computer system validation methodology

Complete guide to computer system validation in 2024

Computer system validation continues to bring a lot of uncertainty and questions, particularly as new publications like the Second Edition of GAMP 5 guidance are rolled out.

"What does modern CSV really demand for electronic quality management system adoption?"

"What will my auditor expect to see when I show them the eQMS software we’ve been using?" 

"Do we still need IQs, OQs and PQs?"

These are common and recurring questions.

We’ve assembled this guide, with the help of computerized system compliance expert Sion Wyn , to answer these questions for you.

Table of contents Computer system validation Computer system validation (CSV) to computer system assurance (CSA) What are regulated companies doing wrong with their computer system validation approach? Computer system validation: quality, not compliance A new approach to computer system validation for electronic quality management systems IQs, OQs and PQs Smarter testing Documentation The Enabling Innovation Good Practice Guide Agile software Service providers Critical thinking The Second Edition of GAMP 5 guidance: what’s changed? Conclusion: ten key takeaways

Computer system validation

What is computer system validation?

It's the process of ensuring that the digital tools used by regulated companies are safe and fit for purpose.

After all, a bug or untested feature in a software system that helps treat patients or helps to produce drugs and devices can have a disastrous impact.

Between 1985 and 1987, the Therac-25 radiation therapy machine subjected patients to massive doses of radiation because of an undetected series of programming errors. Even now, about 24% of FDA medical device recalls are triggered by software faults.

Computer system validation, or CSV, is almost as old as computers themselves.

In 1983, the FDA's Computerized Systems in Drug Establishments , more commonly known as the 'Blue Book', was unveiled. 3 years later, the Guidelines on General Principles of Process Validation followed.

CSV validation in its most current form came in 1997 with the publication of the FDA's General Principles of Software Validation , which were tweaked and revised in 2002.

At its core, computer system validation is about answering the key question:

"Is your software system fit for use in a regulated GxP environment?" 

Computer system validation (CSV) to computer system assurance (CSA)

The primary recent development in the world of computerized system compliance is the shift from computer system validation to computer system assurance.

What’s driving the shift? And what does it entail?

In a nutshell, the FDA wants life science businesses to invest in computerized systems that digitize, automate and accelerate quality and manufacturing processes.

These systems, after all, slice the risk of human error.

They free up manual admin time for continuous improvement and quality assurance work.

And they contribute to faster, safer delivery of life-saving products to patients.

But the requirements of computerized system validation, outlined in the FDA’s 1997 General Principles of Software Validation , were seen to discourage this adoption of digital tools by presenting an image of unnecessary burden to regulated companies.

Written when they were, CSV guidance had to be stretched to match the 21st-century world of CRMs, LIMs and eQMS platforms.

In the absence of updated guidance, many businesses fell back on conservative, time-consuming validation processes for fear of being non-compliant.

Some businesses gave up altogether.

Rather than going through what was perceived as a time-heavy, expensive and laborious validation process, they chose to stick with basic quality management tools like paper and spreadsheets.

After all, they require no rigorous setup and can be applied instantly. By our count, around 49% of life science companies continued to use this ingrained manual approach in 2023, particularly start-up and scale-up businesses.

The consequences of this hesitation to digitize can be profound.

Companies reliant on legacy quality tools continue to spend inordinate amounts of time on paper-pushing and battling leaky, uncontrolled information flows.

Our quality trends survey revealed that over half of life science quality professionals spend a quarter of their working day just populating spreadsheets, producing reports or searching for information.

This saps time from the real quality work of continuously improving product and patient safety. And it blocks the industry best practice outlined in GAMP 5 guidance and FDA CSV guidelines.

“Where there aren’t the tools and systems in place, there aren’t enough resources or energy to put into quality improvement. 80% of the effort should be there, but currently it’s where only 20% of time is spent. This means we’re not focusing on the bigger picture, which is patient safety.” - Sion Wyn

The evolution from CSV validation to CSA aims to make the adoption of compliant computerized system tools simpler, more streamlined and more straightforward.

In the FDA’s words, the ‘least burdensome approach’ is to be followed - as long as the proper care is taken to safeguard the integrity and quality of the products you make.

Instead of producing lots of documents to validate a digital system and show to auditors - who, incidentally, are only interested if there’s a direct high risk to patient safety at play - regulated companies should instead adopt an agile and risk-based assurance approach to the tools they adopt, trusting system vendors to perform their own testing activities and supplementing sensibly for high-risk areas as required.

The logic is clear: 

Computerized system assurance focuses on:

Critical thinking and risk-based adoption of computerized tools

• Jettisoning of unnecessary legacy validation documents, like IQs, OQs and PQs
• Eliminating fear of regulatory inflexibility as a blocker to the adoption of new technology
• A return to the original ‘spirit’ of the GAMP 5 guidance:
• Proving your computerized system is fit for intended use
• Ensuring your computerized system meets the basic baseline of compliance
• Managing any residual risk to patients and to the quality of the final medicinal product

Above all, it’s important to note that CSA isn’t ‘new’ in the strictest sense of the word.

On the contrary, it’s designed to remove the perceived barriers standing between life science companies and the innovative, agile approach to computerized system adoption already outlined in GAMP 5 and its associated Good Practice Guides.

To that end, the emphasis for modern computerized system compliance falls on cultural change within regulated businesses, rather than any dramatic overhaul from the regulators themselves.

The dawn of the CSA age was formally triggered with the FDA's launch of their new draft guidelines, ' Computer Software Assurance for Production and Quality System Software ', in September 2022.

Read our blog post: What do the FDA's new CSA guidelines mean?

What are regulated companies doing wrong with their computer system validation approach?

Computer system validation: quality, not compliance

The shift from computer system validation to computerized system assurance is part of a broader trend being driven by industry bodies such as the FDA and ISPE.

It’s aimed at replacing a stressful, self-inflicted straitjacket of compliance -based computerized system validation activity with measured, sensible, quality -based computerized system assurance actions.

As the Enabling Innovation Good Practice Guide puts it on page 9:

... the US FDA CDRH (Center for Devices & Radiological Health) has identified that an excessive focus on compliance rather than quality may divert resources and management attention toward meeting regulatory compliance requirements rather than adopting best quality practices...

The intended shift can be summarized as follows:

The old approach

1. Regulated business comes into existence and wants to bring a life science product to market

4. Effort is spent on getting to the end goal of compliance and rigid clause-by-clause adherence.

Fear of adopting computerized systems because of the extra burden of validation means the company either sticks with paper OR generates mountains of documentation in tandem with its computer system vendor to show to inspectors, such as installation, operational and performance qualification reports (IQs, OQs & PQs) and complex risk assessments.

The optimal journey

4. Effort is spent on getting to the constant stretch goal of optimal quality, integrity and patient safety, using regulatory requirements as a stepping stone.

Sensible risk-based assessment of eQMS platforms from established industry vendors means computerized system assurance can be performed quickly with minimal burden.

Rather than generating an unnecessary protective layer of compliance documentation themselves, they can lean on the vendor’s own testing activity and perform some additional testing if they feel it’s necessary

5. The auditor arrives and finds appropriate effort has been dedicated to assurance of computerized systems dependent on their risk profile.

The company has applied critical thinking, common sense and a risk-based approach to prove quality and compliance across the business. Because they’ve ditched paper, the auditor can access the data they need at the touch of a button.

The quality manager has a stress-free audit experience, perhaps with a few learning opportunities.

6. Eliminating fear-based compliance work means the auditor can detect clear value-add quality activity and strong management of high-risk systems and processes.

The auditor is confident in the safety and integrity of the product going to the end patient, and might even be able to finish the inspection earlier than planned!

“ Dr Janet Woodcock , former acting commissioner at the FDA, has been saying the same thing for decades: Don’t primarily think compliance, think quality. Don’t think, ‘what would the FDA like?’ Think, ‘what would safeguard the patient and the efficient delivery of drugs?’ If you do that, you’ll keep them happy - rather than thinking the FDA wants you to produce all these documents so they’ll give you an easy ride on inspections.” - Sion Wyn

A new approach to computer system validation for electronic quality management systems

The evolution to computerized system assurance impacts how regulated businesses work with eQMS market vendors.

FDA and GAMP leadership want regulated businesses to strengthen their quality approach by replacing manual paper-based systems with electronic systems.

The new landscape of CSA therefore aims to make eQMS adoption as quick and painless as possible, without businesses subjecting themselves to an unnecessary and time-consuming validation headache.

Good, appropriate CSA work with a reputable eQMS vendor should therefore include these things:

1. IQs, OQs and PQs? RIP!

Installation, operational and performance qualification activity was ‘borrowed’ into CSV from older process validation frameworks in the 1990s, as the industry scratched around for a suitable CSV approach.

They remain appropriate for simple computerized tools, where a linear process of installing, checking operation and checking performance can be performed. 

But the linear nature of IQ, OQ and PQ processes no longer matches modern, non-linear software development lifecycles - and tends to produce the kind of unnecessary paper documentation that regulators don’t wish to see.

Their use in modern eQMS validation activity adds no value, and is symptomatic of the fear of regulatory punishment that the new world of CSA wants to stamp out.

“IQs, OQs and PQs are very ineffective in a typical large-scale modern software development or configuration environment… where those kinds of deliverables are just not a natural or useful part of the lifecycle. But we still have these really strange situations where acceptance testing is performed, then an OQ is added as a kind of ‘layer’, or user acceptance testing is performed and there’s a document with ten signatures on to say that it happened. There’s no reason you should have an IQ, OQ or PQ.” Sion Wyn

The FDA’s General Principles recognized that IQs, OQs and PQs are largely meaningless for software developers back in 1997, and didn’t mandate them. 

That remains the case in the 21st-century world of burndown charts, backlogs, regression testing, and other modern software testing activities. Automated testing tools like CircleCI and GitHub simply don’t produce IQs, OQs or PQs.

Remember : any eQMS vendor you work with doesn’t need to provide IQ, OQ or PQ documents to help you validate their system.  Your FDA inspector won’t ask to see them.  And using them means you aren’t adopting the agile critical thinking of modern CSA.

2) Smarter testing

Regulated businesses adopting an out-of-the-box eQMS in the traditional ‘compliance fear mode’ can fall into the trap of performing unnecessary system testing to try and protect themselves from a future auditor.

Work with a vendor that doesn’t encourage these activities and helps you get your system set up with minimal fuss and effort.

Typical mistakes include:

• Repeating testing activities already performed by the vendor
• Conducting tests on your own ‘instance’ of multi-tenancy software, where the results will be identical
• Testing by default whenever new software updates are rolled out
• (As we’ve seen) demanding IQs, OQs and PQs from your vendor

A reputable eQMS vendor will constantly test their software themselves, and assume the burden of the majority of assurance activity to prove their system meets your needs and intended use.

Perform your own testing only when your critical thinking approach suggests that a feature or new feature might reasonably impact product and patient safety.

Remember : a good eQMS vendor will help you drive a sensible quality and regulatory approach.  Encouraging you to perform non-value-add validation activity means they aren’t prioritizing your real operational needs - and they probably haven’t done their homework!

3) Sensible documentation

It’s okay to lean on your supplier’s provided documentation, especially if you aren’t configuring your eQMS and are using it out of the box.

Focus any of your own additional testing and documentation according to:

• The risk level of operating your eQMS in your particular environment
• Functional requirements, not what you think your auditor will expect to see

The FDA doesn’t prescribe the quantity or format of documented assurance evidence, precisely because it should be appropriate, risk-based and tailored to your specific use case.

The vast majority of the software development and testing is done as part of the eQMS vendor’s own quality management system.

That’s why, according to Sandy Hedberg of USDM Life Sciences, a robust supplier qualification is all that’s really needed for out-of-the-box systems, with extra ad hoc testing by you for any customized features. 

The need for configuration specifications, traceability matrices and test plans will depend on your level of GxP risk and your level of configuration or customization, while effective evaluation of the methodology and tools of your eQMS vendor is key.

Only create assurance documents that are of real value to you . Key questions to answer if you perform your own testing are:

• What was the risk assessment?
• What did you test, and how?
• Who performed the testing, and when?
• What were the results?
• Were there any defects or deviations, and how did you deal with them?

A sensible, concise, preferably digital summary of this activity with a clear conclusion and treatment of risk will make your auditor happy - and critical thinking is the golden thread holding all this decision-making and documenting activity together.

Remember : a reputable eQMS vendor performs and documents their system’s assurance activity themselves, and should provide it to you as you go live. Use it as the core (and probably the majority) of your assurance records!

“If an eQMS supplier is relying on a lot of paper and is up to here with IQs, OQs and PQs, then my critical thinking is telling me that’s not an up-to-date supplier!” - Sion Wyn

The Enabling Innovation Good Practice Guide

GAMP’s Enabling Innovation GPG was published in September 2021 to sit alongside the main GAMP 5 guidance.

It covers 3 key topics:

1. Agile software

Underlines the modern agile nature of software development and how GxP-regulated businesses can adopt and implement modern digital tools to strengthen themselves.

2. IT service provider management

Service providers like cloud eQMS vendors are assuming more and more responsibility for the testing and assurance of computerized tools.

