Large Clinical Testing Laboratory
AI-assisted development boosts product modernization, efficiency, scalability, and future growth trajectory.
Set to launch later this year, Allata's solution will enhance the client's growth and scalability by digitizing paper processes and improving workflows. With AI utilized throughout the SDLC, the new system will ensure compliance, reduce errors, and offer faster response times, resulting in increased efficiency and scalability. The rebuilt system will support regulatory adherence, reduce costs, and serve as a foundation for future growth and enhancements.
The SITUATION
Overcoming LMS Limitations for Future Growth
The client, a clinical testing laboratory focusing on molecular genetics and toxicology testing, faced several obstacles stemming from their current Lab Management System (LMS) limitations. For the past two years, these shortcomings in the system have consistently caused performance issues. The client encountered a significant hurdle in the form of poor system performance. The existing system could not align with the company’s growth objectives, resulting in obstacles for essential business functions and frustrating delays of up to 30 seconds for system responses.
The client is a CLIA-certified and CAP-accredited lab focusing on high-complexity molecular testing with an extensive portfolio of services. From its beginnings, the client differentiated itself from other laboratories through its highly customized Lab Information System. As the client has grown and expanded its service offering, the LIS was updated to fulfill new requirements. After ten years of operation, the client realized it needed to redesign and architect its LIS to support future goals and growth. System limitations and performance constraints became hurdles to handling increased workloads and expanding service offerings.
The IMPACT
Scalable Solutions Boosting Lab Efficiency
If the client had chosen to do nothing to address the challenges with growth over the last two years, it would have faced significant risks. Compliance would have been an ongoing concern, particularly as regulations and requirements continually evolve. Ensuring the system aligned with FDA standards and other regulatory requirements would have been a significant obstacle in obtaining FDA certification. The struggle lies in certain parts of the system needing to be fully compliant, which would have posed a considerable challenge for the company. Addressing compliance issues and meeting regulatory standards would have been crucial for the client’s success and expansion.
As the client looks to expand its capabilities and offerings, it needs a system to support the additional scale and flexibility required. Allata has partnered with the client to design a modular, event-driven system to support its current and ongoing needs. User experience has been reworked, and previously disparate workflows have been consolidated with this redesign, providing a more straightforward and easy-to-use interface for lab techs. Previously, the client digitized manual workflows, resulting in an overall improvement in the user experience. On top of an improved interface, a modular, microservice-based architecture supports scaling at the business-unit level. Configurability, a key focus of the new LIS, provides additional flexibility and adaptability of the system to support new testing procedures. Configurability and modular setup will allow the new LIS to be more easily set up for new testing and validated for compliance. Further, the event-driven nature of the architecture lends itself to better-supporting compliance needs by tracking user activity in the system.
The RESOLUTION
AI-Enhanced Event-Driven Modular Architecture
Allata’s multi-disciplinary team of 25+ consultants implemented an event-driven architecture with modular components. We incorporated generative AI tools to enhance development processes. GitHub copilot significantly increased the team’s capacity by tackling repetitive tasks like writing unit tests and expediting the development workflow. It alleviated the burden of monotonous and time-consuming tasks, enabling developers to allocate more time and focus toward tackling intricate logic. Additionally, we utilized targeted prompt engineering to customize generative AI personas to help generate user stories. By providing the necessary context, architecture, and data model information, business analysts without tech expertise could draft user stories that effectively covered the various aspects that needed consideration by developers.
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