One of the biggest challenges I faced was parsing and making sense of complex FHIR data. FHIR resources like Observation, Condition, and MedicationStatement are highly nested and often inconsistent across different systems. It was difficult to extract meaningful clinical information from such varied formats. To solve this, I built a custom FHIR parser in Python that standardized and cleaned the data before passing it to the AI model.
Another hurdle was fine-tuning the AI's responses. Initially, GPT-4 would sometimes produce overly generic or clinically irrelevant summaries. I had to experiment with prompt engineering and LangChain’s agent tools to guide the AI toward more context-aware and actionable outputs.
I also ran into performance issues while integrating the AI backend with the frontend. Real-time responsiveness was critical, so I optimized the FastAPI backend using async endpoints and minimized payload sizes to reduce latency.
Lastly, handling drug-allergy conflict detection was challenging due to inconsistent medication naming. I tackled this by implementing fallback logic and referencing standardized vocabularies wherever possible.
These obstacles pushed me to deeply understand both healthcare data and AI tooling and ultimately led to a more reliable, smarter clinical assistant.