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Modern Medical Enterprises Absolutely Need Test Automation. Here’s Why.

3 minutes, 38 seconds read

The healthcare industry is getting a comprehensive digital facelift. Digital Health Systems (DHS) that use new digital technologies like artificial intelligence & robotics are delivering smarter healthcare services and better health outcomes to the masses. Health organizations are increasingly relying on them to improve care coordination, chronic disease management and the overall patient experience. These health systems are also alleviating repetitive administrative tasks from the roles of healthcare professionals, allowing them more time to practice actual healthcare.

The Modern Medical Enterprise draws on digital-enabled technologies such as telemedicine, AR/VR and remote-monitoring wearables to diagnose diseases and promote self-care. These applications rely on high-volume processing of patient data on a frequent basis.  Healthcare organizations also need to share/receive this information securely over a distributed network. However, sharing patient information remains a challenge, while the inability to access these records in a time-sensitive manner can affect the time-to-treatment for patients.

Deploying digital health systems that are both compliant to regulatory standards and functionally stable for a large number of concurrent users requires significant manned effort. Moreover, QA teams comprised of manual testers may end up working on repetitive manual test case scenarios that can lead to challenges in scaling or rolling out new features. 

How can the modern healthcare enterprise keep pace with issues posed by the safe deployment of their digital health systems? Automated Testing is a hallmark process of any digital transformation project. It gives enterprises the ability to shorten their release cycles and meet their business needs without affecting productivity or operations across the healthcare value chain. Test Automation also allows medical enterprises to run repeatable and extensible test cases against real-world scenarios.

Test Automation Use Case

The growth of DevOps and the rise of mobile-first applications are responsible for driving the growth of the test automation market globally. Today, enterprises are able to go faster-to-market owing to the technological advancements in quality assurance & testing.

For instance, in the case of a large US-based teleradiology firm that offers enterprise Imaging Solutions for improving patient care — a stable and reliable system mandated custom-built test automation frameworks. The medical technology company provides fast & secure access to diagnostic quality images using any web enabled device. To achieve this, they have built a cloud-based image sharing platform that allows digital image streaming, diagnostic & clinical viewing, and archiving for healthcare organizations.

Medical Image sharing among healthcare organizations is altogether brimming with security risks, and requires a complex network of systems to facilitate its smooth functioning. 

medical imaging system architecture
Medical Image Sharing Process among Healthcare Organizations

Also read – How are Medical Images shared among Healthcare Enterprises? 

In order to fulfil their business objectives, Mantra Labs identified key challenges for their testing requirements, namely —

1. Scalability
The platform must be able to support a high number of concurrent users.

2. Fail-over Control

The platform should behave functionally correct under very high loads with stable fail-over capability.


3. Efficiency & Reliability
The platform must scale rapidly when supporting a large user base & multiple formats with minimal page navigation response time.

Several testing components were deployed along with test automation techniques to address the full range of QA issues, including: functional testing, integration testing, GUI testing, and regression testing. 

Mantra Labs created a federated architecture to ensure near-perfect scaling, and true load & data isolation between different tenant organizations. The federated architecture consists of a number of deployments and a central set of components that stores global information like lists of organizations & users, and provides a centralized messaging service. 

test automation process flow diagram for modern medical enterprises
Mantra Labs Test Automation Process

Test Automation Improves Accuracy & Test Coverage

The entire cycle of bug detection in the UI, API and Server Loads involves several weeks of regression manual efforts. By automating tests, techniques like Stochastic Tests can be applied to detect bugs and reduce the overall cycle time.

Through Mantra Labs deep medical domain expertise, in-depth testing practices, intuitive suggestions for platform scaling and successful test automation efforts — significant business objectives were realised over the course for the client. Mantra was able to achieve over 60% reduction in cycle time, and about 65 per cent improvement in bug detection capability before the release cycle.

Nearly 35% of Executive Management objectives revolve around implementing quality checks early in the product life cycle, which can be achieved through test automation. For further queries and details about automated testing, please feel free to reach us at hello@mantralabsglobal.com

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What’s Next in Cloud Optimization? Can We Optimize Costs Without Sacrificing Performance?

Not too long ago, storing data meant dedicating an entire room to massive CPUs. Then came the era of personal computers, followed by external hard drives and USB sticks. Now, storage has become practically invisible, floating somewhere between data centers and, well, the clouds—probably the ones in the sky. Cloud computing continues to evolve, As cloud computing evolves, optimizing costs without sacrificing performance has become a real concern.  How can organizations truly future-proof their cloud strategy while reducing costs? Let’s explore new-age cloud optimization strategies in 2025 designed for maximum performance and cost efficiency.

Smarter Cloud Strategies: Cutting Costs While Boosting Performance

1. AI-Driven Cost Prediction and Auto-Optimization

When AI is doing everything else, why not let it take charge of cloud cost optimization too? Predictive analytics powered by AI can analyze usage trends and automatically scale resources before traffic spikes, preventing unnecessary over-provisioning. Cloud optimization tools like AWS Compute Optimizer and Google’s Active Assist are early versions of this trend.

  • How it Works: AI tools analyze real-time workload data and predict future cloud resource needs, automating provisioning and scaling decisions to minimize waste while maintaining performance.
  • Use case: Netflix optimizes cloud costs by using AI-driven auto-scaling to dynamically allocate resources based on streaming demand, reducing unnecessary expenditure while ensuring a smooth user experience.

