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PubMed2004

Frequency of laboratory test utilization in the intensive care unit and its implications for large-scale data collection efforts.

OBJECTIVE: Mapping local use names to standardized nomenclatures such as LOINC (Logical Observation Identifiers Names and Codes) is a time-consuming task when done retrospectively or during the configuration of new information systems. The author sought to identify a subset of intensive care unit (ICU) laboratory tests, which, because of their frequency of use, should be the focus of efforts to standardize test names in ICU information systems. DESIGN: The author reviewed the ordering practices in medical, surgical, and pediatric ICUs within a large university teaching hospital to identify the subset of laboratory tests that represented the majority of tests performed in these settings. The author compared the results of his findings with the laboratory tests required to complete several of the most frequently used ICU acuity scoring systems. RESULTS: It was found that between 104 and 202 tests and profiles represented 99% of all testing in the three ICUs. All the laboratory studies needed for six commonly used ICU scoring systems fell into the top 21 laboratory studies and profiles performed in each ICU. CONCLUSION: The author identified a small subset of the LOINC database that should be the focus of efforts to standardize test names in ICU information systems. Mapping this subset of laboratory tests and profiles to LOINC vocabulary will simplify the process of collecting data for large-scale databases such as ICU scoring systems and the configuration of new ICU information systems.

APACHE↗
PubMed2001

Integrated patient data for optimal patient management: the value of laboratory data in quality improvement.

Managed care organizations are shifting from traditional utilization management programs to focus on initiatives that improve the health of an insured population. This strategy requires sophisticated data integration to identify at-risk individuals and track outcomes. Laboratory data are becoming increasingly valuable tools for managed care organizations and healthcare providers. The HEDIS Effectiveness of Care measures have incorporated laboratory data into several key performance indicators. By building a comprehensive repository of laboratory data that includes both procedure codes and laboratory values, managed care organizations can realize substantial savings by avoiding the costly medical record reviews required when administrative data are incomplete. In addition to tracking clinical outcomes, laboratory data provide the ability to risk-stratify a population to target high-risk individuals for case management and disease management interventions. Healthcare organizations face several challenges in the integration of laboratory data into medical databases and practice management software. Confidentiality is a key consideration in view of recent healthcare regulations. Providers of laboratory services should work collaboratively with organizations setting standards for healthcare informatics to facilitate the pooling of data for quality improvement and outcomes research. Health Level Seven, Inc. (HL7), Logical Observation Identifier Names and Codes (LOINC), and Systematized Nomenclature of Medicine (SNOMED) will likely play a key role in this process.

Clinical Laboratory Techniques↗
PubMed2006

Representation of ophthalmology concepts by electronic systems: intercoder agreement among physicians using controlled terminologies.

OBJECTIVE: To assess intercoder agreement for ophthalmology concepts by 3 physician coders using 5 controlled terminologies (International Classification of Diseases 9, Clinical Modification [ICD9CM]; Current Procedural Terminology, fourth edition; Logical Observation Identifiers, Names, and Codes [LOINC]; Systematized Nomenclature of Medicine, Clinical Terms [SNOMED-CT]; and Medical Entities Dictionary). DESIGN: Noncomparative case series. PARTICIPANTS: Five complete ophthalmology case presentations selected from a publicly available journal. METHODS: Each case was parsed into discrete concepts. Electronic or paper browsers were used independently by 3 physician coders to assign a code for every concept in each terminology. A match score representing adequacy of assignment for each concept was assigned on a 3-point scale (0, no match; 1, partial match; 2, complete match). For every concept, the level of intercoder agreement was determined by 2 methods: (1) based on exact code matching with assignment of complete agreement when all coders assigned the same code, partial agreement when 2 coders assigned the same code, and no agreement when all coders assigned different codes, and (2) based on manual review for semantic equivalence of all assigned codes by an independent ophthalmologist to classify intercoder agreement for each concept as complete agreement, partial agreement, or no agreement. Subsequently, intercoder agreement was calculated in the same manner for the subset of concepts judged to have adequate coverage by each terminology, based on receiving a match score of 2 by at least 2 of the 3 coders. MAIN OUTCOME MEASURES: Intercoder agreement in each controlled terminology: complete, partial, or none. RESULTS: Cases were parsed into 242 unique concepts. When all concepts were analyzed by manual review, the proportion of complete intercoder agreement ranged from 12% (LOINC) to 44% (SNOMED-CT), and the difference in intercoder agreement between LOINC and all other terminologies was statistically significant (P<0.004). When only concepts with adequate terminology were analyzed by manual review, the proportion of complete intercoder agreement ranged from 33% (LOINC) to 64% (ICD9CM), and there were no statistically significant differences in intercoder agreement among any pairs of terminologies. CONCLUSIONS: The level of intercoder agreement for ophthalmic concepts in existing controlled medical terminologies is imperfect. Intercoder reproducibility is essential for accurate and consistent electronic representation of medical data.

