Functional Analysis of G6PD Variants Associated With Low G6PD Activity in the All of Us Research Program

Glucose-6-phosphate dehydrogenase (G6PD) protects red blood cells against oxidative damage through regeneration of NADPH. Individuals with G6PD polymorphisms (variants) that produce an impaired G6PD enzyme are usually asymptomatic, but at risk of hemolytic anemia from oxidative stressors, including certain drugs and foods. Prevention of G6PD deficiency-related hemolytic anemia is achievable through G6PD genetic testing or whole-genome sequencing (WGS) to identify affected individuals who should avoid hemolytic triggers. However, accurately predicting the clinical consequence of G6PD variants is limited by over 800 G6PD variants which remain of uncertain significance. There also remains significant variability in which deficiency-causing variants are included in pharmacogenomic testing arrays across institutions: many panels only include c.202G>A, even though dozens of other variants can also cause G6PD deficiency. Here, we seek to improve G6PD genotype interpretation using data available in the All of Us Research Program and using a yeast functional assay. We confirm that G6PD coding variants are the main contributor to decreased G6PD activity, and that 13% of individuals in the All of Us data with deficiency-causing variants would be missed if only the c.202G>A variant were tested for. We expand clinical interpretation for G6PD variants of uncertain significance; reporting that c.595A>G, known as G6PD Dagua or G6PD Açores, and the newly identified variant c.430C>G, reduce activity sufficiently to lead to G6PD deficiency. We also provide evidence that five missense variants of uncertain significance are unlikely to lead to G6PD deficiency, since they were seen in hemi- or homozygous individuals without a reduction in G6PD activity. We also applied the new WHO guidelines and were able to classify two synonymous variants as WHO class C. We anticipate these results will improve the accuracy, and prompt increased use, of G6PD genetic tests through a more complete clinical interpretation of G6PD variants. As the All of Us data increases from 245,000 to 1 million participants, and additional functional assays are carried out, we expect this research to serve as a template to enable complete characterization of G6PD deficiency genotypes. With an increased number of interpreted variants, genetic testing of G6PD will be more informative for preemptively identifying individuals at risk for drug- or food-induced hemolytic anemia.

The Role of HL7 FHIR in the European Project GATEKEEPER

The European Project GATEKEEPER aims to develop a platform and marketplace to ensure a healthier independent life for the aging population. In this platform the role of HL7 FHIR is to provide a shared logical data model to collect data in heterogeneous living, which can be used by AI Service and the Gatekeeper HL7 FHIR Implementation Guide was created for this purpose. Independent pilots used this IG and illustrate the impact of the approach, benefit, value, and scalability.

Pediatric and Young Adult Household Transmission of the Initial Waves of SARS-CoV-2 in the United States: Administrative Claims Study

BACKGROUND

The correlates responsible for the temporal changes of intrahousehold SARS-CoV-2 transmission in the United States have been understudied mainly due to a lack of available surveillance data. Specifically, early analyses of SARS-CoV-2 household secondary attack rates (SARs) were small in sample size and conducted cross-sectionally at single time points. From these limited data, it has been difficult to assess the role that different risk factors have had on intrahousehold disease transmission in different stages of the ongoing COVID-19 pandemic, particularly in children and youth.

OBJECTIVE

This study aimed to estimate the transmission dynamic and infectivity of SARS-CoV-2 among pediatric and young adult index cases (age 0 to 25 years) in the United States through the initial waves of the pandemic.

METHODS

Using administrative claims, we analyzed 19 million SARS-CoV-2 test records between January 2020 and February 2021. We identified 36,241 households with pediatric index cases and calculated household SARs utilizing complete case information. Using a retrospective cohort design, we estimated the household SARS-CoV-2 transmission between 4 index age groups (0 to 4 years, 5 to 11 years, 12 to 17 years, and 18 to 25 years) while adjusting for sex, family size, quarter of first SARS-CoV-2 positive record, and residential regions of the index cases.

RESULTS

After filtering all household records for greater than one member in a household and missing information, only 36,241 (0.85%) of 4,270,130 households with a pediatric case remained in the analysis. Index cases aged between 0 and 17 years were a minority of the total index cases (n=11,484, 11%). The overall SAR of SARS-CoV-2 was 23.04% (95% CI 21.88-24.19). As a comparison, the SAR for all ages (0 to 65+ years) was 32.4% (95% CI 32.1-32.8), higher than the SAR for the population between 0 and 25 years of age. The highest SAR of 38.3% was observed in April 2020 (95% CI 31.6-45), while the lowest SAR of 15.6% was observed in September 2020 (95% CI 13.9-17.3). It consistently decreased from 32% to 21.1% as the age of index groups increased. In a multiple logistic regression analysis, we found that the youngest pediatric age group (0 to 4 years) had 1.69 times (95% CI 1.42-2.00) the odds of SARS-CoV-2 transmission to any family members when compared with the oldest group (18 to 25 years). Family size was significantly associated with household viral transmission (odds ratio 2.66, 95% CI 2.58-2.74).

CONCLUSIONS

Using retrospective claims data, the pediatric index transmission of SARS-CoV-2 during the initial waves of the COVID-19 pandemic in the United States was associated with location and family characteristics. Pediatric SAR (0 to 25 years) was less than the SAR for all age other groups. Less than 1% (n=36,241) of all household data were retained in the retrospective study for complete case analysis, perhaps biasing our findings. We have provided measures of baseline household pediatric transmission for tracking and comparing the infectivity of later SARS-CoV-2 variants.

The Necessity of Interoperability to Uncover the Full Potential of Digital Health Devices

Personalized health care can be optimized by including patient-reported outcomes. Standardized and disease-specific questionnaires have been developed and are routinely used. These patient-reported outcome questionnaires can be simple paper forms given to the patient to fill out with a pen or embedded in digital devices. Regardless of the format used, they provide a snapshot of the patient's feelings and indicate when therapies need to be adjusted. The advantage of digitizing these questionnaires is that they can be automatically analyzed, and patients can be monitored independently of doctor visits. Although the questions of most clinical patient-reported outcome questionnaires follow defined standards and are evaluated by clinical trials, these standards do not exist for data processing. Interoperable data formats and structures would benefit multilingual and cross-study data exchange. Linking questionnaires to standardized terminologies such as the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Logical Observation Identifiers, Names, and Codes (LOINC) would improve this interoperability. However, linking clinically validated patient-reported outcome questionnaires to clinical terms available in SNOMED CT or LOINC is not as straightforward as it sounds. Here, we report our approach to link patient-reported outcomes from health applications to SNOMED CT or LOINC codes. We highlight current difficulties in this process and outline ways to minimize them.

