December 19, 2025
Comments Off on Scaling Telehealth Video Applications: Best Practices for Reliability and Compliance

As telehealth applications become more sophisticated, incorporating AI-based features and rich data pipelines for better, accurate diagnostics, the challenge of delivering a flawless, high-quality call experience at scale becomes exponentially more complex.

Our team at WebRTC.ventures has been building telehealth video applications since the days when we had to convince healthcare providers that video consultations were even viable. Back then, the challenge was adoption. Today, it’s scaling: handling thousands of concurrent calls while maintaining security, compliance, and reliability under peak demand.

In this post, we cover the four pillars for scaling telehealth video infrastructure: security and HIPAA compliance, resilient architecture, intelligent auto-scaling, and proactive monitoring. These are the approaches we’ve refined through years of building and scaling real-world telehealth platforms.

Security and HIPAA Compliance for Telehealth Video at Scale

Rather than being just a technical problem, security breaches are a huge catastrophic business failure that often leads to expensive fines.. As your platform grows, so does your attack surface and compliance burden, therefore, enforcing robust Telehealth HIPAA compliance from day one is the only path forward for any Telehealth solution that takes security seriously.

End-to-End Encryption for ePHI: Beyond WebRTC Basics

While WebRTC provides an important baseline by encrypting video and audio streams in transit, your responsibility doesn’t end there. True HIPAA compliance requires a holistic approach to data protection. This means encrypting all electronic Protected Health Information (ePHI) associated with a patient encounter, including:

  • Recordings: If your application records virtual interactions (and provided you have the patient’s consent), all stored video and audio sessions must be encrypted at-rest, and in-transit while transported to storage location.
  • File Exchanges: Any documents, images, or files containing ePHI shared between patient and provider must be encrypted both in-transit and at-rest.
  • Chat Interactions: Text chat, metadata, and transcripts often contain ePHI and must be encrypted in transit and at rest, with strict role-based access control (RBAC).

In cloud environments such as AWS, services like AWS Key Management Service (KMS) for envelope encryption, IAM for least-privilege access, and S3 bucket policies with audit logging provide the primitives needed to enforce these controls consistently.

All public-facing applications, particularly those handling Protected Health Information (ePHI), require a Transport Layer Security (TLS/SSL) certificate to enforce secure HTTPS communication. Cloud services like AWS Certificate Manager (ACM) help to automatically provision, manage, and renew public and private SSL/TLS certificates.

Authentication and Secrets Management for Telehealth Applications

A secure architecture prevents unauthorized access at the application level. Hardcoding credentials or using insecure authentication methods is a recipe for disaster.

  • Stateless Authentication: Implement modern, secure methods like JSON Web Tokens (JWT). JWTs allow your services to verify user identity without needing to maintain a persistent session state, making your architecture more scalable and resilient.
  • Secrets Management: Never hardcode API keys, database credentials, or encryption keys in your codebase. Use a dedicated secrets vault (like AWS Secrets Manager or HashiCorp Vault) to manage and inject these credentials securely at runtime. This practice is critical for preventing credential leakage and simplifying key rotation.

Building Resilient Telehealth Infrastructure: Multi-Region Architecture

A solid architecture ensures high availability, prevents costly outages, and allows the platform to grow predictably and cost-effectively. Your video application infrastructure must be designed to tolerate failure, not just to operate under ideal conditions.

Infrastructure as Code for Telehealth: Terraform and CloudFormation

Define your entire infrastructure—servers, load balancers, and network configurations—in code using tools like Terraform or AWS CloudFormation.

  • Predictable Scaling: Infrastructure as Code (IaC) makes scaling, rebuilding, or modifying your environment a predictable and less error-prone process.
  • Regional Replication: Easily replicate your entire infrastructure stack across multiple geographic regions to serve a global user base.

High Availability Architecture: Eliminating Single Points of Failure

A single server outage should never result in a dropped patient call. Build redundancy into every layer of your stack:

  • Deploy multiple, redundant media servers to handle real-time video streams.
  • Cluster core services like signaling and session management so that if one node fails, another takes over.
  • Ensure high availability for your STUN/TURN servers, which are critical for establishing peer-to-peer connections in restrictive network environments.

Global Reach, Local Performance: The Importance of Multi-Region Deployment

Latency is the #1 enemy of real-time video. To ensure high-quality, low-latency calls, you must place your infrastructure closer to your users. A multi-region deployment strategy allows you to route patients and providers to the nearest data center, dramatically improving call quality and user experience.

In practice, this typically means regionalizing media servers and TURN infrastructure while keeping control-plane services centralized or lightly replicated.

Smart Traffic Management: Load Balancing for Video and Data

Not all traffic is created equal. Using the right tool for the job is essential for both performance and cost-efficiency.

