Many organizations combine numerous partners in diverse ways across multiple research projects. Even in simpler models, any collaboration with an external partner is typically not static, but evolves over time. Therefore, sponsoring organizations are often changing the business processes around the collaboration frequently and rapidly.

While external collaboration can provide many benefits including improved flexibility, enhanced innovation, and reduced time-to-market, externalized R&D activity introduces unique data and IT challenges. Some of these include:

• Synchronization of partner and in-house data across different transactional systems
• Maintaining secure and appropriately partitioned data
• Harmonizing master data to facilitate high quality data flow
• Developing appropriate long-term plans for data management, including potential data repatriation in an efficient manner
• Protecting intellectual property (IP) and managing joint IP

These challenges can result in additional costs and be potential limitations on the benefits of external collaborations. At the least, they introduce risks for sponsoring organizations. All too often, unfortunately, these data and information management aspects of a collaboration are not fully considered until problems arise. In this blog, we discuss key characteristics of informatics systems for collaborations, along with best practices for implementing a collaboration platform.

Approaches to R&D Collaboration Data Management

Nearly all R&D informatics systems are designed and implemented only to meet internal R&D requirements. Also, organic growth of internal R&D activities often leads to a tangled web of processes and systems with significant assumptions incorporated into the ecosystem. These latent aspects of systems frequently become impactful when considering the flow of data outside of the R&D organization, and how to open internal systems and/or their data to external collaborators. Some examples of system characteristics important in collaborative data flows are:

• User identity and access management processes and technology
• Data access control models
• Processes that require multiple systems with human-only integrations (“sneakernets”)

Sometimes these limitations make it infeasible to use the internal systems and processes with an external collaborator. Although it may seem more efficient to design our systems with external collaborations in mind, the reality of delivering informatics capabilities on budget and in time almost always means this does not happen. When faced with supporting external collaborations, this leaves the following choices:

• Use the collaborator’s system. If the collaborator is in the business of collaborations, they are potentially more likely to have systems that would meet the challenges above.
• Transfer data in email attachments. This lowest common denominator approach and unfortunately tends to be the status quo.
• Implement a new informatics capability.

There are important sub-aspects to the implementation of a new capability. First is the relationship to the existing system(s). If the current system is meeting requirements and is only insufficient for use in a collaboration, then considering how the system might be extended is an appropriate course of action. If the current system is lacking, or there’s a likelihood of long-term multiple collaborations, then a strategic assessment with the development of a roadmap to a solution architecture that meets all needs is essential.

If either a significant extension of an existing system or a new system is needed, then a cloud-first solution architecture should be considered. Cloud-first systems have several distinct qualities that make them a logical choice to meet the needs for R&D collaboration data management. Specifically, these qualities are:

• Configurable by intent
• Based on a tenant model for data and configuration
• Built for automated deployment

Key Characteristics of a Cloud Collaboration Platform

Some important characteristics of potential candidates for a cloud-based R&D collaboration data management solution are:

Configuration. An ideal platform is highly configurable, allowing organizations to define sites, projects and user roles and, along with the access permissions for each. The user authorization mechanism should be able to incorporate company-specific identity and directory systems for ease of use by scientists, rather than having separate identify and password management for the collaboration system. The platform should also support a range of core R&D capabilities, potentially including some of the functionality of:

• A flexible, multi-disciplinary ELN
• A portal that allows scientists for sharing reports, protocols, and other documents
• Data capture, analysis and visualization capabilities

Deployment. An effective cloud-based platform allows quick creation of separate collaboration environments for use with specific partners. Each environment should represent a secure data  partition. Data from an environment should be extractable and be able to be merged into other environments.

Security. Cloud providers should have ISO accreditation for their systems, technology, processes, and data centers. Data should be encrypted at rest and during transit using well-defined current best practice encryption techniques. The collaboration platform data architecture should have strong isolation across tenants and include logging of all system access and use.

Integration. The cloud platform should have a complete and robust programming interface (API) for integration with the internal systems of either organization in the collaboration. The platform must support bi-directional integration and data syncing between on-premise systems and cloud applications.

