Regulatory guidance regarding controls for electronic records include requirements for implementing appropriate controls to ensure the integrity of data. There are multiple areas that the organization needs to review to ensure data integrity controls are established to comply with FDA and other health authority guidance. The GMP guidance states that you need to implement appropriate controls to prevent and detect data integrity issues. That involves controls across the entire lifecycle. The organization needs to look at everything from the actual implementing and validating of the systems, as well as the controls that are needed when configuring, implementing, or installing these systems. Many times, the controls are related to security in terms of accessing the system, user roles that are established for the system, and configuration of the roles along with the system. There are also processes that have to be evaluated in terms of how the data is collected.

The most common challenges organizations face in this area have to do with inadequate controls over stand-alone instruments and systems where they are not networked. Also, the security of these systems are not in place or not strong enough. With respect to security, it is not uncommon to see people sharing a single account to access the systems. This is mainly due to a specific group in the organization being small, but constantly using the instrument or system. This is done for ease of use within the group, however, having this shared account is not traceable to who performed the activities in the system.  It is therefore not a best practice for data integrity and the organization needs to figure out how to better control this aspect of security and access.

There are  different types of systems in an organization that need to be reviewed holistically and relative to the process of data integrity and not just one specific process or validation area.  The organization needs to perform a holistic assessment of the controls applicable to each system to establish the appropriate process and system usage policies. .

2. Lack of adequate User Management Procedures

Many times there is no real procedure or process to periodically review the user roles or the users in the system to ensure that they are still assigned the adequate authority or that they should no longer have access to the system. Because this may happen sometimes, there should be a periodic review and procedures for that aspect. Procedures should also exist to document as to what should happen if someone leaves a company or changes roles.  When a role change occurs, it should be documented and recorded, ideally within one to three days depending upon the process or the procedure. This also needs to be double checked and on a yearly and more frequent basis for more privileged roles like administrators.

The organization needs to have defined procedures and documentation to support authorizing a user at a certain level. These procedures should discuss actual process steps / activities / tasks that a specific type of user performs and should be periodically reviewed to reflect changes in process steps / procedures.

3. Inadequate Audit Trail Review

Data integrity issues also revolve around audit trail challenges.  It is common to have software systems that provide audit trail functionality, especially with the leading systems in the industry. The challenge is understanding what to actually do with the audit trails. Most of the time organizations fall into a routine of just setting it up and forgetting about it. The organization knows it is there, however, they don’t review it. The organization needs to know what is being captured and what isn’t. The organization needs to have someone or a group review particular aspects of the audit trail and do it on a specific schedule. Also understand what parts of the data is being submitted to the quality group for review.

4. Lack of Accountability

If an organization has multiple teams or departments relying on the same data, it’s not always obvious who is ultimately accountable for the integrity of that data. This can sometimes lead to challenges and finger-pointing A better approach is to appoint individuals as data stewards who are responsible for data integrity. This will ensure that there is a sense of accountability for the data.

5. Lack of training

Data accuracy is commonly effected by user error. If users have not been trained consistently, or not trained at all, they can be introducing errors into the data. What is needed is a comprehensive training process to ensure data is handled and entered in properly.

Summary

Organizations are faced with challenges to ensuring data integrity. These challenges impact all aspects of data integrity including data accuracy, validity, and consistency. Given the importance in the Life Sciences industry of data to product quality and patient safety, it is imperative that businesses focus on how to resolve any challenges impacting data integrity. The key challenges are in areas where organizations can make changes to improve. They include identifying and implementing appropriate controls over electronic records, having adequate user management procedures, ensuring audit trail reviews are performed, ensuring someone in the organization has accountability for the data, and that proper training is given to those working with this important company asset.

Why It Matters To You

Data Integrity is an imperative to ensure product quality and patient safety, however, organizations face challenges in this area to ensure that the accuracy, validity, and consistency of data is maintained. In this blog we discuss:

• The top challenges organizations face with ensuring data integrity.
• How user management procedures impact data integrity.
• Why accountability is imperative to data integrity.
• How the audit trail plays a role in data integrity.

About Astrix

For over 25 years, Astrix has been a market-leader in dedicated digital transformation &  staffing services for science-based businesses.  Through our proven laboratory informatics, digital quality & compliance, and scientific staffing services we deliver the highly specialized people, processes, and technology to fundamentally transform how science-based businesses operate.  Astrix was founded by scientists to solve the unique challenges which science-based businesses face in the laboratory and beyond.  We’re dedicated to helping our clients speed & improve scientific outcomes to help people everywhere.