Decision Support Systems, Clinical↗
PubMed2005

Electronic laboratory reporting for the infectious diseases physician and clinical microbiologist.

BACKGROUND: One important benefit of electronic health information is the improved interface between infectious diseases practice and public health. Electronic communicable disease reporting (CDR), given its legal mandate and clear public health importance, is a significant early step in the sifting and pooling of health data for purposes beyond patient care and billing. Over the next 5-10 years, almost all CDR will move to the internet. METHODS: This paper reviews the components of electronic laboratory reporting (ELR), including sifting through data in a laboratory information management system for reportable results, controlled "vocabularies" (e.g., LOINC, Logical Observation Identifiers Names and Codes [Regenstrief Institute], and SNOMED, Systematized Nomenclature of Medicine [College of American Pathologists]), the "syntax" of an electronic message (e.g., health level 7 [HL7]), the implications of the Health Insurance Portability and Accountability Act for ELR, and the obstacles to and potential benefits of ELR. RESULTS: There are several ways that infectious diseases physicians, infection control professionals, and microbiology laboratorians will participate in electronic CDR, including web-based case reporting and ELR, the direct, automated messaging of communicable disease reports from clinical lab information management systems to the appropriate public health jurisdiction's information system. CONCLUSIONS: ELR has the potential to make a large impact on the timeliness and the completeness of communicable disease reporting, but it does not replace the clinician's responsibility to submit a case report with important demographic and epidemiologic information.

Clinical Laboratory Information Systems↗
PubMedDate not supplied

Planned NLM/AHCPR large-scale vocabulary test: using UMLS technology to determine the extent to which controlled vocabularies cover terminology needed for health care and public health.

The National Library of Medicine (NLM) and the Agency for Health Care Policy and Research (AHCPR) are sponsoring a test to determine the extent to which a combination of existing health-related terminologies covers vocabulary needed in health information systems. The test vocabularies are the 30 that are fully or partially represented in the 1996 edition of the Unified Medical Language System (UMLS) Metathesaurus, plus three planned additions: the portions of SNOMED International not in the 1996 Metathesaurus Read Clinical Classification, and the Logical Observations Identifiers, Names, and Codes (LOINC) system. These vocabularies are available to testers through a special interface to the Internet-based UMLS Knowledge Source Server. The test will determine the ability of the test vocabularies to serve as a source of controlled vocabulary for health data systems and applications. It should provide the basis for realistic resource estimates for developing and maintaining a comprehensive "standard" health vocabulary that is based on existing terminologies.

Computer Communication Networks↗
PubMed2004

Medical informatics standards applicable to emergency department information systems: making sense of the jumble.

The adoption of medical informatics standards by emergency department information systems (EDISs) is not universal, despite obvious benefits. Clinicians and administrators looking to obtain an EDIS need to know exactly what the various standards can do for them and how the systems they depend on can be integrated and extended. In addition to the standard methods for systems to communicate (chiefly Health Level 7 [HL7]) and those required for submission of claims (Current Procedural Terminology [CPT]-4, International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM], and X12N), there are several other available standards that are clinically useful and can greatly improve the ability to access and exchange patient information. Major advances in the Unified Medical Language System of the National Library of Medicine have made the patient medical record information standards (Systematized Nomenclature of Medicine [SNOMED], Logical Observation Identifiers, Names, and Codes [LOINC], RxNorm) easily accessible. Detailed knowledge of the arcana associated with the technical aspects of the standards is not needed (or desired) by clinicians to use standards-based systems. However, some knowledge about the commonly used standards is helpful in choosing an EDIS, interfacing the EDIS with the other hospital information systems, extending or upgrading systems, and adopting decision support technologies.

Humans↗
PubMed2003

LOINC, a universal standard for identifying laboratory observations: a 5-year update.

