Compliance & Security
November 5, 2025
Chinmay Chandgude
5 Phases of Medical Device Software Development: Complete Guide to Build a Safe Software
In modern healthcare, software has become the central nervous system of innovation. From wearable ECG monitors and connected insulin pumps to AI-powered diagnostic tools, it’s the code that now determines a device’s performance, safety, and clinical reliability.
According to Deloitte (2024), more than 60 percent of new medical devices rely on embedded or standalone software, while the FDA reports that nearly 75 percent of device recalls occur due to software related issues mostly caused by design flaws, insufficient testing, or incomplete documentation.
Unlike conventional app development, medical device software operates in a high-stakes, regulated ecosystem governed by standards such as IEC 62304, ISO 13485, and FDA 21 CFR Part 820. Every requirement, function, and line of code must be traceable, validated, and auditable because even a minor defect can carry clinical consequences.
This article unpacks the five phases of medical device software development, pinpoints where teams commonly go wrong, and outlines how disciplined engineering leads to safer, more compliant, and more trusted healthcare technology.
Phase 1: Planning and Requirements
Every reliable medical device is designed by understanding what the software must achieve, who it serves, and how it will remain compliant across its lifecycle. Planning starts with defining the intended medical use and identifying the device’s regulatory classification (Class I, II, or III) under FDA medical device software or MDR frameworks. Teams then map which standards will apply, for instance, IEC 62304 for software lifecycle management, ISO 13485 for quality systems, and FDA 21 CFR Part 820 for design control and documentation. These frameworks form the backbone of any audit-ready development process.
A well-structured medical device Software Development Plan (SDP) outlines the project scope, deliverables, team roles, and verification approach. A requirements-traceability matrix ensures that each function, risk, and test can be linked in both directions from idea to implementation. This traceability becomes crucial during audits and change-control reviews.
In parallel, risk management under ISO 14971 identifies hazards, estimates their severity and probability, and defines mitigations. Performing this analysis early prevents design rework and aligns teams on what truly matters for patient safety. Risk documentation must be maintained as a living artifact throughout development and post-market surveillance medical devices updates. Automation in healthcare systems can also streamline documentation and traceability during the planning phase.
Where teams go wrong: Many startups rush into prototyping before establishing proper documentation or risk assessments. Overlooking cybersecurity or data-privacy planning is very common. But it is now a mandatory focus area under the FDA’s 2023 Cybersecurity Guidance.
By the end of Phase 1, you should have:
An approved intended use statement, defined user needs, and documented regulatory classification (FDA/MDR).
A list of applicable standards and controls (IEC 62304, ISO 13485, ISO 14971, FDA 21 CFR Part 820).
A signed medical device Software Development Plan (SDP) with scope, roles, milestones, and verification approach.
An initial Requirements Traceability Matrix (RTM) linking each requirement to risks and tests.
A documented initial risk analysis and mitigation plan (ISO 14971).
A documented cybersecurity and data-protection baseline (access control, encryption, logging).
Phase 2: System and Software Design
Once the requirements are approved, the next step is to translate them into a system architecture: a detailed blueprint showing how the software will function, communicate, and maintain patient safety.
System design determines the overall structure, data flow, and risk-control logic of the device. It specifies how subsystems such as data acquisition, processing, and communication interact, and how the device handles events like data loss or sensor failure. Design documentation must show that safety-critical pathways are protected through redundancy, isolation, and recovery procedures.
Under IEC 62304, each software component is classified according to its potential effect on patient safety: Class A (low risk), Class B (moderate), and Class C (high risk). This classification governs how much documentation, verification, and testing each element requires. Design teams use this model to prioritize controls and validation depth early, preventing costly redesigns later in the lifecycle.
Modular architectures let teams test or update one component without disrupting the entire system, supporting scalability and audit readiness. Interoperability ensures compliance with healthcare data standards like HL7 and FHIR, allowing devices to integrate with hospital systems and electronic health records (EHRs).
Where teams go wrong: Designs that are too rigid or under-documented cause friction later. Over-engineered systems make verification difficult, while minimal design artifacts create inconsistencies between development and QA teams. Neglecting usability or failing to classify software correctly are among the most frequent reasons for FDA design-control deficiencies.
By the end of Phase 2, you should have:
Approved system architecture and data-flow diagrams that show module boundaries, data movement, and safety behavior.
IEC 62304 classification (Class A/B/C) assigned to each software item, with rationale recorded.
Documented design inputs and design outputs under version control (interfaces, behaviors, safety controls).
Completed design review with sign-off from engineering, QA, regulatory, and clinical stakeholders.
