Harmonization of detailed clinical models with clinical study data standards.

INTRODUCTION: This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". BACKGROUND: Data sharing and integration between the clinical research data management system and the electronic health record system remains a challenging issue. To approach the issue, there is emerging interest in utilizing the Detailed Clinical Model (DCM) approach across a variety of contexts. The Intermountain Healthcare Clinical Element Models (CEMs) have been adopted by the Office of the National Coordinator awarded Strategic Health IT Advanced Research Projects for normalization (SHARPn) project for normalizing patient data from the electronic health records (EHR). OBJECTIVE: The objective of the present study is to describe our preliminary efforts toward harmonization of the SHARPn CEMs with CDISC (Clinical Data Interchange Standards Consortium) clinical study data standards. METHODS: We were focused on three generic domains: demographics, lab tests, and medications. We performed a panel review on each data element extracted from the CDISC templates and SHARPn CEMs. RESULTS: We have identified a set of data elements that are common to the context of both clinical study and broad secondary use of EHR data and discussed outstanding harmonization issues. CONCLUSIONS: We consider that the outcomes would be useful for defining new requirements for the DCM modeling community and ultimately facilitating the semantic interoperability between systems for both clinical study and broad secondary use domains.

Auditing consistency and usefulness of LOINC use among three large institutions - using version spaces for grouping LOINC codes.

OBJECTIVES: We wanted to develop a method for evaluating the consistency and usefulness of LOINC code use across different institutions, and to evaluate the degree of interoperability that can be attained when using LOINC codes for laboratory data exchange. Our specific goals were to: (1) Determine if any contradictory knowledge exists in LOINC. (2) Determine how many LOINC codes were used in a truly interoperable fashion between systems. (3) Provide suggestions for improving the semantic interoperability of LOINC. METHODS: We collected Extensional Definitions (EDs) of LOINC usage from three institutions. The version space approach was used to divide LOINC codes into small sets, which made auditing of LOINC use across the institutions feasible. We then compared pairings of LOINC codes from the three institutions for consistency and usefulness. RESULTS: The number of LOINC codes evaluated were 1917, 1267 and 1693 as obtained from ARUP, Intermountain and Regenstrief respectively. There were 2022, 2030, and 2301 version spaces among ARUP and Intermountain, Intermountain and Regenstrief and ARUP and Regenstrief respectively. Using the EDs as the gold standard, there were 104, 109 and 112 pairs containing contradictory knowledge and there were 1165, 765 and 1121 semantically interoperable pairs. The interoperable pairs were classified into three levels: (1) Level I - No loss of meaning, complete information was exchanged by identical codes. (2) Level II - No loss of meaning, but processing of data was needed to make the data completely comparable. (3) Level III - Some loss of meaning. For example, tests with a specific 'method' could be rolled-up with tests that were 'methodless'. CONCLUSIONS: There are variations in the way LOINC is used for data exchange that result in some data not being truly interoperable across different enterprises. To improve its semantic interoperability, we need to detect and correct any contradictory knowledge within LOINC and add computable relationships that can be used for making reliable inferences about the data. The LOINC committee should also provide detailed guidance on best practices for mapping from local codes to LOINC codes and for using LOINC codes in data exchange.

A characterization of local LOINC mapping for laboratory tests in three large institutions.