The Logical Observation Identifier Names and Codes (LOINC) database provides a universal code system for reporting laboratory and other clinical observations. Its purpose is to identify observations in electronic messages such as Health Level Seven (HL7) observation messages, so that when hospitals, health maintenance organizations, pharmaceutical manufacturers, researchers, and public health departments receive such messages from multiple sources, they can automatically file the results in the right slots of their medical records, research, and/or public health systems. For each observation, the database includes a code (of which 25 000 are laboratory test observations), a long formal name, a "short" 30-character name, and synonyms. The database comes with a mapping program called Regenstrief LOINC Mapping Assistant (RELMA(TM)) to assist the mapping of local test codes to LOINC codes and to facilitate browsing of the LOINC results. Both LOINC and RELMA are available at no cost from http://www.regenstrief.org/loinc/. The LOINC medical database carries records for >30 000 different observations. LOINC codes are being used by large reference laboratories and federal agencies, e.g., the CDC and the Department of Veterans Affairs, and are part of the Health Insurance Portability and Accountability Act (HIPAA) attachment proposal. Internationally, they have been adopted in Switzerland, Hong Kong, Australia, and Canada, and by the German national standards organization, the Deutsches Instituts für Normung. Laboratories should include LOINC codes in their outbound HL7 messages so that clinical and research clients can easily integrate these results into their clinical and research repositories. Laboratories should also encourage instrument vendors to deliver LOINC codes in their instrument outputs and demand LOINC codes in HL7 messages they get from reference laboratories to avoid the need to lump so many referral tests under the "send out lab" code.

Clinical Laboratory Information Systems↗
PubMed1999

Advances in data exchange for the clinical laboratory.

The focus of the article is on the nuts and bolts of those standards relevant to the exchange of data between a clinical laboratory and an electronic health record. These include: Health Level 7 (HL7), Logical Observation Identifier Names and Codes (LOINC), Systematized Nomenclature of Human and Veterinary Medicine (SNOMED), and, most recently, the Extensible Markup Language (XML).

Animals↗
PubMed2000

Electronic laboratory-based reporting for public health.

This article describes the role of laboratory-based reporting for public health in the United States and outlines a vision for electronic laboratory-based reporting (ELR). It emphasizes the importance of adoption and implementation of standards to the successful development of ELR. In particular, it describes the role of Health Level 7 as a standard for electronic message formats and the roles of LOINC (Logical Observation Identifiers, Names, and Codes) and SNOMED (Systematized Nomenclature for Human and Veterinary Medicine) as standards for test names and results, respectively. In addition, the article describes ongoing and planned ELR projects

Clinical Laboratory Information Systems↗
PubMed2000

Planning for the future: the Department of Defense Laboratory Joint Working Group and Global Laboratory Information Transfer.

The Department of Defense (DoD) Laboratory Joint Working Group plans unified laboratory strategy under the auspices of the Armed Forces Institute of Pathology Board of Governors. One goal of the Laboratory Joint Working Group is to advocate clinical integration through automation of data transfer between medical treatment facilities using the DoD standard platform, Composite Health Care System (CHCS). A working group project team is implementing global laboratory information transfer, which enables CHCS-to-CHCS communication throughout the DoD. A prerequisite to global laboratory information transfer is the standardization of laboratory test nomenclature across all CHCS systems using LOINC (Logical Observation Identifiers, Names and Codes). This makes possible easier access to information among caregivers and therapeutic and public health disease managers and enhances global surveillance of disease outbreaks and continuity of care. The end result is the first-ever electronic transfer of laboratory results between all DoD facilities.

Clinical Laboratory Information Systems↗
PubMed2001

The creation of an ontology of clinical document names.

The efficient use of documents from heterogeneous computer systems is hampered by differences in document-naming practices across organizations. Using an open-consensus method, the Document Ontology Task Force, with support from the Veterans Health Administration, addressed this pervasive problem by developing a clinical document ontology. Based on the analysis of over 2000 clinical document names, the ontology was used to formulate a terminology model which is currently being used to guide the creation of fully-specified document names in LOINC (Logical Observations, Identifiers, Names and Codes). Incorporation into LOINC will enable homogeneous management of documents in a widely distributed environment and will also give rise to a rich polyhierarchy of document names.

Documentation↗
PubMed2001

Integration of nursing assessment concepts into the medical entities dictionary using the LOINC semantic structure as a terminology model.

Recent investigations have tested the applicability of various terminology models for the representing nursing concepts including those related to nursing diagnoses, nursing interventions, and standardized nursing assessments as a prerequisite for building a reference terminology that supports the nursing domain. We used the semantic structure of Clinical LOINC (Logical Observations, Identifiers, Names, and Codes) as a reference terminology model to support the integration of standardized assessment terms from two nursing terminologies into the Medical Entities Dictionary (MED), the concept-oriented, metadata dictionary at New York Presbyterian Hospital. Although the LOINC semantic structure was used previously to represent laboratory terms in the MED, selected hierarchies and semantic slots required revisions in order to incorporate the nursing assessment concepts. This project was an initial step in integrating nursing assessment concepts into the MED in a manner consistent with evolving standards for reference terminology models. Moreover, the revisions provide the foundation for adding other types of standardized assessments to the MED.