Defined plans for usability evaluation and interoperability (e.g. HL7/FHIR/EHR implementations).
All design artifacts stored, versioned, and audit-ready in the design history file.
Phase 3: Implementation and Coding
Once the architecture is approved, development moves into implementation. In medical device development, coding is a regulated engineering process. Every feature must trace back to a defined requirement, risk control, and verification method, ensuring that nothing is left undocumented or untested.
To maintain consistency and safety, development teams operate under a Quality Management System (QMS) aligned with ISO 13485. The QMS governs version control, peer reviews, defect tracking, and documentation updates. It also enforces change-control mechanisms so that any code modification is evaluated for risk and revalidated where necessary.
For embedded and low-level systems, coding standards such as MISRA C:2012 help prevent unsafe programming patterns, memory errors, and undefined behavior. For connected or cloud-based applications, frameworks like OWASP Secure Coding Practices guide developers in mitigating vulnerabilities related to authentication, data storage, and communication. Together, these controls create a defensible security posture that meets both FDA design control guidance and MDR cybersecurity expectations.
Automation plays a key role in maintaining quality at scale. Tools such as SonarQube, Polyspace, or Veracode are often integrated into CI/CD pipelines to perform static code analysis, detect vulnerabilities, and measure compliance with safety standards. These systems ensure traceability between requirements, commits, and test results .
Where teams go wrong: In many MedTech software lifecycle projects, documentation steps are skipped, peer reviews are reduced to formality, or third-party components are integrated without validation. These shortcuts often lead to Software of Unknown Provenance (SOUP) — code whose origin or quality is unverifiable. SOUP is one of the most frequent causes of nonconformance findings during FDA and ISO audits.
By the end of Phase 3, you should have:
Enforced secure coding standard (e.g. MISRA C, OWASP) applied across the codebase and documented.
Full traceability in place: every commit and feature is linked to an approved requirement in the traceability matrix.
Peer reviews completed and logged for all merged code, with unit tests and defect tracking recorded.
CI/CD pipeline running static analysis, linting, and security scans, with results stored.
SOUP components evaluated, documented (source, version, purpose, risk), and approved.
Risk management file and design documentation updated to reflect any new or changed behavior in the implementation.
Phase 4: Verification, Validation, and Testing
After implementation, the focus shifts from building the product to proving that it works, both technically and clinically. In medical device software development, testing isn’t an optional QA step but a regulatory obligation.
Testing spans multiple layers. Unit testing validates individual code components, integration testing confirms modules interact correctly, and system testing verifies complete end-to-end functionality. Beyond these, usability testing and clinical simulation testing ensure the device performs safely in real-world environments. Each test must be documented, reproducible, and traceable to a specific requirement and risk.
In regulated environments, Continuous Integration/Continuous Deployment (CI/CD) pipelines automate regression tests, security scans, and performance checks, producing verifiable logs for compliance reports. These validation pipelines resemble predictive analytics systems that continuously learn and optimize based on real-time data. Tools such as Jenkins, TestRail, or Azure DevOps are commonly used to record version-controlled evidence for each build. The FDA’s Software Validation in Healthcare Guidance and ISO/TR 80002-1 both emphasize that the absence of documented proof is what typically causes audit failures.
Where teams go wrong: Many organizations treat testing as a single project phase rather than an ongoing process. When verification is postponed until the end of development, defects often surface too late. This forces design changes, repeated validations, or delayed FDA medical device software submissions. Inconsistent test environments, missing traceability, or incomplete logs are frequent findings in FDA Form 483 observations.
By the end of Phase 4, you should have:
An approved Verification & Validation (V&V) plan covering unit, integration, system, usability, and safety testing, with defined pass/fail criteria.
Each test case mapped to a specific requirement and its related risk control.
Automated regression, performance, and security testing running in CI/CD, with stored results.
Objective evidence captured and versioned for each executed test (logs, screenshots, reports).
Usability and (where relevant) clinical simulation testing completed and documented to show safe use in real conditions.
A clean, reviewable test package prepared for FDA submission or internal audit.
Phase 5: Maintenance and Post-Market Surveillance
Once software is deployed, manufacturers are responsible for ensuring that it performs consistently in the field, remains compliant with evolving regulations, and adapts to real-world usage conditions. Post-market surveillance medical devices is an extension of the same design-control principles that governed the initial build.
The maintenance phase involves scheduled updates, defect corrections, and performance enhancements, each governed by the same verification and validation rigor as pre-release code. Even minor software patches require risk assessment under ISO 14971 to confirm they do not inadvertently introduce new hazards or degrade safety functions. Any change that affects device behavior must be re-verified, documented, and, if necessary, submitted for regulatory review.