OBJECTIVES: We characterized the use of laboratory LOINC® codes in three large institutions, focused on the following questions: 1) How many local codes had been voluntarily mapped to LOINC codes by each institution? 2) Could additional mappings be found by expert manual review for any local codes that were not initially mapped to LOINC codes by the local institution? and 3) Are there any common characteristics of unmapped local codes that might explain why some local codes were not mapped to LOINC codes by the local institution? METHODS: With Institutional Review Board (IRB) approval, we obtained deidentified data from three large institutions. We calculated the percentage of local codes that have been mapped to LOINC by personnel at each of the institutions. We also analyzed a sample of unmapped local codes to determine whether any additional LOINC mappings could be made and identify common characteristics that might explain why some local codes did not have mappings. RESULTS: Concept type coverage and concept token coverage (volume of instance data covered) of local codes mapped to LOINC codes were 0.44/0.59, 0.78/0.78 and 0.79/0.88 for ARUP, Intermountain, and Regenstrief, respectively. After additional expert manual mapping, the results showed mapping rates of 0.63/0.72, 0.83/0.80 and 0.88/0.90, respectively. After excluding local codes which were not useful for inter-institutional data exchange, the mapping rates became 0.73/0.79, 0.90/0.99 and 0.93/0.997, respectively. CONCLUSIONS: Local codes for two institutions could be mapped to LOINC codes with 99% or better concept token coverage, but mapping for a third institution (a reference laboratory) only achieved 79% concept token coverage. Our research supports the conclusions of others that not all local codes should be assigned LOINC codes. There should also be public discussions to develop more precise rules for when LOINC codes should be assigned.

Building a comprehensive clinical information system from components. The approach at Intermountain Health Care.

OBJECTIVES: To discuss the advantages and disadvantages of an interfaced approach to clinical information systems architecture. METHODS: After many years of internally building almost all components of a hospital clinical information system (HELP) at Intermountain Health Care, we changed our architectural approach as we chose to encompass ambulatory as well as acute care. We now seek to interface applications from a variety of sources (including some that we build ourselves) to a clinical data repository that contains a longitudinal electronic patient record. RESULTS: We have a total of 820 instances of interfaces to 51 different applications. We process nearly 2 million transactions per day via our interface engine and feel that the reliability of the approach is acceptable. Interface costs constitute about four percent of our total information systems budget. The clinical database currently contains records for 1.45 m patients and the response time for a query is 0.19 sec. DISCUSSION: Based upon our experience with both integrated (monolithic) and interfaced approaches, we conclude that for those with the expertise and resources to do so, the interfaced approach offers an attractive alternative to systems provided by a single vendor. We expect the advantages of this approach to increase as the costs of interfaces are reduced in the future as standards for vocabulary and messaging become increasingly mature and functional.

A national agenda for public health informatics.

The American Medical Informatics Association 2001 Spring Congress brought together the public health and informatics communities to develop a national agenda for public health informatics. Discussions on funding and governance; architecture and infrastructure; standards and vocabulary; research, evaluation, and best practices; privacy, confidentiality, and security; and training and workforce resulted in 74 recommendations with two key themes: (1) all stakeholders need to be engaged in coordinated activities related to public health information architecture, standards, confidentiality, best practices, and research and (2) informatics training is needed throughout the public health workforce. Implementation of this consensus agenda will help promote progress in the application of information technology to improve public health.

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.

Development of a template model to represent the information content of chest radiology reports.

The authors describe the application of a methodology for developing representational models for loosely structured medical domains. The methodology is subdivided in two interrelated tasks: terminology acquisition and template generation. The methodology is applied to the domain of chest radiology, producing a domain-specific lexicon and a series of templates to represent all the relevant clinical information stated on a chest x-ray report. Details about the successive application of the methodology and the resulting versions of the lexicon and templates are presented. The most relevant aspects of the methodology utilization are discussed and compared with evidence from other authors.

A method for the automated mapping of laboratory results to LOINC.

LOINC is emerging as the standard for laboratory result names, and there is great interest in mapping legacy terms from laboratory systems to it. However, the mapping task is non-trivial, requiring significant resource commitment and a good understanding of the LOINC identifying attributes for the laboratory result names. Because the number of results in a laboratory system may range from around 500 to 2000 or more, manual, one-by-one matching, even with the aid of the RELMA matching tool provided by LOINC, is time consuming and laborious. Moreover, human variation may introduce mapping inconsistencies or errors. Through our experience mapping the results from a variety of laboratory systems to LOINC, an automated mapping method has been developed and is described in this paper. This method allows for data from the laboratory information system to be provided in a manner familiar to the submitting technician, and makes use of parsing and logic rules, combined with synonyms, attribute relationships and mapping frequency data, to perform automated matching to LOINC.