Dictionaries, Medical as Topic↗
PubMed2005

Automating identification of adverse events related to abnormal lab results using standard vocabularies.

Laboratory data need to be imported automatically into central Clinical Study Data Management Systems (CSDMSs), and abnormal laboratory data need to be linked to clinically related adverse events. This import of laboratory data can be automated through mapping to standard vocabularies with HL7/LOINC mapping to the metadata within a CSDMS. We have designed a system that uses the UMLS metathesaurus as a common source to map or link abnormal laboratory values to adverse event CTCAE coded terms and grades in the metadata of TrialDB, a generic CSDMS.

Clinical Laboratory Information Systems↗
PubMed2005

Automated mapping of local radiology terms to LOINC.

We developed an automated tool, called the Intelligent Mapper (IM), to improve the efficiency and consistency of mapping local terms to LOINC. We evaluated IM's performance in mapping diagnostic radiology report terms from two hospitals to LOINC by comparing IM's term rankings to a manually established gold standard. Using a CPT-based restriction, for terms with a LOINC code match, IM ranked the correct LOINC code first in 90% of our development set terms, and in 87% of our test set terms. The CPT-based restriction significantly improved IM's ability to identify correct LOINC codes. We have made IM freely available, with the aim of reducing the effort required to integrate disparate systems and helping move us towards the goal of interoperable health information exchange.

Artificial Intelligence↗
PubMed2002

4.2 Clinical records and global diagnostic codes.

A clinical record should include the personal demographic details of the patient, health status, diagnostic information and management/treatment options. However, clinical records are of little use without effective filing and retrieving systems. Coding is therefore necessary to deal with large amounts of differing data and a global coding system could be effectively developed through the use of information technology. The aim of this section was to review the main existing vocabularies and coding systems and to examine ways of improving their global application. It was concluded that global diagnostic codes would be beneficial to the patient, to the profession and to those responsible for strategic decisions concerning the delivery of health care. Extant dental clinical codes are not accepted widely or applied universally. There is an urgent need to identify existing coding systems and to assess their utility and potential for global application. Every effort should be made to include existing codes in the development of a global coding system on which all specialist areas would need to agree. This would require the provision of an overarching interdisciplinary focus and funding should be made available for this development and its implementation.

Cultural Diversity↗
PubMed2005

SPIN query tools for de-identified research on a humongous database.

The Shared Pathology Informatics Network (SPIN), a research initiative of the National Cancer Institute, will allow for the retrieval of more than 4 million pathology reports and specimens. In this paper, we describe the special query tool as developed for the Indianapolis/Regenstrief SPIN node, integrated into the ever-expanding Indiana Network for Patient care (INPC). This query tool allows for the retrieval of de-identified data sets using complex logic, auto-coded final diagnoses, and intrinsically supports multiple types of statistical analyses. The new SPIN/INPC database represents a new generation of the Regenstrief Medical Record system - a centralized, but federated system of repositories.

Confidentiality↗
PubMed2006

Validity of International Classification of Diseases, Ninth Revision, Clinical Modification Codes for Acute Renal Failure.

Administrative and claims databases may be useful for the study of acute renal failure (ARF) and ARF that requires dialysis (ARF-D), but the validity of the corresponding diagnosis and procedure codes is unknown. The performance characteristics of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes for ARF were assessed against serum creatinine-based definitions of ARF in 97,705 adult discharges from three Boston hospitals in 2004. For ARF-D, ICD-9-CM codes were compared with review of medical records in 150 patients with ARF-D and 150 control patients. As compared with a diagnostic standard of a 100% change in serum creatinine, ICD-9-CM codes for ARF had a sensitivity of 35.4%, specificity of 97.7%, positive predictive value of 47.9%, and negative predictive value of 96.1%. As compared with review of medical records, ICD-9-CM codes for ARF-D had positive predictive value of 94.0% and negative predictive value of 90.0%. It is concluded that administrative databases may be a powerful tool for the study of ARF, although the low sensitivity of ARF codes is an important caveat. The excellent performance characteristics of ICD-9-CM codes for ARF-D suggest that administrative data sets may be particularly well suited for research endeavors that involve patients with ARF-D.

Acute Kidney Injury↗