2025 AMIA Panel Proposal

Notes from email thread/SC call:

Tailor this to the AMIA audience, which is mainly physician informaticists

May wish to highlight it will help providers and facilities understand where test differences impact values, decisions, algorithms, research and interoperability

• Focusing on pain points for data exchange and how SHIELD can help

  • meaning of tests be distinct, method type is important, impact on incorrect codification of lab tests results in EHR-s

• Clinicians care about finding the right test to order, where to find the result and what the next step is for patient care

• Focusing on how SHIELD helps improve usability of lab results for providers - example Impact of Chlamydia testing if no or incorrect LOINC is used

  • ELR

  • eCR

  • HEDIS measures

  • Insurance reimbursements

  • data quality inititatives

  • research

We could have one panelist bring up the challenges and others could bring the solutions, or we could divide the breadths of issues and address both – the problem and how SHIELD is helping to fix it – on the technical as well as on the policy level.

Word document sections:

Title:

Automating sharing of high quality lab data and how SHIELD can support the Adoption of Standards for Laboratory Data Interoperability and Usability

Abstract:

Learn the latest about the Systemic Harmonization and Interoperability Enhancement for Laboratory Data (SHIELD) stakeholder initiative focused on US national laboratory interoperability needs. SHIELD’s work is to achieve semantic and clinical interoperability from laboratory ordering to performance of laboratory testing on in vitro diagnostics devices, to laboratory resulting to public health and the EHR, to usage of real-world laboratory data for research, surveillance, clinical trials and post market needs. An aim is to help maintain the integrity and complete meaning of a laboratory test to reduce negative decision-making impacts for optimizing patient care and outcomes and improving the quality and availability of data for research, surveillance and public health needs. Use of standards such as LOINC and SNOMED CT in SHIELD initiatives will be discussed.

Introduction:

Laboratory data in the United States is complex, starting with tagging structured data from the in vitro diagnostics (IVD) device through moving specified data to laboratory information systems (LIS), healthcare organizations and patients. In this presentation, we describe challenges across the lab information ecosystem and proposed solutions to harmonize the process of lab information exchange. Systemic Harmonization and Interoperability Enhancement for Laboratory Data (SHIELD) is a public-private partnership of over 70 organizations representing federal agencies, in vitro diagnostics (IVD) organizations, standards development organizations, medical associations, and other professional laboratory organizations established to build, implement, and support a comprehensive solution that addresses clinical and semantic device interoperability of in vitro diagnostics (IVD) across the nation.

SHIELD strives to create laboratory data interoperability because it will enable accurate public health surveillance, provide confidence to patients and providers in laboratory results and ensure medical researchers that their studies can be reproduced.  Attendees will learn more about current challenges with sharing meaningful, consistent laboratory result information between systems, areas where stakeholders need to collaborate to achieve this level of interoperability and harmonization and how they can contribute. SHIELD’s mission is to “Describe the SAME test the SAME way ANYWHERE in the Healthcare ecosystem”

Panel Members:

1. Jenna Rychert, PhD, ABMM - Medical Director: Operational Informatics, Microbial Immunology, and Customer Support; Director, Laboratory and Clinical IT, ARUP Laboratories

Adjunct Associate Professor, University of Utah School of Medicine

Relevant References:

·       Rychert J. In support of interoperability: A laboratory perspective. Int J Lab Hematol. 2023 Aug;45(4):436-441. doi: 10.1111/ijlh.14113. Epub 2023 Jun 20. PMID: 37337695.

·       Rychert, J. LOINC Management in Reference Labs. 2023 LOINC Conference.

2. Pamela Banning, MLS(ASCP)^cm, PMP – Senior Healthcare Content Developer, Solventum Health Information Systems; Laboratory LOINC Committee Chair

 Relevant References:

·       Banning, P. Mapping in Action: Mapping Local Test Codes to LOINC. 2024 LOINC Conference

·       Banning, P. In-roads: Advancing LOINC Adoption in Clinical Trials. 2023 LOINC Conference

3. _________Marjorie Rallins

Relevant References:

4. _________ Julia Skapik

Relevant References:

Discussion and Conclusion:

Discussion Topics – Jenna Rychert: 

• Keeping LOINC up to date - ensuring the lab has processes in place to get the best LOINC assigned as the test evolves over time and as LOINC evolves over time

• LIS limitations - often the assumption is that there is no need for the LIS to communicate outside of the EHR it is built into

  • HL7 2.5.1 inbound is not possible

  • Assigning SNOMED to qualifiers (Negative, Positive, etc) is not available

  • Mechanisms for assigning LOINC for Microbiology are clunky and don't exist for AP

  • No mechanism to assign LOINC at the order level, only the result level

  • No reflex mechanism outside of the micro module

  • No support for more than one standard code system (standard code system is hardcoded)

  • No standard support for specimen attributes (most have a single bucket they call specimen source, some have type and source site) making it hard to properly describe specimen, particularly for AP and micro (infection control follow up)

  • Requirement to have a result in order to produce a report (for example when a specimen is rejected – that should really not be reported as a result)

• Order level LOINC is often requested by clients, especially for public health reporting but it isn't always practical to attempt to assign a LOINC

• HEDIS measures - we are finding that our clients are trying to provide lab data that that is needed for HEDIS but the LOINC ARUP provides is not on the list of acceptable lab tests (maybe the new LOINC ontology will help address that and the HEDIS panels could define mostly the analytes that should be included rather than kuisting explicit LOINCS as part of the panel (think of it as a valueset definition that gets expanded every time it is called, so that new additions can be considered?))