As we’ve seen, this shifts the emphasis onto regulated businesses from directly performing validation tasks themselves to evaluating and assuring how IT vendors indirectly perform them on their behalf.

The GPG breaks down how regulated businesses can evaluate vendor activity, find reputable providers, and use agreements and contracts to ensure the heavy lifting is done properly by the vendor.

3. Adoption of critical thinking to support the objectives of CSA and the Case for Quality

The Guide emphasizes the importance of ditching unthinking tickbox exercises and replacing them with full subject matter expert-led understanding of your processes, data flows and risks - and how your software’s lifecycle and usage aligns.

“It's a backwards world, entrenched in paper and with resistance to adopting new tools. SaaS can help you in your journey. You'll have a better result. The medical device industry feels like banking 20 years ago, when everyone was allergic to cloud SaaS products because of fear and bureaucracy. But now there are neobanks, and everything's changed. Embrace those companies leading the charge and who can provide you services you haven't had before. It's a good change.” — Daniel Aragao, Chief Technology Officer, InVivo Bionics ( Qualio customer )

The Second Edition of GAMP 5 guidance: what’s changed?

The Second Edition of the ISPE’s GAMP 5 computer system validation guidance was released in July 2022, replacing the First Edition unveiled in 2008.

The Second Edition is right in keeping with the shift to agile risk-based adoption of computerized systems.

Read our post: The 10 key changes in the GAMP 5 Second Edition

Conclusion: ten takeaways for computer system validation in 2024

1. Make quality your operational goal for computerized system adoption, not compliance

2. Don’t waste time on unnecessary documentation like IQs, OQs and PQs

3. Your IT vendor assumes the bulk of the responsibility for assuring the quality and integrity of their systems - it’s your job to assess and qualify them

4. Use critical thinking and risk awareness as the golden thread to inform you if you need to perform extra assurance activity, in which areas, and to what extent

5. Ensure you have in-house understanding of modern computerized system adoption to help you assess and work with suppliers

6. Don’t be afraid of your auditor or inspector

7. Proving you’ve thought about the relationship of your computerized system to the safety of your product and patient is your primary objective. Indirect systems like an eQMS do not require the same level of assurance vigor as an adverse event MDR reporting system

8. The FDA wants you to move from paper to computerized systems: it’ll only make you stronger

9. Don’t work with a vendor stuck in outdated validation activities

10. Industry guidance, from the Case for Quality to GAMP 5’s Second Edition, is remarkably consistent. Do your own reading and make yourself an expert!

Embracing computer system validation in its most modern form is your organization's pathway to a digital evolution that doesn't mean mountains of paper or weeks of effort.

To learn how Qualio uses a modern, expert-approved computer system validation approach for our eQMS software, download our validation datasheet .

Alex has worked in the quality and compliance space for 5 years, producing a range of industry content to help Qualio blog visitors understand the complex and highly regulated environments of modern life science. Since graduating with a master's degree from the University of Cambridge, Alex has produced training courses, webinars, whitepapers, blog posts, e-books and more on a range of life science quality topics, from GxP to ISO 13485. Alex is passionate about the transformational power of a culture of quality and writes extensively about digital quality management, life science company growth and easing compliance burden.

Related Articles

The 10 key changes in the gamp 5 second edition, what do the fda's new csa guidelines mean, guide to gxp compliance: processes, challenges and tools.

QUALIO'S VALIDATION APPROACH

Learn how Qualio uses a modern CSA approach for fast, stress-free validation

• Subscribe to CSols
• Career Opportunities
• Canadian Customers

What Is Computer System Validation, and How Do I Do It Right?

The process of software validation ensures that the system fits its intended use and functions as it should. Computer system validation (CSV) for laboratory informatics is essential because regulated businesses must ensure the safety of their products for consumers, and their laboratory informatics systems (LIMS, ELN, CDS) are an integral part of that. Given its importance, CDS or LIMS validation tends to be seen as confusing and challenging to execute correctly. Of course, it is possible to do it right, and CSols has proof. In almost 30 years of doing CSV , we haven’t heard of any of our clients receiving an FDA 483 form .

Validation is part of the software development life cycle. In this blog, we’ll review what that means and how to do it so that your system will be defendable in a regulatory audit.

Computer System Validation Basics

No discussion of computer systems validation is complete without an overview of the legislation around it. In the United States, the Food and Drug Administration (FDA) regulates specific industries that directly impact consumer health, including pharmaceuticals, cosmetics, and food and beverage. These industries have an added responsibility to ensure their products are safe and their data are secure. The relevant legislation addressing aspects of computer systems validation in the United States comes from the Code of Federal Regulations (CFR), most specifically 21 CFR Part 11 , dealing with electronic records and signatures. Similar government agencies and regulations apply in other countries as well.

Part 11 mandates the requirements for electronic records and signatures to be considered accurate, reliable, readily retrievable, and secure, to replace paper records and handwritten signatures legally. Validating your computer system is the primary means of determining that electronic records and signatures can be used in this way.

The Validation Process

Validation can take many shapes during the computer system life cycle, depending on whether it is a new implementation or an upgrade to an existing system. For new systems that the user hopes can solve a current problem, validation happens from the ground up. For an existing system that needs an upgrade or is expanding the scope of its intended use, the need is to keep the system in a validated state by testing the new capabilities before releasing them into production use. The validation process ends when a system is retired and its data are successfully migrated to a new system or archived. The figure below shows how validation supports the project life cycle.

The Validation Master Plan

Your CDS or LIMS validation master plan guides you through the validation process and becomes a sort of a check-off list to ensure that everything happens as it should. Once you’ve assessed the As-Is state of your system, the validation master plan encompasses all the other steps you’ll take to ensure your system is validated in its current state and fit for its intended use.

The validation master plan should account for requirements gathering , a functional risk assessment , a trace matrix , IQ OQ PQ protocols and testing , and change control procedures with periodic reviews. Each part of the validation master plan is executed in a defined order. Your requirements and the risk assessment should be completed before you move on to developing the trace matrix and then doing the testing. This way, you minimize the risk of having to go back and develop new test cases late in the process.

The Relationship Between Requirements Gathering and Qualification Testing

It is critical to ensure that your requirements and specifications are well defined and approved before validating the computer system. The validation V-Model is commonly used to visualize the relationship between requirements and specifications and the testing performed on them (see diagram below). Qualification testing (down the right side of the V) is designed based on your intended use and the functionality required to meet that use (represented down the left side of the V).

• The User Requirements Specification will document what the users need the system to do, and PQ testing verifies those requirements.
• The Functional Requirements Specification will document the system functionality required to meet the User Requirements Specification, and OQ testing verifies those specifications.
• The Design Specifications will document the system design (e.g., modules, units, etc.), and IQ testing verifies that the system’s installation meets those design requirements.

IQ/OQ/PQ testing is arguably an essential part of the validation process. Successful completion of the testing will verify that your system functions as intended and is fit for its intended use in your environment. It is best practice to approve your user requirements and functional specifications before testing to avoid scope creep and possible re-testing .

To learn more about IQ OQ PQ testing, watch our webinar .

The people writing and executing your IQ/OQ/PQ testing should be thoroughly familiar with your lab informatics system (LIMS, ELN, CDS) and your intended use. If your in-house staff does not have the bandwidth or experience for proper testing, you should work with qualified CSV consultants, like CSols, who have the requisite experience with your informatics systems.

ALCOA+ and Data Integrity

The importance of laboratory informatics data cannot be understated. When you have data in a validated environment, you need to ensure that your data remain secure and reliable. The acronym ALCOA identifies the five basic principles of data integrity : data must be Attributable, Legible, Contemporaneous, Original, and Accurate. More recently, four additional principles have been added, so that the acronym is now ALCOA+. The four additions are Complete, Consistent, Enduring, and Available.

Data integrity is integral to all CDS or LIMS validation activities. Following the principles of ALCOA+ ensures that your system captures, produces, reports, transfers, and stores data that are secure, retrievable at will, and reliable.

The Future of CSV: Computer System Assurance

Although we are still waiting for the FDA to release their expected guidance about computer system assurance (CSA) , it is coming. At its core, CSA reinforces a risk-based approach that expands on the GAMP 5 principles of product and process understanding, quality risk management, and leveraging supplier activities. Risk is assessed based on the big picture of the overall business process. Doing so places more emphasis on test efficiency, focusing on testing that ensures the system is fit for purpose.

Computer System Validation Challenges

Validation of computer systems can involve challenges, including the risk of system failure, restrictive company policies, and increasingly stringent regulatory requirements. Another significant issue is when users need to balance the risk vs. cost equation after risk categories are defined. A risk-based approach to CSV can help to mitigate some of these challenges.

Additional steps you can take to avoid validation problems include the following:

• Ensuring your validation master plan is complete and follows industry best practices and regulations
• Defining the computer system (i.e., hardware, software, people, and processes) that is to be validated
• Providing clear limits for your expected results; i.e., what is acceptable
• Describing and meeting thorough requirements and specifications for your intended use of the software

Properly executing a computer system validation is an involved process, but you can do it when you have the right expertise. If you aren’t confident that your in-house staff has the necessary CSV experience, reach out to us .

Is there anything else you’d like to know about computer system validation that hasn’t been addressed here? Comment below, and we’ll get you an answer.

Categories:

Leave a reply cancel reply.

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed .

Explained in good way about CSV,Thanks for sharing such a great article.

thank you for such a A detailed article

• Pingback: Ensuring The Safety of Your Computer System - Creative Business Leaders

• Core Values
• Differentiators
• Employee & Consultant Reviews
• Client Reviews
• Corporate Social Responsibility
• Quality Assurance
• Regulatory Affairs
• Clinical Operations
• Commissioning, Qualification, and Validation
• Chemistry, Manufacturing, and Controls
• Expert Witness
• Consulting Projects
• Staff Augmentation
• FTE Recruitment
• Functional Service Program
• Browse Consultant CVs
• Start a Consultant Search
• Join Our Consulting Team
• Insider Newsletter
• White Papers
• Case Studies
• Regulations and Standards

28 August 2023

Computer System Validation (CSV) in the FDA-Regulated Industries

In the complex landscape of FDA-regulated industries, Computer System Validation (CSV) is a critical process, ensuring that systems operate consistently and produce results that meet predetermined specifications. CSV is not merely an industry recommendation; it's a regulatory necessity that bridges the gap between technology and regulatory compliance.

This guide dives into CSV's intricacies and sheds light on its best practices, as gleaned from a stimulating conversation with an industry expert, Rashida Ray. The insights drawn from this discussion highlight the core components that drive successful CSV projects.

These components include planning and project management, effective communication, understanding and adherence to FDA regulations, the value of external consultants, and the necessity for continuous improvement in CSV practices.

Understanding and implementing CSV processes can often pose challenges in an industry where regulatory standards are paramount. This guide intends to provide an in-depth understanding of these challenges and offer practical solutions to navigate them successfully.

Need expert CSV support? Contact us today to rapidly access the industry’s top validation consultants.

Meet the contributor

Rashida Ray is a seasoned quality assurance and compliance professional with a robust background in computer system validation (CSV) and auditing in the pharmaceutical, medical device, and biologics sectors. As an industry consultant serving as a Senior Validation Engineer, she offers a deep understanding of regulatory requirements and quality systems, underpinned by over a decade of experience in the industry. In her current role at The FDA Group, she leverages her CSV expertise to evaluate computer systems for clients, ensuring they comply with all relevant regulations and standards. Her skills extend to conducting audits, managing quality systems, and providing guidance on regulatory compliance.

The Basics of CSV: A Brief Review

What is csv.

CSV is a documented process for assuring that a computer system does exactly what it is designed to do consistently and reproducibly. The process ensures that the system meets all predetermined requirements and is fit for its intended use. It helps to ensure data integrity, patient safety, and product quality—and reduce the risk of product recalls and regulatory action.

In the FDA-regulated industries, CSV ensures that all computer systems that control the manufacturing processes are validated. This includes systems for production, quality control, and distribution.

What does CSV involve?

The CSV process involves several steps: planning, specification, programming, testing, documentation, and operation. Each step is crucial and must be performed correctly to ensure the system's validation.

What are the key components of a CSV plan?

A CSV plan includes defining the system, identifying the key users, outlining the intended use, and detailing the validation strategy. It also includes risk assessment, defining responsibilities, and setting timelines.

What are the FDA’s expectations regarding CSV?

The FDA has specific expectations for CSV in the industries it regulates, particularly in the pharmaceutical, medical device, and biotech sectors.

