A framework for comprehensive health terminology systems in the United States: development guidelines, criteria for selection, and public policy implications. ANSI Healthcare Informatics Standards Board Vocabulary Working Group and the Computer-Based Patient Records Institute Working Group on Codes and Structures

AnnoDash, a clinical terminology annotation dashboard

Background

Standard ontologies are critical for interoperability and multisite analyses of health data. Nevertheless, mapping concepts to ontologies is often done with generic tools and is labor-intensive. Contextualizing candidate concepts within source data is also done in an ad hoc manner.

Methods and Results

We present AnnoDash, a flexible dashboard to support annotation of concepts with terms from a given ontology. Text-based similarity is used to identify likely matches, and large language models are used to improve ontology ranking. A convenient interface is provided to visualize observations associated with a concept, supporting the disambiguation of vague concept descriptions. Time-series plots contrast the concept with known clinical measurements. We evaluated the dashboard qualitatively against several ontologies (SNOMED CT, LOINC, etc.) by using MIMIC-IV measurements. The dashboard is web-based and step-by-step instructions for deployment are provided, simplifying usage for nontechnical audiences. The modular code structure enables users to extend upon components, including improving similarity scoring, constructing new plots, or configuring new ontologies.

Conclusion

AnnoDash, an improved clinical terminology annotation tool, can facilitate data harmonizing by promoting mapping of clinical data. AnnoDash is freely available at https://github.com/justin13601/AnnoDash (https://doi.org/10.5281/zenodo.8043943).

ARTICULATE: A European glossary of terms used in oral health professional education

INTRODUCTION

The Erasmus+O-Health-EDU project aims to gain a comprehensive view of oral health professional (OHP) education in Europe, through the development of web-based surveys and online toolkits. A glossary to facilitate a common language through which academic teams could cooperate and communicate more accurately was identified as a key need within the project. The aim of ARTICULATE was thus to create a shared language, with a European focus, for terms and concepts used in the field of OHP education.

METHODS

The methodology was developed from those published for construction of other glossaries with a circular and iterative process: the creation of content and definitions by a group of experts in OHP education, the testing of "fitness for purpose" of the content, and stakeholder consultation. All creation steps were followed by refinements based on testing results and stakeholder comments. The final glossary was then launched as an online resource including a built-in mechanism for user feedback.

RESULTS

The scope and structure of the glossary were mapped out at a workshop with 12 dental education experts from 7 European countries. A total of 328 terms were identified, of which 171 were finally included in ARTICULATE. After piloting with a close group of other colleagues, the glossary was opened for external input. Thirty European Deans or Heads of Education assessed the definition of each term as "clear" or "not clear." A total of 86 definitions were described as "clear" by all individuals. Terms deemed unclear by at least one individual were revisited and changes made to 37 of the definitions. In conjunction with the launch of the glossary, a range of stakeholder organisations were informed and asked to participate in an open global consultation by providing feedback online. Since its launch in June 2021, the ARTICULATE website (https://o-health-edu.org/articulate) has had an average of 500 visits/month. To promote community ownership, forms embedded on the ARTICULATE webpage allow users to give feedback and suggest new terms. A standing taskforce will meet regularly to consider amendments and make changes to ensure that the glossary remains a relevant and up-to-date resource over time.

CONCLUSION

ARTICULATE is a unique, evolving, online glossary of terms relating to OHP education, created as a resource for all interested OHP educators. The glossary is a key output of the O-Health-Edu project, which relies on a comprehensive vision of OHP education to address the future oral health needs of the European population.

Establishing a consensus on wound infection definitions

OBJECTIVES

The aim of this study was to establish an international, interorganisational consensus on wound infection terminology.