The post Top 5 Challenges Impacting Data Integrity in Life Sciences appeared first on Astrix.

Data Quality versus Data Integrity

Data Quality refers to the traits that determine the ability of information to suit an intended function, such as planning, decision making, and operations.

Data Quality is evaluated based on whether it is complete, unique, timely, valid, and consistent.

The data has to be:

• Complete – representative of a large percentage of the total amount of data being processed.
• Unique – free of redundancy and extraneous entries.
• Timely – up-to-date as possible.
• Valid – syntax and structure that is defined by the needs of the business.
• Consistent – represented and stored in a standard acceptable framework.

Data Integrity is determined by factors such as data accuracy, validity, and consistency over time.

• Data Accuracy – data values stored for an object are the correct values.
• Validity – delivery of clean and clear data to the programs, applications and services using it.
• Consistency – each user sees a reliable view of the data, including visible changes made by the user’s own transactions and transactions of other users.

It refers to the lack of unwanted data changes between two updates or modifications to data records. Data integrity is the complete opposite of data corruption, which causes information to be inadequate in meeting intended data needs.

In order to achieve Data Integrity the organization needs to focus on integration, quality, location intelligence, and enrichment. With respect to integration, the data must be effortlessly integrated into a single perspective that can provide organizations with rapid and increased visibility. The data also needs to adhere to acceptable quality standards – complete, unique, valid, timely, and consistent. By utilizing location intelligence, this adds a layer of richness and complexity that provides greater insight and analytics which makes the data much more actionable across the organization. And finally, the enrichment of the data by leveraging external sources of information helps add perspective and meaning.

Why is Data Integrity Important and What are the Benefits

Data Integrity and data quality should both be viewed as top business priorities across Life Sciences organizations. As a result, it’s critical to ensure data quality while also retaining data integrity. Businesses are exposed to a number of dangers if they do not preserve data integrity or ensure data quality. In decision-making and other data-driven business processes, bad data leads to more human error and could potentially impact the profitability of the organization. When businesses talk about data integrity vs. data quality, there needs to be a concerted effort to instill a culture of quality and compliance as well as adherence to industry leading practices through proper training across the organization. .

Data Integrity is important to the organization as it ensures and secures the searchability and traceability of the organization’s data back to its original source as well as establish and showcase the compliance posture of the organization. Maintaining the integrity of data and ensuring its completeness and security is also essential for the organization to have confidence that the data is reliable and has not been compromised.

As the organization collects more and more data it becomes an even higher priority to secure and maintain the integrity of that data. Without integrity and accuracy, the data is worthless. Data loss, corrupted or compromised data can considerably damage the business – as is evident from the many instances across the globe relating to data loss and compromised data.

Summary

Data Quality and Data Integrity are different aspects of data. Data Quality refers to whether or not the data is complete, unique, timely, valid, and consistent, Data Integrity refers to data accuracy, validity, and consistency over time. Both should be viewed as a top priority by the business. The organization is exposed to a significant vulnerabilities if it does not preserve Data Integrity or ensure Data Quality.

Data Integrity is important because it ensures and secures data so that it is searchable and traceable. The benefit that Data Integrity brings to the organization is confidence that the data is complete and secure and has not been compromised. Without the integrity and accuracy, the organizations data is ineffective.

Why it Matters to You

Data Integrity is important to Life Sciences organizations because it ensures confidence that the data is complete and secure, and has not been compromised. The quality of data effects product quality and patient safety, so it is critical to safeguard data integrity.

In this blog we discuss:

• The difference between Data Quality and Data Integrity.
• Key attributes of both Data Quality and Data Integrity.
• Why Data Integrity and Data Quality are important to the organization.
• The key benefits of the organization of Data Integrity.

About Astrix

For over 25 years, Astrix has been a market-leader in dedicated digital transformation &  staffing services for science-based businesses.  Through our proven laboratory informatics, digital quality & compliance, and scientific staffing services we deliver the highly specialized people, processes, and technology to fundamentally transform how science-based businesses operate.  Astrix was founded by scientists to solve the unique challenges which science-based businesses face in the laboratory and beyond.  We’re dedicated to helping our clients speed & improve scientific outcomes to help people everywhere.

The post Why Data Integrity is Important to Life Sciences Organizations appeared first on Astrix.

