Three years ago, I penned my first piece on the role of AI in healthcare, coinciding with the debut of OpenAI's ChatGPT. At that time, I explored the transformative potential of generative AI in the medical field and felt a surge of optimism about the advancements it could bring. With the excitement still fresh in my memory, I decided this holiday weekend was the perfect moment to reflect on that initial article and assess whether we've made real strides in the areas I predicted would be most impactful.
When I first categorized the potential effects of AI on healthcare, I identified three primary domains:
1. Patient care and delivery 2. Research, diagnostics, and treatment 3. Clinical and non-clinical workflow
**Patient Care and Delivery**
I anticipated that AI would enhance patient care and streamline delivery through improved triage systems, error detection, and greater patient engagement. Now, three years later, I’m pleasantly surprised not just by the innovations, but also by the revelation that many of the challenges in healthcare were never truly technical in nature.
The triaging and access segment has blossomed into a market exceeding $100 million, growing 20-fold year over year. Companies like Counsel Health, Doctronic, and Torch Health leverage conversational AI to evaluate symptoms and direct patients toward appropriate care. Meanwhile, scheduling automation tools such as Assort Health and Hello Patient have simplified the often tedious appointment booking process. This aligns closely with the initiative I pursued with Nayroo, aimed at minimizing no-shows by efficiently matching patients with available slots.
The real excitement lies in patient engagement. AI-driven navigation platforms like Hippocratic AI, Ferry Health, and Aidify have taken on roles that were historically neglected—the gaps between clinical visits. They follow up with test results, arrange subsequent appointments, and provide care coordination, even answering queries at odd hours. Personally, I've integrated DoximityGPT into my discharge workflow, allowing me to quickly transform physician summaries into accessible explanations for patients and their families. These updates are not only read but also referenced, ensuring that primary care providers receive crucial information during vulnerable post-discharge phases.
However, it’s important to note that most of these advancements represent improvements in efficiency rather than true transformation. We're enhancing existing processes instead of fundamentally fixing the flaws within them. As a result, we find ourselves automating inefficiencies without addressing their root causes.
**Research, Diagnostics, and Treatment**
The evolution of AI in diagnostics and clinical decision support diverged from my initial expectations. Instead of AI taking over clinical reasoning, we have seen the rise of AI-powered medical references that enhance a clinician's ability to make informed decisions.
UpToDate has introduced Expert AI, which mimics the decision-making process of experienced clinicians while being fully transparent about its assumptions and reasoning. Additionally, OpenEvidence secured $210 million for quick literature analysis, and Doximity acquired Pathway Medical for $63 million, integrating it into DoxGPT. Other platforms like ClinicalKey, DynaMed, and Glass Health have also embraced AI to bolster clinical decision support.
The key takeaway is that the information provided by these tools is not only transparent but also draws from reliable sources, making them valuable additions to the clinician's toolkit rather than replacements for human judgment. If utilized properly, these tools could empower the next generation of healthcare providers to be exceptionally knowledgeable.
**Clinical and Non-clinical Workflows**
I remain a strong advocate for leveraging AI to alleviate administrative burdens, simplify documentation processes, and eradicate redundant tasks. Yet, I’ve grown increasingly skeptical about the so-called AI arms race in this domain.
AI scribes have become ubiquitous in the healthcare landscape, akin to the essential pagers of yesteryear—considered standard by all practitioners. Companies like Abridge, Suki, and Doximity automate clinical documentation, providing much-needed relief to healthcare professionals. However, they do little to address the underlying reasons for excessive documentation demands.
The surge in applications for prior authorization has been significant, with spending skyrocketing from $10 million in 2024 to $100 million in 2025. Platforms like Latent Health and Tandem now automate the filling of insurance forms directly from electronic health records, enabling physicians to submit claims swiftly. Simultaneously, payers utilize their own AI systems, such as Optum Real, for immediate coverage validation. This ongoing race sees both sides pouring billions into making a flawed system more efficient without questioning its very existence.
Furthermore, the Centers for Medicare & Medicaid Services recently introduced AI-driven prior authorization to Traditional Medicare, compensating AI contractors a share of denied claims to review services deemed "unnecessarily used." This move brings controversial aspects of Medicare Advantage into a system that functioned effectively without such burdens.
**Final Thoughts**
Reflecting on what I’ve learned about AI in healthcare over these past three years, it becomes clear that we are addressing symptoms, not fundamental issues.
We’re automating prior authorization processes without analyzing their necessity, accelerating claims processing without reimagining payment structures, and improving documentation workflows without questioning why physicians allocate so much time to record-keeping.
AI has the capacity to enhance healthcare, but only if it shines a light on the misaligned incentives that we’ve previously blamed on the intricacies of the system. Currently, we attribute inefficiencies to complexity, but what occurs when AI eliminates that rationale? When AI can streamline processes in seconds or reduce administrative costs significantly, the issue of inefficiency shifts from capability to incentive.
Healthcare organizations that wish to thrive must confront these misaligned incentives directly and leverage AI to realign them toward enhancing efficiency and patient care. Those that fail to do so will merely manage to operate their flawed systems at an accelerated pace.