Beyond maintenance, post-market surveillance (PMS) focuses on continuously collecting and analyzing real-world data to identify potential safety or performance concerns. This includes user feedback, adverse-event reports, device usage analytics, and regulatory notifications. PMS obligations are defined under both the FDA’s Quality System Regulation (21 CFR Part 820.100) and the European MDR (Article 83), requiring manufacturers to maintain vigilance and report significant incidents promptly.
Where teams go wrong: Many organizations underestimate the resources needed for sustained monitoring. Neglecting software updates, skipping field-data analysis, or failing to submit corrective-action documentation can lead to warning letters or product recalls. The FDA’s recall database consistently lists software configuration errors and unverified patches as leading causes of post-market failures.
By the end of Phase 5, you should have:
A defined change-control procedure for software updates, including approval, testing, and release steps.
Scheduled risk reviews after each release, with updates captured in the risk management file.
A process to collect and analyze field data (performance issues, user complaints, adverse events).
An active post-market surveillance (PMS) plan aligned with FDA 820.100 and EU MDR Article 83.
Automated monitoring in place for deployed versions and cybersecurity vulnerabilities.
Documented corrective actions and a feedback loop that pushes those findings back into design and requirements for the next iteration.
Conclusion:
In today’s MedTech software lifecycle ecosystem, software doesn’t just run medical devices, it defines their clinical integrity. With the rise of connected systems, wearables, and AI-powered diagnostics, maintaining traceability, data security, and post-market vigilance has become a strategic differentiator. The companies that lead the market are those that integrate compliance automation, usability testing, and data-driven surveillance into a unified engineering process.
At Latent, we help medical device software development innovators achieve this balance by accelerating product development while safeguarding compliance and patient safety. Our development framework merges regulatory precision with software agility, enabling:
End-to-end traceability across all lifecycle stages.
Integrated risk management aligned with FDA, MDR, and ISO standards.
Continuous validation pipelines that reduce verification time and audit friction.
Built-in usability and cybersecurity testing aligned with FDA’s 2023 guidance.
Post-market surveillance medical devices that convert device data into actionable compliance insights.
In modern healthcare, software has become the central nervous system of innovation. From wearable ECG monitors and connected insulin pumps to AI-powered diagnostic tools, it’s the code that now determines a device’s performance, safety, and clinical reliability.
According to Deloitte (2024), more than 60 percent of new medical devices rely on embedded or standalone software, while the FDA reports that nearly 75 percent of device recalls occur due to software related issues mostly caused by design flaws, insufficient testing, or incomplete documentation.
Unlike conventional app development, medical device software operates in a high-stakes, regulated ecosystem governed by standards such as IEC 62304, ISO 13485, and FDA 21 CFR Part 820. Every requirement, function, and line of code must be traceable, validated, and auditable because even a minor defect can carry clinical consequences.
This article unpacks the five phases of medical device software development, pinpoints where teams commonly go wrong, and outlines how disciplined engineering leads to safer, more compliant, and more trusted healthcare technology.
Phase 1: Planning and Requirements
Every reliable medical device is designed by understanding what the software must achieve, who it serves, and how it will remain compliant across its lifecycle. Planning starts with defining the intended medical use and identifying the device’s regulatory classification (Class I, II, or III) under FDA medical device software or MDR frameworks. Teams then map which standards will apply, for instance, IEC 62304 for software lifecycle management, ISO 13485 for quality systems, and FDA 21 CFR Part 820 for design control and documentation. These frameworks form the backbone of any audit-ready development process.
A well-structured medical device Software Development Plan (SDP) outlines the project scope, deliverables, team roles, and verification approach. A requirements-traceability matrix ensures that each function, risk, and test can be linked in both directions from idea to implementation. This traceability becomes crucial during audits and change-control reviews.
In parallel, risk management under ISO 14971 identifies hazards, estimates their severity and probability, and defines mitigations. Performing this analysis early prevents design rework and aligns teams on what truly matters for patient safety. Risk documentation must be maintained as a living artifact throughout development and post-market surveillance medical devices updates. Automation in healthcare systems can also streamline documentation and traceability during the planning phase.
Where teams go wrong: Many startups rush into prototyping before establishing proper documentation or risk assessments. Overlooking cybersecurity or data-privacy planning is very common. But it is now a mandatory focus area under the FDA’s 2023 Cybersecurity Guidance.
By the end of Phase 1, you should have:
An approved intended use statement, defined user needs, and documented regulatory classification (FDA/MDR).