Source: Special Interest Briefing on Validation 4.0: Objectives, Working Model, and Relation to QBD

With that in mind, there is a need for a framework that assists in the transition to Validation 4.0 by showing how it maps to the PLC. A Validation 4.0 model highlighting this mapping was discussed in June of 2021 on an ISPE webinar on this topic. This model is the foundation for incorporating Validation 4.0 into the organization. It reviews the PLC stages along with the inter-dependency of some of the activities across the phases.

Product Life Cycle

PLC goes from Discovery, to Development, to Commercialization, and then to Post Market. The Validation life cycle has corresponding steps that provide data and feedback to ensure success. Quality by Design (QbD) attributes such as the Critical Quality Attributes (CQAs), the Critical Process Parameters (CPPs), the Critical Material Attributed (CMAs), and other key considerations would need to be incorporated into the PLC to provide the required visibility as well as monitoring of the overall process.

Quality By Design

Utilizing digital tools as an enabler across all stages of the validation process provides ready access to critical variables, parameters and requirements as they change throughout the process. It is important to note that QbD is not a signal step of the process but rather an iterative approach we use in order to learn more about our product and processes. With QbD, we accumulate information that assists in better understanding the products and processes so as to improve efficiency and optimization across all stages.

Planning & Control Implementation Stage

Stage 1, in the Validation 4.0 life cycle is the planning & control implementation phase. This maps to the discovery and development stage of PLC. In this stage, the organization’s user requirements are captured through process maps and data flows. This involves a digitization of the process leveraging technology. In this stage, the requirements that provide a better understanding of what the organization needs from a process and products perspective are contextualized. Additionally, a risk based assessment is applied that is associated with the process maps and data flows. This is where the risk is measured as it impacts product quality and patient safety. Criticality and vulnerability of the risk are both key considerations relative to this area.

Controls Stage

The Implementation of Controls is the next stage, Stage 2, in the Validation 4.0 Life Cycle. In this phase, we have digital tools in place to collect, report, and act on critical data in real-time.

Verify Controls Stage

The following stage, Stage 3, is the Verify Controls phase. In this stage, the critical data from the on-going process are continually monitored to provide opportunities for optimization as well as implement any changes to the system based on performance monitoring. The digital monitoring is to ensure that the design of the system to control the process and data is providing an acceptable level of risk. Data is leveraged for continuous improvement and optimization of the process. A continual feedback mechanism is required where the data is collected and analyzed in real-time so that the appropriate action on any discrepancies can be taken.

Summary

In order to successfully incorporate Validation 4.0 into the business, there needs to be an understanding of the Product Life Cycle (PLC). Additionally, the requirements associated with the processes and data need to be digitized so that they can be measured throughout the life cycle. This is required in order to ensure that the product quality and patient safety is kept at the highest level. To do this, there is a need for a Validation 4.0 model that maps to the PLC that can be leveraged to assist in ensuring the monitoring of the data and processes via controls. This model needs to incorporate the appropriate technologies that help to report, control, and improve the validation process across the life cycle. The validation process also needs to be continuously monitored to verify that standards are kept in place.

Why It Matters to You

Validation is the key to ensuring that product quality and patient safety are kept at a high standard. Validation 4.0 takes validation to the next level by ensuring that the processes are digitized and has the proper controls throughout the Product Life Cycle. In this blog we discuss:

• A Validation 4.0 model that assists in controlling product quality and patient safety.
• How the Validation Life Cycle maps to the Product Life Cycle.
• How data, processes, risk, and controls play a part in the Validation 4.0 model.
• Key elements of the Validation Life Cycle to consider to help ensure success.

About Astrix

For over 25 years, Astrix has been a market-leader in dedicated digital transformation &  staffing services for science-based businesses.  Through our proven laboratory informatics, digital quality & compliance, and scientific staffing services we deliver the highly specialized people, processes, and technology to fundamentally transform how science-based businesses operate.  Astrix was founded by scientists to solve the unique challenges which science-based businesses face in the laboratory and beyond.  We’re dedicated to helping our clients speed & improve scientific outcomes to help people everywhere.

The post Validation 4.0 Model for a Digital Transformation of the Life Sciences Industry appeared first on Astrix.

Challenges Impacting the Implementation of Pharma 4.0 and Validation 4.0

People, Culture, and Skillset Challenges

Manufacturers and regulators will need to make cultural adjustments and innovate to manage the myriad of data, computing, and automation hazards on the way to full adoption of Pharma 4.0 and Validation 4.0 guidelines and the technologies required to get there. In order to achieve optimization, knowledge and training gaps will have to be addressed in the adoption of a new paradigm and industry infrastructure based on digitized and interconnected enterprise systems that rely on computational power, communications technologies, cybersecurity, and advanced controls.