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.

Automated mapping of observation codes using extensional definitions.

OBJECTIVE: To create "extensional definitions" of laboratory codes from derived characteristics of coded values in a clinical database and then use these definitions in the automated mapping of codes between disparate facilities. DESIGN: Repository data for two laboratory facilities in the Intermountain Health Care system were analyzed to create extensional definitions for the local codes of each facility. These definitions were then matched using automated matching software to create mappings between the shared local codes. The results were compared with the mappings of the vocabulary developers. MEASUREMENTS: The number of correct matches and the size of the match group were recorded. A match was considered correct if the corresponding codes from each facility were included in the group. The group size was defined as the total number of codes in the match group (e.g., a one-to-one mapping is a group size of two). RESULTS: Of the matches generated by the automated matching software, 81 percent were correct. The average group size was 2.4. There were a total of 328 possible matches in the data set, and 75 percent of these were correctly identified. CONCLUSIONS: Extensional definitions for local codes created from repository data can be utilized to automatically map codes from disparate systems. This approach, if generalized to other systems, can reduce the effort required to map one system to another while increasing mapping consistency.

Toward vocabulary domain specifications for health level 7-coded data elements.

The "vocabulary problem" has long plagued the developers, implementers, and users of computer-based systems. The authors review selected activities of the Health Level 7 (HL7) Vocabulary Technical Committee that are related to vocabulary domain specification for HL7 coded data elements. These activities include: 1) the development of two sets of principles to provide guidance to terminology stakeholders, including organizations seeking to deploy HL7-compliant systems, terminology developers, and terminology integrators; 2) the completion of a survey of terminology developers; 3) the development of a process for HL7 registration of terminologies; and 4) the maintenance of vocabulary domain specification tables. As background, vocabulary domain specification is defined and the relationship between the HL7 Reference Information Model and vocabulary domain specification is described. The activities of the Vocabulary Technical Committee complement the efforts of terminology developers and other stakeholders. These activities are aimed at realizing semantic interoperability in the context of the HL7 Message Development Framework, so that information exchange and use among disparate systems can occur for the delivery and management of direct clinical care as well as for purposes such as clinical research, outcome research, and population health management.

The decline and fall of Esperanto: lessons for standards committees.

In 1887, Polish physician Ludovic Zamenhof introduced Esperanto, a simple, easy-to-learn planned language. His goal was to erase communication barriers between ethnic groups by providing them with a politically neutral, culturally free standard language. His ideas received both praise and condemnation from the leaders of his time. Interest in Esperanto peaked in the 1970s but has since faded somewhat. Despite the logical concept and intellectual appeal of a standard language, Esperanto has not evolved into a dominant worldwide language. Instead, English, with all its idiosyncrasies, is closest to an international lingua franca. Like Zamenhof, standards committees in medical informatics have recognized communication chaos and have tried to establish working models, with mixed results. In some cases, previously shunned proprietary systems have become the standard. A proposed standard, no matter how simple, logical, and well designed, may have difficulty displacing an imperfect but functional "real life" system.

Linking a medical vocabulary to a clinical data model using Abstract Syntax Notation 1.

We have created a clinical data model using Abstract Syntax Notation 1 (ASN. 1). The clinical model is constructed from a small number of simple data types that are built into data structures of progressively greater complexity. Important intermediate types include Attributes, Observations, and Events. The highest level elements in the model are messages that are used for inter-process communication within a clinical information system. Vocabulary is incorporated into the model using BaseCoded, a primitive data type that allows vocabulary concepts and semantic relationships to be referenced using standard ASN. 1 notation. ASN. 1 subtyping language was useful in preventing unbounded proliferation of object classes in the model, and in general, ASN.1 was found to be a flexible and robust notation for representing a model of clinical information.

Evaluation of a semantic data model for chest radiology: application of a new methodology.