Here are some of them:

• Regulatory Compliance: The FDA expects that all computer systems used in the design, manufacture, packaging, labeling, storage, installation, and servicing of all finished products intended for human use should comply with 21 CFR Part 11 and other relevant regulations.
• Validation Documentation: The FDA expects firms to maintain comprehensive documentation of the validation process. This includes validation plans, system specifications, test protocols, and test results. The documentation should be sufficient to demonstrate that the system is validated and operates correctly.
• Risk Assessment: The FDA expects companies to perform risk assessments to identify the potential impact of system failures on product quality, patient safety, and data integrity. The level of validation effort should be commensurate with the level of risk.
• Data Integrity: The FDA expects that CSV processes will ensure data integrity. This includes generating accurate and reliable data, preventing unauthorized access or changes to data, and maintaining audit trails.
• Change Control: The FDA expects a robust change control process. Any changes to the system, including software updates or changes to hardware, should be documented and validated to ensure that they do not adversely affect system performance or data integrity.
• Training: The FDA expects that individuals responsible for the design, operation, maintenance, and validation of computer systems are adequately trained to perform their duties.
• Periodic Review: The FDA expects companies to periodically review validated systems to ensure they remain in a state of control and continue to meet their intended purpose.
• Vendor Assessment: If third-party software or systems are used, the FDA expects companies to assess the vendor's ability to provide a product that will meet the user's requirements and regulatory expectations.

Need expert CSV support? Contact us to access the industry’s top validation consultants. We’ve helped hundreds of firms plan and conduct comprehensive CSV projects around the world.

Common CSV Challenges and Best Practices

1. planning.

Teams often fail to fully consider the impact and reach of a system they plan to implement. Siloed thinking limits their perspective of the implications on other departments. They create specifications based on their department's needs without considering that other departments may also use the system, resulting in issues down the line.

The best solution is to create a process map that details how the system will be used, what areas will be affected, and how the data flows through the system. With a process map in hand, you can identify specific requirements based on the system's use in different departments. Understanding how data is collected, where it resides, and how it's manipulated can help identify potential issues with data integrity.

In addition to a process map, it’s also important to establish procedures and technical solutions to control the data , for instance, to know who has access to the system and ensure that unauthorized individuals can't tamper with the raw data, and avoid potential issues with editing final reports. Consider how the new system could affect existing procedures and whether they need to be revised accordingly.

As for the tools to create these process maps, consider platforms like Microsoft Visio, Lucidchart, or even hand-drawing them. However, the most important and often overlooked part is asking the right questions to understand how the system operates and the regulatory or security controls needed around it.

Consider the following questions when structuring your CSV planning and crafting process maps:

"When you have your intended use, create a process map of the entire system, understanding how it will be used and what it will affect. Then, get to know your system better to visualize data integrity aspects like how the data is acquired, where it is, how it's collected, and what measures are in place to ensure it can't be tampered with or deleted. These steps in planning a CSV project are incredibly crucial but very commonly overlooked." — Rashida Ray, CSV Consultant, The FDA Group

2. Communication

“Silo mentality” is both pervasive and insidiously hard to recognize when different departments operate in relative isolation. If left to fester, it can lead to systems or processes that may serve one team’s individual needs but often fail to accommodate the needs of other departments. Without genuine cross-departmental collaboration, CSV, like many other types of projects whose outcomes impact more than one side of the business, suffers from friction and inefficiency.

The key to mitigating this is early and comprehensive communication involving all of the departments impacted by the new system. Too often teams are brought in last minute when their feedback is largely inconsequential due to how far along the process is, making the whole exercise feel counterproductive.

Another commonly overlooked aspect of communication is the regular holding of cross-functional meetings . Regular sessions allow for critical questions to be asked, ideas to be brainstormed, and requirements to be gathered, ultimately facilitating proactive rather than reactive process optimization. Including subject matter experts from each department in these meetings is also beneficial, as their specialized knowledge can offer unique insights and direction that might otherwise not have been obvious.

One of Rashida’s central points revolves around asking the right questions when considering a new system, such as, "What do we need it to do?" and "How can we optimize our existing processes?" This approach ensures the collection of necessary requirements and the selection of a system that meets the needs of most departments.

Similarly, she underscores the importance of keeping everyone informed and included in the process. The sentiment of being necessary and included contributes to a more cohesive working environment and is crucial to successfully implementing new systems. Coupled with this, Ray highlights the importance of critical thinking and mapping processes, where each department's procedures are scrutinized to understand how a new system will affect them and how the process can be optimized.

"What I've seen many times is people don't talk to each other, there are no cross-functional team meetings, even though they know that other departments are affected. So you're creating an even bigger process map at this point, right? Because you have different departments that are affected by this particular system. That would require some cross-functional team meetings, there might be some subject matter experts from each department, some sort of manager from that department, that can really give a lot of good insight on certain things and how the system can be integrated into their own existing processes." — Rashida Ray, CSV Consultant, The FDA Group

3. Understanding Regulations

The third major challenge facing businesses involved in CSV is understanding the complexity of regulations. This challenge emerges from the rapid pace of changes in regulations, the high turnover of personnel within organizations, and the dynamic nature of the regulatory environment itself. 

This issue often arises when companies treat regulatory compliance as an exercise in box-checking, often following regulatory guidelines without understanding their underlying purpose. This lack of understanding typically stems from ineffective training—and can lead to teams unknowingly violating FDA regulations. The most effective solution to a training problem is bringing in an outside party to reveal knowledge gaps with an unbiased perspective and provide the requisite training. Internal training, which is sometimes sufficient, often fails to cover the extent of knowledge gaps.

Treat regulatory compliance as more than a paper exercise, prioritizing patient safety and quality at all times. Focus training on specific regulations like 21 CFR Part 11, Part 820, and other data integrity guidances. Third-party gap assessments can be extremely valuable in identifying blind spots in the organization's understanding and application of these regulations. Again, the importance of a fresh perspective to illuminate potential gaps and provide new solutions can’t be overstated. This includes hiring new employees with different experiences and ideas that can contribute to a richer understanding of regulations and better compliance practices.

In addition, Ray points out the usefulness of bringing in consultants, even when it may not seem obvious, to provide a fresh pair of eyes and help improve processes. The key takeaway from Ray's discussion is the crucial need for external training and consulting to enhance the understanding and effective application of regulations in CSV, underlining the significant role of consultants in maintaining quality and patient safety in compliance activities.

"When you have a project, and regardless of what happened, whether you receive a 483, or there was some sort of audit finding of some kind, from a vendor, audit, etcetera. You have those findings, and you look at those findings as an exercise. And you treat it as if we need to get this done because they told us so. But not because you actually understand and know what those regulations are. And that really comes down to training. And you don't know what you don't know. Yeah, right. You know, you've been doing things a certain way. You don't necessarily understand why that way is not compliant with FDA regulations. You know, you don't understand why performing certain shortcuts is a big problem. So honestly, the only way to do that is to bring someone in from the outside to do the training."  — Rashida Ray, CSV Consultant, The FDA Group

Preparing to Work With a CSV Consultant

Here are a few common scenarios that signal the need for a third-party CSV consultant:

• Implementation of New Systems: Whenever a firm implements a new computer system, it needs to ensure that the system is validated in accordance with FDA regulations. A CSV consultant can provide expert advice on properly validating the system. 
• Upgrades or Changes to Existing Systems: Significant changes or upgrades to a computer system can potentially impact its validated status. A CSV consultant can help determine the impact of these changes and guide the revalidation process, if necessary.
• Audit/Inspection Findings: If an audit or inspection by the FDA or other regulatory body uncovers issues related to computer system validation, a CSV consultant can help address those findings. They can guide the company in correcting deficiencies and preventing similar issues in the future.
• Training Needs: If a company's employees need training on computer system validation, a CSV consultant can provide or help develop a comprehensive training program—and ensure the training meets both regulatory requirements and the company's specific needs.
• Regulatory Updates: When there are significant changes to the regulatory landscape related to CSV, such as new guidance from the FDA, a CSV consultant can assist the firm in understanding and implementing these changes.
• Risk Management Support: A CSV consultant can assist with identifying and managing risks associated with computer systems, which is especially valuable when launching a new product or entering new markets.
• Resource Constraints: If the company doesn't have sufficient in-house expertise or resources to manage CSV activities, a CSV consultant can fill in these gaps.
• Preparation for Regulatory Inspections: To prepare for an FDA inspection, a CSV consultant can conduct a pre-inspection audit (or mock inspection) to identify potential CSV issues and help the company address them proactively.

When preparing to bring in a CSV consultant, setting a foundation that promotes efficient collaboration and desired outcomes is important. The consultant's role, after all, is not just to perform a specific task but to bring a fresh perspective and specialized knowledge that could significantly enhance your team's approach to validation.

But just how much value you can derive from a third-party expert relies heavily on your team's readiness and openness to this professional partnership. With this in mind, Rashida recommends a three-pronged approach to ensure that industry teams maximize the benefits of working with a CSV consultant. 

This strategy revolves around rigorous project planning, clear communication with subject matter experts, and cultivating an open mindset toward change and continuous improvement. These elements are designed to facilitate the consultant's work and foster an environment that invites innovation, transparency, and collective success.

• Plan and Scope Out the Project: Teams should thoroughly outline the specific project processes in a detailed document or visual diagrams (like process maps) before the consultant arrives. This could include the objectives of the project, the technology involved, key stages, dependencies, and the resources needed. This preliminary work—to the extent it can be done without the consultant’s help— will enable the consultant to understand the task more efficiently and effectively, reducing the time spent on initial familiarization.
• Prepare and Communicate with Subject Matter Experts: Teams should compile a comprehensive list of points of contact, including the names, roles, and contact information of SMEs who are relevant to the project—and that the consultant may need to engage. These could be department leads, IT specialists, or quality assurance managers, just to name a few. The team should then notify these individuals about the upcoming project and the consultant's role. Informing SMEs about the consultant's expected outreach helps to reduce surprises and promote smoother communication. Furthermore, having a shared understanding of the project amongst all parties can reduce potential “ information hoarding” and cultivate a sense of collaboration and trust from the start.
• Be Open-Minded and Accepting of Change: Teams should maintain an open mind throughout the project. This includes being open to new ideas, procedures, or technologies the consultant might introduce. It's important to remember that even established procedures can have room for improvement, particularly if issues have been identified in audits. Teams should be prepared to actively engage in constructive dialogue about potential changes rather than dismissing them out of hand because they deviate from 'how things have always been done.' To facilitate this, teams could organize regular project meetings where the consultant can explain their approach, and the team can provide feedback, generating shared understanding and mutual respect.

Get rapid access to the industry's best CSV and data integrity consultants.

Thorough CSV ensures your system stands up to scrutiny, leaving you secure in the knowledge that your data is safe, reliable, and available. Our CSV experts implement systems and obtain “fit for use” certification in the areas of computer and cloud systems validation and data integrity.

Our CSV and data integrity assessment framework can be applied to proprietary and commercially available software. Projects are planned and executed by leading CSV experts with intimate knowledge of current regulatory requirements and years of experience enhancing IT operations, control systems, and data integrity for pharmaceutical, medical device, and biotechnology companies.

Our comprehensive data integrity and computer systems validation services include, but are not limited to:

• Computerized and Cloud System Validation (CCSV) and qualification
• Establishing data integrity infrastructures
• Third-party CMO audits
• Vendor audits
• Mock Pre-Approval Inspection (PAI) audits of data integrity
• Formal risk assessments and risk mitigation strategies
• Planning and remediation assistance for data integrity gaps
• Guidance and development of Data Governance and Data Management Programs
• FDA 483 and warning letter responses for data integrity
• Pre-audit preparation and support during audits
• Training and development of training programs in data integrity and CSV

Learn more about our services and contact us today.

Watch our free webinar to learn more about conducting a thorough gap analysis and a step-by-step process for resourcing and implementing remediation afterward.

Topics: Validation

Sign up for updates from our blog

Proprietary talent selection of former FDA and industry professionals amplified by a corporate culture of responsiveness and execution.

US Toll-Free: 1-833-FDA-GROUP

International: +001 508 926 8330

• Res_Q Platform

What is Computer System Validation?

What is computer system validation and why is it important.

By Sware Team 

Computer System Validation (CSV) is a process used in the pharmaceutical, healthcare, and other regulated industries to ensure that computer systems, particularly those involved in the production of pharmaceuticals or the management of related data, consistently meet their predefined specifications and fulfill their intended purpose.

The primary goal of CSV in pharma is to ensure that these systems are reliable, accurate, and compliant with regulatory requirements.

But what is computer system validation? And what is a validated system for the pharmaceutical and healthcare industries? Let’s look at the CSV meaning, the medical implications that make it a necessity, and how computer system validation in the pharmaceutical industry can offer a range of advantages for meeting regulatory compliance, protecting the operating environment, and ensuring that products meet acceptance criteria.

The History of CSV

CSV is closely tied to the evolution of the computing industry and the increased reliance on computerized systems in regulated environments. As computers became more powerful and accessible, industries started adopting them for various purposes, including manufacturing, finance, and healthcare. With the integration of computers into critical processes, the need for ensuring the reliability and accuracy of computerized systems became apparent.

In the 1970s, the U.S. Food and Drug Administration (FDA) began to recognize the importance of computerized systems in the pharmaceutical and healthcare industries. The FDA established regulations, such as Good Manufacturing Practice (GMP) requirements, that laid the foundation for pharmaceutical process validation in computer systems.

