(P140) PERFORMANCE OF AUTOMATED CASE CREATION, REPORTING, AND CLOSURE OF CHLAMYDIA AND GONORRHEA CASES IN FLORIDA

Background:
In 2022, the Florida Department of Health (FDOH) implemented procedures to automate the processing of electronic laboratory results (ELR) for chlamydia (CT) and gonorrhea (GC). A pre-implementation model estimated that automation would significantly reduce manual data entry by automatically closing 259,418 (45%) CT/GC ELR that either did not meet the surveillance case definition or that were not prioritized for investigation, resulting in substantial time savings for FDOH personnel (11,040 hours saved). We used post-implementation ELR results to compare observed time savings to modeled outcomes.

Methods:
We used CT/GC ELR data reported to the FDOH in December 2022 for the post-implementation model and multiplied the counts by 12 to estimate annualized data. ELR from 2019–2021 were used for the pre-implementation model. Post-implementation ELR results were compared to pre-implementation model predictions (i.e., record closed, requires field investigation, reported with no investigation). Time and cost savings were estimated by assuming one minute for each step that was automated, $50,000 salary for each full-time equivalent (FTE) employee, and 2,080 annual work hours per FTE.

Results:
In total, 44,723 CT/GC ELR, extrapolated to 536,676 ELR annually, were processed using automation, and 65% were automatically closed. Of those determined to be a new infection (15,808 of 44,723 CT/GC ELR), the implemented procedures auto-closed and reported 66% (10,418 of 15,808) of all CT/GC ELR that did not require field investigation, similar to the 67% (259,418 of 386,763) expected based on the pre-implementation model. The implemented procedures would save an estimated 16,140 hours and $388,000.

Conclusion:
Post-implementation evaluation of automation procedures for CT/GC ELR demonstrated high alignment with the pre-implementation model for case closure and reporting new infections. The pre-implementation model under-estimated time savings; automation resulted in greater observed time savings than expected.