Leveraging DevOps to accelerate the delivery of intelligent and autonomous care solutions
By Siddharth Sheth, SVP, MedTech Technology, CitiusTech and Mr. Vinay Kumar Ramchander, AVP – DevOps, CitiusTech
Innovation in healthcare rarely happens in sweeping shifts. Progress is made quietly, often behind the scenes, by solving a single, persistent problem. That’s where DevOps has begun to make a meaningful difference.
For years, healthcare teams have worked around legacy systems that were reliable but rigid. Calls for modernization have been growing steadily—from patients, payers, and increasingly from clinicians and operational teams within care delivery itself. Each step forward must acknowledge a constant reality: lives are involved, and trust cannot be compromised.
For many organizations that succeed with DevOps, transformation doesn’t begin with sweeping plans or large technology rollouts. They begin with one clear goal.
Starting Small with a Tangible Use Case
One healthcare technology provider faced recurring bottlenecks in onboarding new development projects. Project cycles often extended up to eight weeks, slowed by repeated compliance reviews, inconsistent access controls, and the need for coordination between engineering and security teams.
Instead of trying to fix everything at once, the team chose a problem they encountered regularly—a HIPAA compliance check that held up every new project. By automating that task, they brought onboarding time down to a day. The improvement also made their workflow smoother, reduced errors, and helped different teams work together more easily. That single win helped build support for making other improvements across the board.
What Makes Healthcare DevOps Distinct
Fast iteration and continuous delivery have become standard in industries like e-commerce and finance. Healthcare operates under different rules. Here, the consequences of technical missteps can directly affect care outcomes or compromise sensitive patient information. Even a small configuration error can delay a diagnosis or impact patient safety.
That reality shifts how DevOps is applied. The focus is on building systems that behave consistently, meet compliance standards automatically, and support reliable care delivery at every step.
Four Areas That Create Measurable Impact
While every organization’s journey is different, four capabilities consistently deliver value when DevOps is tailored for healthcare settings:
- Consolidated Delivery Platforms
Engineering teams often toggle between multiple tools for deployments, infrastructure, compliance audits, and access management. A unified platform helps reduce friction and lowers the chance of errors introduced by switching contexts.
One hospital reduced deployment-related issues by 40% after consolidating their delivery systems. To reduce deployment risks, they began testing changes with a small set of users before wider rollout. This incremental approach helped catch issues early and ensured that any failures could be contained—without affecting live clinical environments.
- Improving the Developer Experience
In many healthcare environments, developers are held back by slow setup processes and multi-step approvals that make it harder to contribute code efficiently or with confidence. This often leads to slower cycles and fragmented focus.
Modern DevOps platforms help by introducing prebuilt, compliant workflow templates, secure self-service provisioning for environments, and real-time, AI-supported code review tools.
In one case, development teams streamlined dozens of custom scripts into a reusable pipeline that provisioned compliant environments automatically. The result was a noticeable reduction in setup time and greater consistency across projects.
Building on this foundation, DevOps also play a vital role in development and deployment of the Machine Learning Models. The ease of setting up platforms to expedite ML model development and deployment has become a key requirement.
For one specific use case, an orchestration was tailored using a combination of automation tools, and cloud providers. This enabled multiple data scientists to quickly provision ML models and deploy them across in multiple environments with minimal intervention.
As a result, data scientists were able to focus their cognitive load on managing multiple models and extracting the most relevant insights, rather than being burdened by the aspects of provisioning and deployments.
- Embedding Security Throughout the Process
Security and compliance checks often happen too late in the release process. A shift toward security-as-code addresses this by embedding policies directly into the pipeline.
Infrastructure configurations are validated automatically against HIPAA and other regulatory frameworks before deployment. Secrets management, including credential encryption, is handled natively by the platform. If a misconfiguration occurs, policy enforcement systems can revert the change without waiting for manual intervention.
- Improving Visibility into Cloud Spending
Cloud costs in healthcare environments are frequently higher than necessary, often due to underutilized or idle resources. With increased adoption of FinOps principles, organizations are aligning cloud usage with actual demand.
One provider connected cloud resource usage to live patient portal traffic. Development environments were set to pause during off-hours, and idle resources were flagged automatically. Over a quarter, this shift reduced their non-production cloud costs by over 70%.
Onboarding Gains and the Role of AI
New developer onboarding in healthcare settings has historically been a slow process, partly due to complex environments and the need for compliance training. Several organizations have now shortened this cycle by automating foundational steps such as secure workstation setup and embedding policy training into existing workflows.
An EHR provider, for example, reduced onboarding time from 90 days to under 30 through a focused investment in platform automation and documentation.
Artificial intelligence is increasingly supporting these platforms—not by replacing engineers, but by reducing low-value manual tasks. AI now assists in:
- Detecting potentially risky changes before they reach production
- Patching common vulnerabilities automatically
- Enabling deployment interfaces that respond to natural language prompts
These capabilities are most effective when layered onto a system that already has strong processes in place. AI enhances, rather than replaces, well-designed pipelines.
This is AI for Devops
A Measured Path Forward
The demands on healthcare systems aren’t just technical—they’re human. There is a constant expectation for developers and security teams to maintain both speed and precision. Finding that equilibrium can be difficult, especially when technical infrastructure is unstable or when compliance reviews are delayed.
With thoughtfully designed systems that reflect healthcare’s unique context, updates become easier to manage and less risky to implement. Over time, this consistency builds trust and creates space for gradual improvements in care delivery, without causing unnecessary disruption.
In healthcare, lasting improvement tends to unfold gradually: smoother deployments, reduced onboarding delays, and fewer manual approvals holding up progress. These steady improvements, while easy to overlook, are often what enable organizations to scale without sacrificing safety or trust.