A novel approach for standardizing clinical laboratory categorical test results using machine learning and string distance similarity

Standardizing clinical laboratory test results is critical for conducting clinical data science research and analysis. However, standardized data processing tools and guidelines are inadequate. In this paper, a novel approach for standardizing categorical test results based on supervised machine learning and the Jaro-Winkler similarity algorithm is proposed. A supervised machine learning model is used in this approach for scalable categorization of the test results into predefined groups or clusters, while Jaro-Winkler similarity is used to map text terms into standard clinical terms within these corresponding groups. The proposed method is applied to 75062 test results from two private hospitals in Bangladesh. The Support Vector Classification algorithm with a linear kernel has a classification accuracy of 98%, which is better than the Random Forest algorithm when categorizing test results. The experiment results show that Jaro-Winkler similarity achieves a remarkable 99.93% success rate in the test result standardization for the majority of groups with manual validation. The proposed method outperforms previous studies that concentrated on standardizing test results using rule-based classifiers on a smaller number of groups and distance similarities such as Cosine similarity or Levenshtein distance. Furthermore, when applied to the publicly available MIMIC-III dataset, our approach also performs excellently. All these findings show that the proposed standardization technique can be very beneficial for clinical big data research, particularly for national clinical research data hubs in low- and middle-income countries.

[Coding of laboratory parameters using the LOINC system at the Clinical Center of the University of Debrecen]

INTRODUCTION

The research utility of the bulk of the medical data generated at the Clinical Center of the University of Debrecen, which is constituted mainly by the clinical diagnostic laboratory results and medical images, is quite constrained in its present unstandardized form. The primary aim of the Big Data Research and Development project at the University of Debrecen is to facilitate data transformation and standardization to propagate its research utility for the potential end-users. Data generated in the in vitro diagnostic laboratory setting are an ideal candidate for the aforementioned goals. Data generated in Hungarian language in this particular setting are typically acronyms that do not particularly confirm to any standard norms and the transformation of these data using the globally acknowledged Logical Observation Identifiers Names and Codes (LOINC) was the primary goal of this research project. Globally the LOINC is used by healthcare providers, government agencies, insurance companies, software and device manufacturers, researchers and reference laboratories for identifying medical laboratory observations and promote unhindered fluency between various systems.

OBJECTIVE

The aim of the project was to assure compliance of the various routine diagnostic laboratory parameters (n = 448) generated at the Department of Laboratory Medicine of the University of Debrecen to the LOINC system paying particular attention to and accommodating data sensitive to timeline and methodology.

METHODS

Keywords allocated to individual parameters determined by the laboratory were provided by the IT service provider of the facility. The individual codes for the various parameters were manually identified using the search engine of the LOINC database available at http://www.loinc.org, only upon attainment of proficiency in use of the database and ample familiarity with the scientific literature on the topic.

RESULTS

All routine diagnostic laboratory parameters were LOINC coded with no exception. The list of LOINCs' was made available on the https://labmed.unideb.hu/hu/loinc-tablazatok web link of the University of Debrecen.

CONCLUSION

The transformation of diagnostic laboratory parameters to globally recognized LOINCs' improves and further facilitates the international integration of data generated at the University of Debrecen, furthermore propels communications between laboratories and parties of interest beyond international boundaries and borders. Orv Hetil. 2023; 164(27): 1043-1051.

Experience from the Development of HL7 FHIR IG for Gatekeeper Project

The GATEKEEPER (GK) Project was financed by the European Commission to develop a platform and marketplace to share and match ideas, technologies, user needs and processes to ensure a healthier independent life for the aging population connecting all the actors involved in the care circle. In this paper, the GK platform architecture is presented focusing on the role of HL7 FHIR to provide a shared logical data model to be explored in heterogeneous daily living environments. GK pilots are used to illustrate the impact of the approach, benefit value, and scalability, suggesting ways to further accelerate progress.

In support of interoperability: A laboratory perspective

Healthcare in the United States has become increasingly digital since the passage of the HITECH Act in 2009. As a result, there is a growing need to optimize healthcare IT to allow for the interoperable exchange of data. As a result, the Office of the National Coordinator for Health IT has implemented their Final Rule for the 21st Century Cures Act. This requires certified health IT systems to use modernized messaging standards for the safe and secure exchange of data within health information networks and also requires the use of terminology standards including LOINC, SNOMED CT, and UCUM for coding clinical and laboratory data. Given the critical importance of laboratory results in the delivery of healthcare, laboratorians must become familiar with these principles of interoperability. Their clinical laboratory expertise is needed to appropriately structure and code test results to safeguard against improper aggregation or misinterpretation by downstream users and systems.

Automatic Outlier Detection in Laboratory Result Distributions Within a Real World Data Network

Laboratory data must be interoperable to be able to accurately compare the results of a lab test between healthcare organizations. To achieve this, terminologies like LOINC (Logical Observation Identifiers, Names and Codes) provide unique identification codes for laboratory tests. Once standardized, the numeric results of laboratory tests can be aggregated and represented in histograms. Due to the characteristics of Real World Data (RWD), outliers and abnormal values are common, but these cases should be treated as exceptions, excluding them from possible analysis. The proposed work analyses two methods capable of automating the selection of histogram limits to sanitize the generated lab test result distributions, Tukey's box-plot method and a "Distance to Density" approach, within the TriNetX Real World Data Network. The generated limits using clinical RWD are generally wider for Tukey's method and narrower for the second method, both greatly dependent on the values used for the algorithm's parameters.

Mis-mappings between a producer's quantitative test codes and LOINC codes and an algorithm for correcting them

OBJECTIVES

To access the accuracy of the Logical Observation Identifiers Names and Codes (LOINC) mapping to local laboratory test codes that is crucial to data integration across time and healthcare systems.

MATERIALS AND METHODS

We used software tools and manual reviews to estimate the rate of LOINC mapping errors among 179 million mapped test results from 2 DataMarts in PCORnet. We separately reported unweighted and weighted mapping error rates, overall and by parts of the LOINC term.

RESULTS

Of included 179 537 986 mapped results for 3029 quantitative tests, 95.4% were mapped correctly implying an 4.6% mapping error rate. Error rates were less than 5% for the more common tests with at least 100 000 mapped test results. Mapping errors varied across different LOINC classes. Error rates in chemistry and hematology classes, which together accounted for 92.0% of the mapped test results, were 0.4% and 7.5%, respectively. About 50% of mapping errors were due to errors in the property part of the LOINC name.

DISCUSSIONS

Mapping errors could be detected automatically through inconsistencies in (1) qualifiers of the analyte, (2) specimen type, (3) property, and (4) method. Among quantitative test results, which are the large majority of reported tests, application of automatic error detection and correction algorithm could reduce the mapping errors further.

CONCLUSIONS

Overall, the mapping error rate within the PCORnet data was 4.6%. This is nontrivial but less than other published error rates of 20%-40%. Such error rate decreased substantially to 0.1% after the application of automatic detection and correction algorithm.

