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At least 19 recordsLinked to original sources
PubMedDate not supplied

Development of the Logical Observation Identifier Names and Codes (LOINC) vocabulary.

The LOINC (Logical Observation Identifier Names and Codes) vocabulary is a set of more than 10,000 names and codes developed for use as observation identifiers in standardized messages exchanged between clinical computer systems. The goal of the study was to create universal names and codes for clinical observations that could be used by all clinical information systems. The LOINC names are structured to facilitate rapid matching, either automated or manual, between local vocabularies and the universal LOINC codes. If LOINC codes are used in clinical messages, each system participating in data exchange needs to match its local vocabulary to the standard vocabulary only once. This will reduce both the time and cost of implementing standardized interfaces. The history of the development of the LOINC vocabulary and the methodology used in its creation are described.

Classification↗
PubMedDate not supplied

Representing nursing assessments in clinical information systems using the logical observation identifiers, names, and codes database.

In recent years, the Logical Observation Identifiers, Names, and Codes (LOINC) Database has been expanded to include assessment items of relevance to nursing and in 2002 met the criteria for "recognition" by the American Nurses Association. Assessment measures in LOINC include those related to vital signs, obstetric measurements, clinical assessment scales, assessments from standardized nursing terminologies, and research instruments. In order for LOINC to be of greater use in implementing information systems that support nursing practice, additional content is needed. Moreover, those implementing systems for nursing practice must be aware of the manner in which LOINC codes for assessments can be appropriately linked with other aspects of the nursing process such as diagnoses and interventions. Such linkages are necessary to document nursing contributions to healthcare outcomes within the context of a multidisciplinary care environment and to facilitate building of nursing knowledge from clinical practice. The purposes of this paper are to provide an overview of the LOINC database, to describe examples of assessments of relevance to nursing contained in LOINC, and to illustrate linkages of LOINC assessments with other nursing concepts.

Computational Biology↗
PubMed2005

Mapping Department of Defense laboratory results to Logical Observation Identifiers Names and Codes (LOINC).

The Department of Defense (DoD) has used a common application, Composite Health Care System (CHCS), throughout all DoD facilities. However, the master files used to encode patient data in CHCS are not identical across DoD facilities. The encoded data is thus not interoperable from one DoD facility to another. To enable data interoperability in the next-generation system, CHCS II, and for the DoD to exchange laboratory results with external organizations such as the Veterans Administration (VA), the disparate master file codes for laboratory results are mapped to Logical Observation Identifier Names and Codes (LOINC) wherever possible. This paper presents some findings from our experience mapping DoD laboratory results to LOINC.

Forms and Records Control↗
PubMedDate not supplied

Evaluation of the clinical LOINC (Logical Observation Identifiers, Names, and Codes) semantic structure as a terminology model for standardized assessment measures.

OBJECTIVE: The purpose of this study was to test the adequacy of the Clinical LOINC (Logical Observation Identifiers, Names, and Codes) semantic structure as a terminology model for standardized assessment measures. METHODS: After extension of the definitions, 1, 096 items from 35 standardized assessment instruments were dissected into the elements of the Clinical LOINC semantic structure. An additional coder dissected at least one randomly selected item from each instrument. When multiple scale types occurred in a single instrument, a second coder dissected one randomly selected item representative of each scale type. RESULTS: The results support the adequacy of the Clinical LOINC semantic structure as a terminology model for standardized assessments. Using the revised definitions, the coders were able to dissect into the elements of Clinical LOINC all the standardized assessment items in the sample instruments. Percentage agreement for each element was as follows: component, 100 percent; property, 87.8 percent; timing, 82.9 percent; system/sample, 100 percent; scale, 92.6 percent; and method, 97.6 percent. DISCUSSION: This evaluation was an initial step toward the representation of standardized assessment items in a manner that facilitates data sharing and re-use. Further clarification of the definitions, especially those related to time and property, is required to improve inter-rater reliability and to harmonize the representations with similar items already in LOINC.

Databases, Factual↗
PubMed1997

Using Logical Observation Identifier Names and Codes (LOINC) to exchange laboratory data among three academic hospitals.

Using a standard set of names and codes to exchange electronic laboratory data would facilitate multiinstitutional research and data pooling. This need has led to the development of the Logical Observation Identifier Names and Codes (LOINC) database and its test naming convention. We conducted a study which required 3 academic hospitals (in 2 separate medical centers) to extract raw laboratory data from their local information system for a defined patient population, translate tests into LOINC, and provide aggregate data which could then be used to compare laboratory utilization. We found that the coding of local tests into LOINC can often be complex, especially the "Kind of Property" field, and apparently trivial differences in choices made by individual institutions can result in nonmatches in electronically pooled data. In our study, 72-86% of the failures of LOINC to match the same tests between different institutions were due to differences in local coding choices. LOINC has tremendous potential to eliminate the needing for detailed human inspection during the pooling of laboratory data from diverse sites, and perhaps even a built-in capability to adjust matching stringency by selecting subsets of LOINC fields required to match. However, a quality, standard coding procedure at all sites is critical.