Best Practices for Implementing a Cloud Collaboration Platform

There are several best practices that should be followed to successfully implement an effective cloud collaboration platform. These include:

Strategic Planning. One of the most important steps in successfully implementing a cloud collaboration platform is the planning necessary to ensure project success. Towards this end, the first steps in the project should include a thorough workflow and business analysis in order to develop the optimized future-state requirements that guide the technology selection process. In addition, an end-state solution architecture should be developed, along with a strategic roadmap to deployment. Good strategic planning helps ensure the deployment effectively and efficiently meets business and technical needs.

Change Management. It is important to carefully consider the cultural impact, employee training, and new policies that necessary to ensure success of the new collaborative environment. Since collaborative R&D informatics systems by definition involve employees of multiple organizations, attention to change management for these systems is of paramount importance to success.

Efficient Testing. Bandwidth requirements for cloud computing are significant, and load and volume testing are important to ensure that system performs acceptably. Waiting until late in the project to discover that your system is not capable of handling the data transport requirements causes unnecessary scrambling to meet implementation goals.

Effective Validation. As some vendors claim prevalidation for their cloud-based software, it is important to understand exactly the scope of the Install/Operational/Performance Qualifications this covers. Compliance requirements mandate validation in the user’s environment, so prevalidation does not suffice to fully satisfy regulations. Working with the vendor to clearly establish individual and joint responsibilities for validation prevents unnecessary duplication and establishes an overall credible approach.

A Detailed SLA. Working with the vendor to create a detailed SLA is one of the most important things you can do to ensure a successful implementation. Without a well-written SLA, your organization could be in for many unpleasant surprises and additional expenses down the road. In addition to system change management processes and requirements to maintain compliance, an important and often overlooked aspect of the SLA is data storage, including controls of underlying data replication related to availability and disaster recovery.

Conclusion

In today’s increasingly collaborative R&D landscape, creating and managing informatics systems to help scientists handle, analyze and share information is critical for organizations to enhance innovation and remain competitive. Cloud- based collaborative platforms can provide a secure, scalable and flexible approach for handling the wide array of data types, sources and partnerships which are involved in modern collaborative research. These systems allow organizations to spin up robust collaborative environments easily with minimal IT support.

When implemented properly, cloud-based research informatics systems as a complement to R&D collaborations can provide important benefits to your organization:

• Effective use of data produced from the collaboration
• Increased scientist productivity
• Enhanced organizational flexibility and agility
• Reduced IT costs per user

There are attractive benefits to a cloud-based collaboration research informatics system, but implementation of the platform can be a difficult endeavor that requires much skill and planning to execute successfully. The project team should follow a proven, comprehensive methodology in order to ensure that the implementation provides significant business value for your organization.

Astrix Technology Group has over two decades of experience in the laboratory informatics domain. Our professionals bring together the technical, strategic and content knowledge necessary to help you efficiently select, configure, implement and validate a cloud collaboration platform that best meets your needs and keeps your total cost of ownership low. Whether your deployment utilizes public, private, or a hybrid cloud architecture, our experienced and skilled professionals can make the process of implementing or migrating to a cloud collaboration platform far more cost effective and efficient. Contact us today for more information on leveraging the cloud to improve agility, reduce cost and advance collaboration when working on new scientific discoveries and technological innovation.

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• decreases the efficiency of the laboratory workplace
• stifles innovation by forcing scientists into manual activities when they could be doing actual science
• leaves the laboratory vulnerable to having valuable data misplaced, lost or destroyed
• creates data integrity issues due to manual data transcription
• exposes the company to regulatory risk due to data integrity and audit trail challenges

Informatics solutions, such a Laboratory Information Management System (LIMS), have been widely adopted across industries in order to meet these challenges. While LIMS provide a diverse array of functionality that serves to improve laboratory operations, the most important feature of a LIMS may be the ability to automate manual processes. In this blog, we will discuss some of the most important ways that LIMS can help automate your laboratory.