The 1980s saw a significant expansion of computer system usage across various industries. With this increased reliance on computers for critical functions, the concept of validating these systems gained traction. Regulatory agencies began emphasizing the need for CSV validation in their guidelines.  Here are some of those regulatory requirements for IT validation:

• Title 21 CFR Part 11: This regulation outlines the criteria under which electronic records and electronic signatures are considered trustworthy, reliable, and equivalent to paper records and handwritten signatures.
• 21 CFR Part 820 - Quality System Regulation (QSR): This FDA regulation applies to medical device manufacturers and includes requirements for the validation of computerized systems used in the production and control of medical devices.
• Annex 11 to the EU GMP (Good Manufacturing Practice) Guidelines: This annex provides guidance on computerized systems in GMP-regulated environments, including principles for data integrity and the importance of validation.
• GxP (Good Practice) Guidelines: The MHRA emphasizes the importance of applying GxP principles to computerized systems, including GMP (Good Manufacturing Practice), GLP (Good Laboratory Practice), and GCP (Good Clinical Practice).
• Good Practices for Pharmaceutical Quality Control Laboratories: WHO provides guidelines for ensuring the quality and integrity of data generated by computerized systems in pharmaceutical quality control laboratories.
• ISO 13485: This standard specifies requirements for a quality management system for medical devices, including provisions for the validation of software.
• While not specific to validation, HIPAA in the United States mandates the security and privacy of electronic health information, which indirectly requires systems handling this information to be validated for reliability and security.

CSV and Today’s Rapidly Evolving Tech

The 21st century has witnessed rapid advancements in technology, including cloud computing, artificial intelligence, and data analytics. These innovations have introduced new challenges and considerations for validation, requiring the adaptation of validation practices to emerging technologies.

Modern computerized systems are highly complex and interconnected. The integration of systems, reliance on third-party software, and the use of advanced technologies have made computer validation an ongoing and evolving process. Throughout the evolution/changes of technology, the underlying principles of computer software validation have remained consistent. But what is the computer system validation’s role in a modern pharmaceutical validation approach? 

CSV principles ensure that computerized systems are reliable, secure, and compliant with regulatory requirements. Since software validation is such a critical process that helps ensure compliance, it’s natural that the validation method must meet strict operating parameters, ensuring consistency during existing procedures and protecting system data integrity. That’s why computer systems validation in the pharmaceutical industry is so meticulous.

Here are some key aspects of pharmaceutical computer systems validation:

• Comprehensive documentation is crucial throughout the development, implementation, and maintenance of computer systems. This includes specifications, testing protocols, and validation reports.
• Rigorous testing is performed to verify that the system functions as intended. This includes functional testing, performance testing, and sometimes user acceptance testing.
• Any changes made to a validated system must be carefully controlled and documented. Changes can include updates, patches, or modifications to the software or hardware.
• Identifying and assessing potential risks to the integrity and reliability of the computer system is an integral part of the validation process. Mitigation strategies are implemented to manage these risks effectively.
• Systems are often required to maintain detailed audit trails that track user activities and system changes. This is important for traceability and accountability.
• Computer systems must comply with regulatory requirements such as those outlined by agencies like the U.S. Food and Drug Administration (FDA), the European Medicines Agency (EMA), and other relevant regulatory bodies.
• Pharmaceutical computer system validation is considered a lifecycle process, meaning that it begins during the development phase and continues through implementation, maintenance, and eventual retirement of the system.

Why CSV is Important in the Pharma Industry

CSV is critical for industries where the reliability and accuracy of computer systems are paramount, especially in fields like pharmaceutical manufacturing, clinical research, and healthcare, where adherence to strict regulatory standards is essential.

Failing to implement CSV in regulated industries can pose various risks, including legal, financial, operational, and reputational consequences. Regulatory bodies, such as the FDA, EMA, and others, require companies to validate computer systems to ensure data integrity, security, and reliability. Non-compliance with these regulations can lead to regulatory actions, including fines, warning letters, and even product recalls.

Without proper computer system validation in the pharma industry, there's an increased risk of data integrity issues, including inaccuracies, inconsistencies, and unauthorized access or changes to data. This can compromise the quality and reliability of data used in critical processes.

In industries like pharmaceuticals, failure to validate computer systems in manufacturing or quality control processes can lead to the production of substandard or unsafe products, posing risks to patient health and safety.

Unvalidated systems are more prone to errors, failures, and disruptions. This can result in downtime, production delays, and difficulties in maintaining business operations, leading to financial losses.  Inefficient or unreliable systems can hinder productivity and workflow efficiency. This can impact overall business performance and competitiveness.

Unvalidated systems may also lack robust security measures, making them more susceptible to cyber threats and unauthorized access. This can lead to data breaches, loss of sensitive information, and damage to the company's reputation.

Companies operating in regulated industries are subject to audits by regulatory agencies. Lack of computer system validation in pharmaceutical companies can result in increased scrutiny during audits, potentially leading to further investigations and penalties.

Without proper computer system validation in pharma, utilizing the best practices and implementing changes to computer systems becomes challenging. This can hinder innovation and adaptation to new technologies, putting the company at a disadvantage.

To mitigate these risks, organizations should prioritize the implementation of robust CSV processes, adhere to regulatory requirements, and regularly update and validate their computer systems throughout their lifecycle. This not only ensures compliance but also contributes to the overall reliability and success of the business.

The industry has seen this process as somewhat challenging for several reasons. CSV can often be complex.  This complexity often arises from the unique characteristics of the systems being validated and the regulatory environment in which they operate.

Technology evolves quickly, and keeping up with the latest advancements while ensuring compliance with existing regulations can be challenging. The introduction of new technologies, software, and platforms adds complexity to the validation process.

Organizations use a wide range of computerized systems for different purposes, from manufacturing and quality control to data management and reporting. Each type of system may have unique characteristics and validation requirements. Many of those computer systems are interconnected/integrated in modern enterprises. Validating one system often requires consideration of its interactions with other systems, making the validation process more intricate.

Computer systems often have long lifecycles. Validating a system at the time of implementation is just the beginning; maintaining validation throughout the system's lifecycle presents ongoing challenges, especially with updates and changes.

Ensuring the integrity of data generated and managed by computer systems is a critical aspect of CSV. Managing data across various processes while preventing errors, corruption, or unauthorized access adds complexity.  

Despite these challenges, CSV is essential for ensuring the reliability, accuracy, and compliance of computer systems in regulated industries. CSV processes are critical for many reasons.  Besides compliance with regulatory requirements, ensuring the integrity of data is critical, particularly in industries where data accuracy directly impacts product quality and patient safety. CSV helps prevent data corruption, errors, and unauthorized access, thereby maintaining the reliability of information.

Validating systems ensures that the manufacturing processes are controlled and that the final products meet quality standards, ensuring patient safety.

CSV helps identify and manage risks associated with computerized systems. This includes risks related to data integrity, system failures, security breaches, and other factors that could impact the reliability and performance of the system.

CSV requires substantial resources, including time, expertise, and financial investment. Companies must allocate resources for documentation, testing, training, and ongoing maintenance, which can strain budgets and staffing.

CSV involves people at various levels, from system users to validation experts. Ensuring that personnel are adequately trained and understand the importance of following procedures is crucial but can be challenging.

In summary, Computer System Validation is a critical practice in regulated industries to meet regulatory requirements, safeguard data integrity, ensure product quality, manage risks, and maintain the trust of stakeholders. It is an integral part of quality management systems in industries where the reliability of computerized systems directly impacts product safety and effectiveness. 

Talk to the Sware team to start your successful journey today.

RELATED ARTICLES

• Lupe_Grau Cancel

Computerized System Validation CSV

We frequently get asked, "Do you also offer Computerized Systems Validation?" One of the reasons for the interest is certainly: Authorities and notified bodies increasingly address the Computerized System Validation (CSV) in audits. This article introduces regulatory requirements regarding "Computerized Systems Validation" and provides guidance on how you can best meet these requirements.

Computer System Validation CSV: What's that?

A) definition and purpose of csv.

Computerized Systems Validation (CSV) is a documented process of assuring that a computerized system does exactly what it is designed to do.  [1]

b) What does Validation mean in this Context?

In general, validation is the "confirmation, through the provision of objective evidence, that the requirements for a specific intended use or application have been fulfilled" [ISO 9001:2015].

As for medical devices, this involves an "assessment by objective means of whether the specified users are enabled to achieve the specified goals (intended purpose) within the specified context of use". 

This may sound as if only the finished product, here the installed computerized system, must be validated.

However, many regulations go beyond such an understanding. Such as the FDA's requirements: they require the whole development process to be validated; not just its last phase or final product. More on this later.

c) Computerized Systems Validation vs. DQ, IQ, OQ and PQ

DQ, IQ, OQ and PQ - we come across these acronyms in the context of CSV. They stand for:

• DQ: Design Qualification
• IQ: Installation Qualification
• OQ: Operation Qualification
• PQ: Performance Qualification

These qualifications are particularly required in the pharmaceutical environment, e.g. by GMP-directives, and rank among equipment validations, just as CSV. IQ, OQ and PQ comprise certain aspects of validation / qualification:

• IQ: when installing, first inspections at the site of the customer shall ensure: the device was delivered, installed and installed according to the specifications. The documentation is available.
• OQ: checks ideally shortly after IQ shall confirm that the device operates according to specifications - even when reaching the specification limits.
• PQ: These tests address, inter alia, measurement accuracy (incl. calibration and adjustment).

d) Computerized Systems Validation vs. Software Validation

Computerized Systems Validation generally comprises both computer hardware as well as software running on the hardware. Particularly for standard hardware such as PC based systems, Computerized System Validation substantially equates to software validation. In case of specific hardware, it should for example be examined if:

• the software can be installed on the respective hardware
• the overall system operates at a satisfactory pace
• the interplay with neighbor systems (other devices or IT systems) functions as specified

CSV: Who Requires What? Regulatory Requirements

A) overview.

Requirements for validation of computerized systems can be found in:

• FDA  21 CFR part 820.70
• FDA  21 CFR part 11.10
• FDA 21 CFR part 820
• FDA Guidance Document regarding Software Validation (also addressing process software)
• FDA  Computer Software Assurance for Production and Quality System Software
• ISO 13485 , inter alia in chapter 4.1.6, 7.5.2.1 and 8.2.3 
• GMP directives
• GAMP 5 , e.g. regarding the "risk-based approach of testing GxP systems"

b) FDA 21 CFR part 820.70

In 21 CFR part 820.70, the FDA writes:

"When computers or automated data processing systems are used as part of production or the quality system, the manufacturer shall validate computer software for its intended use according to an established protocol. All software changes shall be validated before approval and issuance. These validation activities and results shall be documented. “

c) FDA 21 CFR part 11

21 CFR stipulates in part 11.10:

"Persons who use systems to create, modify, maintain, or transmit electronic records shall employ procedures and controls designed to ensure the authenticity, integrity, and, when appropriate, the confidentiality of electronic records. Such procedures and controls shall include validation of systems to ensure accuracy, reliability, consistent intended performance, and the ability to discern invalid or altered records.”

This demand for validation corresponds with what is required under 21 CFR part 820.70.

d) FDA Computer Software Assurance for production and Quality System Software

This Guidance Document is intended to replace the no longer up-to-date FDA Guidance Document on "Software Validation" in those parts that do not concern the software becoming part of the medical device or the medical device itself.

Requirements

The FDA emphasizes the risk-based approach in terms of critical thinking. Depending on the risk of the software/computerized system, specifically its critical functions, manufacturers must carry out "assurance activities" such as software tests.

On the positive side, the FDA's document distinguishes the development of medical device software from the development of software used in QM systems.

On the negative side, the guidance document can be considered no big success:

Once again, it seems no software engineering experts wrote the guidance. No matter if it concerns definitions, terminology, or methods. The document uses its own concepts.

It is difficult to reduce software quality assurance to analytical quality assurance, especially software testing. Quality cannot be tested in the software. The approaches on classification of software and the proposed test techniques are inconsistent, the examples are not taking into account the concepts presented in full range.

The already large number of CSV specifications becomes even more extensive.

e) ISO 13485:2016

In its latest version, ISO 13485:2016 states the requirements for validation of computerized systems more clearly:

In chapter 4.1.6, it is stipulated that manufacturers shall validate their computer software pursuant to documented procedures. This affects every software used in a process which controls the QM system. Validation shall not only take place before the first use, but also after modifications to the software.

To put it even stronger: in Europe, CSV used to be mandatory only for manufacturing and service processes. Since ISO 13485:2016, this requirement also applies to all computerized systems being used in any of the processes regulated by the QM system. In the context of FDA, this has always been the case.

GAMP 5 applies to medical device manufacturers as well. The authors write:

The scope has been widened [compared to Gamp 4] to include related industries and their suppliers, including biotechnology and systems used in medical device manufacturing (excluding software embedded within the medical devices).

Do you want to impress during audits and leave no questions about CSV unanswered?

Then, the medical device university is just right for you! Here, you will learn everything about computerized systems validation and the corresponding regulations - and you will be able to carry them out independently.

Computerized Systems Validation CSV: How is this done?

At first, you should understand: which requirements for computer system validation do you want to or must meet?