An essential step toward the effective processing of the medical language is the development of representational models that formalize the language semantics. These models, also known as semantic data models, help to unlock the meaning of descriptive expressions, making them accessible to computer systems. The present study tries to determine the quality of a semantic data model created to encode chest radiology findings. The evaluation methodology relied on the ability of physicians to extract information from textual and encoded representations of chest X-ray reports, whilst answering questions associated with each report. The evaluation demonstrated that the encoded reports seemed to have the same information content of the original textual reports. The methodology generated useful data regarding the quality of the data model, demonstrating that certain segments were creating ambiguous representations and that some details were not being represented.

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.

Evaluation of a "lexically assign, logically refine" strategy for semi-automated integration of overlapping terminologies.

OBJECTIVE: To evaluate a "lexically assign, logically refine" (LALR) strategy for merging overlapping healthcare terminologies. This strategy combines description logic classification with lexical techniques that propose initial term definitions. The lexically suggested initial definitions are manually refined by domain experts to yield description logic definitions for each term in the overlapping terminologies of interest. Logic-based techniques are then used to merge defined terms. METHODS: A LALR strategy was applied to 7,763 LOINC and 2,050 SNOMED procedure terms using a common set of defining relationships taken from the LOINC data model. Candidate value restrictions were derived by lexically comparing the procedure's name with other terms contained in the reference SNOMED topography, living organism, function, and chemical axes. These candidate restrictions were reviewed by a domain expert, transformed into terminologic definitions for each of the terms, and then algorithmically classified. RESULTS: The authors successfully defined 5,724 (73%) LOINC and 1,151 (56%) SNOMED procedure terms using a LALR strategy. Algorithmic classification of the defined concepts resulted in an organization mirroring that of the reference hierarchies. The classification techniques appropriately placed more detailed LOINC terms underneath the corresponding SNOMED terms, thus forming a complementary relationship between the LOINC and SNOMED terms. DISCUSSION: LALR is a successful strategy for merging overlapping terminologies in a test case where both terminologies can be defined using the same defining relationships, and where value restrictions can be drawn from a single reference hierarchy. Those concepts not having lexically suggested value restrictions frequently indicate gaps in the reference hierarchy.

Clinical data exchange standards and vocabularies for messages.

Motivation for the creation of electronic data interchange (message) standards is discussed. The ISO Open Systems Interface model is described. Clinical information models, message syntax and structure, and the need for a standardized coded vocabulary are explained. The HIPAA legislation and subsequent HHS transaction recommendations are reviewed. The history and mission statements of six of the most popular message development organizations (MDOs) are summarized, and the data exchange standards developed by these organizations are listed. The organizations described include Health Level Seven (HL7), American Standards for Testing and Materials (ASTM) E31, Digital Image Communication in Medicine (DICOM), European Committee for Standardization (Comité Européen de Normalisation), Technical Committee for Health Informatics (CEN/TC 251), the National Council for Prescription Drug Programs (NCPDP), and Accredited Standards Committee X12 Insurance Subcommittee (X12N). The locations of Internet web sites for the six organizations are provided as resources for further information.

A proposal for incorporating health level seven (HL7) vocabulary in the UMLS Metathesaurus.

The HL7 Vocabulary Technical Committee (TC) was organized to select and maintain the vocabulary used in HL7 messages. The goal is to make implementations of the Version 3 HL7 Standard more plug-and-play compatible. In order to make the vocabulary readily accessible to the public, HL7 is collaborating with the U.S. National Library of Medicine (NLM) to include HL7 vocabulary in the Unified Medical Language System (UMLS) Metathesaurus. This article describes a proposal for how HL7 data elements and coded values can be represented accurately in the relational tables of the UMLS Metathesaurus.

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.

Coupling vocabularies and data structures: lessons from LOINC.

Using LOINC's data model for laboratory test result names as a starting point, an extended model is presented, coupled to a more complete vocabulary model. Justifications for this approach are obtained from a matching experiment that attempts to identify SNOMED terms that correspond to the various components of the LOINC names. Some limitations of LOINC's current vocabulary model, exposed during the matching process, are discussed.