For example, to implement AI and other advanced technologies in pharmaceutical manufacturing, a variety of expertise beyond traditional biology, chemistry, and process engineering will be required. Data scientists, computational and systems engineers, IT specialists, and AI experts, for example, will most likely be needed. At least initially, regulators and business may be fighting for the same tiny pool of talent in these areas. New labor force training standards are undoubtedly on the way, and comprehensive training programs will be required.

There will also be a need to understand the operation of smart devices that will enable monitoring and operating from a distance by operators and supervisors. These technologies will simplify the changeover, setup, and maintenance and the life cycle management. There will also be a need for the workforce to operate cross functionally, integrating several disciplines.

Incorporating best practices and new technologies into the business is required. The difficult part is changing the culture and persuading individuals to accept a new way of doing business after having done things a certain way for many years. Organizational Change Management will be necessary (OCM). OCM is the “application of a systematic process and set of tools for driving the people side of change to accomplish a desired outcome,” according to Prosci, the global leader in change management best practices research. Additionally, strong communications, with a leadership team eager to drive positive experiences through technology, will be required.

Planning and Process Challenges

Because the potential for Pharma 4.0 is so great and so wide-ranging, many pharmaceutical companies are racing to implement the technology and haven’t defined their primary goals and what problems they’re seeking to solve.

If these two important questions are not answered before starting down the path to Pharma 4.0, the direction will be unclear, and many firms will lose sight of their objectives and goals. The organization’s business processes will undergo significant changes as a result of the major technologies required for digital transformation, and those changes must be acknowledged and understood before they are implemented. The company needs to have a strategy and a plan to get there.

Pharma 4.0 and Validation 4.0 require an holistic control strategy. A strategy that follows the product from research to development, to tech transfer, and then through to commercial manufacturing. With this strategy there also needs to be a synergy between digital automation and guidelines and an enhancement of the quality manufacturing focus, where Quality Target Product Profiles are required for all products. With this control strategy, the data from machines and components is immediately available without traversing various systems . The operators’ remarks can be automatically recorded making the continuous improvement process easy to implement.

Technology Challenges

Transitioning to Pharma 4.0 and leveraging Validation 4.0 guidelines requires incorporating new technologies and ensuring that everything works together to produce reliable data across the value chain. The technology needs to provide a means to integrate data and eliminate the silos that may exist throughout the organization today. These data silos can exist across process areas, applications being used, and organizational groups. A few examples are business quality data, IT quality data, product quality data, and supplier quality data. In many situations, the data is not being collected using the same method and this incongruent data has a negative impact on the decision process. Many times, this leads to inefficiencies and sometimes costly inaccuracies.

Organizations need to look at technology solutions across the value chain. From the company’s business partners and the customer perspective, the organization needs to be able to make informed decisions based on data compiled from various sources and to communicate specific aspects of that data accurately to suppliers, vendors, and customers.

Innovative technologies also necessitate new processes and more regulatory scrutiny. Manufacturers must remain nimble and able to adopt new skills in order to improve manufacturing and provide high-quality products.

To connect the essential instruments and equipment, the technology including tools, devices, and IT systems must be on an open platform with common data standards and, most likely, be cloud-based to enable data sharing. Integration and traceability, as well as automation, rely on information systems that eliminate needless manual or human interaction while also lowering regulatory scrutiny. Leveraging the right technology across the organization that is integrated and provides consistent and accurate information is imperative.

Operating within existing regulatory frameworks can be considered another challenge for Pharma 4.0 and technical innovation when it comes to regulatory restrictions. Due to a lack of regulatory precedent, the industry may continue to use old practices even though new processes will lower overall regulatory burden and improve quality in the long run.

Summary

Moving to Pharma 4.0 and incorporating Validation 4.0 guidelines impacts the entire organization. There are challenges that influence the success of an organization meeting its objectives with Pharma 4.0. These challenges are in the areas of people, processes, and technology. To move to this new approach, it will require training of the people and a cultural shift to be successful. It will also involve an understanding of the objectives of the move to Pharma 4.0 and the processes involved and an holistic control strategy. Additionally, with Pharma 4.0, there is also the technology aspect. This requires an understanding of the new technologies so that everything works together to produce reliable data across the value chain.

Why it Matters to You

Before an organization embarks on an initiative to implement Pharma 4.0 and Validation 4.0 guidelines into the business, there needs to be an understanding of the areas that could provide challenges. These challenges impact the success of the organization. In this blog, we discuss:

• The key challenges faced by organizations looking to implement this new approach.
• The impact of training and cultural on the move.
• What a Holistic Control Strategy role is in the implementation.
• Technology consideration when implementing this new approach.

