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Biomedical subjects

S M Huff

Publications and source records attributed to S M Huff.

At least 19 recordsLinked to original sources

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.

Computer Systems↗

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.

Congresses as Topic↗

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↗

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.

Humans↗

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.

Classification↗

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.

Artificial Intelligence↗

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.

Adult↗

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.

Computer Communication Networks↗

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.

Computer Communication Networks↗

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↗

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.

Classification↗

Collaborative medical informatics research using the Internet and the World Wide Web.

The InterMed Collaboratory is an interdisciplinary project involving six participating medical institutions. There are two broad mandates for the effort. The first is to further the development, sharing, and demonstration of numerous software and system components, data sets, procedures and tools that will facilitate the collaborations and support the application goals of these projects. The second is to provide a distributed suite of clinical applications, guidelines, and knowledge-bases for clinical, educational, and administrative purposes. To define the interactions among the components, datasets, procedures, and tools that we are producing and sharing, we have identified a model composed of seven tiers, each of which supports the levels above it. In this paper we briefly describe those tiers and the nature of the collaborative process with which we have experimented.

Computer Communication Networks↗

Experience with a mixed semantic/syntactic parser.

The value of the computerized medical record is derived in part from the availability of medical information in a coded form accessible to manipulation by processes designed for automated decision support, medical research, and computer assistance in the management of health care delivery. To meet these needs medical reports captured and stored as natural language documents must be encoded. Below we discuss an ongoing formative process aimed at developing a natural language understanding system for chest x-ray reports. Comparative data showing the progress of this process is presented.

Hospital Information Systems↗

Interfacing a stand-alone diagnostic expert system with a hospital information system.

Few diagnostic decision-support systems are in routine clinical use, mainly because these systems typically require time-consuming manual data entry. This research investigated the feasibility of reducing manual data entry by integrating a stand-alone diagnostic expert system with an existing comprehensive hospital information system (HIS). A knowledge-based intervocabulary mapping technique was developed to map disparate vocabularies. The results of a retrospective study indicate that transferring clinical data from the HIS to the diagnostic expert system at the beginning of workup significantly reduces the manual data entry required for generating the correct diagnoses for patients.

Diagnosis, Computer-Assisted↗

Decision support in medicine: examples from the HELP system.

Computerized health information systems can contribute to the care received by patients in a number of ways. Not the least of these is through interactions with health care providers to modify diagnostic and therapeutic decisions. Since its beginning, developers have used the HELP hospital information system to explore computerized interventions into the medical decision making process. By their nature these interventions imply a computer-directed interaction with the physicians, nurses, and therapists involved in delivering care. In this paper we describe four different approaches to this intervention. These include: (1) processes that respond to the appearance of certain types of clinical data by issuing an alert informing caregivers of these data's presence and import, (2) programs that critique new orders and propose changes in those orders when appropriate, (3) programs that suggest new orders and procedures in response to patient data suggesting their need, and (4) applications that function by summarizing patient care data and that attempt to retrospectively assess the average or typical quality of medical decisions and therapeutic interventions made by health care providers. These approaches are illustrated with experience from the HELP system.

Adult↗

Designing a controlled medical vocabulary server: the VOSER project.

The authors describe their experience designing a controlled medical vocabulary server created to support the exchange of patient data and medical decision logic. The first section introduces practical and theoretical premises that guided the design of the vocabulary server. The second section describes a series of structures needed to implement the proposed server, emphasizing their conformance to the design premises. The third section introduces potential applications that provide services to end users and also a group of tools necessary for maintaining the server corpus. In the fourth section, the authors propose an implementation strategy based on a common framework and on the participation of groups from different health-related domains.

Clinical Laboratory Information Systems↗

Using digrams to map controlled medical vocabularies.

A program for matching between controlled medical vocabularies has been developed which adopts methods used in the domain of Information Retrieval. This program combines a stemmer based on fragments of words (digrams) with a similarity function. The proposed stemmer did not require any knowledge about word-formation rules and helped the identification of several kinds of word variants. The adopted similarity function assigned the highest score to the best candidate match in 99.0% of the cases.

Algorithms↗

HELP the next generation: a new client-server architecture.

A new client-server based system which is centered around a lifetime data repository (LDR) is under construction. The goal of the new system is to maintain the patient centered decision support aspects of the existing HELP* system while providing an open architecture that supports faster application development and allows execution of applications to be distributed across many computers. These goals are achieved by implementing the system with software components that are commercially available or by adhering to national and international standards for software integration. Keys to successful integration include the use of MS-DOS @, OS/2#, and UNIX Section as operating systems, Microsoft OLE 2.0 as a standard interface to the clinical database, the use of TUXEDO as a transaction/communication manager, and the use of ORACLE [symbol: see text] RDBMS as the underlying database management system.

Computer Communication Networks↗