Discussion Topics (tailor as needed) – Pam Banning

·       Examples of why LOINC is not a total solution for systemic interoperability

·       Keeping LOINC up to date from an IVD manufacturer perspective. SHIELD encourages use of IICC’s LIVD format for communication of approved LOINC terms. Examples of resources needed

·       Types of missing information from LIVD files (Supporting information)

·       Lab LOINC activities in improving LOINC for clinical laboratories; basic interoperability of systems

·       Known content gaps in LOINC

o   Flow Cytometry / Special Cell Immunology

o   Patient Generated Health Data

Discussion Topics - Julia Skapik

Integrating laboratory data across the lifecycle of healthcare: The role of UDI in ensuring valid laboratory data flow

The proposal would then describe multiple views in succession from upstream to downstream:

IVD vendor process

Lab ordering and testing

Transmission of lab results to healthcare entities and patients

Validation and reuse of lab data across downstream use cases (not patient care but things like billing, public health, etc)

Ideally we would at each transition point describe the existing challenges to seamless data transmission and then suggest approaches that would facilitate closing these gaps.

Discussion Topics - Marjorie Rallins

Discussion Topics Steven Emrick

Overall References??:

1. Luu, Laboratory Data as a Potential Source of Bias in Healthcare Artificial Intellgence and Machine Learning Models, Annals of Laboratory Medicine, 2024, October 24, PMID: 39444135 

2. Semantic Interoperability for In Vitro Diagnostic Systems. 1st ed. CLSI report AUTO17. Clinical and Laboratory Standards Institute; 2023.

Suggested Discussion Questions:

Potential Ideas/Outline

1. First panelist gives overview of SHIELD and describes the problem(s) to T up how SHIELD addresses

2. Another panelist, Using Chlamydia as example test, briefly (few slides) describe how LIVD standard can be used by labs for LOINCs needed to map and build in LIS. Lab perspective.

   1. Also how receipt of SCT codes needed for source site and used by labs for PH reporting. value of specimen info, coding, etc. Include/expand on Jenna’s points on support of specimen info?

   2. Focus on how lab name doesn’t have complete info/meaning (maybe compare 2 different chlamydia results form different labs that have same name/difference LOINCs?) value of coding. Lab A Chlamydia vs Lab B Chlamydia

   3. Mention how SHIELD is working to build LIDR to have more complete test details. can mention UDI. (If we have 2 different IVD LIVD maps for Chlamydia, may have UDIs. Otherwise, may need to look up.)

3. Another panelist. Show EHR receiving Lab A Chlamydia and Lab B Chlamydia in EHR. Provider perspective

   1. Are they built separately to support separate coding needs or is a single Chlamydia result built and a generic LOINC is needed? Compare/contrast what lab provides vs what is seen/used in EHR.

   2. Address provider/informaticist needs/questions/concerns

   3. HEDIS measure reporting. Are these distinct tests in the HEDIS value set/LOINCs?

      1. To Jenna’s point is the lab provided LOINC missing from the Hedis value set? and thus burden/issues with quality measure reporting as a result?

   4. What story to tell? comparability, test details, exchanges with other EHRs, HIEs, etc.? Reuse per Julia’s comments

   5. Comparability in

      1. chart review. SHIELD’s future work on flags for clinically significantly different result values. (Desired by clinicians)

      2. algorithms, See FDA AST example (or others Pam’s LOINC presentations) where clinically significant differences.

      3. External exports/exchanges and external lab imports/exchanges. completeness of data vs information loss. See Hung’s SHIELD paper

      4. research.

         1. Data warehouse/data lake Benefits: codifed data can be more easily automated ETL into research Common Data Models such as OMOP, i2b2, FHIR mCode, and PCORI

         2. Clinical research informatics queries/analysis leveraging standards, instead of data element names

         3. EHR research platforms like COSMOS leverage EHR builds/maps

         4. Coding helps reduce biases introduced where test details are lost and values are comingled. Differences due to tests, not disease or treatment effects.

4. Another panelist Public Health Reporting. PH perspective.

   1. ELR:

      1. Briefly describe reporting requirements by law for Chlamydia (including if any source site differences)

      2. Value of LOINC/LIVD/Standards. Show PH perspective (variety of Chlamydia LOINCs they may receive and how it helps reduce their burden)? Perhaps use one of the CDC LIVD maps to show variety of IVD tests/LOINCs?

      3. Mention how standards help PH with automated receipt/processing, per SC discussion?

   2. eCR

      1. Briefly describe provider reporting requirements by law for Chlamydia

      2. Describe how LOINCs are the trigger codes for reporting to reduce clinical burden

      3. Value of LOINC/Standards for PH processing reducing PH burden.

   3. Other PH reporting to mention? Cancer? Similar benefits. Expand on Jenna’s points of (lack pf ) LOINC functionality for AP?

5. Moderator: Summary/QA

6. Near Final Submission