METHODS

This project consisted of definition scoping and a Delphi process to produce a consensus glossary for 18 wound infection terms. Recent guidelines/consensus documents were reviewed to identify 2-4 definitions for each term. An online consensus process was undertaken using the RAND Appropriateness Method, a consensus method for panels to reach agreement. International wound organisations nominated experts to participate, from whom 21 participants were selected to represent different organisations, geographic regions and disciplines. In the first consensus round, each term was presented alongside 2-3 definitions and participants nominated their preferred definition, with the majority vote used to select a baseline definition. The consensus process then proceeded, with participants using a 9-point Likert scale to score their level of agreement or disagreement with the definition for each term. Participants also provided a justification outlining the reason behind their rating. At the end of each round, an index was calculated to provide a quantitative evaluation indicating whether agreement or disagreement had been reached.

RESULTS

Reasoning statements were summarised and the definitions were adjusted to incorporate concepts identified by participants. The adjusted definition was presented in the next consensus round, together with the reasoning statements. Terms for which a final definition was not achieved in three consensus rounds were finalised with preferential voting using 2-3 definitions that had reached consensus.

PROJECT PROGRESS AND SIGNIFICANCE

The project generated a glossary of wound infection terms, endorsed through participation of 15 international organisations, for dissemination of guidelines and clinical decision-making/teaching tools.

Incorporation of Korean Electronic Data Interchange Vocabulary into Observational Medical Outcomes Partnership Vocabulary

OBJECTIVES

We incorporated the Korean Electronic Data Interchange (EDI) vocabulary into Observational Medical Outcomes Partnership (OMOP) vocabulary using a semi-automated process. The goal of this study was to improve the Korean EDI as a standard medical ontology in Korea.

METHODS

We incorporated the EDI vocabulary into OMOP vocabulary through four main steps. First, we improved the current classification of EDI domains and separated medical services into procedures and measurements. Second, each EDI concept was assigned a unique identifier and validity dates. Third, we built a vertical hierarchy between EDI concepts, fully describing child concepts through relationships and attributes and linking them to parent terms. Finally, we added an English definition for each EDI concept. We translated the Korean definitions of EDI concepts using Google.Cloud.Translation.V3, using a client library and manual translation. We evaluated the EDI using 11 auditing criteria for controlled vocabularies.

RESULTS

We incorporated 313,431 concepts from the EDI to the OMOP Standardized Vocabularies. For 10 of the 11 auditing criteria, EDI showed a better quality index within the OMOP vocabulary than in the original EDI vocabulary.

CONCLUSIONS

The incorporation of the EDI vocabulary into the OMOP Standardized Vocabularies allows better standardization to facilitate network research. Our research provides a promising model for mapping Korean medical information into a global standard terminology system, although a comprehensive mapping of official vocabulary remains to be done in the future.

CMIID: A comprehensive medical information identifier for clinical search harmonization in Data Safe Havens

Over the last decades clinical research has been driven by informatics changes nourished by distinct research endeavors. Inherent to this evolution, several issues have been the focus of a variety of studies: multi-location patient data access, interoperability between terminological and classification systems and clinical practice and records harmonization. Having these problems in mind, the Data Safe Haven paradigm emerged to promote a newborn architecture, better reasoning and safe and easy access to distinct Clinical Data Repositories. This study aim is to present a novel solution for clinical search harmonization within a safe environment, making use of a hybrid coding taxonomy that enables researchers to collect information from multiple repositories based on a clinical domain query definition. Results show that is possible to query multiple repositories using a single query definition based on clinical domains and the capabilities of the Unified Medical Language System, although it leads to deterioration of the framework response times. Participants of a Focus Group and a System Usability Scale questionnaire rated the framework with a median value of 72.5, indicating the hybrid coding taxonomy could be enriched with additional metadata to further improve the refinement of the results and enable the possibility of using this system as data quality tagging mechanism.

Ten tips for a text-mining-ready article: How to improve automated discoverability and interpretability

Data-driven research in biomedical science requires structured, computable data. Increasingly, these data are created with support from automated text mining. Text-mining tools have rapidly matured: although not perfect, they now frequently provide outstanding results. We describe 10 straightforward writing tips—and a web tool, PubReCheck—guiding authors to help address the most common cases that remain difficult for text-mining tools. We anticipate these guides will help authors’ work be found more readily and used more widely, ultimately increasing the impact of their work and the overall benefit to both authors and readers. PubReCheck is available at http://www.ncbi.nlm.nih.gov/research/pubrecheck.