Development and Implementation of a Standard Format for Clinical Laboratory Test Results

OBJECTIVES

Surprisingly, laboratory results, the principal output of clinical laboratories, are not standardized. Thus, laboratories frequently report results with identical meaning in different formats. For example, laboratories report a positive pregnancy test as "+," "P," or "Positive." To assess the feasibility of a widespread implementation of a result standard, we (1) developed a standard result format for common laboratory tests and (2) implemented a feedback system for clinical laboratories to view their unstandardized results.

METHODS

In the largest integrated health care system in America, 130 facilities had the opportunity to collaboratively develop the standard. For 15 weeks, clinical laboratories received a weekly report of their unstandardized results. At the study's conclusion, laboratories were compared with themselves and their peers by metrics that reflected their unstandardized results.

RESULTS

We rereviewed 156 million test results and observed a 51% decline in the rate of unstandardized results. The number of facilities with fewer than 23 unstandardized results per 100,000 (Six Sigma σ > 5) increased by 58% (52 to 82 facilities; β = 1.79; P < .001).

CONCLUSIONS

This study demonstrated significant improvement in the standardization of clinical laboratory results in a relatively short time. The laboratory community should create and promulgate a standardized result format.

Data Element Mapping in the Data Privacy Era

Secondary use of health data is made difficult in part because of large semantic heterogeneity. Many efforts are being made to align local terminologies with international standards. With increasing concerns about data privacy, we focused here on the use of machine learning methods to align biological data elements using aggregated features that could be shared as open data. A 3-step methodology (features engineering, blocking strategy and supervised learning) was proposed. The first results, although modest, are encouraging for the future development of this approach.

Building a Comprehensive Clinical Data Repository Using FHIR, LOINC and SNOMED

In 2018 the University Hospital of Giessen (UHG) moved its hospital information system from an in-house solution to commercial software. The introduction of MEONA and Synedra-AIM allowed for the successful migration of clinical documents. The large pool of structured clinical data has been addressed in a second step and is now consolidated in a HAPI-FHIR server and mapped to LOINC and SNOMED for semantic interoperability in multicenter research projects, especially the German Medical Informatics Initiative (MII) and the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium.

Indexing Imaging Reports for Data Sharing: A Study of Mapping Using RadLex Playbook and LOINC

New use cases and the need for quality control and imaging data sharing in health studies require the capacity to align them to reference terminologies. We are interested in mapping the local terminology used at our center to describe imaging procedures to reference terminologies for imaging procedures (RadLex Playbook and LOINC/RSNA Radiology Playbook). We performed a manual mapping of the 200 most frequent imaging report titles at our center (i.e. 73.2% of all imaging exams). The mapping method was based only on information explicitly stated in the titles. The results showed 57.5% and 68.8% of exact mapping to the RadLex and LOINC/RSNA Radiology Playbooks, respectively. We identified the reasons for the mapping failure and analyzed the issues encountered.

ELaPro, a LOINC-mapped core dataset for top laboratory procedures of eligibility screening for clinical trials

Background

Screening for eligible patients continues to pose a great challenge for many clinical trials. This has led to a rapidly growing interest in standardizing computable representations of eligibility criteria (EC) in order to develop tools that leverage data from electronic health record (EHR) systems. Although laboratory procedures (LP) represent a common entity of EC that is readily available and retrievable from EHR systems, there is a lack of interoperable data models for this entity of EC. A public, specialized data model that utilizes international, widely-adopted terminology for LP, e.g. Logical Observation Identifiers Names and Codes (LOINC®), is much needed to support automated screening tools.

Objective

The aim of this study is to establish a core dataset for LP most frequently requested to recruit patients for clinical trials using LOINC terminology. Employing such a core dataset could enhance the interface between study feasibility platforms and EHR systems and significantly improve automatic patient recruitment.

Methods

We used a semi-automated approach to analyze 10,516 screening forms from the Medical Data Models (MDM) portal’s data repository that are pre-annotated with Unified Medical Language System (UMLS). An automated semantic analysis based on concept frequency is followed by an extensive manual expert review performed by physicians to analyze complex recruitment-relevant concepts not amenable to automatic approach.

Results

Based on analysis of 138,225 EC from 10,516 screening forms, 55 laboratory procedures represented 77.87% of all UMLS laboratory concept occurrences identified in the selected EC forms. We identified 26,413 unique UMLS concepts from 118 UMLS semantic types and covered the vast majority of Medical Subject Headings (MeSH) disease domains.

Conclusions

Only a small set of common LP covers the majority of laboratory concepts in screening EC forms which supports the feasibility of establishing a focused core dataset for LP. We present ELaPro, a novel, LOINC-mapped, core dataset for the most frequent 55 LP requested in screening for clinical trials. ELaPro is available in multiple machine-readable data formats like CSV, ODM and HL7 FHIR. The extensive manual curation of this large number of free-text EC as well as the combining of UMLS and LOINC terminologies distinguishes this specialized dataset from previous relevant datasets in the literature.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01611-y.

Preparing Laboratories for Interconnected Health Care

In an increasingly interconnected health care system, laboratory medicine can facilitate diagnosis and treatment of patients effectively. This article describes necessary changes and points to potential challenges on a technical, content, and organizational level. As a technical precondition, electronic laboratory reports have to become machine-readable and interpretable. Terminologies such as Logical Observation Identifiers Names and Codes (LOINC), Nomenclature for Properties and Units (NPU), Unified Code for Units of Measure (UCUM), and SNOMED-CT can lead to the necessary semantic interoperability. Even if only single “atomized” results of the whole report are extracted, the necessary information for correct interpretation must be available. Therefore, interpretive comments, e.g., concerns about an increased measurement uncertainty must be electronically attached to every affected measurement result. Standardization of laboratory analyses with traceable standards and reference materials will enable knowledge transfer and safe interpretation of laboratory analyses from multiple laboratories. In an interconnected health care system, laboratories should strive to transform themselves into a data hub that not only receives samples but also extensive information about the patient. On that basis, they can return measurement results enriched with high-quality interpretive comments tailored to the individual patient and unlock the full potential of laboratory medicine.

Aligning an interface terminology to the Logical Observation Identifiers Names and Codes (LOINC®)

Objective

Our study consists in aligning the interface terminology of the Bordeaux university hospital (TLAB) to the Logical Observation Identifiers Names and Codes (LOINC). The objective was to facilitate the shared and integrated use of biological results with other health information systems.

Materials and Methods

We used an innovative approach based on a decomposition and re-composition of LOINC concepts according to the transversal relations that may be described between LOINC concepts and their definitional attributes. TLAB entities were first anchored to LOINC attributes and then aligned to LOINC concepts through the appropriate combination of definitional attributes. Finally, using laboratory results of the Bordeaux data-warehouse, an instance-based filtering process has been applied.

