Case studies
Enhancing Leafwell’s Data Platform for the Future
Overview
Leafwell has a telemedicine platform providing access to medical cannabis consultations and certifications. It connects patients with licensed physicians, streamlining the process of legally obtaining medical marijuana cards. Leafwell’s platform also offers educational resources on medical cannabis, helping patients navigate complex regulations and help new users get a grasp on why it’s beneficial to use cannabis-based substances. A key component of Leafwell's services is its data platform, which integrates real-world data, clinical outcomes, and machine learning to optimize treatments and contribute to medical cannabis research. This system supports the company's goal of advancing medical cannabis use through data-driven insights.
Challenges
Leafwell aimed to enhance its existing data platform to support its growing user base and to continue advancing its research efforts. As the company scaled, its current architecture faced limitations in terms of scalability, performance, and reliability. Leafwell required a next-generation data platform that could efficiently handle larger datasets, provide faster analytics, and support a more robust research infrastructure. The challenge was to identify the architectural and technological improvements necessary to achieve these goals while aligning with the company’s long-term objectives.
Solution and Technological Approach
Having significant expertise in designing and building data platforms in Healthcare, Strongbytes partnered with Leafwell to tackle this challenge. The engagement began with a thorough assessment of Leafwell’s existing data infrastructure. The approach followed several key steps:
- Stakeholder Engagement: Strongbytes engaged with Leafwell’s key stakeholders to understand their business objectives, current data usage patterns, and anticipated growth. This helped establish a clear vision of what the new platform needed to achieve.
- Analysis of Current Infrastructure: Strongbytes conducted a deep analysis of Leafwell’s data lake architecture, processing tools, and performance metrics. This review uncovered several bottlenecks and limitations that impacted scalability and performance.
- Identification of Pain Points: The assessment revealed challenges related to data processing speed, scalability of the current architecture, and the ability to efficiently integrate new data sources for medical research. Additionally, security measures were evaluated to ensure compliance with healthcare regulations.
- Technology Exploration: Strongbytes explored various technology options to enhance the platform’s architecture. Each option was carefully evaluated based on Leafwell’s requirements, with a comparison of the pros and cons of each technology.
- High-Level Architecture Proposal: Using the insights gathered from the assessment, Strongbytes developed a high-level architecture proposal for the next version of the data platform. This proposal included recommendations for improving scalability, performance, and data processing efficiency while aligning with Leafwell’s research and business objectives.
Outcome
With Strongbytes’ expert guidance, Leafwell had a clear path to upgrading its data platform. The proposed architecture addressed key bottlenecks and enhanced scalability, ensuring that Leafwell can support its growing user base and research initiatives. The new platform design enabled faster and more reliable data processing, improving the accuracy of patient outcomes and furthering their mission to provide safe and effective treatments.
This collaboration exemplifies Strongbytes’ ability to deliver cutting-edge solutions that support data-driven healthcare companies like Leafwell in achieving their mission.
Business Impact
Improved scalability to handle a growing volume of data and users
Enhanced performance and reliability of data processing
More robust support for future initiatives, improving insights into medical cannabis efficacy
A future-proof architecture aligned with long-term business and research goals
Technologies Used
AWS, Amazon Redshift, Airflow, Airbyte, dbt, Metabase
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