Lab Matters Fall 2024 | Page 87

APHL 2024 POSTER ABSTRACTS

• Create an alert and notification process so that data quality and validation teams can follow-up when incorrect data is entered or when there are data discrepancies

• Develop a way to update changes in LIMS when changes are made to the Vital Stats database , or send alerts to staff when such changes are made .

• Improve staff lookup capabilities when searching FVS

New Features / Functions that were developed as part of the Florida NBS and Vital Stats Data Matching Application and included :

• Matches for 4 fields ( Birth date and time / MRN – medical record number / Mother address / Birth hospital ) ( Other data elements can be added in the future )

• Flag or alert for data that does not match – so that validation staff can follow up and correct

• Auto populate specific missing fields for a dropdown / Create acceptance window for each auto populated field

• Auto populate notes stating data were pulled from FVS with date / time / DEO stamp

• Develop algorithm to match birth file to NBS order

• Process to import / update birth file with most current data

• Develop an interface to conduct matching between the FVS and LIMS

• Ability to overwrite / house deceased data

Post-implementation data management includes FVS Interface monitoring and maintenance and alert and notification of data quality , validation and follow-up .

Presenter : Eddie Gonzalez Loumiet , lmin @ ruvos . com

Modernization of PLR Code Set to Support Public Health

S . Brumley 1 , J . Patterson 2 , S . Shakib 1 , A . Liu 3 , Clinical Architecture 1 , The St John Group / APHL Contractor 2 , Inductive Health / APHL

Contractor 3

Background : The Association of Public Health Laboratories ( APHL ) represents labs that monitor and detect public health threats . As part of this effort , APHL created and maintains the Public Health Lab Result ( PLR ) code system for labs to capture concepts not currently in SNOMED CT ( SCT ). Historically , these concepts were created and managed internally on a spreadsheet , resulting in a system that was error prone , had limited collaboration functionality and led to hundreds of duplicate concepts . APHL recognized the need for modernizing the PLR code system and partnered with Clinical Architecture ( CA ), a terminology management software vendor , to help with this effort . Utilization of CA ’ s Symedical as the software solution greatly reduced manual mapping efforts , improved collaboration with partners and helped standardize the code set .

Methods : APHL utilized Symedical and its web-based adaptive workflow ( AWF ) to support the PLR management lifecycle . After acquiring a SCT namespace identifier , a reconciliation occurred to remove duplicate concepts that already existed in either the PLR code system or SCT . The transition to creating an APHL SCT extension was accomplished by formalizing a process and workflow for authoring new concepts that met practical requirements for PLR and adhered to SCT best practices . Ongoing maintenance occurs by mapping PLR to SCT codes to identify duplicates . As new APHL SCT extension codes are created , the tracking of submissions to SNOMED is accomplished via AWF and the concepts are published in Symedical Viewpoint for public access .

Results : This poster illustrates the creation of an SCT extension management lifecycle and ongoing efforts to ensure public health labs have access to concepts in real time , thereby eliminating the need to wait for the next SCT release . The initial go live occurred in early 2022 , where 300 duplicate PLR concepts were identified and then inactivated or superseded . To date , over 1800 mappings have been created between PLR and existing SCT codes .

Lessons Learned : The utilization of Symedical has greatly improved the creation and maintenance of APHL terminology by removing many of the limitations caused by using a spreadsheet for code system creation . Symedical can quickly identify potential duplicate concepts when a new term is created . Instead of manually mapping over 6,000 concepts after each SCT release , the tooling utilizes machine generated computable definitions to automate the mapping process , thereby reducing both mapping errors and the level of effort . Additionally , the functionalities within Symedical allow terminologists to work collaboratively when mapping APHL SCT extension terms to SCT concepts . Lastly , the customized templates and workflows facilitate the submission of APHL extension codes to SNOMED . With a more robust terminology service , APHL is better able to support public health labs and terminology standards .

Presenter : Sarah Brumley , sarah _ brumley @ clinicalarchitecture . com

Modernizing Electronic Laboratory Reporting

M . Malai 1 , S . Brumley 2 , Datapult , an Association of Public Health Laboratories company 1 , Clinical Architecture 2

Background : An important aspect of data modernization is to eliminate redundant efforts to build a national infrastructure that ’ s resilient , adaptable and sustainable . There is an opportunity to modernize electronic laboratory reporting to create more efficient processes that eliminate the need to establish separate data interfaces between each laboratory and public health agency . Using reporting hubs that allow one interface to reach many disease surveillance systems , laboratories and public health agencies can save hundreds of hours of work . Datapult , working together with Clinical Architecture , developed a system to address the challenges of long onboarding times and intensive manual intervention faced by both the reporting laboratory and the receiving public health department when it came to reportable lab data .

Methods : To achieve a process that produces the highest quality laboratory reporting files each time , our team developed a modular framework that first identified the smallest units of data that would be used to identify a test and result . Those units are then combined to reflect the specific rules for each jurisdiction in our inferencing engine .

The inferencing engine can detect the results from a laboratory to determine whether the combined test type and test answer are reportable to public health . Then , our system focuses on normalizing the CSV or HL7 file to ensure data complies with the HL7 v2.5.1 ELR format .

Results : Our process improves the efficiency of onboarding by using one system that can be used to report to all state , local and territorial disease surveillance systems . Laboratories only need to