• Minimal requirements:  either way, you must validate your finished computer system.
• Additional requirements:  if you develop the system yourself or have it developed, you must at least document the whole development process.

Both variants are addressed in the following.

The actual validation

If you intend to validate the "finished" (possibly already installed) computerized system, you must know the requirements for the computerized system. If these requirements are lacking, you must deduce them in retrospect.

Unfortunately, many companies and, in part, even authorities, lump different types of requirements together.

• Stakeholder requirements and purpose : those are the truly important objectives and requirements different stakeholders such as users, operators and legislation have to be able to reach their respective aims. If you have, for example, a laboratory information system, one requirement would be: the user must be able to recognize via the system whether the patient has hepatitis.
• System requirements specification : this requirement describes what the system must be able to do to fulfill the stakeholder requirements. Ideally, those system or software requirements are specified as black box requirements. In the case of laboratory information system, such a requirement would be that the system shell display all patients with at least one positive hepatitis value in red and bold font.

Strictly speaking, validation is an examination of whether the first type of requirements is met. However, it can be observed that in practice, the fulfillment of rather system or software requirements is examined during validations. However, technically speaking, this is actually a verification.

In the chapter "Step-by-Step Instructions" you can read about how to conduct this type of validation.

The complete development process

When "validating" according to the FDA Software Validation Guidance Documents, you will document the complete development process such as

• Software requirements
• Verification of software requirements including traceability to stakeholder requirements
• Software architecture and detailed design
• Verification of software architecture and detailed design including traceability to software requirements
• Software code, code reviews and unit tests including traceability to software architecture and detailed design respectively
• Integration and system tests including traceability to software architecture and software requirements respectively

IEC 62304 and IEC 82304 as well as the aforementioned FDA documents provide you with valuable information on how to define and implement such a development process.

Computerized Systems Validation: Step-by-Step Instructions

To validate a computerized system, proceed as follows:

0. Decide whether the system must be validated

Describe the intended use of the system. Evaluate whether it is supposed to be used in one of the processes that is regulated by your QM systems. If this is the case the system must be validated.

1. Document requirements

If the requirements for your software and computerized system are not or not completely documented, catch up on this retroactively.

• Display : Describe the appearance of your graphical user interface (GUI) and what must be displayed.
• Algorithms:  If this display depends on calculations, then state them.
• Input:  Describe which data the user can enter in which formats. Also, how the system shall react in the case of a wrong entry.
• Navigation: Specify the navigation through the system, i.e. which "screens" the system shall display when certain selections are made, e.g. by clicking.
• Access right : An aspect of the aforementioned requirement is the specification of role concepts, authorization and authentication.
• System context: Describe with which adjacent systems your system will interact with.
• Interoperability: Specify how they are proceeded. Name the standard (e.g. TCP/IP, HTTP, bus systems), formats, classification systems, authorizations etc. Do not forget to determine how your system shall react if data is transmitted in a wrong format or volume, in an unexpected frequency etc.
• Authorizations:  Describe how adjacent systems shall authenticate and authorize on your system.
• Performance: Specify how fast your system must be able to react to data transfers. This requirement will probably depend on the number of transactions, data volumes or the like.
• Hardware:  Specify the minimum requirements for hardware such as CPU, RAM, hard drive, screen size, screen resolution etc.
• Operating System: Also determine the operating system which is required at least.
• Other Software:  Do you allow for other software such as anti-virus programs? Do you even suppose this, e.g. in form of a database? Do not forget to specify these requirements.

2. Specify test cases

The next step is to specify test cases, i.e. to determine

• Which data  the user or the adjacent system shall enter or transfer.
• Which prerequisites  must be met, e.g. regarding the software version or existing data.
• The procedure , e.g. the sequences in which to proceed i.e. to use the system, to input test data, to record results.
• Pass/fail criteria:  which values do you expect? Which outcomes meet the requirements?
• Test documentation  is also part of the specification.

3. Execute and document tests

Finally, you must execute, document and evaluate the tests according to the test specifications.

Further Tips

The following thoughts may help you with your Computerized Systems Validation:

• Systematic derivation of test data:  you should not just come up with test data but systematically derive them using black box test methods such as testing methods based on equivalence classes, boundary values, decision tables, errors or conditions. Software testers can do that.
• Not just the happy path:  not only examine if your system reacts as specified but also if it deals properly with wrong or incomplete entries.
• Risk-based approach:  if you are overwhelmed with the tests' scope, focus on the most critical functionality sections, i.e. those leading to the highest risks in cases of errors.
• Regression:  Computerized Systems Validation is not a one-time thing but has to be repeated each time you make changes to the system or software. Thus, automating the tests is recommended. There are tools with which you can semi automatically record and play back user interface tests as well.

Further Information

Click here to also read the article on AAMI TIR 36 and IEC 80002-2. Both articles offer specific assistance to validation of "Computerized Systems".

Change history:

• 2022-09-15: FDA Guidance Computer Software Assurance for Production and Quality System Software added

Prof. Dr. Christian Johner

A quick overview: Our

Starter-Kit

Always up to date: Our

Back To Top

Privacy settings

We use cookies on our website. Some of them are essential, while others help us improve this website and your experience.

Individual Cookie Settings

Only accept required cookies.

Privacy Notes Imprint

Here is an overview of all cookies use

Required Cookies

These cookies are needed to let the basic page functionallity work correctly.

Show Cookie Informationen

Hide Cookie Information

Provide load balancing functionality.

Provides functions across pages.

Hubspot Forms

Used for the google recaptcha verification for online forms.

Cookies for Statistics

Statistic cookies anonymize your data and use it. These information will help us to learn, how the users are using our website.

Google Analytics

Tracking and analys of traffic on our websites.

Cookies for Marketing

Marketing cookies from thrid parties will be used to show personal advertisment. They use them to track users outside of their own web page.

Keeping track of a visitor's identity. It is passed to HubSpot on form submission and used when deduplicating contacts. It contains an opaque GUID to represent the current visitor. It also introduces cookies from linked in for marketing reasons.

LinkedIn conversion tracking.

Cookies for external Content

Content for Videoplatforms und Social Media Platforms will be disabled automaticly. To see content from external sources, you need to enable it in the cookie settings.

Google Maps

Used to display google maps on our Websites. Google uses cookies to identify and track users.

The Complete Guide to Computer System Validation: IQ, OQ, PQ,

In our industry, Computer system validation is the process of ensuring that a computer system is fit for use and is functioning according to user requirements. The FDA defines computer software validation as “confirmation by examination and provision of objective evidence that software specifications conform to user needs and intended uses, and that the particular requirements implemented through software can be consistently fulfilled” ​(FDA, 2002)​. To be compliant with 21 CFR Part 11, companies must demonstrate that their computer systems are secure, accurate, and reliable.

Blue Mountain Regulatory Asset Manager (RAM) is a 21 CFR Part 11 compliant intelligent asset management software, designed exclusively for the life sciences. Blue Mountain RAM features built-in best practices and combines the capabilities of an Enterprise Asset Manager (EAM), a Computerized Maintenance Management System (CMMS), and a Computerized Calibration Management System (CCMS) all while ensuring GMP compliance.

Topics Covered:

• Let’s get Started

Continuing Validation

Importance of computer systems validation, let’s get started.

If you use computers or automated data processing systems for manufacturing or quality assurance purposes, the FDA requires that you validate the computer’s intended use by following a specific protocol. Below we will go through the steps required to conduct your own computer systems validation.  We’re going to Discover , Plan then Execute everything you need to be up-to-date and compliant with all the policies and procedures we’ve discussed.

• Discover – Define the Scope of the Validation

First, we need to identify the specific system(s) that need to be validated and define their intended use. In order to discover all the systems and functions that will need to be included in your scope, you may need to familiarize yourself with other stakeholders and end users in your organization. From this list, you must now identify all the testing methods and their deployments in order to meet all the potential requirements of the systems themselves and the software running on them.

Software requirements are typically given by vendors. Special consideration should be taken when formulating end-user processes. Documented requirements and risk analysis of the system help to define the scope of the evidence needed to show that the software is validated for its intended use.

Once you have all the systems, their functionality, and the tests needed to satisfy the accurate operation of you’re ready to create your validation plan.

• Plan – Create a Validation Plan

Your plan should include a detailed description of the validation process, including the testing methods that will be used to ensure the system’s accuracy and reliability. During this step, we would use our user requirements and system specifications to write our qualification protocol documents. Keep your Quality team involved in validating our plan-making to ensure the process is thoroughly developed.

Installation Qualification (IQ)

The IQ captures software/system installation requirements. The document outlines testing to ensure it is compliant with appropriate codes and design intentions.

Operational Qualification (OQ)

The OQ document captures system performance expectations. It serves as a detailed review of the software startup and base operation. Tests outlined in the OQ are executed to ensure the system is accurate, reliable, and secure.

Performance Qualification (PQ)

The PQ document captures the end-user requirements of the system. Tests outlined in the PQ should reflect processes that end users will encounter, establishing them as effective and reproducible

21 CFR Part 11 Controls to include:

• Data should be stored in an electronic format that is as trustworthy as paper records (and is archivable).
• The system must ensure the trustworthiness of electronic signatures
• The system must have access controls to ensure only authorized users have access.
• Password controls (including complexity and expiry) must be available in the system
• The system must be able to generate an audit trail of every activity performed in the system.
• Execute – Perform the Validation

The Validation will be performed in the new system’s environment. First, we execute the IQ, which tests that the system has been installed and set up according to the design specification. Then We execute the OQ to ensure all functionality specified is present and working properly.

Finally, we execute the PQ to ensure the system is fit for end users. Our last task is to create a summary document signing off that those specifications are met and the system is ready to go live.

At Blue Mountain, we offer plans to help along the way. Our teams of QA and Software analysts work together to ensure your system is fully validated. Our IQs, OQs, and PQs are written to ensure all controls of the software are captured. We even write custom PQs to your specification.

Computer systems validation concepts are an ever-evolving component of running an FDA 21 CFR Part 11 compliant computer system. As more features are identified and added to a system, the process will become cyclical. With each upgrade to a system, Blue Mountain repeats the IQ, OQ, and PQ processes. We identify the components to be installed, we validate that the new feature is working as intended, then we perform tests for its intended use.

Your first validation of a new system will be far from its last.

Software validation can increase the usability and reliability of the device, resulting in decreased failure rates, fewer recalls and corrective actions, less risk to patients and users, and reduced liability to device manufacturers. Testing software through validation can help to cut down expenses in the future as it makes it more efficient and inexpensive to make modifications and test them for accuracy.

General Principles of Software Validation; Final Guidance for Industry and FDA Staff 

What is Computer System Validation CSV in the Pharma Industry? (getreskilled.com)

FDA Guidance for Industry – Part 11, Electronic Records; Electronic Signatures — Scope and Application (fda.gov)

What is Computer System Validation and How Do You Do It? (validationcenter.com)

GET UPDATES

" * " indicates required fields

Software & Computer System Validation

Meet 21 cfr part 11, annex 11 and gmp requirements.

Driving Efficiency and Compliance in Health Product Industries

A computerized system can include hardware, software, its peripherals, interfaces, equipment, users, and operating procedures. Today, in the health products industry that encompasses pharmaceuticals, biologics, vaccines, biotechnology, natural health products, medical devices, cosmetics and allied industries, software and hardware components are used for the purposes of data processing, data storage, and process control.

AXSource Consulting is committed to understanding your unique situation and supporting a complete Computer System Validation (CSV) program, ensuring strict regulatory compliance.

According to US FDA,  “ Computer system validation is the process of providing a high degree of assurance through documented evidence that a computer system consistently meets its pre-determined or intended use or quality attributes such as accuracy, security, reliability, and functionality .”

We have performed Computer System Validation projects for custom, off-the-shelf (OTS) and highly configurable Enterprise Resource Planning (ERP) computer system applications such as:

We are fortunate to have  AXSource Infotech  that supports our design, development, and implementation efforts.

In addition to ERP, we have validated Laboratory Information Systems (LIMS/LIS), Electronic Data Capture (EDC) systems, and medical device software in “GxP” environments. All projects conform to recognized quality standards such as ISPE’s Good Automated Manufacturing Practices (GAMP 5 Guide: Compliant GxP Computerized Systems), US FDA 21 CFR Part 11 Regulations & FDA Software Validation Guidance, and EMA’s Annex 11 Regulations on Computerised Systems.

AXSource Consulting can assist in identifying the area of the regulated process or the medical devices that require validation. We can provide you with expert advice throughout the entire project lifecycle whether you are using an Agile Methodology or System Development Life Cycle (SDLC) approach.

Under FDA software validation requirements for medical devices,  “ any software used to automate any part of the device production process or any part of the quality system must be validated for its intended use, ” as required by 21 CFR §820.70(i). This requirement applies to any software used to automate device design, testing, component acceptance, manufacturing, labeling, packaging, distribution, complaint handling, or to automate any other aspect of the quality system. We have ample experience performing medical device software validation for clients all over the world.

Cost Savings

AXSource consultants prefer to support clients interactively to enable a smooth transition to client in-house IT and quality teams and most importantly to provide cost savings.