Academic Medical Centers↗
PubMed1998

Combining laboratory data sets from multiple institutions using the logical observation identifier names and codes (LOINC).

A standard set of names and codes for laboratory test results is critical for any endeavor requiring automated data pooling, including multi-institutional research and cross-facility patient care. This need has led to the development of the logical observation identifier names and codes (LOINC) database and its test-naming convention. This study is an expansion of a pilot study using LOINC to exchange laboratory data between Columbia University Medical Center in New York and Barnes Hospital at Washington University in St. Louis, where we described complexities and ambiguities that arose in the LOINC coding process (D.M. Baorto, J.J. Cimino, C.A. Parvin, M.G. Kahn, Proc. Am. Med. Inf. Assoc. 1997). For the present study, we required the same two medical centers to again extract raw laboratory data from their local information system for a defined patient population, translate tests into LOINC and provide aggregate data which could then be used to compare laboratory utilization. Here we examine a larger number of tests from each site which have been recoded using an updated version of the LOINC database. We conclude that the coding of local tests into LOINC can often be complex, especially the 'Kind of Property' field and apparently trivial differences in choices made by individual institutions can result in nonmatches in electronically pooled data. In the present study, 75% of failures to match the same tests between different institutions using LOINC codes were due to differences in local coding choices. LOINC has the potential to eliminate the need for detailed human inspection during the pooling of laboratory data from diverse sites and perhaps even a built-in capability to adjust matching stringency by selecting subsets of LOINC fields required to match. However, a quality standard coding procedure is required and examples highlighted in this paper may require special attention while mapping to LOINC.

Clinical Laboratory Techniques↗
PubMed1996

Logical observation identifier names and codes (LOINC) database: a public use set of codes and names for electronic reporting of clinical laboratory test results.

Many laboratories use electronic message standards to transmit results to their clients. If all laboratories used the same "universal" set of test identifiers, electronic transmission of results would be greatly simplified. The Logical Observation Identifier Names and Codes (LOINC) database aims to be such a code system, covering at least 98% of the average laboratory's tests. The LOINC database should be of interest to hospitals, clinical laboratories, doctors' offices, state health departments, governmental healthcare providers, third-party payors, organizations involved in clinical trials, and quality assurance and utilization reviewers. The fifth release of the LOINC database, containing codes, names, and synonyms for approximately 6300 test observations, is now available on the Internet for public use. Here we describe the LOINC database, the methods used to produce it, and how it may be obtained.

Clinical Laboratory Information Systems↗
PubMedDate not supplied

Adequacy of evolving national standardized terminologies for interdisciplinary coded concepts in an automated clinical pathway.

PURPOSE: The purpose of this analysis was to determine the adequacy of evolving national standardized terminologies with regard to coded data elements (concepts) in an automated clinical pathway designed to drive adherence with the American College of Cardiology (ACC)/American Heart Association (AHA) Guidelines for Evaluation and Management of Chronic Heart Failure. METHOD: Concepts were identified in a previously developed automated clinical pathway and associated tools. Once identified, concepts were categorized according to the conceptual domains identified by Campbell et al. (1997). A review of evolving national standardized terminologies and coding systems was initiated to determine if the identified concepts had corresponding representation in one of these coding systems. Available codes were then evaluated for adequacy with respect to national guideline adherence measures put forth by the Centers for Medicare/Medicaid Services (CMS) and Joint Commission on Accreditation of Healthcare Organizations (JCAHO). RESULTS: The concept domain model put forth by Campbell et al. (1997) worked well for organizing concepts and for providing a useful framework for data analysis. Using our method, 260 unique pathway concepts were identified, of which, 91.9% (239) are represented by one or more of the standardized coding systems. Logical Observation Identifiers Names and Codes (LOINC) and SNOMED CT alone represented 86.2% of the concepts. Seventy percent (70%) of the clinical pathway concepts are represented using the Health Insurance Portability and Accountability Act (HIPAA) mandated national terminologies alone. Less than 50% of CMS and JCAHO guideline adherence concepts were found to have representation in the HIPAA mandated terminologies. The addition of Logical Observation Identifier Names and Codes (LOINC) and SNOMED CT improved representation up to 86.4%, but did not include representation of all concepts necessary for complete electronic monitoring of guideline adherence. CONCLUSIONS: Evolving national standardized terminologies provided matching terms for the majority of the data elements in the automated clinical pathway. Standard clinical terminologies with granular terms such as LOINC and SNOMED CT are required to represent the depth and detail of certain procedures and guideline-based care. Gaps exist in Health Insurance Portability and Accountability Act (HIPAA) mandated terminologies for representing interdisciplinary concepts in national adherence measures.

