Presentation Catalogue

CLEM MCDONALD LECTURE

Terminology Mapping’s Key Role in Improving Care: The Experience of the INPC

John P. Kansky
President/CEO
Indiana Health Information Exchange

LOINC 101: Understanding LOINC concepts and uses

Pamela Banning, MLS(ASCP)cm, PMP(PMI)
3M Medical Informatics

Jami Deckard, MS
Regenstrief Institute

LOINC is the universal standard for identifying health measurements, observations, and documents. It is now ubiquitous in health data systems worldwide and is an essential ingredient of system interoperability. This tutorial presents an overview of LOINC and its use around the world, discusses the LOINC concept model and data structures, and describes the resources available for implementing LOINC. If you are new(ish) to LOINC, this session should be your starting point.

Specific examples of ongoing collaborative research and obstacles using physiologic time-series signals

Gilles Clermont
University of Pittsburgh Medical Center/VA Pittsburgh Health System

Many physicians and engineers have been using physiologic time-series signals to build a new class of predictive algorithms in monitored patients. These physiologic times series are typically waveform of vital signs digitized at 100-500Hz. Alongside these extremely high-density data, a numerical signal is generated every second 1Hz. For example, blood pressure a wave with a particular shape, with a maximum (systolic) and diastolic pressures. There are several vendors around generating waveform density signals. Several groups are now developing ML-based engines based on those signals. As a consequence, there is a growing need for interoperability of the input into those engines, and tagging LOINC codes to 1HZ and >100Hz signals would be of great help to those developing such analytics. The closest there exists right now is a LOINC code for moderate frequency output from a wearable signal such as Fitbit generate heart rate. Our problem is a lot richer and very different.

Tapping into the power of LOINC searches

John Hook
Regenstrief Institute

Brenée Mitchell, MS
Regenstrief Institute

The secret to finding the appropriate LOINC code is all about knowing how to effectively search the database. You will learn the tips and tricks necessary to craft effective LOINC searches regardless of whether you're using RELMA or search.loinc.org. We will show you how to combine keywords, filter, and sort to quickly find exactly what you are looking for, as well as show you how to avoid common pitfalls.

The German Corona Consensus Dataset (GECCO): A standardized dataset for COVID-19

Christine Haas
BfArM

Sylvia Thun
Berlin Institute of Health (BIH)

Kai Heitmann
hih – health innovation hub of the Federal Ministry of Health, Berlin

The current COVID-19 pandemic has led to a surge of research activity. To support and accelerate these activities as well as to improve comparability in an increasing segmentation of information and interoperability a uniform dataset has been established. The “German Corona Consensus Dataset” (GECCO) uses international terminologies and health IT standards.

Generating FDA-ready submission datasets for new COVID-19 therapies directly from Electronic Health Records using the new COVID-19 LOINC codes

Jozef Aerts
XML4Pharma

By using precoordinated LOINC codes, the generation of clinical research FDA submission datasets from EHRs can be fully automated. When using the classic process, such submission dataset generation usually takes months. In order to enable this automation, we extended the existing LOINC-CDISC mapping for laboratory tests with additional mappings for vital signs and the new COVID-19 LOINC codes and deployed these as RESTful web services. The presentation will contain a demo showing the retrieval of all relevant COVID-19 information from an FHIR-enabled EHR, with the generation of submission-ready FDA datasets, in just minutes. We will further discuss how the use of precoordinated LOINC and SNOMED-CT can considerably increase efficiency in clinical research.

AI Algorithms coding with international standards: ICD-10, LOINC and SNOMED CT for the epidemiological surveillance environment

M. Rodríguez Naque
BITAC

M. Prieto
SD Information systems- SES

L. Lozano
SD Information systems- SES

G. Calero
BITAC

G. González Gacio
BITAC

V. Bérez Baldrich
BITAC

A. Mas Mota
BITAC

The rise of emerging and re-emerging reportable diseases such as SARS-COV-2, multidrug-resistant tuberculosis (MDR-TB), Ebola, and related information needs have to lead to increasing interest in reportable disease surveillance and notification systems. Control of Notifiable Diseases requires accurate and efficient surveillance, via the provision of reporting system at all levels. To achieve a new and simple model, it is necessary to design, process, and facilitate the flow of information as well as reporting systems. Our goal is to address this challenge through the CTMAP application, a tool for encoding health data. ICD10-CM (WHO) is the mandatory universal classification for diagnoses in the National Health System (SNS). LOINC (Regenstrief Institute) and SNOMED CT (Snomed International) are the most widely used terminologies in the field of laboratory information systems. This project is part of the digital strategy to improve healthcare processes in the Extremadura Health Service (SES), incorporating CAC (Computer Assisted Coding) and PLN (Natural Language Processing) technology.