About Astrix

For over 25 years, Astrix has been a market-leader in dedicated digital transformation &  staffing services for science-based businesses.  Through our proven laboratory informatics, digital quality & compliance, and scientific staffing services we deliver the highly specialized people, processes, and technology to fundamentally transform how science-based businesses operate.  Astrix was founded by scientists to solve the unique challenges which science-based businesses face in the laboratory and beyond.  We’re dedicated to helping our clients speed & improve scientific outcomes to help people everywhere.

The post Pharma 4.0 and Validation 4.0 – The Challenges of Implementing appeared first on Astrix.

The Benefits of Validation 4.0

Validation 4.0 ensures new technologies are adopted and validated to meet patient safety and product quality criteria and follow the Pharma 4.0 operating model. Given that Validation 4.0 enables Pharma 4.0, there are a number of benefits that Life Sciences organization will see including:

• Improved Quality – By focusing on the critical thinking and the risk management required to validate the various technologies like smart devices within the ecosystem, it assists in ensuring optimal quality is met. The validation includes visibility into the data exchanged through support networks of suppliers, CDMOs, CMOs, and other external organizations that are part of the value chain.  By incorporating the guidelines of Validation 4.0 organizations can ensure a high level of data integration across the organization as well as with suppliers. It can also enhance visibility and resiliency of the value chain.

• Lowers the Cost of Operations –  In leveraging new technologies as part of the Pharma 4.0 framework and incorporating the guidelines of Validation 4.0, the organization can lower the overall cost of Quality across the organization.

• Lowers the Risk By leveraging Validation 4.0 strategies, the organization can also lower the overall risk. By incorporating the new technologies of Pharma 4.0 and leveraging Validation 4.0 guidelines the organization can better mitigate risk. This requires an approach that focuses on incorporating design aspects and controls into the value chain at various phases and continuously rather than a holistic check towards the end of the process, thus attempting to control and mitigate risks throughout the process.

• Provides a Faster Time to Market Validation 4.0 also assists in helping the organization to get products out to market faster. By leveraging technologies of Pharma 4.0 and the Validation 4.0 guidelines, the organization can better control the quality and safety in real-time and manage any deviation, thereby augmenting a faster time to market of products.

• Better Visibility Across Value Chain By incorporating the new technologies of Pharma 4.0  along with the guidelines of Validation 4.0, the organization will have a complete and concise view across the value chain in order to ensure better control of the various internal and external processes.

Summary

By leveraging the guidelines of Validation 4.0 the Life Sciences organization can receive significant benefits. It can improve quality of the products along with  lowering the cost of operations. There will also be a lower risk given the focus on controls throughout the value chain and at various phases to continuously check for issues. Additionally, the organization will have a quicker time to market by leveraging new technologies and Validation 4.0. Another significant benefit, will be the visibility across the value chain to ensure things are running smoothly from both an  internal and external perspective.

Why it Matters to You

When implementing any new technology or guidelines, it is critical to understand the benefits you will receive by doing so. Validation 4.0 is a key component of Pharma 4.0 and will provide significant benefits to any Life Sciences organization. In this blog we discuss:

• The key benefits of Validation 4.0 to the Life Sciences organization.
• How the organization’s product quality and product safety are impacted.
• How it lowers the cost of operations and the risk level.
• How it improves the time to market and visibility across the value chain.

About Astrix

For over 25 years, Astrix has been a market-leader in dedicated digital transformation &  staffing services for science-based businesses. Through our proven laboratory informatics, digital quality & compliance, and scientific staffing services we deliver the highly specialized people, processes, and technology to fundamentally transform how science-based businesses operate.  Astrix was founded by scientists to solve the unique challenges which science-based businesses face in the laboratory and beyond.  We’re dedicated to helping our clients speed & improve scientific outcomes to help people everywhere.

The post Validation 4.0 – The Benefits to the Life Sciences Industry appeared first on Astrix.

Quality by Design

The more the organization understands regarding the key aspects of their products and processes, the better they can concentrate their efforts towards finding and implementing appropriate controls to improve quality. The approach should focus on incorporating design aspects and controls into the value chain at various phases and continuously rather than a holistic check towards the end of the process, thus attempting to mitigate risks throughout the process.