Results

We found a small overlap between the tokens constituting the labels of TLAB and LOINC. However, the TLAB entities have been easily aligned to LOINC attributes. Thus, 99.8% of TLAB entities have been related to a LOINC analyte and 61.0% to a LOINC system. A total of 55.4% of used TLAB entities in the hospital data-warehouse have been mapped to LOINC concepts. We performed a manual evaluation of all 1-1 mappings between TLAB entities and LOINC concepts and obtained a precision of 0.59.

Conclusion

We aligned TLAB and LOINC with reasonable performances, given the poor quality of TLAB labels. In terms of interoperability, the alignment of interface terminologies with LOINC could be improved through a more formal LOINC structure. This would allow queries on LOINC attributes rather than on LOINC concepts only.

An OpenEHR Template with the Integrated German LOINC Terms

An OpenEHR template based on LOINC terms in German language (LOINC-DE) has been created for the structured clinical data capture. The resulting template includes all terms available in LOINC-DE, which can be selected from the drop-down menu for clinical data capture. The template can be used as an independent laboratory form or it can be customized for local needs. This approach presents the possibility to include terminologies in EHR when capturing patient data.

Use of LOINC and SNOMED CT with FHIR for Microbiology Data

Infectious diseases due to microbial resistance pose a worldwide threat that calls for data sharing and the rapid reuse of medical data from health care to research. The integration of pathogen-related data from different hospitals can yield intelligent infection control systems that detect potentially dangerous germs as early as possible. Within the use case Infection Control of the German HiGHmed Project, eight university hospitals have agreed to share their data to enable analysis of various data sources. Data sharing among different hospitals requires interoperability standards that define the structure and the terminology of the information to be exchanged. This article presents the work performed at the University Hospital Charité and Berlin Institute of Health towards a standard model to exchange microbiology data. Fast Healthcare Interoperability Resources (FHIR) is a standard for fast information exchange that allows to model healthcare information, based on information packets called resources, which can be customized into so-called profiles to match use case- specific needs. We show how we created the specific profiles for microbiology data. The model was implemented using FHIR for the structure definition, and the international standards SNOMED CT and LOINC for the terminology services.

Use of EHRs in a Tertiary Hospital During COVID-19 Pandemic: A Multi-Purpose Approach Based on Standards

This work aims to describe how EHRs have been used to meet the needs of healthcare providers and researchers in a 1,300-beds tertiary Hospital during COVID-19 pandemic. For this purpose, essential clinical concepts were identified and standardized with LOINC and SNOMED CT. After that, these concepts were implemented in EHR systems and based on them, data tools, such as clinical alerts, dynamic patient lists and a clinical follow-up dashboard, were developed for healthcare support. In addition, these data were incorporated into standardized repositories and COVID-19 databases to improve clinical research on this new disease. In conclusion, standardized EHRs allowed implementation of useful multi- purpose data resources in a major Hospital in the course of the pandemic.

A Multi-User Terminology Mapping Toolbox

Semantic interoperability is a major challenge in multi-center data sharing projects, a challenge that the German Initiative for Medical Informatics is taking up. With respect to laboratory data, enriching site-specific tests and measurements with LOINC codes appears to be a crucial step in supporting cross-institutional research. However, this effort is very time-consuming, as it requires expert knowledge of local site specifics. To ease this process, we developed a generic manual collaborative terminology mapping tool, the MIRACUM Mapper. It allows the creation of arbitrary mapping workflows involving different user roles. A mapping workflow with two user roles has been implemented at University Hospital Erlangen to support the local LOINC mapping. Additionally, the MIRACUM LabVisualizeR provides summary statistics and visualizations of analyte data. We developed a toolbox that facilitates the collaborative creation of mappings and streamlines the review as well as the validation process. The two tools are available under an open source license.

Postcoordination of LOINC Codes in SNOMED CT

The objectives of this paper are to analyze the terminologies SNOMED CT and Logical Observation Identifiers Names and Codes (LOINC) and to provide a guideline for the translation of LOINC concepts to SNOMED CT. Verified research data sets were used for this study, so this experiment is replicable with other research data. 50 LOINC concepts of frequently performed laboratory services were translated to SNOMED CT. Information would be lost with pre-coordinated mapping but the compositional grammar of SNOMED CT allows for the linking of individual concepts into complicated postcoordinated expressions including all embedded information in LOINC concepts. All information can thus be transferred smoothly to SNOMED CT.

Towards Unified Data Exchange Formats for Reporting Molecular Drug Susceptibility Testing

Background

With the rapid development of new advanced molecular detection methods, identification of new genetic mutations conferring pathogen resistance to an ever-growing variety of antimicrobial substances will generate massive genomic datasets for public health and clinical laboratories. Keeping up with specialized standard coding for these immense datasets will be extremely challenging. This challenge prompted our effort to create a common molecular resistance Logical Observation Identifiers Names and Codes (LOINC) panel that can be used to report any identified antimicrobial resistance pattern.

Objective

To develop and utilize a common molecular resistance LOINC panel for molecular drug susceptibility testing (DST) data exchange in the U.S. National Tuberculosis Surveillance System using California Department of Public Health (CDPH) and New York State Department of Health as pilot sites.

Methods

We developed an interface and mapped incoming molecular DST data to the common molecular resistance LOINC panel using Health Level Seven (HL7) v2.5.1 Electronic Laboratory Reporting (ELR) message specifications through the Orion Health™ Rhapsody Integration Engine v6.3.1.

Results

Both pilot sites were able to process and upload/import the standardized HL7 v2.5.1 ELR messages into their respective systems; albeit CDPH identified areas for system improvements and has focused efforts to streamline the message importation process. Specifically, CDPH is enhancing their system to better capture parent-child elements and ensure that the data collected can be accessed seamlessly by the U.S. Centers for Disease Control and Prevention.

Discussion

The common molecular resistance LOINC panel is designed to be generalizable across other resistance genes and ideally also applicable to other disease domains.

Conclusion

The study demonstrates that it is possible to exchange molecular DST data across the continuum of disparate healthcare information systems in integrated public health environments using the common molecular resistance LOINC panel.

COVID-19 TestNorm: A tool to normalize COVID-19 testing names to LOINC codes

Large observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on coronavirus disease 2019 (COVID-19). Data normalization is a key challenge for the secondary use of EHRs for COVID-19 research across institutions. In this study, we addressed the challenge of automating the normalization of COVID-19 diagnostic tests, which are critical data elements, but for which controlled terminology terms were published after clinical implementation. We developed a simple but effective rule-based tool called COVID-19 TestNorm to automatically normalize local COVID-19 testing names to standard LOINC (Logical Observation Identifiers Names and Codes) codes. COVID-19 TestNorm was developed and evaluated using 568 test names collected from 8 healthcare systems. Our results show that it could achieve an accuracy of 97.4% on an independent test set. COVID-19 TestNorm is available as an open-source package for developers and as an online Web application for end users (https://clamp.uth.edu/covid/loinc.php). We believe that it will be a useful tool to support secondary use of EHRs for research on COVID-19.

