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BACKGROUND: Merlin, Florida’s reportable disease surveillance system, began receiving electronic laboratory reporting (ELR) results in 2005. Historically, ELR results were automatically routed to the appropriate county health department (CHD) task list within minutes of receipt. The CHD user then manually assigned the appropriate disease and overall result, and decided whether to create a case based on the information in the ELR result. In 2011, Florida began implementing auto-case creation logic, which automated the process of assigning the appropriate disease and overall result, deciding whether a case should be created, and populating case information from the ELR (e.g., demographics). While very successful, disease-specific development was required to implement auto-case creation, which was time and resource intensive. In 2014, Florida redesigned the auto-case creation logic to alleviate the need for disease-specific software code development, allowing for implementation of auto-case creation and reporting for all diseases.
METHODS: Merlin logic for auto-case creation was redesigned to depend on user-defined scenarios rather than disease-specific software code development. Users can create and update these scenarios at any time. A sequence of steps evaluates all incoming ELR results and depending on the user-defined scenarios, assigns the appropriate disease and overall result, evaluates whether a case should be created, and populates the available case data from the ELR result. Users define specific combinations of diseases, laboratory test types, laboratory results, and counties, providing significant flexibility, particularly as test types change over time. Additional functionality provided by this logic will be presented in more detail.
RESULTS: In 2014, over 56,000 cases of reportable disease cases and over 420,000 laboratory results were captured in Merlin. Over 90% of the laboratory results were received via ELR and over 60% of the cases were initially identified and created based on the receipt of an ELR result. Auto-case creation saves an estimated 1.3 minutes per case, which could result in an overall savings of over 300 hours per year. Results associated with additional functionality provided by this logic will be presented in more detail.
CONCLUSIONS: Leveraging automation to streamline work processes reduces person driven data entry and should be considered a best practice. Improving and alleviating data entry processes allows staff to focus more on case investigations, follow-up, and outbreak detection activities. Disease detection and response is improved by not depending on manual actions. The decision support logic implemented here for auto case-creation is generalizable and could be used by other states.