AI. By learning from our past, we can support a future where artificial intelligence becomes the powerful, reliable ally it is poised to be.