Building an I2B2-Based Population Repository for Clinical Research

The present work provides a real-world case of the connection process of a hospital, 12 de Octubre University Hospital in Spain, to the TriNetX research network, transforming a compilation of disparate sources into a single harmonized repository which is automatically refreshed every day. It describes the different integration phases: terminology core datasets, specialized sources and eventually automatic refreshment. It also explains the work performed on semantic normalization of the involved clinical terminologies; as well as the resulting benefits the InSite platform services have enabled in the form of research opportunities for the hospital.

Data Integration into OMOP CDM for Heterogeneous Clinical Data Collections via HL7 FHIR Bundles and XSLT

Data integration is an important task in medical informatics and highly impacts the gain out of existing health information data. These tasks are using implemented as extract transform and load processes. By introducing HL7 FHIR as an intermediate format, our aim was to integrate heterogeneous data from a German pulmonary hypertension registry into an OMOP Common Data Model. First, domain knowledge experts defined a common parameter set, which was subsequently mapped to standardized terminologies like LOINC or SNOMED-CT. Data was extracted as HL7 FHIR Bundle to be transformed to OMOP CDM by using XSLT. We successfully transformed the majority of data elements to the OMOP CDM in a feasible time.

Standardizing Germany's Electronic Disease Management Program for Bronchial Asthma

Disease management programs coordinate and manage treatment between physicians and across sectors of the healthcare system. The aim is to reduce existing care deficits (overuse, underuse and misuse) and thus improve the quality and cost-effectiveness of care. To facilitate the treatment of chronic diseases such as asthma, it is important to continuously document a patient's medical history. For this purpose, it is necessary to be able to integrate and exchange data from and between multiple different information systems. Aiming to ensure interoperability across electronic documentation systems, this paper proposes the standardization of the KBV's (National Association of Statutory Health Insurance in Germany) specification for the electronic Disease Management Program (eDMP) for bronchial asthma. Therefore, international standards like SNOMED CT, LOINC and UCUM were chosen to encode clinical information, while evaluating their suitability with the scoring system ISO/PRF TR 21564. The resulting analysis showed that most of the terms had either a complete or partial equivalent term in one of the terminology systems. Therefore, future implementations of the eDMP for bronchial asthma that utilize standard terminologies could benefit from data integration from different sources like electronic health records and reduce redundancies in data capture and storage.

Aggregation and Visualization of Laboratory Data by Using Ontological Tools Based on LOINC and SNOMED CT

With the proliferation of digital communication in healthcare, the reuse of laboratory test data entails valuable insights into clinical and scientific issues, basically enabled by semantic standardization using the LOINC coding system. In order to extend the currently limited potential for analysis, which is mainly caused by structural peculiarities of LOINC, an algorithmic transformation of relevant content into an OWL ontology was performed, which includes LOINC Terms, Parts and Hierarchies. For extending analysis capabilities, the comprehensive SNOMED CT ontology is added by transferring its contents and the recently published LOINC-related mapping data into OWL ontologies. These formalizations offer rich, computer-processable content and allow to infer additional structures and relationships, especially when used together. Consequently, various reutilizations are facilitated; an application demonstrating the dynamic visualization of fractional hierarchy structures for user-supplied laboratory data was already implemented. By providing element-wise aggregation via superclasses, an adaptable, graph representation is obtained for studying categorizations.

Implementing LOINC - Current Status and Ongoing Work at a Medical University

The Logical Observation Identifiers, Names and Codes (LOINC) is a common terminology used for standardizing laboratory terms. Within the consortium of the HiGHmed project, LOINC is one of the central terminologies used for health data sharing across all university sites. Therefore, linking the LOINC codes to the site-specific tests and measures is one crucial step to reach this goal. In this work we report our ongoing efforts in implementing LOINC to our laboratory information system and research infrastructure, as well as our challenges and the lessons learned. 407 local terms could be mapped to 376 LOINC codes of which 209 are already available to routine laboratory data. In our experience, mapping of local terms to LOINC is a widely manual and time consuming process for reasons of language and expert knowledge of local laboratory procedures.

Documenting social determinants of health-related clinical activities using standardized medical vocabularies

OBJECTIVES

Growing recognition that health is shaped by social and economic circumstances has resulted in a rapidly expanding set of clinical activities related to identifying, diagnosing, and intervening around patients' social risks in the context of health care delivery. The objective of this exploratory analysis was to identify existing documentation tools in common US medical coding systems reflecting these emerging clinical practices to improve patients' social health.

MATERIALS AND METHODS

We identified 20 social determinants of health (SDH)-related domains used in 6 published social health assessment tools. We then used medical vocabulary search engines to conduct three independent searches for codes related to these 20 domains included in common medical coding systems (LOINC, SNOMED CT, ICD-10-CM, and CPT). Each of the 3 searches focused on one of three clinical activities: Screening, Assessment/Diagnosis, and Treatment/Intervention.

RESULTS

We found at least 1 social Screening code for 18 of the 20 SDH domains, 686 social risk Assessment/Diagnosis codes, and 243 Treatment/Intervention codes. Fourteen SDH domains (70%) had codes across all 3 clinical activity areas.

DISCUSSION

Our exploratory analysis revealed 1095 existing codes in common medical coding vocabularies that can facilitate documentation of social health-related clinical activities. Despite a large absolute number of codes, there are addressable gaps in the capacity of current medical vocabularies to document specific social risk factor screening, diagnosis, and interventions activities.

CONCLUSIONS

Findings from this analysis should help inform efforts both to develop a comprehensive set of SDH codes and ultimately to improve documentation of SDH-related activities in clinical settings.

Unit conversions between LOINC codes

Logical Observation Identifiers Names and Codes (LOINC) is the most widely used controlled vocabulary to identify laboratory tests. A given laboratory test can often be reported in more than 1 unit of measure (eg, grams or moles), and LOINC defines unique codes for each unit. Consequently, an identical laboratory test performed by 2 different clinical laboratories may have different LOINC codes. The absence of unit conversions between compatible LOINC codes impedes data aggregation and analysis of laboratory results. To develop such conversions, a computational process was developed to review the LOINC standard for potential conversions, and multiple expert reviewers oversaw and finalized the conversion list. In all, 285 bidirectional conversions were identified, including conversions for routine clinical tests such as sodium, magnesium, and human immunodeficiency virus (HIV). Unit conversions were applied to the aggregation of laboratory test results to demonstrate their usefulness. Diverse informatics projects may benefit from the ability to interconvert compatible results.

