Chandra Vikram · Healthcare AI Engineer

I'm Chandra Vikram, a healthcare AI engineer, and my path here wasn't a straight line. I did my Master's in Health Informatics at Indiana University; before that, I studied pharmaceutical sciences in my bachelor's, because I thought medicine meant molecules. What kept pulling me back was watching how often a good clinical decision still depended on whether the right piece of information reached the right person at the right time, and how rarely the systems made that easy. That conviction became a presentation at the AMIA FHIR App Challenge in San Francisco and production work at the intersection of clinical data, FHIR interoperability, and AI. Coming in from outside engineering taught me something I keep returning to: the most useful clinical AI gets built by people fluent in both languages: the clinic and the codebase.

I believe healthcare AI, done with rigour and with clinicians firmly in the loop, is among the most transformative technologies of our generation, and that the wrong systems do real harm. Every project below is built around that distinction: explicit safety mechanisms, evaluation pinned against published baselines, and failure modes named before any benchmark number is claimed. Each entry links to a preliminary manuscript describing the problem, the approach, and the papers I'm reading as supporting work. Every citation links out; the references are one click away.

Index of Works

FHIR-grounded infrastructure, clinical benchmarks, safety and alignment, voice and telehealth agents, knowledge graphs, and retrieval evaluation. Nothing here is finished, and nothing is being claimed. This is a working notebook.

01	Triagemind	Triage	A four-agent ED triage system with calibrated uncertainty, red-flag screens, and structured handoff, pinned to Sax et al.'s 32.2% ESI mistriage baseline.
02	Graphcore	GraphRAG	Microsoft GraphRAG methodology applied to clinical guideline corpora, the first cost-quality Pareto curve for clinical GraphRAG.
03	Atrium	FHIR	A reference Model Context Protocol server exposing FHIR clinical data via SMART-on-FHIR launch.
04	Reason·Med	Fine-tune	An open clinical reasoning model trained via continued pretraining, SFT, and GRPO on Qwen3-8B.
05	Chaincite	Citations	Clinical RAG benchmark measuring citation correctness AND faithfulness, built on Wallat et al.'s ICTIR 2025 distinction.
06	Pharos	Chronic	Long-horizon voice agent for HF and type-2 diabetes with persistent memory and clinician oversight loop. Targets 80% adherence at 12 weeks.
07	Longitude	Long-context	Diagnostic reasoning over decade-long longitudinal patient records, 150k–500k tokens each.
08	Oracle	Diagnosis	A differential-diagnosis agent that emits evidence-grounded reasoning traces, citation per claim.
09	Auris	Scribe	Ambient clinician-patient voice to validated FHIR resources, end-to-end.
10	Medigraph	Knowledge-graph	Patient-centric clinical knowledge graph from FHIR + clinical notes + UMLS / SNOMED / RxNorm / LOINC. Composes with Atrium.
11	Asclepius	Red-team	An adversarial benchmark for medical LLM jailbreaks and sycophantic capitulation.
12	Calline	Voice	Voice-first after-hours nurse triage agent: streaming ASR, gpt-realtime, sub-second TTS, uncertainty-gated escalation.
13	Caliper	Benchmark	A FHIR-grounded extension of HealthBench with a public cross-model leaderboard.
14	Conscience	Alignment	Constitutional AI applied to clinical decision support, fine-tuned on Qwen3-8B.
15	Telesight	Telehealth	A three-phase telehealth copilot covering pre-visit chart prep, intra-visit CDS with Five-Rights gating, and post-visit instructions plus coding.
16	Chartwalker	Computer-use	A Claude-driven agent navigating a real EHR interface with a deterministic grading harness.
17	Ragprobe	Adversarial	Adversarial robustness benchmark for clinical RAG: PoisonedRAG / BadRAG / GARAG / Phantom / indirect-injection on clinical corpora.
18	Vestibule	Transitions	Post-discharge transition agent with a 24h/48h/72h/7d voice-call cadence pinned against Jencks's 19.6% 30-day Medicare readmission baseline.