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            Mushrooms, Goats, and Machine Learning: What do they all have in common? You may never know unless you get started exploring the fundamentals of Machine Learning with Dr. Tim Oates, Synaptiq's Chief Data Scientist. You can read and visualize his new book in Python, tinker with inputs, and practice machine learning techniques for free. 
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                  IT ARCHITECTURE AND INFRASTRUCTURE

                  Modernizing a Legacy Disease Surveillance System with AI for a State Government Agency

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                  Learn how Synaptiq modernized a legacy disease surveillance system by transforming its application and data pipelines.

                  Problem:

                  Michigan’s Department of Health and Human Services needed to modernize its legacy disease surveillance system, a 20-year old system essential for timely and accurate public health monitoring. Previous attempts to upgrade the system had fallen short due to limitations of the outdated technology stack and system architecture, poor system performance, insufficient data quality controls, inflexible data model, counterintuitive user experience as well as a rigid development process that stifled adaptability. The state's ability to track public health initiatives was limited as a result.  

                   

                  Because of the complexity of the problem and need to minimize downtime, the State of Michigan reached out to Blue Bench Advisors and Synaptiq to devise a comprehensive solution while state employees continued to work in the existing system.

                  Solution:

                  Synaptiq and Blue Bench Advisors worked with the State of Michigan to build a modern cloud-native application alongside the legacy system using the strangler pattern—a strategy for gradually replacing legacy systems by incrementally building new functionality around the existing infrastructure. Recognizing the limitations of the legacy logic, a read-only design for the new system was leveraged to ensure data integrity and avoid conflicts during the transition from the old to new system. 

                   

                  This new application was powered by a Databricks Lakehouse that consolidates the disparate data sources powering MDSS, moving data between them, and implementing analytics workflows on both structured and unstructured data sources. The team developed patterns for leveraging cloud-native Infrastructure as Code with legacy on-premises systems, integrating data from a legacy Oracle database, ingesting health records from HL7 files, integrating data from real time API’s, reshaping data into a medallion architecture and leveraging Databricks to perform ETL on all of these while populating an ElasticSearch index for real time search and record display.

                   

                  The resulting robust system architecture allowed the legacy and new systems to run side by side, minimizing disruptions during the transition period. This included carefully designing everything from the user interface to the underlying data schema, ensuring clean, compatible data interoperability that supports both systems. This approach maintained continuity and built a strong foundation for future innovation and scalability while ensuring a user-centric, conflict-free modernization process while steadily phasing out the legacy system.

                  Outcome:

                  The State of Michigan's Department of Health and Human Services successfully launched the modernized disease surveillance system to an initial set of users and is currently gathering feedback before statewide launch. Among other performance improvements, the modernized systems ability to search, filter and facet is over 100x more efficient and reliable than the old system.
                   
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