Computational Biology↗
PubMed2005

Contributing pain assessment concepts to a controlled terminology.

The objectives of this project were to 1) use a multi-disciplinary pain assessment information model to identify concepts required in Logical Observation Identifiers, Names and Codes (LOINC), 2) submit the proposed LOINC codes to the LOINC committee, and 3) have LOINC names and codes created for others to use.

Humans↗
PubMed2003

The map to LOINC project.

We describe a pilot project to standardize local laboratory test names to Logical Observation Identifier Names and Codes (LOINC) at five Indian Health Service (IHS) medical facilities. An automated mapping tool was developed to assign LOINC codes. The laboratory test names not mapped to LOINC by the mapping tool were assigned LOINC codes manually. The results achieved matched current benchmarks.

Clinical Laboratory Techniques↗
PubMed2006

Standardizing laboratory data by mapping to LOINC.

The authors describe a pilot project to standardize local laboratory data at five Indian Health Service (IHS) medical facilities by mapping laboratory test names to Logical Observation Identifier Names and Codes (LOINC). An automated mapping tool was developed to assign LOINC codes. At these sites, they were able to map from 63% to 76% of the local active laboratory tests to LOINC using the mapping tool. Eleven percent to 27% of the tests were mapped manually. They could not assign LOINC codes to 6% to 19% of the laboratory tests due to incomplete or incorrect information about these tests. The results achieved approximate other similar efforts. Mapping of laboratory test names to LOINC codes will allow IHS to aggregate laboratory data more easily for disease surveillance and clinical and administrative reporting efforts. This project may provide a model for standardization efforts in other health systems.

Clinical Laboratory Techniques↗
PubMed2002

Introduction of a hierarchy to LOINC to facilitate public health reporting.

Public health reporting of laboratory results requires unambiguous identification of the test performed and the result observed. Some laboratories are currently using Logical Observation Identifier Names and Codes (LOINC) for the electronic reporting of laboratory tests and their results to public health departments. Initial use revealed inconsistent identification and use of LOINC concepts by laboratories and public health agencies and an inability to systematically extend, for public health use, the tables when adding new concepts. We applied simple, logical rules to existing LOINC concepts to facilitate the creation of a hierarchy of concepts and to allow the identification and specification of appropriate terms for public health reporting and subsequent data aggregation. The hierarchy also allows the systematic addition of new concepts further supporting public health reporting. Application of the hierarchy is illustrated by using all laboratory LOINC concepts assigned to the subset of microbiology test types (CLASS MICRO).

Clinical Laboratory Information Systems↗
PubMed2005

Document ontology: supporting narrative documents in electronic health records.

Electronic health records (EHRs) are beginning to manage an increasing volume of narrative data, such as clinical notes pertaining to admission, patient progress, shift change, follow-up, consultation, procedures, etc. These documents fall into a wide variety of classes, based on who is writing them, for what purpose, and in which location, suggesting the need for a document ontology (DO) to model our knowledge of health care documents and their properties. This paper focuses on one aspect of the Health Level 7 (HL7)/ Logical Observation Identifiers, Names, and Codes (LOINC) DO, the Subject Matter Domain (SMD). We created a new polyhierarchical structure for the SMD that combines the current value lists from the LOINC database with another value list from the American Board of Medical Specialties (ABMS). We refined and evaluated the new structure through expert review of the ontology, a survey of medical specialty boards, and specification of SMDs for a corpus of clinical notes.

Attitude of Health Personnel↗
PubMed2005

Development of an information model for storing organ donor data within an electronic medical record.

OBJECTIVE: To develop a model to store information in an electronic medical record (EMR) for the management of transplant patients. The model for storing donor information must be designed to allow clinicians to access donor information from the transplant recipient's record and to allow donor data to be stored without needlessly proliferating new Logical Observation Identifier Names and Codes (LOINC) codes for already-coded laboratory tests. DESIGN: Information required to manage transplant patients requires the use of a donor's medical information while caring for the transplant patient. Three strategies were considered: (1) link the transplant patient's EMR to the donor's EMR; (2) use pre-coordinated observation identifiers (i.e., LOINC codes with *(wedge)DONOR specified in the system axes) to identify donor data stored in the transplant patient's EMR; and (3) use an information model that allows donor information to be stored in the transplant patient's record by allowing the "source" of the data (donor) and the "name" of the result (e.g., blood type) to be post-coordinated in the transplant patient's EMR. RESULTS: We selected the third strategy and implemented a flexible post-coordinated information model. There was no need to create new LOINC codes for already-coded laboratory tests. The model required that the data structure in the EMR allow for the storage of the "subject" of the test. CONCLUSION: The selected strategy met our design requirements and provided an extendable information model to store donor data. This model can be used whenever it is necessary to refer to one patient's data from another patient's EMR.