When is system or device validation required?

When is the right time to validate the system or device? To what extent is the validation necessary?

The extent of validation is commensurate to the risk posed by the system in terms of patient safety, the accuracy and security of the data involved, and/or the nature of the change. At AXSource, we evaluate the purpose and risks associated with each system prior to designing a validation strategy with our clients. Although not recommended by agencies,  AXSource  has also performed numerous retrospective validation of numerous existing computerized systems.

How does Software Validation apply to me?

• Automated operation and/or control in “GxP” (Manufacturing, Laboratory, Clinical, Distribution) environments.
• Software used as a medical device or as a component of a medical device.
• Controlling of health product (medical device, pharmaceutical, natural health product, biologics in humans or animals) manufacturing or control process, e.g., weighing, mixing, compounding, labeling, automated inspection systems, laboratory information system.
• Used in traceability and inventory control of health product (raw materials, components, bulk, finished products, devices).
• Used in recording health product manufacturing and control batch history information (electronic Batch Record) including e-Signature.
• Used in collecting & archiving clinical data to order to perform a medical assessment of risk & treatment.
• Used in Environmental Control provisions of health product process, e.g., HVAC.
• Used in critical supply of utilities in health products manufacturing, e.g., CIP/SIP, Purified Water.
• Used in Laboratory to inspect and test health products.
• Used in data records (word processing, spreadsheets, databases) to ensure integrity.
• Wireless technology or interface employed for any of the above functions.
• Definition of acceptance criteria with statistical significance limits.
• List of various statistical methods to be employed for validation & statistics. analysis (accuracy, precision, standard deviation, confidence interval, Pareto analyses, T-test, etc.)
• Need and determine the basis for any re-validation activity, if required.

Deliverables

Documentation deliverables during the IQ, OQ and PQ phases of the computer system validation / software validation, include but are not limited to the following:

Partner With Us for Computer System Validation and Medical Device Software Validation Needs!

Digital Transformation Regulatory & Quality

Careers Contact Us

Blogs News Whitepaper Webinars

Copyright © 2024 - All Rights Reserved - AXSource | Privacy Policy

• Microsoft D365 F&O / Dynamics AX
• Microsoft D365 CE / Dynamics CRM
• Microsoft D365 BC / Dynamics NAV
• Microsoft Power BI
• ERP/CRM Implementation & Support
• Application Migration/Upgrade
• Business Consulting & Project Planning
• Development, Reporting & Data Migration
• Interface Design/Integration
• Application Managed Services (ASAP)
• Regulatory Strategy
• Regulatory Submissions
• eCTD Publishing
• SPL & XML Labelling
• Quality Assurance & Quality Control
• Regulatory Inspections & Audits
• Professional Training
• Licensing & Importation
• Software & CSV
• Process Validation
• Clinical Operations
• Pharmacovigilance
• Whitepapers

She offers outstanding qualifications in pre-marketing government liaison with the EU, FDA & Health Canada, Notified Bodies, legal support, implementation of compliance programs including Quality Management System design, certified quality auditing, validation (specializing in software system validation), risk management/mitigation, and process improvement. Nav is a US patent holder for a Quality and compliance Solution (QCS™ software) fully integrated with Microsoft’s Dynamics D365 Finance & Operations solution.

Navneet has volunteered as a member or chair in many industry associations (CAPRA, PSG, MEDEC/MEDTEC CANADA, EFC/MIISC, PDA, ISQA & RAPS). More recently, she has been responsible for board governance for the Halton Police Service Board & Ontario Energy Board (OEB). 

QbD » Blog » What is the Agile model in Computerized System Validation? 

Life Sciences Insights

Sharing expert knowledge via our latest blog posts

What is the Agile model in Computerized System Validation? 

• Jonathan Boel, Division Head Software Solutions & Services at the QbD Group
• Software Services & Solutions
• March 28, 2023

Pharmaceutical, clinical, MD, and IVD companies pursuing robustness in their software validation activities can use either the V-model or the Agile model . Both are Computer System Validation (CSV) methods that can be applied according to GAMP 5 .

The Good Automated Manufacturing Practice (GAMP) framework is a set of guidelines that provide a structured approach to validation in regulated industries, including pharmaceuticals, biotechnology, and medical devices. GAMP 5 is the latest version of this framework, released in 2008 and updated in 2022 .

This blog post is about the Agile model and is a follow-up to the V-model blog post we shared earlier.

Enjoy reading!

A complete guide to Computer System Validation

This +100 page guide aims to bring context and define the necessary and appropriate strategies for the validation of computerized systems for   Pharmaceutical Industries, Biologics, Biotechnology, Blood Products, Medicinal Products, and Medical Devices, used in activities related to compliance with Good Practices (GxP). Download it now  for free:

FREE E-BOOK

Why validate your computerized systems?

Using software for your life science activities requires a high level of integrity and security in the data processing. The computerized system must be designed to operate in a consistent and reproducible manner, in accordance with applicable GxP regulations, to ensure the expected final product quality. Validating this system provides documented evidence that it does exactly what it is intended to do. In order to achieve this, you can use either the V-model or the Agile model .

The Agile model in CSV

The Agile model is based on a continuous exchange between the client and the validation team. The stages of the project rely on constant feedback from the customer to make progress.

Agile development methods have become increasingly popular in recent years because they offer a flexible and iterative approach to software development that can help teams quickly deliver high-quality products.

However, it can be challenging to validate agile development processes in regulated industries, where strict quality standards must be met to ensure patient safety.

The V-model in CSV

In a V-model , the ‘V’ can stand for validation and verification, but it also refers to the shape of its common graphical representation. 

It is a variant of the Waterfall model of software development, which describes a sequential procedure in which each step must be completed before the next can be started. Since cycles are not possible here, the V shape appears.

Validation according to the Agile model

Agile development is an iterative approach to software development that emphasizes collaboration, flexibility, and rapid delivery . Unlike traditional software development methods following the V-model or Waterfall approach – which involve a sequential process of planning, designing, coding, testing, and deployment – the Agile model focuses on delivering working software in small steps, with frequent feedback from users and stakeholders.

While this approach can be very effective for developing software quickly , it can be challenging to validate in regulated industries .

Validation is the process of demonstrating that a product or process meets predetermined requirements and specifications and is suitable for its intended use. In regulated industries, validation is required to ensure patient safety, product quality, and compliance with regulatory standards.

To validate agile development processes, organizations must demonstrate that they have a robust quality management system in place that can manage the risks associated with agile development. This includes implementing appropriate controls, procedures, and documentation to ensure software meets regulatory requirements.

GAMP 5 framework for agile development

The GAMP 5 framework provides a structured approach to validation that can be applied to agile development processes. The framework is based on a risk-based approach , identifying potential risks and implementing appropriate controls to mitigate those risks.

The GAMP 5 framework consists of 4 categories :

• Category 1 – Infrastructure software
• Category 3 – Non-configured products
• Category 4 – Configured products
• Category 5 – Customized applications

To validate agile development processes according to GAMP 5, organizations must follow the framework’s five-phase approach :

Figure 1 – GAMP 5’s five-phase approach

Determine the scope and strategy of the validation effort and develop a validation plan that includes identification of critical processes and requirements.

The delivered description of system functionality is provided by the list of completed agile artifacts, such as epics and user stories, taken directly from agile software development tools.

Configure and/or code

In this step, the supplier relies on its expertise to implement the customer’s requirements. All functionalities documented in the previous steps are implemented.

Verify that the software meets user requirements and design specifications through testing and other validation activities, such as peer reviews and code inspections.

Develop a design specification that describes how the software will be designed, including the software architecture, data model and interfaces.

In agile development, these 5 phases are typically conducted in iterative cycles , with each cycle resulting in a working software increment . At the end of each cycle, the software increment is validated to ensure that it meets the user requirements and design specifications.

Special considerations when using the Agile model

Integral part of the development process.

In an agile environment, validation must be an integral part of the development process , with validation activities performed in each sprint. This approach allows validation to be built into the development cycle, rather than being an afterthought or bottleneck.

One way to achieve this is by using automated testing tools that can validate functionality quickly and reliably. Automated testing tools can be integrated into the development cycle, allowing testing to be performed automatically after each code change.

Documentation

Another important consideration when validating computerized systems in an agile environment is documentation. Regulatory requirements typically require that computerized systems be documented thoroughly, including requirements, design, testing, and maintenance.

In an agile environment, documentation should be incorporated into the development cycle , with documentation updated in each sprint. Documentation should be clear, concise, and easy to understand, allowing developers, testers, and stakeholders to understand the system’s functionality and validate that it meets the specified requirements.

Communication

Communication is also essential when validating computerized systems in an agile environment. Agile development emphasizes collaboration and communication, and this applies to validation as well.

Developers, testers, and stakeholders should communicate frequently, with regular feedback loops throughout the development cycle . This approach allows issues to be identified and addressed quickly, reducing the risk of delays or failures.

Validation strategy

Finally, it’s important to establish a validation strategy and plan before starting development. The validation strategy should outline the validation approach, including the scope, objectives, and acceptance criteria.

The validation plan should provide a roadmap for validation activities, including testing, documentation, and communication. These documents should be updated throughout the development cycle, reflecting changes in the system’s functionality or regulatory requirements.

Conclusion: use GAMP 5 and the Agile model to your advantage

Validating agile development processes in regulated industries requires a risk-based approach that considers the unique challenges and complexities of agile development.

The GAMP 5 framework provides a structured approach to validation that can be applied to agile development processes, enabling organizations to develop high-quality software products that meet regulatory requirements and ensure patient safety. By following the five-phase approach outlined in GAMP 5, organizations can validate their agile development processes – following the Agile model – whilst being compliant.

In conclusion, validating computerized systems following the Agile model requires a proactive and collaborative approach . By incorporating validation into each sprint, using automated testing tools, communicating frequently, and documenting thoroughly, developers, testers, and stakeholders can work together to ensure that computerized systems meet regulatory requirements and are fit for their intended use.

With these best practices in place, organizations can successfully implement agile development methodologies while maintaining compliance with regulatory requirements.

Need help and assistance? QbD is here for you! Don’t hesitate to contact us to come up with a pragmatic and customized approach for your business.

Expert knowledge in Computer Systems Validation

Did you find this article interesting? Thanks for sharing it with your network:

Read more experts content

• Regulatory Updates
• Whitepapers

Table of Contents

Stay up to date with life sciences insights.

• October 24-26
• Barcelona, Spain

Come visit our booth at CPHI Barcelona 2023

Come to see the QbD Group at stand #3G73 at CPHI Conference in Barcelona. And after the conference…Eat & Connect with lifescience professionals at our QbD’s CPHI Networking Drink .

• Create an account
• Profile Settings
• What we do Life Sciences Expertise Computer Software Assurance Data Integrity Cybersecurity Medical Device UDI & MDR GxP Training & Education Compliance Simplified Cloud Assurance as a Platform Cloud Assurance Certified Audits & Risk Assessments Validation & Qualification Quality Management Processes GxP Technologies ProcessX & GxP Workflow Automation Automated Application Lifecycle Management Technical Development & GxP Configuration Digital Cloud & AI System Implementation AI & Machine Learning in Life Sciences
• Who we work with Adobe Acrobat Assent Atreo.io Box DocuSign Google Cloud Microsoft Okta Oracle PTC QAD Salesforce ServiceNow Sorcero UL's ComplianceWire Veeva ZenQMS
• Expertise Domains Clinical Labs Manufacturing Quality Supply Chain Industries Biotech Medical Device Pharma Other Pre-Commercial Staffing
• Recommended
• Case Studies
• News & Events
• White Papers
• Leadership Team
• Subject Matter Experts

Compliance Simplified

Validation & qualification.

Learn how USDM helped establish a risk-based, phase-appropriate, pragmatic approach for compliance to achieve an audit-ready position in less than 6 weeks.

Full-service computer software, equipment, and process validation in regulated life sciences environments.

From methodology development through end-user training, USDM ensures that your systems are compliant. Our validation best practices and test automation capabilities significantly decrease your implementation and validation time.

Whether you’re still using the traditional Computer System Validation (CSV) approach or you’re ready to take the first steps to a more modern Computer Software Assurance (CSA) approach to improve your quality and efficiency, USDM can help!

Meeting validation requirements ensures that life sciences organizations achieve regulatory compliance and helps them maintain product quality and safety. But who’s responsible for what in the validation process? Download our Validation Requirements and Responsibilities white paper to explore options for offloading some of your validation burdens and bridging any gaps in validation responsibilities.

Computer System Validation for Life Sciences GxP Environments

USDM has experience qualifying, verifying, and validating the myriad systems, equipment, and processes that are found in most life sciences GxP environments, both on-premises and cloud-based. Our expertise includes, but is not limited to:

• Blood and Plasma Systems
• Building Management/Environmental Control Systems
• Corrective and Preventive Actions (CAPA) Systems
• Clinical Systems (CDMS, CTMS, EDC, eTMF, ePRO, IRT)
• Content Management Systems
• Laboratory Systems and Equipment (ELN, Freezer Management, LIMS)
• Manufacturing Systems and Equipment
• Process Validation
• Quality Management Systems (LMS, Quality Document Management, QMS)
• Regulatory Publishing and Submissions
• Software as a Medical Device (SAMD)
• UDI & Serialization

Have a question about how we can work with your specific GxP system setup? Fill out our  contact form with your system requirements and we’ll review them.