Your published research is already being processed with automated tools, and text mining will become more common; this Community Page article describes how you can help these tools process your work more accurately, including a web tool, PubReCheck.

Development of LactaPedia: A lactation glossary for science and medicine

During the last decade, there have been several publications highlighting the need for consistent terminology in breastfeeding research. Standard terms and definitions are essential for the comparison and interpretation of scientific studies that, in turn, support evidence‐based education, consistency of health care, and breastfeeding policy. Inconsistent advice is commonly reported by mothers to contribute to early weaning. A standard language is the fundamental starting point required for the provision of consistent advice. LactaPedia (www.lactapedia.com) is a comprehensive lactation glossary of over 500 terms and definitions created during the development of LactaMap (www.lactamap.com), an online lactation care support system. This paper describes the development of LactaPedia, a website that is accessible free of charge to anyone with access to the Internet. Multiple methodological frameworks were incorporated in LactaPedia's development in order to meet the needs of a glossary to support both consistent health care and scientific research. The resulting LactaPedia methodology is a six‐stage process that was developed inductively and includes framework to guide vetting and extension of its content using public feedback via discussion forums. The discussion forums support ongoing usability and refinement of the glossary. The development of LactaPedia provides a fundamental first step towards improving breastfeeding outcomes that are currently well below World Health Organisation recommendations globally.

Automated mapping of laboratory tests to LOINC codes using noisy labels in a national electronic health record system database

Objective

Standards such as the Logical Observation Identifiers Names and Codes (LOINC®) are critical for interoperability and integrating data into common data models, but are inconsistently used. Without consistent mapping to standards, clinical data cannot be harmonized, shared, or interpreted in a meaningful context. We sought to develop an automated machine learning pipeline that leverages noisy labels to map laboratory data to LOINC codes.

Materials and Methods

Across 130 sites in the Department of Veterans Affairs Corporate Data Warehouse, we selected the 150 most commonly used laboratory tests with numeric results per site from 2000 through 2016. Using source data text and numeric fields, we developed a machine learning model and manually validated random samples from both labeled and unlabeled datasets.

Results

The raw laboratory data consisted of >6.5 billion test results, with 2215 distinct LOINC codes. The model predicted the correct LOINC code in 85% of the unlabeled data and 96% of the labeled data by test frequency. In the subset of labeled data where the original and model-predicted LOINC codes disagreed, the model-predicted LOINC code was correct in 83% of the data by test frequency.

Conclusion

Using a completely automated process, we are able to assign LOINC codes to unlabeled data with high accuracy. When the model-predicted LOINC code differed from the original LOINC code, the model prediction was correct in the vast majority of cases. This scalable, automated algorithm may improve data quality and interoperability, while substantially reducing the manual effort currently needed to accurately map laboratory data.

Terminology Services: Standard Terminologies to Control Health Vocabulary

Healthcare Information Systems should capture clinical data in a structured and preferably coded format. This is crucial for data exchange between health information systems, epidemiological analysis, quality and research, clinical decision support systems, administrative functions, among others. Structured data entry is an obstacle for the usability of electronic health record (EHR) applications and their acceptance by physicians who prefer to document patient EHRs using “free text”. Natural language allows for rich expressiveness but at the same time is ambiguous; it has great dependence on context and uses jargon and acronyms. Although much progress has been made in knowledge and natural language processing techniques, the result is not yet satisfactory enough for the use of free text in all dimensions of clinical documentation. In order to address the trade-off between capturing data with free text and at the same time coding data for computer processing, numerous terminological systems for the systematic recording of clinical data have been developed. The purpose of terminology services consists of representing facts that happen in the real world through database management in order to allow for semantic interoperability and computerized applications. These systems interrelate concepts of a particular domain and provide references to related terms with standards codes. In this way, standard terminologies allow the creation of a controlled medical vocabulary, making terminology services a fundamental component for health data management in the healthcare environment. The Hospital Italiano de Buenos Aires has been working in the development of its own terminology server. This work describes its experience in the field.

