Virtual care went from a nice-to-have to a must-have during the COVID-19 pandemic and while in-person visits are starting to pick up again, telemedicine is here to stay. Its growth will continue: According to Health-tech companies, the telemedicine market was valued at $143.49 billion in 2024. It is predicted to be $167.74 billion in 2025 and reach $584.99 billion by 2033 at a growth rate of 16.9%.

Telehealth platforms handle sensitive health information, manage appointments, stream live video and link to patient EMRs — and, of course, must be fully HIPAA-compliant. Anything that goes wrong, and we mean anything, can harm patient care, erode trust and bring on a whole bunch of regulatory headaches. For DevOps teams that build and run these platforms, having a system that’s rock solid and always available is no longer a nice-to-have; it’s a patient safety feature.

This guide is all about where observability, site reliability engineering (SRE) and uptime management come together in telehealth. Based on research, real case studies and hard-won experience of actually managing DevOps teams in healthcare, it outlines a bunch of practical steps you can take to get your production platform ready for production while meeting all the compliance and user-experience requirements.

Why Telehealth Reliability Matters

Telehealth users expect the same uptime as traditional clinical systems. According to a 2024 survey of healthcare organizations, 93% of patients expect digital health services to be available 24/7 and downtime incidents cost an average of $7,900 per minute. Hospitals and clinics operate on thin margins, and outages have a ripple effect: Staff have to go back to paper processes, appointment backlogs grow, and patient safety declines. The 2024 CrowdStrike-related Microsoft outage cost the healthcare industry about $1.94 billion, with individual organizations losing about $64.6 million.

Regulators are paying attention. North Memorial Health, for example, paid $1.55 million after a HIPAA violation related to inadequate safeguards and lack of business-associate agreements. Downtime during compliance incidents damages not only finances but also patient trust. As Mitesh Rao, former chief patient safety officer at Stanford Health Care, said, telehealth outages “affect every aspect of patient care.” DevOps teams need to engineer telehealth platforms that deliver continuous availability while meeting security and regulatory requirements.

Beyond Monitoring: Building Observability

**What is Observability? **

Monitoring checks if specific metrics are within thresholds. Observability goes further; it provides a holistic view of system health by correlating metrics, logs, traces and events. Bri Morgan of Splunk says healthcare observability is “the path to achieving resiliency across mission-critical services.” Observability tools let IT teams see into the system’s internal state based on its external behavior and gather insights into clinical and operational workflows. John Wilson of SolarWinds adds that observability “helps identify security threats and potential breaches” and helps with HIPAA compliance.

Observability is different from monitoring in its predictive and diagnostic capabilities. It includes distributed tracing, a forensic technique that visualizes the flow of requests across microservices to find bottlenecks and anomalies. Predictive analytics can forecast potential issues so organizations can respond proactively. Patrick Lin, general manager of observability at Splunk, says observability integrates with DevSecOps so teams can catch security issues before they hit production without slowing down the development cycle.

**Telehealth‑Specific Observability **

Telehealth platforms bring unique challenges. Observability must cover everything from network to application — including video streaming and its performance, asynchronous messaging, EHRs, billing systems, remote monitoring devices and device health too. Take Codebridge’s advice for a moment: Setting service level objectives (SLOs) right from the get-go is a good place to start — 99.95% uptime, video join times of five seconds or less and call drop rates under 2%. Those SLOs translate patient expectations into real targets — something to aim for.

Observability isn’t just about catching errors — though that’s part of it. You need to see the whole picture: All those user journeys & interactions that are happening. For example, if patients are bailing out on authentication all the time, metrics are just the first place to look. You need to dig deeper to find out if it’s the identity provider, network or the timeouts that are the problem. You should be able to see interactions with all those peripheral devices — digital stethoscopes and blood pressure monitors. Remote site observability ensures your telehealth services are running smoothly, patient data and device health are getting checked and you’re keeping an eye on medical equipment too. DevOps teams want to be able to get that device data, so they can pick up on data quality issues before it’s too late for clinicians.

As the world moves on, more and more of these observability platforms are including AIOps — AI-driven analytics that can spot anomalies and say ‘here’s how to fix it’. That AI observability stuff can collect and analyze all the data — events, metrics, logs and traces, to name a few — spot system issues before they become problems, keep downtime to a minimum and ensure services such as EHRs, telehealth and imaging are always available. Those AI models can even spot the really subtle stuff, like a slow memory leak or the odd bit of network congestion that you might otherwise miss.

**Implementing Observability **

• Instrument all your bits and bobs — use open standards like OpenTelemetry to add monitoring to your code, infrastructure and networks. Get a handle on metrics (latency, throughput and error rates) and logs (the lowdown on what’s happening, in context) — and don’t forget about those cross-service workflows, captured via distributed tracing. For telehealth, pay attention to video streaming (looking at WebRTC metrics), call quality (packet loss and jitter can be a real problem) and device connectivity.

• Gather all your data in one place and get some visibility across the board. Use a unified observability platform to collect telemetry data and spot problems as they appear, across different parts of the system and make sure distributed tracing ties user actions back to the processes that are actually working on them. This way you can stop teams from bouncing between different dashboards to try and figure things out.

• Set up your alerts and anomaly detection to save you time and stress. Use machine learning to spot when things are behaving strangely and get real-time alerts when there’s an issue. Simplify fixing issues with runbook automation to resolve problems as soon as they arise.