Data Integrity by Design

Data is centric to the organization. It needs to be visible to those that need to access it and leverage it across the organization. Validation is impacted by bad data and specifically when data is not integrated across functions of the organization. Integrated data is critical to Validation 4.0.

Integrated Environments

Current technologies like IoT have provided a means to capture and make visible key information across the entire value chain. This information is key to ensuring Validation 4.0.

Modern Documentation

A critical area to consider regarding validation is documentation. Going away from a paper-based approach to a digital approach where all the data is centralized versus in multiple places is imperative

The post Validation 4.0 and Quality 4.0 in the Life Sciences Industry appeared first on Astrix.

There are several important factors to consider to ensure a successful transition to CSA. They are focused on those areas that can provide a major impact to your CSA implementation.

• Leveraging Agile Methodologies

  • Leverage Scrum – Scrum is an approach that relies on teams working together in short phases, enabling rapid feedback, continual improvement, and fast adaptation to change to accomplish an objective. By incorporating this agile approach with the CSA implementation the organization will be able to bring in efficiencies and optimization into their processes supporting CSA.
  • Test-driven development (TDD) – TDD is a software development methodology that focuses on establishing unit test cases before writing actual code. It’s a method that combines programming, unit testing, and refactoring in an iterative. TDD will enable organizations and teams to reduce the amount of documented testing that would need to be performed as part of the system release.
  • Follow Behavioral-driven development- BDD is an agile software development process in which an application is documented and designed around the behavior that a user expects to see when interacting with it. BDD will help reduce the rigor and the complexities of developing test cases from the requirements alone – incorporating the learnings from the BDD sessions into the testing phase will greatly reduce the time for the testing phase as well as bring in efficiencies.
  • Introduce early testing techniques – By incorporating configuration and experimentation in lower environments to find defects early, organizations can improve process efficiencies across the SDLC as well get to release faster.

• Leveraging automation and digital technology

  • Continuous Integration – The method of automating the integration of code changes from various contributors into a single software project is known as continuous integration (CI). It’s a key DevOps best practice that allows developers to merge code changes into a common repository, from which builds and tests can be executed. Using this approach, organizations can test more holistically and focus more on the integration testing rather than the functional testing.
  • Continuous Delivery (CI/CD) – This refers to the ability to securely and swiftly deploy updates of any kind, such as new features,  configuration changes, bug fixes, and experimentation, into production or into the hands of users. By adopting CI/CD into the deployment / release processes, quality is incorporated and hence tested at various phases of the process, thus reducing the final testing that needs to be done prior to the release of the product. This further results in a reduction of the workload for the Quality team.
  • Employ Automated Controls and Quality Management System (QMS) tools – By leveraging automated controls throughout the organization the quality function can receive feedback quickly from systems that are able to collect that data and provide it in a consolidated fashion. A QMS or Enterprise Quality Management System (EQMS) is an important component to any digital quality framework. The objective of EQMS is to manage content and business processes for quality and compliance across the value chain. This EQMS platform integrates with the IT architecture and data model and facilitates cross-functional communication and collaboration.

It is essential that the EQMS is not siloed. Quality information should be collected as data and leveraged across the organization to make informed decisions.

The EQMS has to also have an interface to other systems whether it is ERP, PLM,  supplier quality, vendor management, or other enterprise systems integral to the organization. Those interfaces are critical because that is where data resides, and access to that data is vital for decision making.

EQMS also needs to be mobile. It can’t be at one particular location or region or within one area. The EQMS has to provide the ability to look at data wherever, whenever, and however needed. Having this visibility to the pertinent data allows teams to make decisions faster as well as implement controls that prevent issues and non-conformances further in the processes.

Conclusion

As we’ve outlined, there are multiple methodologies and technologies to consider when the organization is looking to transition to CSA. By leveraging the right mix of tools and methodologies, the organization’s move to CSA will result in lower risks during the transition.

Additionally, it is equally important to have the right skill sets (internal and external) to assist with implementing these approaches and tools.  Knowing the various methods and technology and being able to apply them are effectively two distinct requirements.

Why it Matters to You

Organizations making the transition from Computer System Validation (CSV) to Computer Systems Assurance (CSA) will benefit from this information in the following ways. It will:

• Assist in identifying and implementing efficiencies and optimization of the processes supporting CSA.
• Enable organizations and teams to reduce the amount of documented testing that would need to be performed as part of the system release.
• Provide a way to greatly reduce the time for the testing phase as well as provide for efficiencies.
• Enable tests to be done more holistically and to focus on the integration testing rather than the functional testing.
• Reduce the final testing that needs to be done prior to the release of the product.

