Not Every Hospital Looks Like an App—Until It Has To
Most hospitals weren’t built for real-time video consults, AI chatbots, or cloud-native operations.
But telemedicine changed that.
Healthcare software today juggles multiple systems, global compliance, and non-stop uptime—making it more than just a tech project. It’s an architectural challenge.
In India alone, 140M+ teleconsults have already taken place on eSanjeevani.
Telemedicine is no longer optional.
Microservices: Not Because It’s Trendy—Because It’s Necessary
When you’re processing video consults, generating prescriptions, syncing EMRs, and handling patient bills—tight coupling is a death trap.
We broke the hospital management system into the following services:
services:
patient-service:
doctor-service:
billing-service:
emr-service:
teleconsult-service:
ai-triage-service:
prescription-service:
- Services communicate via an internal API Gateway
- Kafka handles asynchronous events (e.g. appointment booked → EMR + email update)
Decouple the chaos. Scale what matters. Leave the rest alone.
HIPAA Isn’t a Checkbox—It’s a Core Architecture Principle
If your system touches PII, you’re liable. HIPAA isn’t an afterthought.
Minimal HIPAA Dev Checklist:
- AES-256 encryption for all data at rest
- HTTPS-only traffic
- OAuth2 with RBAC
- Audit logs for every action
- Mask sensitive values in logs
- Rotate keys regularly
We also added:
- Real-time logging using ELK stack
- Append-only logs for GDPR events
- Slack alerts for abnormal behavior (e.g. HR accessing EMR at 3AM)
AI That Actually Helps (Not Just Claims to Replace Doctors)
We built an AI-powered teleconsultation platform with practical tools for clinicians—not gimmicks.
Real-world Use Cases:
- Symptom Checker → Triage patients & auto-suggest specialists
- SOAP Generator → Converts doctor’s input into structured clinical notes
- Compliance Reminders → Auto-remind patients post-treatment
Example API:
@app.post("/check-symptoms")
def analyze(text: str):
doc = nlp(text)
symptoms = [ent.text for ent in doc.ents if ent.label_ == "SYMPTOM"]
return {"symptoms": symptoms}
Real-Time Video via WebRTC + Live SOAP Notes
We used WebRTC for doctor-patient calls, backed by Coturn + Kubernetes ingress.
Fallback to relay servers in low-bandwidth areas.
Transcriptions handled by OpenAI Whisper, then parsed into:
- Subjective
- Objective
- Assessment
- Plan
Doctors can edit it. No one likes being locked in by an AI guess.
CI/CD & DevOps: Make It Fast, Make It Safe
Every microservice had its own:
- GitHub repo
- CI pipeline (GitHub Actions)
- Docker → Helm → K8s
Deployment Strategy:
- Argo Rollouts for canary deployments
- Mozilla SOPS for encrypting secrets in Git
- Configs decrypted during pipeline using GCP KMS
Data-Driven Care: More Than Just Logs
Every interaction emits structured events:
{
"event": "video_consult_started",
"doctor_id": "d235",
"patient_id": "p493",
"timestamp": "2025-06-19T09:03:21Z"
}
Dashboards powered by Grafana + Prometheus.
What We Tracked:
- No-show patterns
- Department-wise delay metrics
- Doctor efficiency
- Pharmacy restocking forecasts
Failure Is Not an Exception—It’s a Constant
Telemedicine systems fail. That’s not the point.
The point is whether you recover fast.
Our biggest saves:
- Kafka topic overflow (bad cron job)
- SMS gateway outage on vaccination day
- Video call dropped due to bad ingress config
Our Recovery Stack:
- Hystrix circuit breakers
- Exponential backoff retries
- Dead-letter queues
- Real-time Slack alerts
Final Thoughts
We didn’t start with a clean slate. We started with hospitals buried in Excel sheets and broken IVRs and offered them our telemedicine software.
Now:
50+ clinics.
Doctors spend time on care, not admin.
And yes, the engineers sleep better.
Key Takeaways:
- Keep your HMS modular
- Bake in HIPAA from day 1
- Build for failure, not just success
- AI should augment, not replace
Want to build something similar?
Let’s connect → sales@nzcares.com
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