A smart laboratory is an umbrella term for different software and hardware tools that simplify or automate the data management and processes in the lab. Laboratory digitalization or digital transformation is very complex and can have different scopes, depending on your laboratory requirements.
First, let us explain the main differences between three crucial terms in digital transformation: digitization, digitalization, and digital transformation.
Digitization is a process that changes information from analog to digital form.
Example: Converting hand-written notes into digital notes with OCR.
Digitalization is a process of employing digital technologies and information to transform business operations.
Example: Implementation of an ELN system into a research laboratory. With the new system in place, the laboratory will ensure data integrity, increase efficiency and improve communication and collaboration.
Digital transformation is a process that aims to improve an organization by triggering significant changes to its properties through combinations of information, computing, communication, and connecting technologies (Vial, 2019). Digital transformation only happens when leadership recognizes the strategic importance of making profound organizational changes to the company that is customer-driven rather than technology-driven.
Example: A company may decide to run several digitalization projects, but just implementing new systems will not lead to a digital transformation of a company. The management will, however, have to decide that the company will continue to innovate in the digital space and that this will be one of the company’s no. 1 priority. In the long run, this may completely change how the company operates. For example, a production line may be completely AI-driven, customer interaction is online and paperless and/or a company may acquire another company with digital technology that fits into the long-term vision of the company.
Data integrity describes the degree to which the data are complete, consistent, and accurate throughout their entire life cycle.
Example: Results generated by an instrument go directly to a LIMS system. In the LIMS system, an audit trail guarantees full traceability over any manipulation of the data, and electronic signatures prevent manipulation of unauthorized sources.
This article will summarize 6 key challenges you need to address when starting on a laboratory digitalization journey.
1. Understanding your processes and data flows
It is essential that you understand your laboratory processes. Even if you are not planning to digitalize your workflows, with this knowledge you will have a better insight into your workflows and data flows. And with better insight, you can strive to optimize your current practices.
While you might believe that you know all that is happening in your laboratory, it often turns out that there are bottlenecks and workflows that you were not even aware of. This is to be expected, but your goal is to recognize and better manage these pain-points.
As a first step, we strongly recommend that you make a laboratory processes map. Then you will need to decide which process you are mapping, which activities it consist of, what is their order and how it starts and ends. To draw the most accurate processes map, you must include your employees in this process. They are the end-users and will provide the most valuable information on actual workflows happening in their work routine.
Once you have identified critical bottlenecks you can introduce either simple or more complex solutions that solve them. The end goal should be increased efficiency and productivity of your laboratory.
You can read a step-by-step guide on doing laboratory process mapping in our article Laboratory processes mapping.
2. Data management
Laboratories produce an enormous amount of data that needs to be managed correctly for this data to actually provide added value. You can manage your data to a different extent, depending on how much you are already digitalized.
If you are just starting, we suggest that you think about how you can transform your data from analog to digital format. There are many software tools that you can consider and will help you to document, store, analyze, share and manage the experimental data in a digital form.
Once you have a central data repository, such as ELN or LIMS, you might want to consider integrations laboratory devices into one seamlessly connected network. You can do this by using APIs or middleware solutions that handle the communication between devices and the central data repository. This level of digitalization has three direct effects:
- Prevents errors during the manual data transfer
- Reduces time required for manual data transfer and verifications of data transfers
- Helps to improve data integrity
Some laboratories might also want to connect their central laboratory software with other third-party software, such as Enterprise Resource Planning systems (ERPs), production systems, or data analytics software. In any case, we highly recommend that you anticipate at least some of the integrations so that you can select the right laboratory system, which can support third-party integrations in the first place.
Laboratory automation is essential for laboratories that have repetitive and/or high-throughput processes.
It is important to note that automation does not need to be complete right from the start. Currently, the most frequent use of automation in laboratories is partial automation. Only the most routine processes that do not have a lot of variability between runs are fully automated, as these bring the best return on investment.
In a fully automated lab, liquid handling systems and robotic arms can perform the assays and transfer the containers between different devices. With automation, you will aim to rule out the errors that originate in the manual work.
