Contrary to conventional wisdom, large medical practices are predicted to outpace major hospital systems in AI adoption. Practices' more modern, cloud-based infrastructure allows them to deploy AI tools more quickly than hospitals, which are often hindered by legacy technology, complex governance, and slower ROI realization on new tech.

Despite industry rhetoric, healthcare technology development overwhelmingly prioritizes physicians over patients. This creates a significant gap, as the ultimate end-user's needs are often an afterthought in solution design.

Electronic Health Record (EHR) companies have historically used proprietary formats to lock in customers. AI's ability to read and translate unstructured data from any source effectively breaks these data silos, finally making patient data truly portable.

An "AI arms race" is underway where stakeholders apply AI to broken, adversarial processes. The true transformation comes from reinventing these workflows entirely, such as moving to real-time payment adjudication where trust is pre-established, thus eliminating the core conflict that AI is currently used to fight over.

Beyond technological and regulatory hurdles, a crucial barrier to healthcare innovation is complacency within leadership. Executives must be more curious and proactive in understanding emerging technologies to drive meaningful change.

Chronic disease patients face a cascade of interconnected problems: pre-authorizations, pharmacy stockouts, and incomprehensible insurance rules. AI's potential lies in acting as an intelligent agent to navigate this complex, fragmented system on behalf of the patient, reducing waste and improving outcomes.

Unlike the top-down, regulated rollout of EHRs, the rapid uptake of AI in healthcare is an organic, bottom-up movement. It's driven by frontline workers like pharmacists who face critical staffing shortages and need tools to manage overwhelming workloads, pulling technology in out of necessity.

The primary reason multi-million dollar AI initiatives stall or fail is not the sophistication of the models, but the underlying data layer. Traditional data infrastructure creates delays in moving and duplicating information, preventing the real-time, comprehensive data access required for AI to deliver business value. The focus on algorithms misses this foundational roadblock.

Simply patching existing Electronic Health Records is insufficient. The next generation must be architected from the ground up with three core principles: offline functionality for resilience, a mobile-native experience, and generative AI at their core.