Labs that are implementing a laboratory information management system (LIMS) generally start with “happy path” use cases. These ideal or expected workflows represent the most efficient, smoothest route to complete a task. But a system that only meets these straightforward requirements will quickly run into trouble in a production environment.
Experienced LIMS implementers know it’s also critical to support unexpected events. When the LIMS is implemented to meet these additional requirements, labs can properly manage their entities and workflows under a wider range of real world scenarios.
In this post, we’ll share five things labs should consider when moving beyond the happy path during a LIMS implementation.
What is the happy path?
In the context of software development, the happy path refers to the ideal and intended flow of actions and interactions within a software application or system. It represents the sequence of steps where everything goes according to plan, and all features and functionalities work as expected.
For lab staff, a happy path experience is a smooth and error-free process while they use the software to manage laboratory operations, such as sample tracking, data analysis, inventory management, and communication with colleagues. The software provides accurate results and timely notifications through intuitive user interfaces. The result? Enhanced efficiency, productivity, and satisfaction.
Lab workflows in the real world
While the happy path is what everyone hopes to achieve, in the real world, things don’t always go according to plan. Humans make mistakes — chemicals are spilled, equipment is not cleaned thoroughly, measurements are misread, data is entered incorrectly, or the wrong buttons are pushed. Furthermore, lab equipment might be calibrated incorrectly or substances can evaporate. When these issues arise, the LIMS needs to handle them through error handling and the provision of alternate paths.
As a software consultant working in the laboratory sector, Semaphore can guide labs through a LIMS implementation. Our process includes discovering a lab’s unique requirements, implementing the requirements, demonstrating incremental progress, performing user acceptance testing (which could result in further changes), validating the LIMS in the lab, and maintaining and supporting the LIMS.
Ensuring the LIMS works beyond the happy path
The requirements discovery phase is a vital part of a successful LIMS implementation. The more we can uncover at this key stage, the more efficiently we can produce a LIMS that meets the needs of a lab.
Here are five of the things we focus on with clients that you should consider if you’re undertaking your own LIMS implementation:
1. Tracking and managing all entities
Within a lab environment, there are numerous entities to track and manage. These entities include instruments, personnel, protocols, samples, freezers, and more. Each entity plays a critical role in the smooth operation of the lab and in maintaining business as usual. Pay careful attention to each to be sure they are accounted for beyond the simple happy path in your LIMS implementation.
2. Integrating instruments and technologies
We’ve talked before about the importance of integrating instruments and other pieces of software within the lab’s software stack. Integrations can help your lab operate more efficiently, streamlining workflows, and enabling scaling throughput and business growth. Within every integration, there is a happy path, but it’s still essential to consider exceptions and errors that could occur and how they will be managed. Using liquid handler robots as an example, a transfer of liquids may not occur as expected, containers might be in the wrong deck positions, or the wrong containers might be on the deck. Implemented correctly, your LIMS should be able to handle these types of errors.
Another consideration is the use of software engineering best practices. Integrations not leveraging these principles can be fragile and inefficient. If your lab is developing instrument integrations, electronic medical or health record (EMR/EHR) integrations, or enterprise software integrations, be sure your team or external consultants are experienced in this area.
3. Reducing errors and avoiding duplicate data
One way to keep workflows on the happy path is to reduce the incidence of errors (human or machine) and minimize the potential for duplicate data. At Semaphore, we recommend labs take advantage of automation wherever possible. Automation reduces manual processing, significantly decreasing the number of transcription and data entry errors. It also has the benefit of freeing up your staff to focus their efforts on potentially life-changing innovations and breakthroughs.
While automation does not mean your lab will operate flawlessly with 100% efficiency all the time — you’ll still need to build in error and exception handling — any time you add automation, you should expect to see incremental improvements in speed and efficiency across the lab.
4. Accommodating changes to workflows
It’s also important to implement a LIMS in such a way that it can be adapted for future changes. The happy path today might not accommodate the equipment your lab purchases tomorrow or the new protocol you adopt in a year. Implementing the LIMS to support additional modules and changes to workflows is the best way to future-proof your business.
Often we’ve found that labs’ custom legacy solutions, many built in-house, contain hard-coded components that are not easily updated. While these solutions might have supported the lab’s happy path originally, over time, they quickly become outdated and a hindrance to changes the lab needs to make to stay competitive. In some cases, the answer is to replace these legacy systems with more modern modular systems or custom applications that are better equipped to meet current and future requirements.
5. Supporting collaboration and data exchange
In today’s fast-moving world, labs must be able to support collaboration and the exchange of data within the lab and with external partners. Implementing FAIR (findable, accessible, interoperable, and reusable) data principles and data provenance enables data sharing between instruments and systems in the lab and facilitates collaboration with other organizations across the sector. Tracking data provenance is vital for auditability, reproducibility, complying with regulations, and ensuring patient safety. At the same time, FAIR data supports broader initiatives like open science that have the potential to make a global impact.
Beyond the happy path
The happy path is an important factor in a LIMS implementation. Your lab should have clear standard operating procedures, which describe the happy path for all workflows — and the LIMS must be able to support these at a bare minimum.
However, a comprehensive and well-designed implementation will ensure the LIMS can track and manage all entities. It will also have built-in integrations with other lab equipment and software, robust error handling, the ability to accommodate changes and upgrades, and support for internal and external collaboration.