Join the Digitalization Journey

On March the 7^th 2023, the first forum was organized around the topic of Digitalization in R&D Labs, focusing on electronic laboratory notebook (ELN) solutions and their role in the lab of the future. The next event will take place on June 13th register below 

Some of the topics that have been discussed through the first event: 

1. How to choose a solution – ELN or other?

   The large number of solutions in the market, where vendors claim a wide range of applications and functionality can be challenging.  Although the number of alternatives is appreciated, it places a burden on the customer companies. Finding the best candidate solution to implement implies a large effort in terms of time and resources to understand the pros and cons of each solution, including what is the core application and functionality where fitness-for-purpose is excellent and where it is a stretch.

2. Defining an ELN?

   We should stop suggesting that ELN solutions are a paper-on-glass version of a paper notebook where text and files are attached. In a future where data must be captured in a format and structure that ensures re-usability, it is advised to carefully discuss and define the purpose and main priorities for an ELN solution for a given organization. The main purpose can, for example, be regarded as the solution to structure cross-functional workflows.  The solution be used for task management and collaboration, enabling the collection of data and metadata as part of the workflows but not being the system of storage.

3. Engaging the organization to succeed

   Digitalization effort only succeeds if there is an investment in engaging the organization in the change(s) before, during, and after a project. While many times, organizational change management activities are included in the scope of the project, they are less often part of the project analysis and initiation phases, and very rarely included in the operational phase. Inclusion oiin the operational phase would correspond to activities that ensure continued adoption and best use of the digital solution. 

4. Integrations between IT systems and lab instruments

   For most established companies (not starting up), there is an already existing IT landscape of multiple solutions. It will never be replaced by one all-encompassing platform from a single vendor. Therefore, a landscape of new, not-so-new and legacy solutions must be managed proactively.  This requires a strategy for how integrations are to be established and managed.  

 

To continue these engaging conversations, a second event is planned for June 13^th, 2023 at the NNIT headquarters.

Register now:  Digitalization of R&D Labs Series (benchling.com)

If you find the topic and related discussions interesting but are not in the Copenhagen area, please let us know as we are discussing the best formats to engage with a wider life sciences community across Europe and globally.

 

The 2^nd event took place on June 13^th, 2023 with separate discussion groups that focused on two different topics:

• Organizational change management as part of the implementation project of a lab digital solution.
• Building a manageable IT landscape: Integrating new solutions with instruments and other existing IT Solutions.

 

The key points summarizing the lively discussions between the event participants are summarized below.

Organizational change management as part of the implementation project of a lab digital solution

Discussion Key Points

There is a broad recognition that technology is getting even stronger and changing ways of working. Therefore, it is important to take a holistic approach. New technology is not simply a tech project implementation.  It also represents changed ways of working, doing, and maybe even thinking and doing. We must look at change holistically and plan for change accordingly.

Communication is vital but it can be difficult to cut through all the noise in larger corporations.  You must be clever and adaptable to get the message through efficiently.  Communication is different in each company, so what works in one may not work in another. Any message must fit into the communication landscape.

Effort must be put into creating the right type of environment for change to happen successfully. This includes  good training, support on the ground, and post Go-Live support for people and leaders to play the part of coach.

It is important to spread the load and ensure that people on the ground feel that they are part of the change. Give them the possibility to play a part in enacting the change and the responsibility to drive it on the ground. One successful approach is to establish a clear role of superusers, as change agents, trainers, and first support. Building a manageable IT landscape: Integrating new solutions with instruments and other existing IT Solutions 

Discussion Key Points

There are several barriers to establishing an integrated landscape that is manageable:

• R&D labs are diverse and constantly changing and therefore implementing and enforcing standards is a challenge. Nonetheless, some degree of standardization is essential to succeed in creating an integrated IT landscape.
• Legacy equipment and software that is loved by its users in R&D labs and it is costly to replace.
• There are downsides to selecting any architecture which must be recognized and mitigated. There are no perfect solutions, despite what may be promised. 
  • Hub architectures create single points of failure and are therefore fragile, and potentially vulnerable. Point-to-point integrations are prone to bugs and require a higher maintenance effort.
  • There is a mental fatigue associated with building and maintaining a complex integration model vs. the need to interrogate and understand connections.

 

Standardization was highlighted as the first crucial step to succeed in building an integrated IT landscape that enables the R&D Labs. Standardization is, however, challenging and therefore will only succeed with enforcement and incentives from management via KPIs, for example.

The group also discussed the importance of focusing first on the integrations that bring the most value to the organization – identifying and solving the right problem.

Since we all agreed that the conversations must continue, a third face-to-face networking event is planned for the Autumn (November 21^st) at the NNIT headquarters in Søborg.

Register now: http://events.benchling.com/digitalizationofrdlabsseries3 

If you find the topic and related discussions interesting but are not in the Copenhagen area, please let us know as we are discussing the best formats to engage with a wider life sciences community across Europe and globally.

 

The 4th Lab Series forum held on March 12, 2024, focussed on the Digitalization of R&D Labs within the Nordic Life Sciences sector.

The forum focused on two main discussion topics:

• The first topic discussed the challenges of maintaining instrument data connectivity and the impact of manual data transfer.
• The second topic was about integrating AI algorithms for data analysis and decision-making in a laboratory setting. 

Marie Helene Andersson, Corporate Principal Data Partner at LEO Pharma, was the keynote speaker who discussed "Empowering scientists through the fusion of generative AI and digital skills.