LabRS: A Rosetta stone for retrospective standardization of clinical laboratory test results

Objective

Clinical laboratories in the United States do not have an explicit result standard to report the 7 billion laboratory tests results they produce each year. The absence of standardized test results creates inefficiencies and ambiguities for secondary data users. We developed and tested a tool to standardize the results of laboratory tests in a large, multicenter clinical data warehouse.

Methods

Laboratory records, each of which consisted of a laboratory result and a test identifier, from 27 diverse facilities were captured from 2000 through 2015. Each record underwent a standardization process to convert the original result into a format amenable to secondary data analysis. The standardization process included the correction of typos, normalization of categorical results, separation of inequalities from numbers, and conversion of numbers represented by words (eg, "million") to numerals. Quality control included expert review.

Results

We obtained 1.266 × 109 laboratory records and standardized 1.252 × 109 records (98.9%). Of the unique unstandardized records (78.887 × 103), most appeared <5 times (96%, eg, typos), did not have a test identifier (47%), or belonged to an esoteric test with <100 results (2%). Overall, these 3 reasons accounted for nearly all unstandardized results (98%).

Conclusion

Current results suggest that the tool is both scalable and generalizable among diverse clinical laboratories. Based on observed trends, the tool will require ongoing maintenance to stay current with new tests and result formats. Future work to develop and implement an explicit standard for test results would reduce the need to retrospectively standardize test results.

Implementing LOINC: Current Status and Ongoing Work at the Hannover Medical School

The Logical Observation Identifiers, Names and Codes (LOINC) is a common terminology used for standardizing laboratory terms. Within the HiGHmed consortium, LOINC is used as a central terminology for health data sharing across all university hospital sites. Therefore, linking the LOINC codes to the site-specific tests and measures is one crucial step to reach this goal. In this work we report our ongoing work in implementing LOINC to the laboratory information system, our challenges and lessons learned.

A computable pathology report for precision medicine: extending an observables ontology unifying SNOMED CT and LOINC

Background

The College of American Pathologists (CAP) introduced the first cancer synoptic reporting protocols in 1998. However, the objective of a fully computable and machine-readable cancer synoptic report remains elusive due to insufficient definitional content in Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT) and Logical Observation Identifiers Names and Codes (LOINC). To address this terminology gap, investigators at the University of Nebraska Medical Center (UNMC) are developing, authoring, and testing a SNOMED CT observable ontology to represent the data elements identified by the synoptic worksheets of CAP.

Methods

Investigators along with collaborators from the US National Library of Medicine, CAP, the International Health Terminology Standards Development Organization, and the UK Health and Social Care Information Centre analyzed and assessed required data elements for colorectal cancer and invasive breast cancer synoptic reporting. SNOMED CT concept expressions were developed at UNMC in the Nebraska Lexicon© SNOMED CT namespace. LOINC codes for each SNOMED CT expression were issued by the Regenstrief Institute. SNOMED CT concepts represented observation answer value sets.

Results

UNMC investigators created a total of 194 SNOMED CT observable entity concept definitions to represent required data elements for CAP colorectal and breast cancer synoptic worksheets, including biomarkers. Concepts were bound to colorectal and invasive breast cancer reports in the UNMC pathology system and successfully used to populate a UNMC biobank.

Discussion

The absence of a robust observables ontology represents a barrier to data capture and reuse in clinical areas founded upon observational information. Terminology developed in this project establishes the model to characterize pathology data for information exchange, public health, and research analytics.

Single-Center Experience Implementing the LOINC-RSNA Radiology Playbook for Adult Abdomen/Pelvis CT and MR Procedures Using a Semi-Automated Method

The LOINC-RSNA Radiology Playbook represents the future direction of standardization for radiology procedure names. We developed a software solution ("RadMatch") utilizing Python 2.7 and FuzzyWuzzy, an open-source fuzzy string matching algorithm created by SeatGeek, to implement the LOINC-RSNA Radiology Playbook for adult abdomen and pelvis CT and MR procedures performed at our institution. Execution of this semi-automated method resulted in the assignment of appropriate LOINC numbers to 86% of local CT procedures. For local MR procedures, appropriate LOINC numbers were assigned to 75% of these procedures whereas 12.5% of local MR procedures could only be partially mapped. For the standardized local procedures, only 63% of CT and 71% of MR procedures had corresponding RadLex Playbook identifier (RPID) codes in the LOINC-RSNA Radiology Playbook, which limited the utility of RPID codes. RadMatch is a semi-automated open-source software tool that can assist radiology departments seeking to standardize their radiology procedures via implementation of the LOINC-RSNA Radiology Playbook.

Report on the Project for Establishment of the Standardized Korean Laboratory Terminology Database, 2015

The National Health Information Standards Committee was established in 2004 in Korea. The practical subcommittee for laboratory test terminology was placed in charge of standardizing laboratory medicine terminology in Korean. We aimed to establish a standardized Korean laboratory terminology database, Korea-Logical Observation Identifier Names and Codes (K-LOINC) based on former products sponsored by this committee. The primary product was revised based on the opinions of specialists. Next, we mapped the electronic data interchange (EDI) codes that were revised in 2014, to the corresponding K-LOINC. We established a database of synonyms, including the laboratory codes of three reference laboratories and four tertiary hospitals in Korea. Furthermore, we supplemented the clinical microbiology section of K-LOINC using an alternative mapping strategy. We investigated other systems that utilize laboratory codes in order to investigate the compatibility of K-LOINC with statistical standards for a number of tests. A total of 48,990 laboratory codes were adopted (21,539 new and 16,330 revised). All of the LOINC synonyms were translated into Korean, and 39,347 Korean synonyms were added. Moreover, 21,773 synonyms were added from reference laboratories and tertiary hospitals. Alternative strategies were established for mapping within the microbiology domain. When we applied these to a smaller hospital, the mapping rate was successfully increased. Finally, we confirmed K-LOINC compatibility with other statistical standards, including a newly proposed EDI code system. This project successfully established an up-to-date standardized Korean laboratory terminology database, as well as an updated EDI mapping to facilitate the introduction of standard terminology into institutions.

Standardizing Physiologic Assessment Data to Enable Big Data Analytics

Disparate data must be represented in a common format to enable comparison across multiple institutions and facilitate Big Data science. Nursing assessments represent a rich source of information. However, a lack of agreement regarding essential concepts and standardized terminology prevent their use for Big Data science in the current state. The purpose of this study was to align a minimum set of physiological nursing assessment data elements with national standardized coding systems. Six institutions shared their 100 most common electronic health record nursing assessment data elements. From these, a set of distinct elements was mapped to nationally recognized Logical Observations Identifiers Names and Codes (LOINC®) and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT®) standards. We identified 137 observation names (55% new to LOINC), and 348 observation values (20% new to SNOMED CT) organized into 16 panels (72% new LOINC). This reference set can support the exchange of nursing information, facilitate multi-site research, and provide a framework for nursing data analysis.