Some laboratories are automated to the level where no human interaction is needed. Robotic arms take samples from the storage, transfer them to the analytical device, and the scientist can check the data in the ELN. With this, we are entering the IoT world, where all laboratory things are seamlessly connected. The data flow from devices to the cloud, where they are automatically processed.
Some of the most successful companies in digital transformation are already going beyond the automation of laboratory processes. This can include different approaches and processes, so here are two examples:
Full business process automation
That also includes non-research processes such as handling incoming samples and materials, invoicing, shipping, and reporting. This calls for integration with ERPs.
Artificial intelligence (AI)
AI can be introduced to different stages, departments, and processes in the company, from quality control, decision-making, reporting, advanced analytics, and predicting trends or creating scientific hypotheses. The goal is to reduce the time scientists spend at the bench and instead invest it into areas where they can add greater value.
3. Finances
When you are considering going through digital transformation, you will certainly need to think about the financial aspect. If we wish to make a fully informed decision, It is crucial to understand the short- and long-term digitalization benefits.
Return on investment (ROI) is a commonly used metric, where you consider all the benefits and costs concerning a specific investment. We provided a list of short- and long-term savings related to digitalization and ROI calculations for digitalization of laboratory process in the blog post Digitalization – Return on investment.
4. Regulation compliance
The pharmaceutical industry was among the early adopters of software in science. The software was first mostly used by their QA departments, where human error could have a significant financial consequence. Digitalization brought great results and became a gold standard in the industry. That was followed by a regulatory framework that strives to provide a legal framework that ensures software quality.
There are essential regulations now that apply to digital transformation in science. One of the most widely used is for sure Title 21 CFR Part 11. It is an FDA regulation on electronic records and electronic signatures that applies to pharmaceuticals, medical device manufacturers, biotech companies, and many others. This regulation sets the criteria for trustworthy and reliable electronic records and electronic signatures. . Also, it defines the principle by which electronic records are equivalent to paper records.
5. Digital culture
A very important part of digital transformation is digital culture. But adopting digital culture does not mean that you need to completely change what you have built, but rather implement your current values to your digital concept.
Although you might provide great vision and values for your company’s digital culture, this does not assure that the adoption of digital culture will be smooth. On the contrary, you may expect to experience resistance to the changes you are making from your employees. While this resistance is completely natural and normal, the way you address it, answer people’s questions, and involve them in the process is crucial to successful user adoption.
If you are thinking about establishing a digital culture, we wrote a blog post with a step-by-step guide on Building a digital culture that dives more into the topic.
6. Digital security
Data security and digital security, in general, are two essential topics that you need to address. When all devices are connected to the internet and can be controlled remotely, it is even more important to prevent unauthorized access.
Access control
Access control limits access to the data only to the authorized people and prevent unauthorized people from accessing it. User authorization is done through the user management features that are available in all enterprise-level systems. You can assign different roles and permissions to different people. For example, most users can only generate new data, some users can review data, and only a few can modify them. This, combined with encryption, ensures the technical safety of data. But you still need to be aware of the possibility of social hacking, i.e., tricking the authorized users into providing access to unauthorized users.
Audit trail
Another requirement to maintain data integrity is an audit trail. Audit trail means that there is a log of all data changes, either due to user actions or automated workflows. The audit trail mechanism stores the old and the new version of the data. This allows full traceability of the changes from the original version of the data.
For the digital transformation, you will most likely be using an external partner to provide you with different digital tools. Understandably, you have certain concerns in this regard since you will have to give away some control over your data. That is why you must choose carefully and ask the vendors questions about how they process the data and if they comply with cybersecurity standards and access rights.
Conclusion
- Digital transformation is a complex process, but it will increase the value of your laboratory and company.
- The world is moving into the digital era, and laboratories will need to digitalize.
- Digitalization does not have to happen all at once. It is better to do it step-by-step, but with a strategy behind it to support it.
- It is essential to map your processes and data flows to ensure a more successful digital transformation.
- Your investment in digitalization will not only pay off in the long run, as there are also short-term savings that you will experience.
- Some legislations regulate digital data in the scientific environment.
- Digital culture will be a very important part of your digital transformation, and it takes time to get fully established.
- Security of your digital data is of high importance, and you should carefully choose the right partners and solutions.