Databases as Topic↗
PubMed2006

A system for automated lexical mapping.

OBJECTIVE: To automate the mapping of disparate databases to standardized medical vocabularies. BACKGROUND: Merging of clinical systems and medical databases, or aggregation of information from disparate databases, frequently requires a process whereby vocabularies are compared and similar concepts are mapped. DESIGN: Using a normalization phase followed by a novel alignment stage inspired by DNA sequence alignment methods, automated lexical mapping can map terms from various databases to standard vocabularies such as the UMLS (Unified Medical Language System) and LOINC (Logical Observation Identifier Names and Codes). MEASUREMENTS: This automated lexical mapping was evaluated using three real-world laboratory databases from different health care institutions. The authors report the sensitivity, specificity, percentage correct (true positives plus true negatives divided by total number of terms), and true positive and true negative rates as measures of system performance. RESULTS: The alignment algorithm was able to map 57% to 78% (average of 63% over all runs and databases) of equivalent concepts through lexical mapping alone. True positive rates ranged from 18% to 70%; true negative rates ranged from 5% to 52%. CONCLUSION: Lexical mapping can facilitate the integration of data from diverse sources and decrease the time and cost required for manual mapping and integration of clinical systems and medical databases.

Algorithms↗
PubMed2005

Toward semantic interoperability in home health care: formally representing OASIS items for integration into a concept-oriented terminology.

OBJECTIVE: The authors aimed to (1) formally represent OASIS-B1 concepts using the Logical Observation Identifiers, Names, and Codes (LOINC) semantic structure; (2) demonstrate integration of OASIS-B1 concepts into a concept-oriented terminology, the Medical Entities Dictionary (MED); (3) examine potential hierarchical structures within LOINC among OASIS-B1 and other nursing terms; and (4) illustrate a Web-based implementation for OASIS-B1 data entry using Dialogix, a software tool with a set of functions that supports complex data entry. DESIGN AND MEASUREMENTS: Two hundred nine OASIS-B1 items were dissected into the six elements of the LOINC semantic structure and then integrated into the MED hierarchy. Each OASIS-B1 term was matched to LOINC-coded nursing terms, Home Health Care Classification, the Omaha System, and the Sign and Symptom Check-List for Persons with HIV, and the extent of the match was judged based on a scale of 0 (no match) to 4 (exact match). OASIS-B1 terms were implemented as a Web-based survey using Dialogix. RESULTS: Of 209 terms, 204 were successfully dissected into the elements of the LOINC semantics structure and integrated into the MED with minor revisions of MED semantics. One hundred fifty-one OASIS-B1 terms were mapped to one or more of the LOINC-coded nursing terms. CONCLUSION: The LOINC semantic structure offers a standard way to add home health care data to a comprehensive patient record to facilitate data sharing for monitoring outcomes across sites and to further terminology management, decision support, and accurate information retrieval for evidence-based practice. The cross-mapping results support the possibility of a hierarchical structure of the OASIS-B1 concepts within nursing terminologies in the LOINC database.

Dictionaries, Medical as Topic↗
PubMedDate not supplied

Extending the LOINC conceptual schema to support standardized assessment instruments.

OBJECTIVE: To extend the Clinical LOINC (Logical Observation Identifiers, Names, and Codes) semantic schema to support (1) the representation of common types of assessment instruments and (2) the disambiguation of versions and variants that may have differing reliability and validity. DESIGN: Psychometric theory and survey research framework, plus an existing tool for implementing many types of assessment instruments (Dialogix), were used to identify and model the attributes of instruments that affect reliability and validity. Four modifications to the LOINC semantic schema were proposed as a means for completely identifying, disambiguating, and operationalizing a broad range of assessment instruments. MEASUREMENTS: Assess the feasibility of modeling these attributes within LOINC, with and without the proposed extensions. RESULTS: The existing LOINC schema for supporting assessment instruments was unable to consistently meet either objective. In contrast, the proposed extensions were able to meet both objectives, because they are derived from the Dialogix schema, which already performs those tasks. CONCLUSION: These extensions to LOINC can facilitate the use, analysis, and improvement of assessment instruments and thereby may improve the detection and management of errors.

Data Collection↗