Application of a Regenstrief RELMA V.6.6 to Map Russian Laboratory Terms to LOINC

BACKGROUND

Manual mapping of laboratory data to Logical Observation Identifiers Names and Codes (LOINC) requires a major effort. Application of the LOINC mapping assistant RELMA V.6.6 can reduce the effort required for mapping. The goal of the paper is to perform a semi-automated mapping of Russian laboratory terms to LOINC.

METHODS

A semi-automated mapping of the 2563 terms from two clinics in Russia was performed. The first step was automatic mapping using RELMA V.6.6 and LOINC V.2.48 Russian translation by Yaroslavl state medical academy. The second step was a manual expert mapping. To evaluate the correctness of mapping all the mapped terms were reviewed by two experts.

RESULTS

The paper presents the results of semi-automatic mapping of Russian laboratory terms to LOINC. Two clinics (A and B) and a laboratory service participated in the project. The following results were achieved: mapping of 86% terms from Clinic A and 87% from Clinic B. It has to be mentioned that 99% of terms used in 2014 were mapped. In total 2398 out of 2563 were mapped.

DISCUSSION

The required effort was reasonable and the price of mapping and maintenance was considered as relatively low in comparison to manual methods.

CONCLUSION

RELMA V.6.6 and LOINC V.2.48 offer the opportunity of a low effort LOINC mapping even for non-English languages. The study proved that the mapping effort is acceptable and mapping results are on the same level as the manual mapping.

Learning from the crowd while mapping to LOINC

OBJECTIVE

To describe the perspectives of Regenstrief LOINC Mapping Assistant (RELMA) users before and after the deployment of Community Mapping features, characterize the usage of these new features, and analyze the quality of mappings submitted to the community mapping repository.

METHODS

We evaluated Logical Observation Identifiers Names and Codes (LOINC) community members' perceptions about new "wisdom of the crowd" information and how they used the new RELMA features. We conducted a pre-launch survey to capture users' perceptions of the proposed functionality of these new features; monitored how the new features and data available via those features were accessed; conducted a follow-up survey about the use of RELMA with the Community Mapping features; and analyzed community mappings using automated methods to detect potential errors.

RESULTS

Despite general satisfaction with RELMA, nearly 80% of 155 respondents to our pre-launch survey indicated that having information on how often other users had mapped to a particular LOINC term would be helpful. During the study period, 200 participants logged into the RELMA Community Mapping features an average of 610 times per month and viewed the mapping detail pages a total of 6686 times. Fifty respondents (25%) completed our post-launch survey, and those who accessed the Community Mapping features unanimously indicated that they were useful. Overall, 95.3% of the submitted mappings passed our automated validation checks.

CONCLUSION

When information about other institutions' mappings was made available, study participants who accessed it agreed that it was useful and informed their mapping choices. Our findings suggest that a crowd-sourced repository of mappings is valuable to users who are mapping local terms to LOINC terms.

Supporting interoperability of genetic data with LOINC

Electronic reporting of genetic testing results is increasing, but they are often represented in diverse formats and naming conventions. Logical Observation Identifiers Names and Codes (LOINC) is a vocabulary standard that provides universal identifiers for laboratory tests and clinical observations. In genetics, LOINC provides codes to improve interoperability in the midst of reporting style transition, including codes for cytogenetic or mutation analysis tests, specific chromosomal alteration or mutation testing, and fully structured discrete genetic test reporting. LOINC terms follow the recommendations and nomenclature of other standards such as the Human Genome Organization Gene Nomenclature Committee's terminology for gene names. In addition to the narrative text they report now, we recommend that laboratories always report as discrete variables chromosome analysis results, genetic variation(s) found, and genetic variation(s) tested for. By adopting and implementing data standards like LOINC, information systems can help care providers and researchers unlock the potential of genetic information for delivering more personalized care.

Assessing the adequacy of the HL7/LOINC Document Ontology Role axis

The healthcare landscape is changing, driven by innovative care models and the emergence of new roles that are inter-professional in nature. Currently, the HL7/LOINC Document Ontology (DO) aids the use and exchange of clinical documents using a multi-axis structure of document attributes for Kind of Document, Setting, Role, Subject Matter Domain, and Type of Service. In this study, the adequacy of the Role axis for representing the type of author documenting care was assessed. Experts used a master list of 220 values created from seven resources and established mapping guidelines. Baseline certification, licensure, and didactic training were identified as key parameters that define roles and hence often need to be pre-coordinated. DO was inadequate in representing 82% of roles, and this gap was primarily due to lack of granularity in DO. Next steps include refinement of the proposed schema for the Role axis and dissemination within the larger standards community.

Next generation phenotyping using the unified medical language system

Background

Structured information within patient medical records represents a largely untapped treasure trove of research data. In the United States, privacy issues notwithstanding, this has recently become more accessible thanks to the increasing adoption of electronic health records (EHR) and health care data standards fueled by the Meaningful Use legislation. The other side of the coin is that it is now becoming increasingly more difficult to navigate the profusion of many disparate clinical terminology standards, which often span millions of concepts.

Objective

The objective of our study was to develop a methodology for integrating large amounts of structured clinical information that is both terminology agnostic and able to capture heterogeneous clinical phenotypes including problems, procedures, medications, and clinical results (such as laboratory tests and clinical observations). In this context, we define phenotyping as the extraction of all clinically relevant features contained in the EHR.

Methods

The scope of the project was framed by the Common Meaningful Use (MU) Dataset terminology standards; the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), RxNorm, the Logical Observation Identifiers Names and Codes (LOINC), the Current Procedural Terminology (CPT), the Health care Common Procedure Coding System (HCPCS), the International Classification of Diseases Ninth Revision Clinical Modification (ICD-9-CM), and the International Classification of Diseases Tenth Revision Clinical Modification (ICD-10-CM). The Unified Medical Language System (UMLS) was used as a mapping layer among the MU ontologies. An extract, load, and transform approach separated original annotations in the EHR from the mapping process and allowed for continuous updates as the terminologies were updated. Additionally, we integrated all terminologies into a single UMLS derived ontology and further optimized it to make the relatively large concept graph manageable.

Results

The initial evaluation was performed with simulated data from the Clinical Avatars project using 100,000 virtual patients undergoing a 90 day, genotype guided, warfarin dosing protocol. This dataset was annotated with standard MU terminologies, loaded, and transformed using the UMLS. We have deployed this methodology to scale in our in-house analytics platform using structured EHR data for 7931 patients (12 million clinical observations) treated at the Froedtert Hospital. A demonstration limited to Clinical Avatars data is available on the Internet using the credentials user “jmirdemo” and password “jmirdemo”.