Pooled COVID-19 Testing: Mapping and Interoperability Considerations

Andrea Pitkus, PhD, MLS(ASCP)CM
University of Wisconsin School of Medicine and Public Health

Learn about the types of COVID-19 pooled testing, how they are reported, and considerations in how these data are used. Test build highlights and implications for mapping to LOINC and SNOMED CT and populating HL7 fields for messaging to public health, EHRs, and data warehouses will be discussed. Distinctions will be made with pooled testing used for screening, diagnostic and surveillance purposes. Limitations, cautions, and interoperability aspects will be presented.

Building Blocks of LOINC: Parts and how they link to other ontologies

David Baorto, Ph.D., MD
Regenstrief Institute

Parts are the LEGO bricks used to build LOINC terms. These reusable pieces have many other applications within LOINC, including those related to descriptions and name building, and also can be used to link to resources outside of LOINC, such as external ontologies. This session, suitable for both newcomers and experienced LOINCers, will teach you the purpose behind LOINC Parts and how you can take advantage of their capabilities.

Automated hierarchical crosswalk between LOINC Laboratory tests and SNOMED

Polina Talapova, MD
Odysseus Data Services

Eduard Korchmar
Odysseus Data Services

It is known that the purpose of the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is to standardize the representation format of healthcare data. It has been adopted by the Health Data Sciences and Informatics (OHDSI) collaborative, a multi-stakeholder, to create open-source solutions that bring out the value of observational health data through large-scale analytics. In the OMOP CDM, such ontologies as SNOMED CT and LOINC are used as Gold standards for the transformation of raw Observations and Measurements into the standardized ones. Moreover, Regenstrief Institute and SNOMED International have formed a long-term collaborative relationship with the objective of developing coded content to support order entry and result reporting.

Overview of LOINC FHIR Terminology Services

Swapna Abhyankar, MD
Regenstrief Institute

Come learn how to access LOINC content programmatically through LOINC's FHIR Terminology Services (TS) API. In this session, you will learn how to get the latest release of LOINC from our FHIR TS, including terms, Parts, Answer lists, and Groups. In addition, we will review the specialized ConceptMaps and ValueSets that are available, as well as our representation of LOINC panels as FHIR Questionnaires. We will look at the relationships between LOINC terms and Parts, including those found in the LOINC Multiaxial hierarchy. And finally, we will discuss our upcoming launch of CodeSystem versioning, which will allow us to publish pre-release terms, such as those related to the COVID-19 pandemic, as part of our FHIR TS.

The LHC LOINC validator and the numbers and kinds of mapping errors in 190 million mapped clinical observations

Clem McDonald, MD
Chief Health Data Standards Officer
National Library of Medicine, National Institutes of Health

LHC is developing a validator for mappings between local and LOINC codes with its primary focus on laboratory tests. It depends strongly on the relationship between reported units of measures and the property of the LOINC term, as does the Regenstrief map checker, but with some differences. (https://lhcforms.nlm.nih.gov/fhir/loinc-mapping-validator/). We obtained a de-identified dataset containing information about PCORnet mappings. PCORnet is a consortium of 60+ DataMarts with access to a collective 9 billion mapped tests. The current work is based on 190 million observations from two DataMarts but we anticipate getting similar data from additional DataMarts. Our dataset includes a record for every unique combination of a local unit string, local test name, and the mapping to LOINC codes. Each dataset record also carries the unique pattern, a number of records with that unique pattern, and, for numerical values, the median value of the related set of observations. We will be obtaining tranches from additional PCORnet DataMarts. The goal of our research is to use this data to identify and classify the kinds of mapping errors that occur and try to prevent and/or automatically correct them. In this session, I will describe the LHC LOINC mapping validator and UCUM validator, on which it depends, and present the early results of our analysis.

