Leveraging Automation to Deliver High-Quality MedTech Software

Randy Horton

Published on: June 17th, 2025

July 2025 Webinar Banner Post without Bernhard

Executive Summary

Medical device manufacturers are accustomed to working towards high standards of quality. However, as software increasingly drives MedTech products, traditional quality tools such as SOPs and manual validations often struggle to keep pace with the malleable nature of software and data.

In a recent Orthogonal webinar, AWS and Orthogonal experts discussed how the tools and techniques of software automation are transforming MedTech quality management. They shared practical strategies for developing, validating, and scaling software-enabled medical device systems that meet both regulatory requirements and real-world needs, all while enhancing patient safety and device effectiveness, ultimately resulting in improved patient outcomes that surpass those achieved through traditional methods.

The Shift from Manufacturing-Minded Quality to Software-Driven Assurance

Historically, MedTech quality frameworks were shaped by challenges and ideas emanating from the R&D and the manufacturing of physical devices. These frameworks focused on biology, chemistry, and physics, and proved effective when quality meant producing identical products at scale. However, software and data (i.e., “digital”) have different properties. Software evolves quickly and can adjust its behavior based on state, context, and configuration. While quality challenges for physical devices involve working with the constraints of the physical world, digital quality challenges often stem from navigating the absence of such constraints.

As a result, traditional quality methods designed for the physical world, such as SOPs and manual checks, often don’t adapt and scale well in the digital world, increasing the risk of quality issues. To address this, MedTech software development must leverage digital automation to ensure consistency, reliability, and the ability to manage continuous change safely.

“We have to think about the design phase differently. We’re only designing things we’ve never designed before. If we’ve designed it before, we should be taking advantage of automation, we should be reusing, etc, so forth.” – Larkin Lowrey

The bottom line is that automation isn’t optional; it’s an operational necessity, enabling MedTech software to function safely, reliably, and efficiently at scale.

Automation as the Backbone of Modern MedTech Quality

In this context, automation refers to the use of software-driven systems and tools that can execute tasks, such as testing, deployment, monitoring, and infrastructure provisioning, without requiring manual intervention. Unlike physical world automation, which often focuses on robotics and mechanized processes, software automation enables systems to self-monitor, self-test, and self-correct at speed and scale.

Panelists strongly emphasized the importance of treating automation as essential for developing compliant, scalable, and safe medical device software systems.

Testing at Speed and Scale: Automated test suites identify regressions swiftly and consistently.
Continuous Deployment with Confidence: Continuous Integration and Continuous Deployment (CI/CD) pipelines enable small, frequent, continuously validated changes.
Monitoring in Production: Tools like AWS CloudWatch (and similar tools from other cloud service providers) verify quality post-deployment, identifying issues undetectable in test environments.
Environment Reproducibility: Infrastructure as Code (IaC) enables teams to accurately recreate any environment for debugging and testing, providing confidence that systems will behave consistently in production environments.

These capabilities aren’t merely beneficial; they’re mission-critical for connected medical device software, directly impacting patient safety. For example, automated backups and recovery testing in cloud systems allow developers to proactively verify disaster recovery capabilities rather than relying on assumptions that backups are being made correctly, are recoverable, and will function properly when needed.

Documenting Quality Without Drowning in Paperwork

In traditional quality management, documentation is written by humans, often in extensive detail. These practices were the best available tools for managing quality in an earlier era of physical-device manufacturing. However, in the context of modern software systems, new options are available. Today, modern software can generate more accurate documentation within the system itself:

Logs over Logsheets: Verifiable automation-generated records capture every deployment and change.
Runbooks over SOPs: Real-time automated runbooks reflect actual system operations, eliminating discrepancies.

“As soon as you’ve automated a process or a part of a process, that system, documentation, and training documentation supplant and replace the SOP because there is no value.” – Ian Sutcliffe

The automation itself becomes the documentation, defining and governing the process rather than simply generating supporting materials. It not only improves accuracy but also aligns naturally with compliance demands, providing inherently auditable records.

“You’re able to do credible integration tests by actually having it in an actually deployed environment. And the economy of this is so good that you will literally do this on every pull request for a code change that developers do.” – Larkin Lowrey

Rethinking Team Dynamics: Aligning Hardware Rigor with Software Agility

Effective MedTech quality in the modern era should combine the best elements of both mindsets and help bridge the differences, allowing teams to collaborate effectively without compromising safety or effectiveness

However, the webinar highlighted a key cultural clash: meticulous hardware engineers who aim to eliminate risk before release versus agile software developers more comfortable releasing software early and then regularly making iterative improvements. This clash commonly occurs in established organizations transitioning to automation and software-driven development.

Panelists recommended practical alignment strategies:

Software Professionals Can Learn from Hardware Professionals: Teams should adopt rigorous validation, proactive risk management, and design with failure prevention in mind.
Hardware Professionals Can Learn from Software Professionals: Teams should incorporate iterative development, rapid feedback loops, and continuous real-world performance monitoring.

The Advantages of Cloud Infrastructure

The rise of the cloud has been fueled by a level of automation that wasn’t previously possible in traditional data center environments. Ian Sutcliffe described several advantages of cloud infrastructure for MedTech, especially when compared to traditional data center infrastructure:

Built-in Security and Reliability: Cloud-native services offer encryption, automated backups, and failover capabilities.
Disaster Recovery: Automated routines allow regular testing and validation of recovery procedures.
Long-term Performance Monitoring and Assurance: Cloud monitoring identifies gradual degradation due to data growth, enabling proactive intervention.

AI’s Practical Role in MedTech Automation

Artificial Intelligence is already enhancing how automation is used to ensure quality, enabling systems to respond more intelligently and proactively. These AI-enabled efficiencies enable teams to scale their automation strategies more effectively, reducing the manual effort required to maintain rigorous quality controls. As a result, AI doesn’t just assist automation, it amplifies its role in ensuring software reliability, compliance, and safety.

Let’s look at AI use cases such as:

Predictive Monitoring: AI detects system anomalies, preventing patient-impacting failures. AI models can detect subtle patterns in operational data, uncovering issues early, such as manufacturing defects or unexpected software behaviors, significantly improving device reliability and patient safety.
Infrastructure and Code Generation: Generative AI accelerates infrastructure setup, test development, and validation processes, making it easier and faster to implement high-quality automation across systems.

However, as the speakers noted, AI doesn’t replace responsibility; it enhances existing good practices and quickly reveals deficiencies. Human oversight remains essential. AI helps people perform their jobs more efficiently and effectively, but it does not replace the critical thinking, judgment, and accountability that only human professionals can provide, especially in MedTech, where quality and safety are paramount.

Final Takeaway: Automation Builds Trust

When implemented properly, automation raises the bar on the consistent execution of tasks such as:

Consistency across environments
Traceability of all changes
Early detection and correction of failures before harm occurs

“If you strive for quality, if you adopt a culture of quality over compliance… compliance will be a natural byproduct.” – Ian Sutcliffe

These capabilities enable medical device manufacturers to consistently deliver levels of device quality that earn trust from regulators, clinicians, and patients.

Both panelists made a persuasive case that now is the time for MedTech manufacturers to fully embrace software automation as one of the industry’s most effective tools for delivering safer, more reliable MedTech products at a higher velocity than ever before.

Further resources

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Randy Horton, VP of Solutions and Partnerships, Orthogonal