Recent Developments in Clinical Terminologies — SNOMED CT, LOINC, and RxNorm

Bodenreider, Oliver; Cornet, Ronald; Vreeman, Daniel J.

doi:10.1055/s-0038-1667077

Cited by 132 publications

(78 citation statements)

References 15 publications

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“…Health information technology uses controlled terminologies to condense the information to a set of codes that can be manipulated more easily and automatically in data processing. We observed the adoption of both common [ 272 , 273 ] and specialized terminologies (eg, Anatomical Therapeutic Chemical Classification [ 274 ], human phenotype ontology [ 275 ], Gene Ontology [ 276 ]). The most broadly used were International Classification of Diseases (ICD)-9 and 10 for the classification of diseases, systematized nomenclature of medicine-clinical terms (SNOMED-CT) for a variety of medical domains, Logical Observation Identifiers, Names, and Codes for laboratory observations, and current procedural terminology for common procedures ( Table 1 ).…”

Section: Resultsmentioning

confidence: 99%

What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions

Gagalova¹,

Elizalde²,

Portales-Casamar³

et al. 2020

JMIR Form Res

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Background Integrated data repositories (IDRs), also referred to as clinical data warehouses, are platforms used for the integration of several data sources through specialized analytical tools that facilitate data processing and analysis. IDRs offer several opportunities for clinical data reuse, and the number of institutions implementing an IDR has grown steadily in the past decade. Objective The architectural choices of major IDRs are highly diverse and determining their differences can be overwhelming. This review aims to explore the underlying models and common features of IDRs, provide a high-level overview for those entering the field, and propose a set of guiding principles for small- to medium-sized health institutions embarking on IDR implementation. Methods We reviewed manuscripts published in peer-reviewed scientific literature between 2008 and 2020, and selected those that specifically describe IDR architectures. Of 255 shortlisted articles, we found 34 articles describing 29 different architectures. The different IDRs were analyzed for common features and classified according to their data processing and integration solution choices. Results Despite common trends in the selection of standard terminologies and data models, the IDRs examined showed heterogeneity in the underlying architecture design. We identified 4 common architecture models that use different approaches for data processing and integration. These different approaches were driven by a variety of features such as data sources, whether the IDR was for a single institution or a collaborative project, the intended primary data user, and purpose (research-only or including clinical or operational decision making). Conclusions IDR implementations are diverse and complex undertakings, which benefit from being preceded by an evaluation of requirements and definition of scope in the early planning stage. Factors such as data source diversity and intended users of the IDR influence data flow and synchronization, both of which are crucial factors in IDR architecture planning.

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Section: Resultsmentioning

confidence: 99%

What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions

Gagalova¹,

Elizalde²,

Portales-Casamar³

et al. 2020

JMIR Form Res

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“…The use of automated coding assistance for assigning billing codes is now a routine part of HIM practice in the US. With the increased recognition of the need for greater interoperability and data sharing, standards such as RxNorm for medications and Logical Observation Identifiers Names & Codes (LOINC) for laboratory tests have been proposed 7 . Laboratory tests in particular are often described differently in different hospital systems, and there is a need to map local test names to a standard that can be used to share data for clinical and research purposes.…”

Section: Resultsmentioning

confidence: 99%

Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management

Bloomrosen¹,

Berner

2019

Yearb Med Inform

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Objectives: To summarize the recent literature and research and present a selection of the best papers published in 2018 in the field of Health Information Management (HIM) and Health Informatics. Methods: A systematic review of the literature was performed, with the help of a medical librarian, by the two editors of the HIM section of the International Medical Informatics Association (IMIA) Yearbook. In order to include papers that would address the special theme of the 2019 Yearbook on artificial intelligence (AI) as well as HIM, we searched bibliographic databases for HIM-related papers with an AI focus using both Medical Subject Headings (MeSH) descriptors and keywords in titles and abstracts. A shortlist of 15 candidate best papers was first selected by section editors before being peer-reviewed by independent external reviewers. Results: While there were a significant number of manuscripts that addressed issues relevant to HIM, there were virtually none with MeSH headings indicating an HIM focus. Manuscripts that were considered related to the HIM field in terms of the practice of health information management as well as the profession included those that examined using machine learning and other AI approaches to identify protected health information in clinical text to aid with de-identification, automated coding approaches to translate free-text into standardized codes, and natural language processing approaches to extract clinical data to assist with populating cancer and other registries. Conclusions: The papers discussed in the HIM section reflect the special theme of the use of AI in healthcare on issues particularly relevant to the field of HIM. This synopsis discusses these papers and recommends that HIM practitioners be more involved in research and that researchers in AI and related areas recognize the applicability and relevance of their work to the field of HIM.

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“…Necessary patient-related information on which the CDSS is evaluated will be extracted from different primary data sources of the clinical routine (patient data management system, laboratory information system and so on) 11. These data will be represented in an interoperable syntactic format (HL7 FHIR)12 and semantically annotated with standard terminologies (LOINC, SNOMED CT and so on) 13. Annotated HL7 FHIR resources will be stored in a ‘Health Data Storage’ of the respective DIC, enabling analytical methods to query all data based on internationally consented code systems or value sets.…”

Section: Methods and Analysismentioning

confidence: 99%

Hospital-wide ELectronic medical record evaluated computerised decision support system to improve outcomes of Patients with staphylococcal bloodstream infection (HELP): study protocol for a multicentre stepped-wedge cluster randomised trial

et al. 2020

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IntroductionStaphylococci are the most commonly identified pathogens in bloodstream infections. Identification of Staphylococcus aureus in blood culture (SAB) requires a prompt and adequate clinical management. The detection of coagulase-negative staphylococci (CoNS), however, corresponds to contamination in about 75% of the cases. Nevertheless, antibiotic therapy is often initiated, which contributes to the risk of drug-related side effects. We developed a computerised clinical decision support system (HELP-CDSS) that assists physicians with an appropriate management of patients with Staphylococcus bacteraemia. The CDSS is evaluated using data of the Data Integration Centers (DIC) established at each clinic. DICs transform heterogeneous primary clinical data into an interoperable format, and the HELP-CDSS displays information according to current best evidence in bacteraemia treatment. The overall aim of the HELP-CDSS is a safe but more efficient allocation of infectious diseases specialists and an improved adherence to established guidelines in the treatment of SAB.Methods and analysisThe study is conducted at five German university hospitals and is designed as a stepped-wedge cluster randomised trial. Over the duration of 18 months, 135 wards will change from a control period to the intervention period in a randomised stepwise sequence. The coprimary outcomes are hospital mortality for all patients to establish safety, the 90-day disease reoccurrence-free survival for patients with SAB and the cumulative vancomycin use for patients with CoNS bacteraemia. We will use a closed, hierarchical testing procedure and generalised linear mixed modelling to test for non-inferiority of the CDSS regarding hospital mortality and 90-day disease reoccurrence-free survival and for superiority of the HELP-CDSS regarding cumulative vancomycin use.Ethics and disseminationThe study is approved by the ethics committee of Jena University Hospital and will start at each centre after local approval. Results will be published in a peer-reviewed journal and presented at scientific conferences.Trial registration numberDRKS00014320.

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Recent Developments in Clinical Terminologies — SNOMED CT, LOINC, and RxNorm

Cited by 132 publications

References 15 publications

What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions

What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions

Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management

Hospital-wide ELectronic medical record evaluated computerised decision support system to improve outcomes of Patients with staphylococcal bloodstream infection (HELP): study protocol for a multicentre stepped-wedge cluster randomised trial

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