HJNO Jul/Aug 2026

U.S. HEALTHCARE JOURNALS I  JUL / AUG 2026 31 promised better coordination. Patient portals promised greater engagement. Telemedicine promised expanded access. Wearable de- vices promised continuous insight into human health. Each innovation delivered meaningful benefits, yet none fundamentally altered the underlying structure of healthcare delivery. One reason is that these technologies largely served as tools for managing information. They stored data, displayed data, transmitted data, and made data more accessible. They improved our ability to capture information, but they did relatively little to help us synthesize it. Artificial intelligence represents a fundamentally differ- ent category of technology. Previous generations of healthcare technol- ogy functioned primarily as systems of record. Their purpose was to document what hap- pened. More recent advances have begun evolving into systems of intelligence, capable of identifying patterns, generating predictions, and surfacing insights that might otherwise go unnoticed. Increasingly, however, AI is push- ing healthcare toward something entirely new: systems of action. A system of action does not simply present information and wait for a cli- nician to determine the next step. It continu- ously analyzes data, identifies opportunities, prioritizes interventions, and recommends spe- cific actions that can improve outcomes. Rather than serving as a passive repository, it becomes an active participant in care delivery. This dis- tinction may sound subtle, but its implications are profound. For decades, one of healthcare’s great- est challenges has not been a lack of medi- cal knowledge. In fact, the volume of medical knowledge now exceeds the ability of any in- dividual clinician to fully absorb and apply it. New clinical trials are published daily. Guide- lines are updated continuously. Therapeutic options expand each year. The challenge is no longer generating evidence. The challenge is reliably translating that evidence into routine clinical practice. It is often estimated that it takes approximately 17 years for evidence- based discoveries to become widely adopted in everyday care. During those years, patients experience preventable complications, avoid- able hospitalizations, and missed opportunities In the first installment of this series on the future of healthcare, I argued that healthcare’s technological revolution has, thus far, delivered decidedly mixed results. Electronic medical records, patient portals, telemedicine, remote monitoring, and countless digital innovations have undoubtedly improved certain aspects of healthcare delivery. Patients today have greater access to information than ever before. Clini- cians can retrieve records instantly, communi- cate electronically, and no longer have to deci- pher the notoriously illegible handwriting that we physicians are known for. Yet despite these advances, many of health- care’s most fundamental problems remain stubbornly intact. Costs continue to rise. Ac- cess remains uneven. Outcomes vary dramati- cally from one community to the next. Physi- cians and nurses report unprecedented levels of burnout. Patients often find themselves navi- gating a fragmented system that seems orga- nized around transactions rather than relation- ships, encounters rather than outcomes, and documentation rather than care. The lesson from the past several decades is not that technology has failed. Rather, it is that technology alone is insufficient. Digitizing a flawed system does not necessarily transform it. In many cases, it simply allows the system to perform its existing functions more efficiently, whether those functions create value or not. This distinction matters because healthcare now stands at the threshold of another techno- logical revolution — one that may prove more consequential than any that came before it — artificial intelligence. Artificial intelligence has rapidly become the dominant topic in healthcare innovation. Venture capital investment is pouring into the space. Technology companies are racing to build increasingly sophisticated models. Health systems are experimenting with ambi- ent documentation, automated inbox manage- ment, and AI-assisted clinical decision support. The excitement is understandable. For the first time, healthcare has access to technology ca- pable not merely of storing and transmitting information, but of interpreting it at a scale and sophistication previously unattainable. And yet, amid all of the enthusiasm, there is a risk that we will repeat the mistakes of the past. The question is not whether AI is powerful. It clearly is. The question is whether we will deploy it in ways that fundamentally improve health and health outcomes — or simply use it to auto- mate the inefficiencies and problems of the system we already have. History suggests that technologies rarely cre- ate transformation by themselves. The auto- mobile was not simply a faster version of horse and carriage. It required a complete redesign of transportation infrastructure. The internet was not simply an incremental improvement over the traditional postal service. It fundamen- tally altered how information moved around the world. Likewise, the future of healthcare will not be determined solely by the capabilities of artificial intelligence. It will be determined by whether we are willing to redesign the systems and care models into which that intelligence is deployed. If we simply layer AI onto a health- care system that remains organized around volume-driven fee-for-service transactions, fragmented care delivery, and administrative complexity, we should not expect dramatically different results. We may document faster. We may code more efficiently. We may process larger volumes of information. But we will still be operating within the same fundamentally flawed architecture. If, however, AI is combined with delivery sys- tem redesign, team-based care, and payment models that reward health outcomes rather than volume-driven activity, something very different becomes possible. For the first time, we may have technology capable of helping us build a healthcare system that is proactive rather than reactive, longitudinal rather than episodic, and truly centered on the needs of patients. That possibility represents far more than another incremental improvement in soft- ware. It represents the beginning of a new era in healthcare. The dawn of AI has arrived. The more important question is what we choose to do with it. Why This Time Really Is Different Healthcare has experienced no shortage of technological breakthroughs over the past several decades. Electronic medical records “The real problem is not whether machines think but whether men do.” — B. F. Skinner