A list of applicable standards and controls (IEC 62304, ISO 13485, ISO 14971, FDA 21 CFR Part 820).
A signed medical device Software Development Plan (SDP) with scope, roles, milestones, and verification approach.
An initial Requirements Traceability Matrix (RTM) linking each requirement to risks and tests.
A documented initial risk analysis and mitigation plan (ISO 14971).
A documented cybersecurity and data-protection baseline (access control, encryption, logging).
Phase 2: System and Software Design
Once the requirements are approved, the next step is to translate them into a system architecture: a detailed blueprint showing how the software will function, communicate, and maintain patient safety.
System design determines the overall structure, data flow, and risk-control logic of the device. It specifies how subsystems such as data acquisition, processing, and communication interact, and how the device handles events like data loss or sensor failure. Design documentation must show that safety-critical pathways are protected through redundancy, isolation, and recovery procedures.
Under IEC 62304, each software component is classified according to its potential effect on patient safety: Class A (low risk), Class B (moderate), and Class C (high risk). This classification governs how much documentation, verification, and testing each element requires. Design teams use this model to prioritize controls and validation depth early, preventing costly redesigns later in the lifecycle.
Modular architectures let teams test or update one component without disrupting the entire system, supporting scalability and audit readiness. Interoperability ensures compliance with healthcare data standards like HL7 and FHIR, allowing devices to integrate with hospital systems and electronic health records (EHRs).
Where teams go wrong: Designs that are too rigid or under-documented cause friction later. Over-engineered systems make verification difficult, while minimal design artifacts create inconsistencies between development and QA teams. Neglecting usability or failing to classify software correctly are among the most frequent reasons for FDA design-control deficiencies.
By the end of Phase 2, you should have:
Approved system architecture and data-flow diagrams that show module boundaries, data movement, and safety behavior.
IEC 62304 classification (Class A/B/C) assigned to each software item, with rationale recorded.
Documented design inputs and design outputs under version control (interfaces, behaviors, safety controls).
Completed design review with sign-off from engineering, QA, regulatory, and clinical stakeholders.
Defined plans for usability evaluation and interoperability (e.g. HL7/FHIR/EHR implementations).
All design artifacts stored, versioned, and audit-ready in the design history file.
Phase 3: Implementation and Coding
Once the architecture is approved, development moves into implementation. In medical device development, coding is a regulated engineering process. Every feature must trace back to a defined requirement, risk control, and verification method, ensuring that nothing is left undocumented or untested.
To maintain consistency and safety, development teams operate under a Quality Management System (QMS) aligned with ISO 13485. The QMS governs version control, peer reviews, defect tracking, and documentation updates. It also enforces change-control mechanisms so that any code modification is evaluated for risk and revalidated where necessary.
For embedded and low-level systems, coding standards such as MISRA C:2012 help prevent unsafe programming patterns, memory errors, and undefined behavior. For connected or cloud-based applications, frameworks like OWASP Secure Coding Practices guide developers in mitigating vulnerabilities related to authentication, data storage, and communication. Together, these controls create a defensible security posture that meets both FDA design control guidance and MDR cybersecurity expectations.
Automation plays a key role in maintaining quality at scale. Tools such as SonarQube, Polyspace, or Veracode are often integrated into CI/CD pipelines to perform static code analysis, detect vulnerabilities, and measure compliance with safety standards. These systems ensure traceability between requirements, commits, and test results .
Where teams go wrong: In many MedTech software lifecycle projects, documentation steps are skipped, peer reviews are reduced to formality, or third-party components are integrated without validation. These shortcuts often lead to Software of Unknown Provenance (SOUP) — code whose origin or quality is unverifiable. SOUP is one of the most frequent causes of nonconformance findings during FDA and ISO audits.
By the end of Phase 3, you should have:
Enforced secure coding standard (e.g. MISRA C, OWASP) applied across the codebase and documented.
Full traceability in place: every commit and feature is linked to an approved requirement in the traceability matrix.
Peer reviews completed and logged for all merged code, with unit tests and defect tracking recorded.
CI/CD pipeline running static analysis, linting, and security scans, with results stored.
SOUP components evaluated, documented (source, version, purpose, risk), and approved.
Risk management file and design documentation updated to reflect any new or changed behavior in the implementation.
Phase 4: Verification, Validation, and Testing
After implementation, the focus shifts from building the product to proving that it works, both technically and clinically. In medical device software development, testing isn’t an optional QA step but a regulatory obligation.
