Life Science companies are rapidly developing thanks to new technological advances becoming available at record speed. Almost every stage of any product development cycle can be optimized with the help of IT or automation today.
This opens a wide pool of possibilities on one hand, but raises a number of challenges from technological and IT management point of view on the other.
Software development in Life Science must adhere to high-quality standards, constantly evolving compliance and regulations (whereas applied), rapid requirements changes, as well as rigid testing and validation practices. At the same time, it is expected that a solution is delivered as soon as possible (since competitors are also up-and-doing), that it’s stable and flexible despite all the complexity hiding behind.
Navigating these hurdles requires a special set of skills and experience from an IT team involved in a project since even minor development mistakes can lead to major harm for a business (i.e. intellectual property leakage, reputational damage, regulatory penalties etc.) and put at risk health and lives of its customers.
After years of working on complex Life Science IT projects (such as lab automation software or AI-powered solution for automated microbiological analysis) we can underline five key factors that need to be taken into account while building software for Life Science projects:
Deep understanding of business
This is a ‘must-have’ for any project we’re doing, but LifeScience has its peculiarities. Here the IT provider needs to get into scientific details, biological or medical data and existing workflows related to the project in order to understand what exactly the client wants to achieve, their context, limitations, environment etc.
Developers cannot create great software for laboratories working in isolation and having no clue how and where their code will be used. That’s why we always insist on visiting the client’s operations, observe how things work in real life, what tools and technologies are being used, accurately gather requirements to suggest the most optimal IT solution.
Moreover, the IT provider shall be familiar with all the standards of the industry and how to work with them effectively. Risk assessment (FMEA analysis), impact assessment, installation & operational qualification protocol, validation plan, functional traceability matrix – there are just a few examples of the documentation we are dealing with while working on Life Science projects.
Software transparency
It’s not enough to understand, we also strive for our solution to be easily understood by creating quality software with clear functionality and logic.
Trust is essential in Life Science. Unless people understand the principles behind Life Science software, they are unlikely to use it (or allow using it): No doctor would agree using analysing software for X-ray images without understanding how it works. No patient would sign up for personal medical treatment generated through an unknown algorithm. And no authority would allow selling medicine without a clear explanation of its discovery path, effectiveness and safety.
To avoid these situations we also prepare detailed technical documentation, precise delivery process and QA procedures description to be followed and provide proofs of compliance.
IT systems flexibility
Life Science and Pharma products are subject to rapid changes due to discoveries occurring regularly and constantly evolving regulations around them. That’s why creating flexible IT systems that are easy to alter and scale is a priority and one of the main challenges here.
This need can be addressed through:
- implementing a hybrid project management system where developers follow agile methodologies (to ensure continuous delivery and flexibility of the process) while high-level management (risk, budget, documentation, etc.) is done in PRINCE or another customary for a particular company way (to avoid disruptions in processes that are already complex enough );
- an early assessment of the possibility of adapting modular architecture or microservices (to makes changes technically possible);
- thorough documenting and validation.
Quality Assurance for Life Science projects
Quality Assurance is, with no doubts, of the greatest importance for Life Science projects. Not only because it’s often a regulatory requirement and necessary for mitigating the risks, but also because its effective implementation can give a competitive advantage and speed up the production cycle.
IT providers must pay special attention to establishing and following functional requirements, quality standards, validation documentation, and automated validation scripts when building software for Life Science projects. They also ought to provide evidence of these procedures to be followed.
Openness to new tech and R&D pilots
Life Science, where data and accuracy is a key, benefits greatly from adopting new technologies. Artificial intelligence (AI), machine learning (ML) and deep learning (DL) already help Life Science companies to reduce the need for human control over the processes, scale operations and optimize the work behind complex biological tests to a few mouse clicks.
Being open to adopting new tech is not simply an advantage. Companies that miss this chance – trail far behind.
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