Laboratory Automation With LIMS

A few of the many ways that a good commercial LIMS can help automate your laboratory environment include:

Workflow Automation

Workflow automation is one of the biggest opportunities offered by LIMS for laboratory productivity improvement. Most LIMS allow complex workflow automations with simple drag and drop functionality. Some LIMS even offer out-of-the-box (OOB) laboratory execution system (LES) functionality for extremely detailed workflow automation capabilities.

Note that many organizations make the mistake of automating inefficient workflows and processes with their LIMS. A thorough workflow analysis prior to a LIMS integration or upgrade allows for the production of a set of optimized requirements detailing process improvements that will guide the implementation for maximum productivity and efficiency enhancement.

Instrument Integration

Integrating your laboratory instruments with your LIMS is a highly effective way to automate your laboratory. Most commercial LIMS have out-or-the-box (OOB) instrument integration capabilities, along with a comprehensive application programming interface (API) that allows for customized integrations with instruments. Once instruments are integrated with LIMS, test results flow directly into the LIMS database without the need for manual transcription. This effectively eliminates manual transcription errors and serves to guard against data integrity issues. Two-way interfaces can be set up between the instrument and the LIMS so that data can flow in both directions. This allows for more complex automation that can drive big productivity gains through controlling the instrument from the LIMS.

Instrument Calibration and Maintenance

Manually maintaining instrument calibration and maintenance records can be both a hassle and a source of workflow inefficiencies in your laboratory. Modern LIMS come with the ability to automate the process of tracking this information. Additionally, lab managers will be able to seamlessly access the up-to-date calibration and maintenance records to confirm that instruments are ready for use before work assignments are made.

Systems Integration

Besides instruments, LIMS can be integrated with many other laboratory and enterprise applications/systems to improve automation across the organization. These integrations often are accomplished via the LIMS’ API, although they can also come as a standard OOB interface. Some examples of systems that are commonly interfaced with LIMS include: ELN, SDMS, LES, MES, ERP, inventory management system, data visualization and analytics, etc.

Reporting

Reporting can be a source of substantial inefficiencies in scientific laboratories. LIMS allow automation in the generation and delivery of reports, allowing scientists to spend more time doing the science that drives innovation and helps the organization remain competitive.

Training

Laboratories in industries adhering to GxP regulations need to make sure that all scientists and technicians are properly trained for the tests and other procedures that they perform. As with instrument calibration and maintenance, modern LIMS come with OOB functionality to automate training schedules and records. Additionally, providing the actual training courses/videos, as well tracking verification of training/retraining, through the LIMS will provide for further workflow automation efficiency gains.

Testing Results Review and Approval

All QA/QC results on a particular sample in a laboratory typically need to be reviewed and approved before the material can proceed to the next step in the production process. Modern LIMS can facilitate automation here by enabling a “by exception only” review process, where staff only have to do a review if the result is assigned a “Fail” or “Alert” status. In addition, the review process will be significantly more efficient since all the information that has been collected about the sample/batch (i.e., test results, batch information, etc.) will be easily available to the reviewer within the LIMS. This is a huge efficiency upgrade over having to go to all the various individual instruments involved to track down test results on a particular sample.

Conclusion

The digital revolution is rapidly changing the laboratory environment. Large amounts of data generated in modern laboratories, along with high throughput requirements, are necessitating the use of informatics solutions such LIMS to improve laboratory automation. We have presented just a small sampling of the many ways in which a properly implemented LIMS can help facilitate laboratory automation for your organization.

In order to ensure that your LIMS implementation optimizes laboratory productivity and efficiency and maximizes business value for your organization, however, it is important to follow a comprehensive and proven methodology. Astrix Technology Group has over 20 years experience helping scientific organizations implement and integrate LIMS in the laboratory. If you would like to have an initial, no obligations consultation with an Astrix informatics expert to discuss laboratory automation or your overall laboratory informatics strategy, please feel free to contact us.

The post Utilizing Laboratory Information Management Systems (LIMS) for Lab Automation appeared first on Astrix.