About Astrix

For over 25 years, Astrix has been a market-leader in dedicated digital transformation & dedicated staffing services for science-based businesses.  Through our proven laboratory informatics, digital quality & compliance, and scientific staffing services we deliver the highly specialized people, processes, and technology to fundamentally transform how science-based businesses operate.  Astrix was founded by scientists to solve the unique challenges which science-based businesses face in the laboratory and beyond.  We’re dedicated to helping our clients speed & improve scientific outcomes to help people everywhere.

The post Computer Systems Assurance – Methodologies and Technologies to Consider appeared first on Astrix.

Cloud technology is no longer thought of as an elusive and uncertain technology environment. The cloud has become mainstream with a computing infrastructure offering extensive computing power, copious data storage, scalability, stability, and a reduced data security risk. Given its benefits across the organization, the quality function is a perfect area to leverage this technology to improve operational efficiencies.

The Cloud’s Impact on the Quality Function

Moving certain key aspects of the quality function to the cloud provides life sciences organizations the ability to centralize data, become more flexible, and enables the empowerment of the employees. Cloud-based quality management systems provide a means to centralize data and better integrate systems, to provide flexibility in operations, as well as a means to ensure knowledge sharing and remote capability.

Enables Centralization of Data and Systems

By leveraging the cloud and centralizing the quality data of the business, it standardizes information and also provides greater visibility into the data across the organization. It also has the ability to fundamentally transform the business.

By centralizing the quality data, it establishes a shared method of interaction across the business and eliminates the confusion that is caused by different locations and groups having their own systems, naming conventions and standard operating procedures.

System-to-system integration is also facilitated along with streamlined reporting and data analysis. With standardized naming conventions, the quality management system can easily collect data across various facilities without sophisticated database requests. This provides for the ability to gain additional insights on the quality of processes and systems across the enterprise.

Provides Flexibility To Adapt to External or Internal Factors

All companies strive to maximize flexibility with their operations so that they can change direction quickly when either internal or external factors affect the organization. Businesses want to have the ability to adjust to demand increases or decreases and hopefully safeguard profitability. A cloud quality management system provides this agility and flexibility. The system can provide the essential data people need to collaborate and work efficiently across functions and geographies. By incorporating a cloud quality management system, you gain maximum flexibility in pursuing the organization’s quality goals.

Provides for Cross-Pollination of Knowledge

Employees of the organization attain knowledge of processes and systems over the years. For example, Plant floor supervisors who have worked in the organization for years, have an intuition as to how to run their production operations. This knowledge is engrained in these experienced employees.

With a cloud-based quality system, that experience and knowledge can be transformed into a digital knowledgebase. In cloud-based quality systems, it is simple to capture and convert that inherent knowledge into explicit rules, procedures, processes, and workflows to ensure that they are consistently applied. This cross-pollination enables the organization to keep the processes running when faced with issues such as resource availability, system downtime, etc.

Facilitates Remote Operations

Providing the ability for employees to work remote is becoming an important reality. Most manufacturers are limiting the number of employees on the shop floor at one time. They are separating the floor into zones and limiting who can enter each zone. With this type of setup, it might require that a production supervisor do their job from an office on site without physically walking the floor.

Cloud-based Quality management systems of today provide the means to do this. Many also integrate with other plant software systems.

Easily Deployed and Maintained

An on-premises quality system requires hardware, licenses, and infrastructure. With a Cloud-based system, a majority of the overhead is eliminated. You only need a computer and to buy subscriptions to use the cloud-based system. The deployment is extremely easy from the user setup perspective.

With a cloud-based quality system, updates are done without a major interaction of company resources. Additionally, there are no servers to purchase and setup and no software to install. There is also no requirement to do time-consuming software upgrades. The organization always has the latest and greatest software version on any device with a browser.

Conclusion

The cloud provides a way for organizations to significantly improve their quality function. By leveraging the cloud a business can centralize their data and integrate their systems to improve operations and reporting.

Cloud applications also provide for flexibility in operations, cross-pollination of information, and the ability for employees to easily work remotely. Additionally, cloud based quality management systems require the least IT resources and cost, and are incredibly easy to deploy and maintain.

