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.

AI's most significant impact won't be on broad population health management, but as a diagnostic and decision-support assistant for physicians. By analyzing an individual patient's risks and co-morbidities, AI can empower doctors to make better, earlier diagnoses, addressing the core problem of physicians lacking time for deep patient analysis.

Shifting from text to voice for CRM data entry will fundamentally change data quality. It enables the capture of conversational nuances from doctors that are lost in text summaries, leading to richer insights for content and strategy.

The next evolution in personalized medicine will be interoperability between personal and clinical AIs. A patient's AI, rich with daily context, will interface with their doctor's AI, trained on clinical data, to create a shared understanding before the human consultation begins.

The most significant opportunity for AI in healthcare lies not in optimizing existing software, but in automating 'net new' areas that once required human judgment. Functions like patient engagement, scheduling, and symptom triage are seeing explosive growth as AI steps into roles previously held only by staff.

An effective AI strategy in healthcare is not limited to consumer-facing assistants. A critical focus is building tools to augment the clinicians themselves. An AI 'assistant' for doctors to surface information and guide decisions scales expertise and improves care quality from the inside out.

The widespread use of AI for health queries is set to change doctor visits. Patients will increasingly arrive with AI-generated analyses of their lab results and symptoms, turning appointments into a three-way consultation between the patient, the doctor, and the AI's findings, potentially improving diagnostic efficiency.

AI assistants can democratize medical knowledge for patients. By processing personal health data and doctor's notes, these tools can explain complex conditions in simple terms and suggest specific questions to ask medical professionals, improving collaboration.

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.