2. Serverless and Function-as-a-Service (FaaS) Evolution

That seamless experience where everything just works the moment you need it—serverless computing is making cloud management feel exactly like that. Serverless computing eliminates idle resources, cutting down costs while boosting cloud performance. You only pay for the execution time of functions, making it a cost-effective cloud optimization technique.

  • How it works: Serverless computing platforms like AWS Lambda, Google Cloud Functions, and Azure Functions execute event-driven workloads, ensuring efficient cloud resource utilization while eliminating the need for constant infrastructure management.
  • Use case: Coca-Cola leveraged AWS Lambda for its vending machines, reducing backend infrastructure costs and improving operational efficiency by scaling automatically with demand. 

3. Decentralized Cloud Computing: Edge Computing for Cost Reduction

Why send all your data to the cloud when it can be processed right where it’s generated? Edge computing reduces data transfer costs and latency by handling workloads closer to the source. By distributing computing power across multiple edge nodes, companies can avoid expensive, centralized cloud processing and minimize data egress fees.

  • How it works: Companies deploy micro data centers and AI-powered edge devices to analyze data closer to the source, reducing dependency on cloud bandwidth and lowering operational costs.
  • Use case: Retail giant Walmart leverages edge computing to process in-store data locally, reducing latency in inventory management and enhancing customer experience while cutting cloud expenses.

4. Cloud Optimization with FinOps Culture

FinOps (Cloud Financial Operations) is a cloud cost management practice that enables organizations to optimize cloud costs while maintaining operational efficiency. By fostering collaboration between finance, operations, and engineering teams, FinOps ensures cloud investments align with business goals, improving ROI and reducing unnecessary expenses.

  • How it works: Companies implement FinOps platforms like Apptio Cloudability and CloudHealth to gain real-time insights, automate cost optimization, and enforce financial accountability across cloud operations.
  • Use case: Early adopters of FinOps were Adobe, which leveraged it to analyze cloud spending patterns and dynamically allocate resources, leading to significant cost savings while maintaining application performance. 

5. Storage Tiering with Intelligent Data Lifecycle Management

Not all data needs a VIP seat in high-performance storage. Intelligent data lifecycle management ensures frequently accessed data stays hot, while infrequently used data moves to cost-effective storage. Cloud-adjacent storage, where data is stored closer to compute resources but outside the primary cloud, is gaining traction as a cost-efficient alternative. By reducing egress fees and optimizing storage tiers, businesses can significantly cut expenses while maintaining performance.

  • How it’s being done: Companies use intelligent storage optimization tools like AWS S3 Intelligent-Tiering, Google Cloud Storage’s Autoclass, and cloud-adjacent storage solutions from providers like Equinix and Wasabi to reduce storage and data transfer costs.
  • Use case: Dropbox optimizes cloud storage costs by using multi-tiered storage systems, moving less-accessed files to cost-efficient storage while keeping frequently accessed data on high-speed servers. 

6. Quantum Cloud Computing: The Future-Proof Cost Gamechanger

Quantum computing sounds like sci-fi, but cloud providers like AWS Braket and Google Quantum AI are already offering early-stage access. While still evolving, quantum cloud computing has the potential to process vast datasets at lightning speed, dramatically cutting costs for complex computations. By solving problems that traditional computers take days or weeks to process, quantum computing reduces the need for excessive computing resources, slashing operational costs.

  • How it works: Cloud providers integrate quantum computing services with existing cloud infrastructure, allowing businesses to test and run quantum algorithms for complex problem-solving without massive upfront investments.
  • Use case: Daimler AG leverages quantum computing to optimize battery materials research, reducing R&D costs and accelerating EV development.

7. Sustainable Cloud Optimization: Green Computing Meets Cost Efficiency

Running workloads when renewable energy is at its peak isn’t just good for the planet—it’s good for your budget too. Sustainable cloud computing aligns operations with renewable energy cycles, reducing reliance on non-renewable sources and lowering overall operational costs.

  • How it works: Companies use carbon-aware cloud scheduling tools like Microsoft’s Emissions Impact Dashboard to track energy consumption and optimize workload placement based on sustainability goals.
  • Use case: Google Cloud shifts workloads to data centers powered by renewable energy during peak production hours, reducing carbon footprint and lowering energy expenses. 

The Next Frontier: Where Cloud Optimization is Headed

Cloud optimization in 2025 isn’t just about playing by the old rules. It’s about reimagining the game entirely. With AI-driven automation, serverless computing, edge computing, FinOps, quantum advancements, and sustainable cloud practices, businesses can achieve cost savings and high cloud performance like never before.

Organizations that embrace these innovations will not only optimize their cloud spend but also gain a competitive edge through improved efficiency, agility, and sustainability. The future of cloud computing in 2025 isn’t just about cost-cutting—it’s about making smarter, more strategic cloud investments.

At Mantra Labs, we specialize in AI-driven cloud solutions, helping businesses optimize cloud costs, improve performance, and stay ahead in an ever-evolving digital landscape. Let’s build a smarter, more cost-efficient cloud strategy together. Get in touch with us today!

Are you ready to make your cloud strategy smarter, cost-efficient, and future-ready with AI-driven, serverless, and sustainable innovations?

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