Conclusions

Despite its inherent complexity, the UMLS can serve as an effective interface terminology for many of the clinical data standards currently used in the health care domain.

A corpus-based approach for automated LOINC mapping

OBJECTIVE

To determine whether the knowledge contained in a rich corpus of local terms mapped to LOINC (Logical Observation Identifiers Names and Codes) could be leveraged to help map local terms from other institutions.

METHODS

We developed two models to test our hypothesis. The first based on supervised machine learning was created using Apache's OpenNLP Maxent and the second based on information retrieval was created using Apache's Lucene. The models were validated by a random subsampling method that was repeated 20 times and that used 80/20 splits for training and testing, respectively. We also evaluated the performance of these models on all laboratory terms from three test institutions.

RESULTS

For the 20 iterations used for validation of our 80/20 splits Maxent and Lucene ranked the correct LOINC code first for between 70.5% and 71.4% and between 63.7% and 65.0% of local terms, respectively. For all laboratory terms from the three test institutions Maxent ranked the correct LOINC code first for between 73.5% and 84.6% (mean 78.9%) of local terms, whereas Lucene's performance was between 66.5% and 76.6% (mean 71.9%). Using a cut-off score of 0.46 Maxent always ranked the correct LOINC code first for over 57% of local terms.

CONCLUSIONS

This study showed that a rich corpus of local terms mapped to LOINC contains collective knowledge that can help map terms from other institutions. Using freely available software tools, we developed a data-driven automated approach that operates on term descriptions from existing mappings in the corpus. Accurate and efficient automated mapping methods can help to accelerate adoption of vocabulary standards and promote widespread health information exchange.

A multi-part matching strategy for mapping LOINC with laboratory terminologies

OBJECTIVE

To address the problem of mapping local laboratory terminologies to Logical Observation Identifiers Names and Codes (LOINC). To study different ontology matching algorithms and investigate how the probability of term combinations in LOINC helps to increase match quality and reduce manual effort.

MATERIALS AND METHODS

We proposed two matching strategies: full name and multi-part. The multi-part approach also considers the occurrence probability of combined concept parts. It can further recommend possible combinations of concept parts to allow more local terms to be mapped. Three real-world laboratory databases from Taiwanese hospitals were used to validate the proposed strategies with respect to different quality measures and execution run time. A comparison with the commonly used tool, Regenstrief LOINC Mapping Assistant (RELMA) Lab Auto Mapper (LAM), was also carried out.

RESULTS

The new multi-part strategy yields the best match quality, with F-measure values between 89% and 96%. It can automatically match 70-85% of the laboratory terminologies to LOINC. The recommendation step can further propose mapping to (proposed) LOINC concepts for 9-20% of the local terminology concepts. On average, 91% of the local terminology concepts can be correctly mapped to existing or newly proposed LOINC concepts.

CONCLUSIONS

The mapping quality of the multi-part strategy is significantly better than that of LAM. It enables domain experts to perform LOINC matching with little manual work. The probability of term combinations proved to be a valuable strategy for increasing the quality of match results, providing recommendations for proposed LOINC conepts, and decreasing the run time for match processing.

LOINC in prehospital emergency medicine in Germany - experience of the `DIRK´-project

BACKGROUND

Treatment of patients picked up by emergency services can be improved by data transfer ahead of arrival. Care given to emergency patients can be assessed and improved through data analysis. Both goals require electronic data transfer from the emergency medical services (EMS) to the hospital information system. Therefore a generic semantic standard is needed.

OBJECTIVES

Objective of this paper is to test the suitability of the international nomenclature Logical Observation Identifiers Names and Codes (LOINC) to encode the core data-sets for rescue service protocols (MIND 2 and MIND 3). Encoding diagnosis and medication categories using ICD-10 and ATC were also assessed.

METHODS

Protocols were broken down into concepts, assigned to categories, translated and manually mapped to LOINC codes. Each protocol was independently encoded by two healthcare professionals and in case of discrepancies a third expert was consulted to reach a consensus.

RESULTS

Currently 39% of parameters could be mapped to LOINC. Additional use of other coding systems such as International Statistical Classification of Diseases and Related Health Problems (ICD-10) for diagnoses and Anatomical Therapeutic Chemical Classification System (ATC) for medications increases the rate of 'mappable' parameters to 56%.

CONCLUSIONS

Although the coverage is low, mapping has shown that LOINC is suitable to encode concepts of the rescue services. In order to create a generic semantic model to be applied in the field our next step is to request new LOINC codes for the missing concepts.

Implementation and management of a biomedical observation dictionary in a large healthcare information system

OBJECTIVE

This study shows the evolution of a biomedical observation dictionary within the Assistance Publique Hôpitaux Paris (AP-HP), the largest European university hospital group. The different steps are detailed as follows: the dictionary creation, the mapping to logical observation identifier names and codes (LOINC), the integration into a multiterminological management platform and, finally, the implementation in the health information system.

METHODS

AP-HP decided to create a biomedical observation dictionary named AnaBio, to map it to LOINC and to maintain the mapping. A management platform based on methods used for knowledge engineering has been put in place. It aims at integrating AnaBio within the health information system and improving both the quality and stability of the dictionary.

RESULTS

This new management platform is now active in AP-HP. The AnaBio dictionary is shared by 120 laboratories and currently includes 50 000 codes. The mapping implementation to LOINC reaches 40% of the AnaBio entries and uses 26% of LOINC records. The results of our work validate the choice made to develop a local dictionary aligned with LOINC.

DISCUSSION AND CONCLUSIONS

This work constitutes a first step towards a wider use of the platform. The next step will support the entire biomedical production chain, from the clinician prescription, through laboratory tests tracking in the laboratory information system to the communication of results and the use for decision support and biomedical research. In addition, the increase in the mapping implementation to LOINC ensures the interoperability allowing communication with other international health institutions.

Implementation of a platform dedicated to the biomedical analysis terminologies management

BACKGROUND AND OBJECTIVES

Assistance Publique - Hôpitaux de Paris (AP-HP) is implementing a new laboratory management system (LMS) common to the 12 hospital groups. First step to this process was to acquire a biological analysis dictionary. This dictionary is interfaced with the international nomenclature LOINC, and has been developed in collaboration with experts from all biological disciplines. In this paper we describe in three steps (modeling, data migration and integration/verification) the implementation of a platform for publishing and maintaining the AP-HP laboratory data dictionary (AnaBio).

MATERIAL AND METHODS

Due to data complexity and volume, setting up a platform dedicated to the terminology management was a key requirement. This is an enhancement tackling identified weaknesses of previous spreadsheet tool. Our core model allows interoperability regarding data exchange standards and dictionary evolution.

RESULTS

We completed our goals within one year. In addition, structuring data representation has lead to a significant data quality improvement (impacting more than 10% of data). The platform is active in the 21 hospitals of the institution spread into 165 laboratories.