First discussion topic: Unlocking Laboratory Efficiency key insights: 

The integration of IT solutions with laboratory instruments is a critical challenge in the ever-evolving landscape of laboratory technology. Our recent discussion group explored this topic in depth, shedding light on the complexities, challenges, and strategies for maintaining these integrations effectively.

The Growing Complexity of Instrument Integrations

As laboratories strive for full digitalization of workflows, the complexity of instrument integrations continues to escalate. The goal is to minimize the manual intervention required by scientists and technicians in executing scientific experiments while ensuring comprehensive capture of the generated data. However, achieving this goal is hindered by various factors, including the lack of adherence to data standards by instrument vendors.

Addressing Integration Management

During our session, we examined what it means to manage integrations effectively. This involves aligning and finding compromises between multiple stakeholders with diverse needs and priorities, ranging from wet lab workers to data scientists and IT administrators. Moreover, ensuring IT security and compliance while maintaining agility poses a significant challenge.

We emphasized the importance of adhering to FAIR principles to ensure data generated from integrations is Findable, Accessible, Interoperable, and Reusable. Additionally, maintaining stable data flows and instrument connectivity is crucial, particularly in implementing fully digitalized workflows.

Navigating Future Ambitions and Barriers

Looking ahead, we discussed future ambitions and the barriers to achieving them. While the ideal scenario is fully digitalized workflows and seamless integrations, a pragmatic approach is favoured. Concrete projects with achievable goals and short durations, focusing on specific use cases, are recommended. These initiatives not only enhance efficiency and productivity but also facilitate the reuse of valuable data.

From an NNIT perspective, managing instrument and IT application integrations requires active management and a risk-based approach. This approach balances reactiveness and proactiveness, considering factors such as business impact, regulatory requirements, and cost implications.

Conclusion

In conclusion, maintaining integrations between IT solutions and laboratory instruments is a multifaceted challenge. By addressing the complexities, managing stakeholders effectively, and adopting a pragmatic approach, laboratories can unlock efficiency and derive greater value from their data. At NNIT, we're committed to supporting organizations in navigating these challenges and achieving their digitalization goals.

Second discussion topic: AI in R&D Labs: Use Cases, Challenges, Concerns and Future Expectations 

In the rapidly evolving landscape of laboratory technology, Artificial Intelligence (AI) stands as a promising frontier, offering solutions to streamline processes, enhance efficiency, and potentially revolutionize entire industries. However, as discussed in the recent Digitalization in R&D Labs Series Event #4, the journey towards fully harnessing the potential of AI is not without its challenges. 

Use Cases: 

At the heart of the discussion were various compelling use cases where AI has begun to make its mark: 

• SQL Query Creation: AI is empowering end-users to effortlessly generate SQL queries from vast company data lakes, facilitating quicker and more accurate data analysis. 
• Document Interaction: The concept of 'chatting with documents' is gaining traction, with AI being explored to swiftly extract information from text-heavy documents like Standard Operating Procedures (SOPs). 
• Training Partner: AI is emerging as a training partner for internal documents, potentially replacing traditional methods reliant on extensive SOPs and quality document repositories. 

 

Challenges: 

Despite the promising use cases, several challenges impede the seamless integration and utilization of AI in laboratory settings: 

• Data Integration: Highly customized Electronic Lab Notebook (ELN) solutions or in-house ELN systems pose integration challenges with AI providers, necessitating extensive preparatory steps. 
• Complexity vs. Expectations: Initial attempts into AI experimentation have encountered complexities beyond expectations due to e.g. lack of available resources with the required technical knowledge or misaligned data standards. This has tempered the initial momentum surrounding AI implementation. 
• Security Concerns: Participants voiced widespread security concerns and a general mistrust towards AI companies' data usage practices, leading to restrictions on employee access to AI tools. 
• Data Maturity Journey: Many companies struggle with data access and governance issues, hindering effective AI utilization. Centralizing data and ensuring proper governance are deemed essential prerequisites.

 

Innovation vs. Efficiency: 

Discussions delved into the balance between leveraging AI for efficiency gains and fostering game-changing innovation. While AI's current focus leans towards efficiency, concerns over transparency in AI models and the potential for true innovation were raised. 

AI Expectations: 

Over the next year, AI's primary focus is expected to remain on efficiency-driven use cases. Achieving game-changing AI may be slow but not unattainable, contingent on overcoming past failures, driving adoption through high-value cases, and adapting to shifting workforce demographics. 

In conclusion, while AI holds immense promise in laboratory settings, realizing its full potential requires addressing challenges, fostering innovation, and managing expectations effectively. As the industry navigates towards an AI-enabled future, collaboration, adaptability, and a nuanced understanding of AI's capabilities will be key to success. 

From an NNIT perspective, the most effective approach to unlocking the value of AI involves pursuing two concurrent paths: 

• Continuous Improvement Path: Focuses on short to medium term, on demonstrating the value of AI through continuous improvement. This involves strategically selecting use cases to showcase how AI can pragmatically address cumbersome and time-consuming tasks, freeing up employees to focus on more meaningful work. Simultaneously, this path allows for the development of AI capabilities and the fostering of trust. 
• Explorative Disruptive Path: Focuses on the long term, shifting emphasis to an explorative and disruptive approach. This involves identifying business areas and processes ripe for revaluation and disruption to leverage AI capabilities effectively. The goal is to drive scientific and operational innovation within the company, ensuring that AI is integrated seamlessly into its core operations. 

Conclusion:

AI has great potential in laboratory environments, but we need to tackle challenges, encourage innovation, and manage expectations to fully realize its benefits. As the industry moves toward an AI-driven future, collaboration, adaptability, and a nuanced understanding of AI's capabilities will be crucial for success.