What is USDM’s Computer System Validation (CSV) methodology?

Our current methodology aligns with GAMP (Good Automated Manufacturing Practice) best practices and includes the following: 

• Vendor Audit
• Validation Plan
• Part 11 and Annex 11 Assessments
• Risk and Impact Assessments
• User Requirements and Functional Specification
• IQ/OQ/PQ/UAT Protocols, Test Scripts, and Execution Assistance
• Traceability Matrix
• Administration, Use, and Operation SOPs
• Business Process SOPs
• Validation Summary Report 

Specific documents and deliverables will depend on GAMP category. 

Standard Operating Procedures (SOPs) for your IT, Validation, or GxP Processes

Standard Operating Procedures (SOPs) are written procedures that ensure essential job tasks are performed according to approved procedures. USDM offers two SOP solutions:

Pre-packaged SOP solutions: This includes templates for select IT or Quality processes based on common regulatory requirements and industry best practices.

Support for creating customized SOPs that include the pre-packaged SOPs, plus additional customized or original SOP development to meet your specific needs and intended use cases.

Pre-Packaged SOP Templates

Pre-packaged SOP templates include select IT or Quality processes based on common regulatory requirements and industry best practices.

IT SOP templates and related forms include:

• Change Control
• Security Administration
• Record Retention
• Data Integrity Policy and Procedures
• System Access and Password Control
• System Administration
• Audit Trail Review
• Electronic Signature Policy and Procedure (includes Acknowledgement Form)
• Backup and Restore
• Disaster Recovery

Validation SOP templates and related forms include:

• Computer System Validation
• Part 11 Assessment
• Master Validation Plan
• Risk Assessment
• Master Inventory List
• Decommissioning
• Periodic Review and Assessment of Validated Systems
• Configuration Management
• SDLC for Custom Systems
• Change Management
• System Validation Plan
• User Requirements Specification
• Functional Requirements Specification
• Risk Assessment Form
• Configuration Specification
• Test Protocols and Test Script
• Deviation Form
• Trace Matrix
• Validation Summary Report
• Regulated Systems Governance (4 phases: planning, implementation, production, and retirement)
• Roles and responsibilities aligned with your organization’s capabilities
• IT System Inventory
• CSA approach to initial validation and release management
• Regulatory Applicability Assessment (RAA) Form
• ERES Compliance Assessment Form
• IT Risk Assessment
• IT Change Control for Regulated Systems and form (for GxP and SOX)
• Periodic Review and form

Supporting Process SOP templates include:

• Vendor/Supplier Management Program
• Data Integrity Program
• Risk Management
• Document Management
• Infrastructure Qualification

Customized Standard Operating Procedures (SOPs)

If your medical device, pharmaceutical, or biotech organization requires a more tailor-made solution, USDM works with your team to upgrade your quality system. We’ll review the current state of your SOPs and create new ones to achieve your goals.

Discounted pricing is available for customized SOPs in quantities of 1-5, 6-10, or 10+ to meet your unique business need. Customized SOPs include pre-packaged SOPs.

Additionally, USDM can provide related services such as:

Computer Software Assurance

• Audits & Assessments
• Quality Management Processes
• GxP Training & Education
• Medical Device UDI & MDR

What SOPs Do I Need?

For start-up and pre-commercial companies, read our  best practices approach  for implementing and validating your first GxP-regulated IT systems on your journey to commercialization.

For commercialized and larger companies, learn how USDM helped a large medical device manufacturer  upgrade their quality system  by revising 400 SOPs and generating CND codes to prepare for EUDAMED submissions.

Or  read this case study  to learn how USDM created standardized processes that saved our customer, a global contract biopharmaceutical manufacturer, over half a million dollars for five sites over two years.

Get in touch with our team to discuss your validation needs.

Related services.

USDM Cloud Assurance

Cloud Assurance is a managed service that offloads your vendor release management and maintenance of ongoing system updates, patches, and changes to keep you compliant.

USDM can assess your CSV process and recommend CSA changes based on your quality of documentation, testing, SOPs/WIs, use of automation, performance on audits, and more.

GxP Cloud Platforms

Your success depends on how well you harness cloud technology to enable your workforce to work from anywhere, build platforms that differentiate, and innovate faster. 

Resources that might interest you

Your IT Roadmap – Guidance for Early-Stage Life Sciences Startups

• Biotech , CMO , CRO , Medical Device , Pharma
• Clinical , Information Technology , Research & Development

Computer Software Assurance (CSA) Guidance

• Sandy Hedberg
• - February 3, 2022
• Biotech , Medical Device , Pharma
• Clinical , Information Technology , Manufacturing , Quality Assurance , Regulatory

Top 5 Opportunities to Improve Compliance Maturity

• Various Authors
• - February 23, 2022
• Clinical , Information Technology , Manufacturing , Quality Assurance , Research & Development

Your Compliance and Technology Today

• - January 28, 2022
• Information Technology , Regulatory

FDA Perspectives on Cloud Technologies

• Jim Macdonell

Standard Operating Procedures (SOPs) – Best Practices for Emerging Life Sciences Companies

• Erin Christy
• - March 12, 2022
• Quality Assurance , Safety

USDM Thought Leaders

What Is Data Validation Testing: Tools, Techniques, Examples

Businesses constantly face the challenge of ensuring the accuracy and reliability of the data they handle. With adequate validation processes, even minor data entry or processing errors can have significant consequences. These errors can lead to incorrect insights, flawed decision-making, and damaged reputations, ultimately impacting the bottom line.

This is where data validation testing emerges as a crucial solution. It provides a safety net, ensuring that only accurate and reliable data is processed and utilized for critical operations and decision-making.

The article will explore what is data validation, as well as what are data validation tools, techniques, and examples. 

What is Data Validation Testing?

Data validation testing is a pivotal aspect of software testing that focuses on verifying data accuracy, completeness, and reliability within a system. It involves validating data inputs, outputs, and storage mechanisms to meet predefined criteria and adhere to expected standards.

The primary goal of data validation testing is to identify and rectify any errors, inconsistencies, or anomalies in the data being processed by a software system. This validation process typically involves comparing the input data against specified rules, constraints, or patterns to determine its validity.

Data validation testing can encompass various techniques, including manual inspection, automated validation scripts, and integration with validation tools and frameworks. It is essential for ensuring data integrity, minimizing the risk of data corruption or loss, and maintaining the overall quality of software systems.

What are Data Validation Techniques?

Some standard data validation techniques include:

Manual Inspection

This involves human review and data verification to identify errors, inconsistencies, or anomalies. Manual inspection is recommended for validating small datasets or data that require subjective judgment.

Automated Validation

Automated validation techniques use scripts, algorithms, or software tools to perform systematic checks on data inputs, outputs, and storage. These techniques efficiently validate large datasets and repetitive tasks and offer quick identification of errors and deviations from expected standards.

Range and Constraint Checking

Range checking verifies that data values fall within predefined ranges or thresholds. Constraint checking ensures data adheres to specified rules, formats, or patterns, such as data type, length, format, or allowed characters.

Data Integrity Constraints

These are rules defined within a database schema to enforce data integrity, such as primary key constraints, foreign key constraints, unique constraints, and check constraints. These constraints help maintain data consistency and prevent invalid data entries.

Cross-Field Validation

This technique involves validating relationships between multiple data fields to ensure consistency and coherence. For example, validating that the start date of an event is before the end date or that the sum of values in multiple fields equals a predefined total.

Data Profiling

Data profiling involves analyzing the structure, quality, and content of data to identify patterns, anomalies, and inconsistencies. This technique helps uncover data quality issues and informs the design of validation rules and processes.

Statistical Analysis

Statistical techniques, such as regression analysis, hypothesis testing, and outlier detection, can be used to assess the distribution, variability, and relationships within datasets. Statistical analysis helps identify data outliers, trends, and patterns that may require further investigation or validation.

What are Data Validation Testing Tools?

Data validation testing tools are software applications or frameworks designed to facilitate data validation within a system. These tools automate verifying data inputs, outputs, and storage against predefined criteria, rules, or standards. 

Some common data validation testing tools include:

Datameer helps you streamline data preparation and transformation, effortlessly converting raw data for faster analysis. With Datameer, you can independently clean, enrich, and structure data without relying on IT assistance. This empowers you to accelerate your data preparation tasks and customize data according to your specific analysis needs.

Key Features

• Native Snowflake Integration: Designed explicitly for Snowflake, it lets you manage the entire data lifecycle, from exploration to sharing trusted datasets. ‍
• Automatic Encoding for Machine Learning: It automates data encoding for machine learning, converting categorical data into a suitable format for ML algorithms. By streamlining this process, Datameer enhances the accuracy and effectiveness of your predictive analytics and ML models, facilitating better decision-making.

Informatica

Informatica is a versatile data management platform that empowers you to execute crucial data quality operations like deduplication, standardization, enrichment, and validation. It enables you to identify, rectify, and monitor data quality issues across cloud-based and on-premises environments. With its built-in connectors, you can effortlessly link up with diverse source systems like databases, file systems, or SaaS applications. 

• Streamlined Data Preparation: With Informatica, you can simplify data preparation by leveraging comprehensive profiling capabilities supported by pre-built rules and accelerators. This empowers you to understand your data's structure, quality, and completeness, facilitating effective data preparation processes. ‍
• Enhanced Performance with Parallel Processing: Informatica helps to enhance performance through parallel processing, allowing you to execute multiple tasks concurrently. This optimizes resource utilization and reduces processing time, resulting in improved efficiency and throughput for your data processing tasks.

Talend is a comprehensive data integration and quality that allows you to streamline your data management processes. It offers functionalities for data extraction, transformation, and loading across systems. This includes data profiling, cleansing, and standardization to ensure consistency and accuracy.

• Data Transformation Capabilities: With Talend, you have access to a wide range of data transformation techniques, such as filtering, sorting, aggregating, and joining data. This allows you to transform and manipulate data according to your specific requirements efficiently. ‍
• Data Security and Compliance: It prioritizes data security and compliance needs by implementing role-based access controls and adhering to regulations such as GDPR and HIPAA. This ensures that your sensitive data remains protected and that your organization complies with relevant regulations.

Alteryx is a powerful data preparation and analytics platform that enables you to uncover timely insights through its intuitive workflow design capabilities. With Alteryx, you can seamlessly connect to diverse sources, both on-premises and in the cloud, streamlining data transformation within a unified environment. 

• Data Quality Recommendations: Alteryx provides tailored data quality recommendations, assisting you in enhancing the accuracy and reliability of your data. ‍
• Customizable Data Processing: With Alteryx, you can customize data processing through transformation, filtering, and alignment. ‍
• Data Profiling and AI-powered Validation: Alteryx offers data profiling and AI-powered data validation capabilities, such as anomaly detection and pattern recognition, enabling efficient analysis and verification of data quality.

Data Ladder

Data Ladder is a comprehensive data quality solution that empowers you to prepare data for analysis. It offers a robust set of functionalities like data profiling, cleansing, and deduplication. This streamlines your data preparation process by allowing you to understand your data and validate it for accuracy.

• Interactive Interface: The platform offers a visual and interactive interface, enabling you to process your data in a no-code environment. ‍
• Data Import: It simplifies data integration by allowing you to import data from various sources like customer relationship management (CRM) systems and marketing automation platforms. 

Ataccama One

Ataccama One provides access to a comprehensive data management solution equipped with robust data quality and validation features. You can continuously manage data quality, autonomously detect anomalies and irregularities, and set custom validation rules as per your requirements.

• Effortless Data Management: With Ataccama One, you have access to comprehensive data management features such as data profiling, cleansing, enrichment, and cataloging. These features empower you to effortlessly manage your data assets and track data lineage, contributing to improved accuracy and reliability. ‍
• Integrated AI and ML: Ataccama One offers integrated AI and ML capabilities to streamline your data management tasks. This integration enhances the accuracy of data quality checks and enables automation, allowing you to optimize your data processes efficiently.

Data Validation Testing Examples

Some data validation testing examples include:

Missing Value Validation

This validation is often implemented into data processing methods, such as Extract Transform Load (ETL) processes. During the transformation step, you can apply validation testing to identify rows with missing data. Once checked, you can either delete that row or add a pre-defined value. 

Email Address Validation

Email address validation can be implemented to ensure that the entered email addresses are correctly formatted, including the presence of the "@" symbol and a valid domain name. This validation prevents you from entering incorrect or incomplete email addresses, ensuring effective and reliable communication channels.

Numeric Range Validation

Numeric range validation requires verifying that the numeric values you enter fall within a specified range, such as ensuring your age input is between 18 and 100. This validation helps ensure that the entered data meets predefined criteria, maintaining accuracy and preventing errors.