  • Application Load Balancers (ALB): Use ALBs for standard web traffic (HTTPS) and signaling (WebSockets). They operate at the application layer and can make intelligent routing decisions.
  • Network Load Balancers (NLB): Required for latency-sensitive real-time traffic such as UDP-based media and TURN relay. NLBs operate at Layer 4, preserve source IPs, and handle high-throughput, low-latency traffic without connection termination, making them suitable for WebRTC media paths.

Auto-Scaling Telehealth Video: Triggers, Session Management, and Cost Optimization

Auto-scaling is the key to financial efficiency and a great user experience. This means controlling infrastructure costs by precisely matching resources to real-time demand, ensuring your platform performs flawlessly during peak hours without over-provisioning during quiet periods. This is the core of effective WebRTC scaling.

Scaling Triggers for Media Servers: Beyond CPU Metrics

Finding the optimal scaling mechanism for a real-time communication application will vary and requires proper performance testing and monitoring. But one thing is true: relying solely on a traditional metric, such as CPU utilization, is a common mistake as it often doesn’t accurately reflect the load on media servers.

Instead, implement an observability strategy that allows you to correlate application level metrics (which reflects your application-specific behavior) with infrastructure usage metrics (which reflects available resources).

One example can be to track the amount of users connected to sessions in a given media server, or the amount of users whose media data is being relayed through a TURN server, and correlate this with traditional metrics such as network out and CPU usage. Then, conduct performance testing to identify breaking points and set appropriate scaling events. This ensures you are scaling based on the actual user load.

Graceful Scale-In: Preventing Session Drops During Auto-Scaling

When demand decreases, your auto-scaling system will begin terminating instances. The problem? Abruptly terminating a media server can disconnect every patient and provider in an active session on that instance. 

The solution is to implement a “drain-to-terminate” mechanism. Before an instance is terminated, a script or function marks the server as “draining,” preventing it from accepting new sessions. It then waits for all existing sessions to complete naturally before allowing the instance to be shut down.

Independent Scaling: Decoupling Media Servers and TURN Infrastructure

Different parts of your application have different scaling needs. For example, your media servers might be applying CPU-demanding processing for recording or transcoding media data, while your STUN/TURN servers manage a high amount  of network requests.

Deploy these components as independent services, each with its own auto-scaling group and triggers, to scale each part of your infrastructure efficiently.

Telehealth Performance Monitoring: Client-Side Metrics and Real-Time Analytics

Proactive telehealth performance monitoring translates directly to quality of care. This means having the visibility to identify and fix issues before they impact the patient experience, protecting your brand’s reputation for reliability.

Client-Side WebRTC Metrics: RTT, Jitter, and Packet Loss

Server-side metrics are important, but they don’t tell you what the user is actually experiencing. You must collect client-side WebRTC metrics to get a direct view into real-world call quality. Key metrics to track include:

  • Round-Trip Time (RTT): The latency between the user and your servers.
  • Jitter: The variation in packet arrival time, which can cause distorted audio and video.
  • Packet Loss: The percentage of data packets that fail to reach their destination.
  • Other metrics such as Frames Per Second (FPS), Codec, and ICE connection status.

Purpose-built, open-source video-conferencing tools such as Peermetrics can be integrated into your application to collect and analyze critical client-side performance metrics.

Centralized Logging for Telehealth: ELK Stack and CloudWatch

When a patient reports a poor call experience, you need end-to-end visibility across your infrastructure. By aggregating logs from media servers, signaling services, load balancers, and web applications into a centralized platform (such as ELK Stack, SigNoz or AWS CloudWatch), support and engineering teams can rapidly correlate events, trace failures across system boundaries, and pinpoint root causes with confidence.

Performance Testing for Telehealth: Load Testing with Loadero and CoTURN

Do not wait for real traffic spikes to expose system limits! Combine user-level testing with Loadero and Playwright (for realistic end-to-end call flows) with purpose-built infrastructure load tools.

For TURN and media layers, use protocol-level tools such as CoTURN’s built-in clients (turnutils_uclient, turnutils_peer) to generate allocations, relay traffic, and long-lived sessions, along with media-server load utilities (e.g., Jitsi Meet Torture, Livekit, Janus or Mediasoup scripts). These tools let you stress signaling, ICE, and relay capacity directly—independent of the browser.

This layered approach validates auto-scaling, uncovers bottlenecks, and ensures the platform can withstand sudden demand surges without degradation.

Scaling Telehealth Video with Confidence

Successfully scaling telehealth video is a strategic imperative. By focusing on these four practices of Security, a Resilient Architecture, Tailored Auto-Scaling, and Proactive Monitoring, you can build a reliable and high-performing service that meets the needs of patients and providers today and tomorrow.

Building or scaling a telehealth application is a complex undertaking. The team at WebRTC.ventures specializes in creating and scaling secure, compliant, and reliable telehealth platforms. Contact us today and let’s make it live!

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