Results: Without additional staffing, the combination of a robust sampling and data review protocol and the electronic accessioning process eliminated error and reduced sample processing time. The processing efficiencies allow what would have taken up to a week to be achieved in hours. The permanently attached cryogenic barcoded sample label made processing activities more efficient and the location code ensured the correct sample storage location for future retrieval. Conclusion: The developed sample management system proved to be an effective and accurate tool for processing large number of samples for population study. Presenter: Collin Riker, New Jersey Department of Health, Ewing, NJ, email@example.com When Does a Public Health Laboratory Reject Specimens? A Look at Specimen Rejection by Facility and Error Type During a Measles Virus Outbreak in Brooklyn, NY, 2018 A. DeVito 1 , C. Mahle 1 , U. Siemetzki-Kapoor 1 , M. Iwamoto 2 , J. Rosen 2 , J. Rakeman 1 ; 1 New York City Public Health Laboratory, 2 New York City Department of Health and Mental Hygiene PHL received specimens for measles testing from 47 submitters, five of which were considered to be high volume. Forty-seven specimens (15.8%), representing 55 test orders, were rejected for testing. Thirty one of these specimens (66.0%) were rejected due to specimen hemolysis. The remaining specimens were rejected due to various specimen integrity issues such as insufficient specimen quantity, sample received without proper transport medium, improper storage of the specimen, old collection date, or requests to cancel the order. Of 16 rejected specimens received from high volume submitters, the most common rejection error was specimen hemolysis (n=11, 64.7%). Hemolysis was the reason for rejection of 20 specimens submitted from low volume submitters (n=31, 64.5 %). No specimen was rejected due to incomplete submission forms with errors that could not be fixed. The most common reason for specimen rejection was specimen hemolysis, and there was no difference in the most common reason for rejection between high and low volume submitters. Submissions with errors in paperwork or specimen integrity cause slowdowns in the testing process due to time spent correcting errors, while specimen rejection can cause additional delays that may involve the patient returning to the provider for a specimen to be recollected. These issues impact the speed at which results are reported to the provider and Health Department for follow up. Improving practices in 82 LAB MATTERS Summer 2019 Presenter: Andrea DeVito, New York City Department of Health and Mental Hygiene, New York, NY, firstname.lastname@example.org A User-friendly Shiny Web Application for Choosing Pool Sizes When Testing Pooled Specimens C. Bilder 1 , B. Hitt 1 , J. Tebbs 2 , C. McMahan 3 ; 1 University of Nebraska- Lincoln, 2 University of South Carolina, 3 Clemson University Background: High volume screening of clinical specimens for infectious diseases is often made possible by a process known as pooled testing. This algorithmic process involves testing portions of specimens from separate individuals together as one unit (or “pool”) to detect infection. Follow-up retesting is performed on members of positive-testing pools to decode the positive members from the negative ones. An important decision needed prior to implementation of pooling is what pool size to use. Choosing too large of a pool size can lead to a large number of retests, perhaps even resulting in a total number of tests larger than what would occur by individually testing specimens. Choosing too small of a pool size can also lead to an overall larger number of tests than necessary. Methods: To help laboratories choose a pool size, we developed a Shiny web application that leverages the power of the R statistical software package. This application uses analytical derivations and computer simulation methods to emulate the pooling process. A user-friendly web interface is provided so that users do not need experience with R to perform calculations. Results and conclusions: The application calculates the expected number of tests and the expected accuracy for commonly used pooling algorithms. This application can also determine the “optimal” pool size(s) based on minimizing the expected number of tests. Access to the application is available through our www.chrisbilder.com/shiny website. This research is supported by Grant R01 AI121351 from the National Institutes of Health. Presenter: Christopher Bilder, University of Nebraska-Lincoln, email@example.com Analytical Validation of a Sample-to-Sequence Pipeline for Non-Targeted Pathogen Detection in Clinically Relevant Matrices K. Parker 1 , B. Knight 1 , H. Wood 1 , D. Yarmosh 1 , J. Russell 1 , J.R. Aspinwall 1 , K. Werking 1 , P. Chain 2 , P.E. Li 2 , R. Winegar 1 , 1 MRIGlobal, 2 Los Alamos National Laboratory The ability to identify an unknown infectious agent in a clinical sample is often limited by the tools available to the clinician. Current microbial and molecular methods are complicated by factors such as fastidious growth conditions, the need to perform a series of differential growth tests, and the challenges of designing large panels of molecular assays that are both sensitive and specific over a broad range of organisms. Next generation sequencing (NGS) provides a means for unbiased detection of pathogens from a variety of clinical matrices. With a non-targeted approach, NGS has PublicHealthLabs @APHL APHL.org In October 2018, the NYC Public Health Laboratory (PHL) began responding to a measles outbreak in Brooklyn. Measles virus testing includes real-time PCR testing of nasopharyngeal (NP) swabs and IgM and IgG testing of serum or blood samples. Requirements for testing clinical specimens include completing a paper submission form, proper labeling of the specimen, and proper collection, storage, and transport of the specimen. DOHMH works to ensure that any correctable error (i.e., missing or illegible information) is resolved through follow up with the submitter. Between 10/1 and 12/14, PHL received 419 requests for measles virus testing on 298 specimens. We examined specimen rejection rates by type of submission error and type of facility (high volume submitters were those that submitted >100 specimens of any type in 2018; all others were considered low volume submitters). the pre-analytical phase especially during an outbreak is of critical importance for timely initiation of testing, follow up, and public health action.