Why It Matters to You

Life Sciences organizations have an opportunity leveraging the cloud-based quality management systems to:

• Lower their cost of operations
• Centralize data and better integrate systems and processes
• Improve reporting across the organization
• Improve flexibility of the quality function and cross-pollination of knowledge

About Astrix

For over 25 years, Astrix has been a market-leader in dedicated digital transformation & dedicated staffing services for science-based businesses.  Through our proven laboratory informatics, digital quality & compliance, and scientific staffing services we deliver the highly specialized people, processes, and technology to fundamentally transform how science-based businesses operate.  Astrix was founded by scientists to solve the unique challenges which science-based businesses face in the laboratory and beyond.  We’re dedicated to helping our clients speed & improve scientific outcomes to help people everywhere.

The post Quality Management in the Cloud The Technology Advantage and How it Impacts the Quality Function appeared first on Astrix.

• Determine how the software will be used in the organization

Is the software affecting the quality of the product or the safety of the patients? If this isn’t the case, you won’t need the same level of assurance as you would for software that affects the product or patient safety. Document management, change management, and audit management software, for example, do not require the same level of testing as device software.

• Determine if there is the potential to influence product quality, patient safety, or system integrity.

CSV validation of computer systems focuses on evaluating the severity of the impact and the likelihood of failure, which leads to risk prioritization. Conversely, with CSA, the emphasis is on calculating the risk/impact on patient safety and product quality, as well as the implementation approach of the software program functionality.

The first step is to determine the degree of impact of the specific software on patient risk and product quality. If the risk is high in one or both of these areas,  more time should be spent in the testing phase. If the impact is very low on these areas, testing can be reduced.

When looking at the software itself, there are different degrees of implementation risk depending on the software’s origin. If the software is out-of-the-box with the pertinent documentation, then it should most likely be of lower risk.  If however there is a need to configure or completely customize the software, there is then a significant increase in the risk  to the situation.

• Wherever feasible, make use of vendor documentation.

If the software vendor’s documentation is audited and validated, then there is no need to reproduce this documentation. Use the vendor’s documentation and validation if it is of high quality, based on an initial assessment of the vendor provided documentation.

• Based on risk, conduct the specific level of testing required for that system or function.

With CSA, which is a risk-based approach, the focus is on critical thinking and professionals determining which systems and functions provide the greatest risk to patient safety and product quality. The highest level of testing should be applied to these areas. To those other low to no risk areas a significantly lower level of testing should be applied.

Testing with Computer System Assurance

Now that we’ve defined the process to follow to determine which areas are high-risk to patient safety or product quality, the next question to answer is, how is testing performed in a CSA context?

In the past, test scripts were written in great detail and were not as concerned with whether or not the system and its functions had a direct impact on patient safety or product quality. With CSA, this has changed. There are now new approaches to testing i.e., Scripted Testing , Unscripted Testing, and Ad-hoc Testing.

Scripted Testing is the traditional form of testing that has been done with CSV. It requires a step-by-step test procedure, required results, and it is pass/fail. However, the difference with CSA is that Scripted Testing is only applied to higher risk systems and features that directly impact the product or patient safety.

Unscripted Testing is less detail oriented than Scripted Testing. It is used to test the lower risk systems or functions that do not directly impact the product or patient safety. They do however impact the quality system. With Unscripted Testing there needs be a test goal and a pass/fail, however, there is no step-by-step test procedure.

Ad-hoc Testing – a third testing method can be used on those systems and functions that are low business risk to the quality area. This testing is performed without planning and required documentation.

Summary

The new CSA risk-based approach to systems validation requires the professional to spend more time focused on critical thinking and less time on documentation. The objective is to focus on those areas that have the largest impact to patient safety, product quality, or the quality system overall.

With this new process, CSA involves different levels of testing. Scripted Testing is used for those systems and functions of high risk to patient safety and product quality. Unscripted Testing is used for those systems and functions with low impact to these areas, however, they do have an impact to the quality system. And finally, Ad-hoc testing is used on those systems with low risk to the business.

Why it Matters to You

This new CSA risk-based approach is important for you to learn about because:

• It will potentially lower the cost of quality by requiring less time in testing and documentation.
• It will drive the team to achieve higher, quality, and productivity.
• Helps to maximize the use of validation and project resource expertise.

 About Astrix

For over 25 years, Astrix has been a market-leader in dedicated digital transformation & dedicated staffing services for science-based businesses.  Through our proven laboratory informatics, digital quality & compliance, and scientific staffing services we deliver the highly specialized people, processes, and technology to fundamentally transform how science-based businesses operate.  Astrix was founded by scientists to solve the unique challenges which science-based businesses face in the laboratory and beyond.  We’re dedicated to helping our clients speed & improve scientific outcomes to help people everywhere

The post Computer Systems Assurance – What are the Steps and How to Test? appeared first on Astrix.