Testing spans multiple layers. Unit testing validates individual code components, integration testing confirms modules interact correctly, and system testing verifies complete end-to-end functionality. Beyond these, usability testing and clinical simulation testing ensure the device performs safely in real-world environments. Each test must be documented, reproducible, and traceable to a specific requirement and risk.
In regulated environments, Continuous Integration/Continuous Deployment (CI/CD) pipelines automate regression tests, security scans, and performance checks, producing verifiable logs for compliance reports. These validation pipelines resemble predictive analytics systems that continuously learn and optimize based on real-time data. Tools such as Jenkins, TestRail, or Azure DevOps are commonly used to record version-controlled evidence for each build. The FDA’s Software Validation in Healthcare Guidance and ISO/TR 80002-1 both emphasize that the absence of documented proof is what typically causes audit failures.
Where teams go wrong: Many organizations treat testing as a single project phase rather than an ongoing process. When verification is postponed until the end of development, defects often surface too late. This forces design changes, repeated validations, or delayed FDA medical device software submissions. Inconsistent test environments, missing traceability, or incomplete logs are frequent findings in FDA Form 483 observations.
By the end of Phase 4, you should have:
An approved Verification & Validation (V&V) plan covering unit, integration, system, usability, and safety testing, with defined pass/fail criteria.
Each test case mapped to a specific requirement and its related risk control.
Automated regression, performance, and security testing running in CI/CD, with stored results.
Objective evidence captured and versioned for each executed test (logs, screenshots, reports).
Usability and (where relevant) clinical simulation testing completed and documented to show safe use in real conditions.
A clean, reviewable test package prepared for FDA submission or internal audit.
Phase 5: Maintenance and Post-Market Surveillance
Once software is deployed, manufacturers are responsible for ensuring that it performs consistently in the field, remains compliant with evolving regulations, and adapts to real-world usage conditions. Post-market surveillance medical devices is an extension of the same design-control principles that governed the initial build.
The maintenance phase involves scheduled updates, defect corrections, and performance enhancements, each governed by the same verification and validation rigor as pre-release code. Even minor software patches require risk assessment under ISO 14971 to confirm they do not inadvertently introduce new hazards or degrade safety functions. Any change that affects device behavior must be re-verified, documented, and, if necessary, submitted for regulatory review.
Beyond maintenance, post-market surveillance (PMS) focuses on continuously collecting and analyzing real-world data to identify potential safety or performance concerns. This includes user feedback, adverse-event reports, device usage analytics, and regulatory notifications. PMS obligations are defined under both the FDA’s Quality System Regulation (21 CFR Part 820.100) and the European MDR (Article 83), requiring manufacturers to maintain vigilance and report significant incidents promptly.
Where teams go wrong: Many organizations underestimate the resources needed for sustained monitoring. Neglecting software updates, skipping field-data analysis, or failing to submit corrective-action documentation can lead to warning letters or product recalls. The FDA’s recall database consistently lists software configuration errors and unverified patches as leading causes of post-market failures.
By the end of Phase 5, you should have:
A defined change-control procedure for software updates, including approval, testing, and release steps.
Scheduled risk reviews after each release, with updates captured in the risk management file.
A process to collect and analyze field data (performance issues, user complaints, adverse events).
An active post-market surveillance (PMS) plan aligned with FDA 820.100 and EU MDR Article 83.
Automated monitoring in place for deployed versions and cybersecurity vulnerabilities.
Documented corrective actions and a feedback loop that pushes those findings back into design and requirements for the next iteration.
Conclusion:
In today’s MedTech software lifecycle ecosystem, software doesn’t just run medical devices, it defines their clinical integrity. With the rise of connected systems, wearables, and AI-powered diagnostics, maintaining traceability, data security, and post-market vigilance has become a strategic differentiator. The companies that lead the market are those that integrate compliance automation, usability testing, and data-driven surveillance into a unified engineering process.
At Latent, we help medical device software development innovators achieve this balance by accelerating product development while safeguarding compliance and patient safety. Our development framework merges regulatory precision with software agility, enabling:
End-to-end traceability across all lifecycle stages.
Integrated risk management aligned with FDA, MDR, and ISO standards.
Continuous validation pipelines that reduce verification time and audit friction.
Built-in usability and cybersecurity testing aligned with FDA’s 2023 guidance.
Post-market surveillance medical devices that convert device data into actionable compliance insights.
Chinmay Chandgude
Chinmay Chandgude is a partner at Latent with over 9 years of experience in building custom digital platforms for healthcare and finance sectors. He focuses on creating scalable and secure web and mobile applications to drive technological transformation. Based in Pune, India, Chinmay is passionate about delivering user-centric solutions that improve efficiency and reduce costs.