ONC & LOINC: A Look at the Future of Health IT Standards and Lab Interoperability

Avinash Shanbhag
Office of the National Coordinator for Health Information Technology (ONC)

Vaishali Patel
Office of the National Coordinator for Health Information Technology (ONC)

Tracy Okubo
Office of the National Coordinator for Health Information Technology (ONC)

Members of the ONC team will discuss the future of their collaboration work with LOINC and share insights from recent studies related to lab interoperability and health IT.

Occupational Health and LOINC: A Need for Standards

David Carlson
Enterprise Health

Richard Hammel, MD, MPH, FACPM, FACOEM
Enterprise Health

David and Rich from Enterprise Health, one of the leading companies for providing occupational health and compliance solutions, will discuss the state of coding standards (or in most cases the lack of them) in many segments of the occupational health industry, such as hearing conservation programs and the use of Audiometry testing (including equipment calibration records) to identify and monitor work-related hearing loss along with the use of employee questionnaires and noise monitoring (area and personal) to assess noise exposure levels. They will discuss working with LOINC to start getting codes for some of these tests and measurements in place, including the recently added Audiometry Testing panel and codes.

Sharing our experience of LOINC implementation in MainEDC™

Anna Polikarova
Data Management 365

Sharing Your Pain

Tess Settergren, MHA, MA, RN-BC
Independent

Susan A. Matney, Ph.D., RNC-OB, FAAN, FACMI, FHIMSS, FAMIA
Intermountain Healthcare

Kay Lytle, DNP, RN-BC, NEA-BC, CPHIMS, FHIMSS
Duke University Health System

Nathan Davis, MSHI, MT(ASCP)
Intermountain Healthcare

Electronic health record interoperability data standards will enable or enhance clinical decision support, coordination of care, quality measurement, research, and other purposes related to improving patient and population outcomes, and reducing healthcare costs. Pain is a pervasive health issue associated with high healthcare costs and variable patient outcomes. A pain information model was derived through the analysis of aggregated electronic health record metadata from eight healthcare systems. The model was refined through consensus and the content was mapped to existing LOINC terms and SNOMED CT concepts, using ONC's nursing data interoperability standards. Content requests were submitted where gaps existed. The terminology content was aligned with Fast Health Interoperability Resource (FHIR) elements and revised as needed. Developers of prioritized pain assessment scales were engaged to supply instrument validity and reliability evidence and to ensure accuracy of LOINC submissions. During this session, the presenters will discuss the challenges and discoveries identified during model development, terminology mapping, and curation. Terminology changes triggered by the alignment with the standard terminologies, logical models, and FHIR profile development will be described. Logical models and FHIR profiles will be demonstrated.

Problem Concept Maps (ProMaps): Use of LOINC for Mitigation of Cognitive Load/Considerations in the Utilization of LOINC in the Problem Concept Maps (PCM) Clinical Decision Support Tool

Andrea Pitkus, PhD, MLS(ASCP)CM
University of Wisconsin School of Medicine and Public Health

Joel Buchanan, MD
UW Health at Madison, WI

Excess cognitive load is an unintended consequence of Electronic Health Record (EHR) implementation and a significant contributor to physician burnout. Cognitive load can be mitigated by providing automatic data summaries on a single screen. These automatic data summaries function as a clinical decision support tool providing a Problem-Oriented View (POV), which is displayed on-the-fly using Problem Concept Maps (ProMaps). We have successfully built Problem Concept Maps using LOINC codes for laboratory results and RxNorm codes for medications. We will explain how ProMaps are built using a Modified Delphi technique on Google Drive to generate consensus among specialists at six academic medical centers.

Research results showing the effect of the POV using ProMaps on physician workflows at several US medical centers in a simulation environment will be presented. Statistically significant scores were obtained for faster and more accurate data retrieval, resulting in higher usability scores and lower cognitive load scores.

Our team will share some of the issues and challenges throughout our journey to assist others to better realize the value of LOINC in clinical decision support. Whether you are a laboratory professional or clinical informaticist building clinical decision support tools, rules, or FHIR apps with LOINC, learn about needs with data encoded with LOINC in driving interoperability and quality implementations.