Date Format Validation

Date format validation checks that the entered dates into a form adhere to a valid format, such as MM/DD/YYYY or YYYY-MM-DD. This validation ensures consistency in date formatting across your data entries, preventing errors and facilitating accurate data processing and analysis.

While ensuring data quality through validation remains important, a modern approach would be validating data after centralizing it through transformation. This means simplifying the process by bringing together data from different sources and formats into one platform. Tools like Airbyte can help you streamline this process, making data management more efficient and enhancing analytical capabilities. 

Some of the Best Practices to Follow when Performing Data Validation Testing

Effective data validation testing is essential for ensuring data quality. Here are some key practices to follow:

1. Understand Your Data

Before you start your data validation testing, it is crucial to have a clear understanding of your data ecosystem. You must also understand the data formats used by each source. 

Define clear data quality expectations based on your business needs. Then, you can design a layout for determining a testing plan, what type of test will be performed, and the duration of the entire process.

2. Perform Data Profiling and Sampling

Utilize data profiling tools to check your data for structure, content, and potential issues. Identify errors, anomalies, or missing values in your datasets.

You can leverage data sampling techniques to validate small datasets for inconsistencies or faults. This can provide insights before testing the entire dataset.

3. Implement Data Validation Testing

This phase focuses on executing tests to ensure that data adheres to pre-defined rules and standards. For instance, data validation testing can be done to ensure data quality. This might invlove eliminating anomalies or errors in your dataset, addressing missing values, validating data formats, and ensuring data integrity across data sources.  

You can also leverage automated data validation testing tools to streamline various processes and reduce human error. These tools can perform various checks, including validating data types and spotting errors. 

4. Continuous Monitoring

Implement real-time data validation testing as it enables you to rectify your data for any inaccuracies as and when they are generated. In addition, conduct regular audits on your datasets to discover any recurring issues.

5. Data Validation Testing Team

When performing data validation testing, you must have a skilled cross-functional team to coordinate during the process. This team can include different professionals, such as data analysts, IT specialists, and data engineers, to oversee the entire process and ensure the successful completion of your business operations.

Easily Manage Data Integration Needs with Airbyte

Airbyte is a powerful data integration platform designed to streamline the process of connecting and synchronizing data from various sources. With its user-friendly interface, Airbyte simplifies data replication tasks, allowing you to efficiently manage data pipelines and ensure data consistency and accuracy.

Here are some features:

• Pre-built Connectors: Airbyte has a comprehensive library of pre-built connectors covering a wide range of cloud applications, databases, and data warehouses. This extensive selection allows you to integrate data from various sources into your data pipeline seamlessly. ‍
• Standardized Testing for Connectors: Airbyte provides pre-built Connector Acceptance Tests (CATs) that can be used to test the functionality of various data connectors. These tests help ensure that connectors adhere to the Airbyte Specification, promoting consistent data transfer across different sources and destinations.  ‍
• Change Data Capture (CDC): Airbyte supports CDC for specific databases. CDC allows you to capture only the changes made to the data since the last sync rather than the entire dataset. This significantly reduces the amount of data that needs to be validated, especially for frequently changing datasets. ‍
• PyAirbyte: PyAirbyte is a Python library that provides programmatic interaction for the Airbyte connectors. It allows you to define connections, initiate syncs, and monitor workflows within your Python code.

Effective data validation is crucial for ensuring the accuracy, completeness, and reliability of your business data. By implementing robust validation processes and techniques, you can mitigate the risk of errors, inconsistencies, and data quality issues. Focusing on validating your data inputs, outputs, and storage mechanisms will help you maintain data integrity and trustworthiness, facilitating informed decision-making and enhancing operational efficiency.

About the Author

Table of contents, get your data syncing in minutes, join our newsletter to get all the insights on the data stack., integrate with 300+ apps using airbyte, integrate and move data across 300+ apps using airbyte., related posts.

• Python for Machine Learning
• Machine Learning with R
• Machine Learning Algorithms
• Math for Machine Learning
• Machine Learning Interview Questions
• ML Projects
• Deep Learning
• Computer vision
• Data Science
• Artificial Intelligence
• Stratified K Fold Cross Validation
• Cross-validation on Digits Dataset in Scikit-learn
• Recursive Feature Elimination with Cross-Validation in Scikit Learn
• Cross Validation in Machine Learning
• Generalisation Performance from NNET in R using k-fold cross-validation
• Training Neural Networks with Validation using PyTorch
• Cross validation in R without caret package
• SVM with Univariate Feature Selection in Scikit Learn
• Time Series Cross-Validation
• Receiver Operating Characteristic (ROC) with Cross Validation in Scikit Learn
• Y Scrambling for Model Validation
• How to do nested cross-validation with LASSO in caret or tidymodels?
• Joint Feature Selection with multi-task Lasso in Scikit Learn
• Random Forest Classifier using Scikit-learn
• ML | Kaggle Breast Cancer Wisconsin Diagnosis using KNN and Cross Validation
• How to split the Dataset With scikit-learn's train_test_split() Function
• Feature Transformations with Ensembles of Trees in Scikit Learn
• K-fold Cross Validation in R Programming
• Repeated K-fold Cross Validation in R Programming

Cross-Validation Using K-Fold With Scikit-Learn

Cross-validation involves repeatedly splitting data into training and testing sets to evaluate the performance of a machine-learning model. One of the most commonly used cross-validation techniques is K-Fold Cross-Validation. In this article, we will explore the implementation of K-Fold Cross-Validation using Scikit-Learn, a popular Python machine-learning library.

Table of Content

What is K-Fold Cross Validation?

K-fold with scikit-learn, visualizing k-fold cross-validation behavior, logistic regression model & k-fold cross validating, cross-validating different regression models using k-fold (california housing dataset), advantages & disadvantages of k-fold cross validation, additional information, conclusions, frequently asked questions (faqs).

In K-Fold cross-validation, the input data is divided into ‘K’ number of folds, hence the name K Fold. The model undergoes training with K-1 folds and is evaluated on the remaining fold. This procedure is performed K times, where each fold is utilized as the testing set one time. The performance metrics are averaged across K iterations to offer a more reliable evaluation of the model’s performance.

Example: Suppose we specified the fold as 10 (k = 10), then the K-Fold cross-validation splits the input data into 10 folds, which means we have 10 sets of data to train and test our model. So for every iteration, the model uses one fold as test data and the remaining as training data (9 folds). Every time, it picks a different fold for evaluation, and the result is an array of evaluation scores for each fold.

Let’s look at how to implement K-Fold cross-validation using Scikit-Learn . To achieve this, we need to import the KFold class from sklearn.model_selection. Let’s look at the KFold class from Scikit-Learn, its parameters, and its methods.

sklearn.model_selection.KFold(n_splits=5, *, shuffle=False, random_state=None) PARAMETERS: n_splits ( int, default=5): Number of folds. Must be at least 2. shuffle ( bool, default=False): Whether to shuffle the data before splitting into batches. Note that the samples within each split will not be shuffled random_state ( int, default=None): When shuffle is True, random_state affects the ordering of the indices, which controls the randomness of each fold. Otherwise, this parameter has no effect. METHODS: get_metadata_routing(): Get metadata routing of this object. get_n_splits( X=None, y=None, groups=None ): Returns the number of splitting iterations in the cross-validator. Here X,y and groups are objects. split( X, y=None, groups=None ): Generate indices to split data into training and test set. Here X is an array which holds number of samples and number of features, y is the target variable for supervised learning problems, groups is the samples used while splitting the data into training / test set.

Let’s create a synthetic regression dataset to analyse how the K-Fold split works. The code is as follows:

In the above code we created a synthetic regression dataset by using make_regression() method from sklearn. Here X is the input set and y is the target data (label). The KFold class divides the input data into four folds using the split() method. Hence, it has a total of four iterations (4 folds). Hope you noticed that for the entire iterations, the train index and test index are different, and it also considered the entire data for training. Let’s check the number of splits using the get_n_splits() method. 

We can create a classification dataset and visualize the behaviour of K-Fold cross-validation. The code is as follows:

Using the make_classification() method, we created a synthetic binary classification dataset of 100 samples with 20 features and prepared a K-Fold cross-validation procedure for the dataset with 10 folds. Then we displayed the training and test data for each fold. You can notice how the data is divided among the training and test sets for each fold.

Let’s visualise K-Fold cross validation behavior in Sklearn. The code is as follows:

in the above code, we used matplotlib to visualize the sample plot for indices of a k-fold cross-validation object. We generated training or test visualizations for each CV split. Here, we filled the indices with training or test groups using Numpy and plotted the indices using the scatter() method. The cmap parameter specifies the color of the training and test sets, and the lw parameter sets the width of each fold. Finally, by using the set() method, we formatted the X and Y axes.

Now let’s create a logistic regression model and cross-validate it using K-Fold. The code is as follows:

In the above code, we make use of the cross_val_score() method to evaluate a score by k-fold cross-validation. Here, we passed the logistic regression model and evaluation procedure (K-Fold) as parameters. The accuracy is the evaluation metric (scoring parameter) that we used to score the dataset.

Now it’s time to cross-validate different regression models using K-Fold, and we can analyze the performance of each model. Let’s make use of the California Housing dataset from Sklearn. The code is as follows:

Here we make use of the fetch_california_housing() method from the sklearn dataset. The dataset consists of 20,640 samples and 9 features (including the label).

Here, the dataset contains only numerical features, and there are no missing values. So we don’t need to deal with text attributes or missing values; all we need to do is scale the features.

Let’s scale the features and apply K-Fold to the dataset.

Here, we scaled the features using the StandardScaler() method from Sklearn and passed the scaled features to the fit_transform() method. Then we prepared the K-Fold validation procedure, where we set the folds as 10 and mixed the dataset by setting the shuffle parameter as true. 

Let’s visualise the split using matplotlib.

K-Fold with Shuffle

We make use of the same plot_cv_indices() method (explained above) to visualize the data split. Hope you noticed that in the above plot diagram, the training and test sets got shuffled up. This is because we set the shuffle parameter in K-Fold as true. This helps in considering data from different section.

Now let’s create different regression models and apply K-fold cross validation. The code is as follows:

In the above code, we created three different regression models (Linear, Decision Tree and Random Forest regression) and identified the prediction error using cross-validation for each model. The cross_val_score() method makes use of neg_mean_squared_error as an evaluation metric (scoring parameter) and K-fold as the cross-validation procedure. Here, we randomly split the training set into 10 distinct subsets called folds. So the K-Fold cross-validation feature can train and evaluate the model 10 times by picking a different fold each time and training on the other 9 folds.

You can notice that the decision tree has a mean prediction error of $72002, whereas the linear regression score is $72933. The Random Forest Regressor seems to be a promising model with a prediction error of $49642.

Once you have identified a promising model, you can fine tune the particular model and increase the model performance .

Advantages of K-Fold Cross Validation

• It has a great positive impact on reducing underfitting and overfitting. It considers most of the data for training and validation.
• Model performance analysis on each fold helps to understand the variation of input data and also provides more insights to fine-tune the model.
• It can efficiently handle unbalanced data and be used for hyperparameter tuning.

Disadvantage of K-Fold Cross Validation

• The approach can be computationally expensive.

Apart from K-Fold cross-validation, there are a few other variations of K-Fold techniques. A few of them are:

• Repeated K-Fold : It can be used when one requires to run KFold n times, producing different splits in each repetition.
• Stratified K-Fold : It is variation of K-Fold which returns startified sample
• Group K-Fold :It is a variation of k-fold which ensures that the same group is not represented in both testing and training sets.
• StratifiedGroupKFold : It is a cross-validation scheme that combines both StratifiedKFold and GroupKFold.

We have discussed the importance of K-Fold cross-validation technique in machine learning and gone through how it can be implemented using Sklearn. Hope you understood how K-fold methodology can increase model performance by avoiding overfitting and underfitting. We also analyzed the performance of different regression models, which helped us choose the most promising model for prediction.

Q. What is K in K fold cross validation?

K represents the number of folds, or subsets, into which the dataset is divided for cross-validation. Based on the K value, the dataset splits and creates k number of folds. For example, if k = 10, then it becomes 10-fold cross-validation.

Q. Is there any formal rule to choose K value?

There is no formal rule, but normally one performs k-fold cross-validation using k = 5 or k = 10, as these values suffer neither from excessively high bias nor from very high variability.

Q. What are the factors to consider while choosing K value?

The value of k is chosen in such a way that the data samples (train/test) should be large enough to represent the broader dataset. Choosing k as 10 results in a model with low bia and modest variance. Choosing k as n (n = size of the dataset) ensures each sample is used in a holdout dataset. This is called leave-one-out cross-validation.

Q. What is cross_val_score() method?

The corss_val_score() method evaluates a score by cross-validation, and it returns the array of scores of the estimator for each run of the cross-validation. Here we can specify the cross-validation procedure to validate the model (in our case, it is k-fold).

Please Login to comment...

Similar reads.

• AI-ML-DS With Python
• Data Science Blogathon 2024
• Machine Learning

Improve your Coding Skills with Practice

What kind of Experience do you want to share?