• Put data privacy at the top of your list — observability has to comply with HIPAA and GDPR. Make sure any sensitive data stays encrypted while it’s in transit and when it’s stored, limit who can access it with role-based controls and draw up business associate contracts. Additionally, don’t forget that HIPAA needs end-to-end encryption, audit trails and frameworks such as NIST and zero-trust architecture. Your data pipelines have to fit the bill, too.

Site Reliability Engineering: Turning Metrics into Reliability

SRE is the application of software engineering to operations. Google made it popular by treating reliability as a product feature and quantifying it through service level indicators (SLIs), SLOs and error budgets. Healthcare can benefit a lot from SRE.

According to a 2024 research paper, Google’s SRE team maintains 99.99% uptime for critical services and the Cleveland Clinic reduced critical incidents by 40% and MTTR by 60% after adopting SRE. A HIMSS survey found 70% of healthcare IT professionals believe SRE practices improve system reliability and performance.

**SRE Practices for Telehealth **

Design for graceful degradation, so if the video doesn’t work, for example, the system can fall back to audio or chat. Provide asynchronous options such as secure messaging or offline notes to ensure care continues even when live sessions drop.

Do Chaos Engineering: Simulate node failures, network partitioning and API timeouts to see how the system behaves. In telehealth scenarios, testing should include streaming services, device connectivity and EHR integration. Use the results to enhance failover strategies and client-side resiliency.

Track Patient-Centric SLIs: CPU and memory metrics are not enough. Monitor metrics such as join time, call drop rate, video quality (jitter, packet loss), authentication latency, prescription fulfillment success and device connectivity. These SLIs are patient-facing. When you put multiple microservices together, use distributed tracing to correlate patient-level transactions across authentication, scheduling, video streaming, EHR integration and billing.

Automate Remediation: Combine observability data with automated runbooks to reduce MTTD and MTTR. According to IRJET, advanced monitoring allowed one hospital to detect and resolve 80% of incidents before they impacted end users; 75% of organizations using advanced monitoring reported improved availability and reduced downtime.

Prioritize Patient Data Integrity: There’s no use being reliable if the data is wrong. SRE practices should include thorough validation and error handling. The Cleveland Clinic reduced errors in data entry by 80% and increased the accuracy of records to 95% by implementing full data validation.

DevOps Playbook for Telehealth

Now that we have the theory, let’s turn it into a playbook for DevOps teams to build telehealth platforms.

1. Know the Domain

Telehealth is regulated and patient-centric. Know HIPAA, HITECH and regional regulations. Map data flows and ensure PHI is secure. During planning, involve compliance specialists and clinicians to understand functional requirements (e.g., e-prescriptions and remote monitoring) and non-functional requirements (e.g., uptime, latency and security). As an example of how domain expertise drives success, ScienceSoft telemedicine solutions enable audiovisual patient-doctor communication, patient monitoring and remote care delivery while reducing healthcare costs and increasing care accessibility. This shows that telehealth software must balance technical complexity with clinical usability.

1. Define Reliability Objectives and Architecture

Set SLOs for each user journey: Session setup time, video quality, messaging latency, device connectivity and data synchronization. Document error budgets and attach it to business objectives. Build architecture diagrams showing redundancy, failover paths, encryption layers and integration points. Decide to use commercial video SDKs or build a custom WebRTC infrastructure. As an example, Codebridge recommends 99.95% uptime, with call drop rates less than 2%; adjust to your clinical use cases.

1. Instrument and Automate

Instrument from day one: OpenTelemetry in microservices; quality metrics in video streaming components; log for EHR integration; infrastructure as code (Terraform, Ansible) and CI/CD pipelines for consistent environments and repeatable deployments; SAST and DAST in your pipeline and performance tests; canary deployments and feature flags to control the blast radius of new features. For example, Northwell Health’s 95% error reduction is proof of a strong CI/CD pipeline.

1. OperateWithSRE Principles

Create on-call rotations and incident response playbooks. Use runbooks to automate standard remediation tasks, such as scale pods and restart services. Run game days and chaos experiments to validate failover. Track incident metrics: MTTD, MTTR; have blameless postmortems. SRE is not about heroics; it’s about creating systems that self-heal and enable engineers to focus on automation instead of firefighting.

1. Continuously Improve Based on Observability Data

Observability should be the driving force that guides where your development and ops teams focus their efforts. Take a close look at those system traces to figure out where you’re really getting bottlenecked — then use that info to tweak code and snag those pesky database queries and network routing issues. Don’t forget predictive analytics — it helps you stay a step ahead to spot impending hardware doom before it becomes a disaster.

Throw those observability dashboards up on the screen for your clinical stakeholders to see, so they can get a handle on how the platform is doing and maybe even make a strong case for some much-needed investments. In the words of LogicMonitor, you can proactively sniff out issues and keep EHR, telehealth and imaging systems always accessible when you use a hybrid observability platform powered by AI.

1. Prioritize Compliance and Trust

Reliability and compliance go hand in hand — so don’t forget it. Encrypt that data, set up proper access controls and keep track of all those audit trails. In addition, make sure you’re in compliance with the major security frameworks such as NIST CSF and zero-trust network segmentation — that’s a given. Then toss in some regular risk assessments and penetration tests for good measure. Document all those data flows, have all the necessary business associate agreements in place with your vendors and make darn tootin’ sure the customer feels safe knowing their data is protected and their care won’t be put in jeopardy from some avoidable security screw-up.

Conclusion

Observability and SRE are key to uptime and reliability in telehealth platforms. By doing proactive monitoring, automated response and DevOps, teams can make every virtual visit seamless, secure and always available when patients need it most.