Using reliable and well-built software tools can help reduce costs. Kneat, for example, provides a comprehensive solution built to drive Good Manufacturing Practices.
Getting senior stakeholders involved and excited about digital validation involves demonstrating clear benefits to their fundamental interests. For IT, this might mean reducing cycle time or costs; for Quality, it might be around data integrity and audit trails; for Finance, it could be about ROI.
Real-time Data Accessibility
Table of Contents
A validation software solution or program helps you to validate data as it is being collected quickly. This prevents errors and ensures that all information is high quality and valid. This helps to reduce data-related costs, improve decision-making and business operations, and mitigate industrial and cybersecurity risks.
Using state-of-the-art software validation applications allows life science companies to quickly capture data from instruments and equipment and plug it into validation protocols, batch records, and log forms. This saves countless hours of manual processing and dramatically reduces cycle times. It also eliminates the need for duplicate data entry and gives authorized users a real-time view of change impact.
Life sciences regulations – such as FDA 21 CFR Part 11 – require validation of all computer systems impacting product or patient safety. This is known as computer system validation (CSV), and improperly performing it can lead to costly implementation delays, rework, team integration issues, and post-deployment defects.
CSV involves internal and external software testing to verify that stakeholders’ requirements are represented accurately in the software specification. This can be accomplished through either a static checking process, where the conditions are written and assessed in detail, or by releasing prototypes and asking stakeholders to evaluate them.
Reduced User Errors
The most cost-effective way to reduce user errors is through validation. Whether you use a point-in-time validation process or an automated Continuous Validation (CV) system, the guarantee will ensure that the data your users enter into your application is correct. This will greatly reduce the number of errors and help your team save time.
Validation will identify incorrect data and the source of those errors so you can address them and prevent future issues. This will save you valuable time as well as money.
When a validation error occurs, it’s important to provide the user with an error message and explain why and how to fix the problem. For example, an invalid entry such as ’13’ in the month field could generate an error if a form field only accepts certain characters. To avoid this issue, including a character check in your validation library is important.
Often, the error messages provided could be more precise, leading to confusion among the user community and a rise in support tickets. This is why it is essential to make sure that your validation library contains error messages that are short and to the point, allowing the user to quickly understand what they need to do to resolve the error.
Increased Management Oversight
Proper validation practices are vital for regulated industries to ensure that products meet quality and performance standards. Managing software validation using paper processes can be error-prone and time-consuming. Digital validation software solutions help to streamline the process and allow teams to focus on other essential tasks.
Managing changes in a validation environment requires careful oversight to prevent impacting the entire system and maintain good documentation practice (GDocP). A company supports best-practice change management by integrating with MasterControl, allowing you to automatically evaluate the impact of new changes on both requirements and the system. You can also track the status of all change requests with a real-time dashboard displaying change history, impact, and the total effort required to complete each request.
Most life sciences companies must follow GxP guidelines for pharmaceuticals, biotechnology, and medical devices when validating a computer system. Failure to perform this critical process correctly can lead to costly rework, team integration issues, and post-deployment defects.
With a lean, automated validation solution, Life Sciences teams can significantly reduce cycle times and documentation burdens while improving productivity and visibility. You can build a strong business case for investing in validation software by demonstrating how the technology enhances compliance and risk-prevention costs.
In an age when a single industrial cybersecurity breach or product recall can ruin a company’s reputation and potentially lead to costly litigation, the need for robust data validation processes is as great as ever. Fortunately, life sciences companies can reduce manual processes and cycle times and improve productivity, visibility, and accountability with a state-of-the-art, lean validation software application that manages the entire validation process from planning to deployment.
To make a case for investing in a digital validation solution, business leaders should start by conducting an inventory of existing tools and quantifying the costs per department associated with each. This can help leaders identify opportunities to cut costs and improve return on investment.
Another key component of a digital validation solution is end-to-end testing. This is essential in complex, data-centric systems to ensure that data flows correctly throughout the system and can be accessed by stakeholders. It also helps ensure that errors are not introduced during the development and testing phases, as a failure to detect and correct a mistake can cost a company significantly in terms of lost revenue.
The best part of automated validation is that it frees up SMEs and staff to focus on more critical initiatives, particularly when the industry suffers from a talent shortage. Moreover, the ability to automatically test software for functionality and compliance before deployment can save development and operating costs in the long run, especially given that bad data is estimated to cost the industry $700 billion annually.