A review of medical terminology standards and structured reporting

Much effort has been invested in standardizing medical terminology for representation of medical knowledge, storage in electronic medical records, retrieval, reuse for evidence-based decision making, and for efficient messaging between users. We only focus on those efforts related to the representation of clinical medical knowledge required for capturing diagnoses and findings from a wide range of general to specialty clinical perspectives (e.g., internists to pathologists). Standardized medical terminology and the usage of structured reporting have been shown to improve the usage of medical information in secondary activities, such as research, public health, and case studies. The impact of standardization and structured reporting is not limited to secondary activities; standardization has been shown to have a direct impact on patient healthcare.

From Concepts and Codes to Healthcare Quality Measurement: Understanding Variations in Value Set Vocabularies for a Statin Therapy Clinical Quality Measure

Objective

To understand the impact of distinct concept to value set mapping on the measurement of quality of care.

Background

Clinical quality measures (CQMs) intend to measure the quality of healthcare services provided, and to help promote evidence-based therapies. Most CQMs consist of grouped codes from vocabularies - or 'value sets' - that represent the unique identifiers (i.e., object identifiers), concepts (i.e., value set names), and concept definitions (i.e., code groups) that define a measure's specifications. In the development of a statin therapy CQM, two unique value sets were created by independent measure developers for the same global concepts.

Methods

We first identified differences between the two value set specifications of the same CQM. We then implemented the various versions in a quality measure calculation registry to understand how the differences affected calculated prevalence of risk and measure performance.

Results

Global performance rates only differed by 0.8%, but there were up to 2.3 times as many patients included with key conditions, and differing performance rates of 7.5% for patients with 'myocardial infarction' and 3.5% for those with 'ischemic vascular disease'.

Conclusion

The decisions CQM developers make about which concepts and code groups to include or exclude in value set vocabularies can lead to inaccuracies in the measurement of quality of care. One solution is that developers could provide rationale for these decisions. Endorsements are needed to encourage system vendors, payers, informaticians, and clinicians to collaborate in the creation of more integrated terminology sets.

Embedding Nursing Interventions into the World Health Organization's International Classification of Health Interventions (ICHI)

OBJECTIVE

The International Classification of Health Interventions, currently being developed, seeks to span all sectors of the health system. Our objective was to test the draft classification's coverage of interventions commonly delivered by nurses, and propose changes to improve the utility and reliability of the classification for aggregating and analyzing data on nursing interventions.

MATERIALS AND METHODS

A 2-phase content mapping method was used: (1) three coders independently applied the classification to a dataset comprising 100 high-frequency nursing interventions; (2) the coders reached consensus for each intervention and identified reasons for initial discrepancies.

RESULTS

A consensus code was found for 80 of the 100 source terms; for 34% of these, the code was semantically equivalent to the source term, and for 64% it was broader. Issues that contributed to discrepancies in Phase 1 coding results included concepts in source terms not captured by the classification, ambiguities in source terms, and uncertainty of semantic matching between "action" concepts in source terms and classification codes.

DISCUSSION

While the classification generally provides good coverage of nursing interventions, there remain a number of content gaps and granularity issues. Further development of definitions and coding guidance is needed to ensure consistency of application.

CONCLUSION

This study has produced a set of proposals concerning changes needed to improve the classification. The novel method described here will inform future health terminology and classification content coverage studies.

Nursing Classification and Terminology Systems

A number of terminologies exist that represent concepts of relevance to nurses, although none of these is in use by Australian nurses. Without consensus, nursing language and definitions incorporated in clinical information systems now being implemented will continue to vary considerably. The result will be an inability to compare nursing practice, or to aggregate data for research purposes, or to collect national statistical data to demonstrate the significance of nurses' contributions to health care. This article provides an international historical overview of nursing terminology developments relative to what is happening in Australia, brief reviews of the many available nursing terminologies, an update of this work relative to activities being undertaken towards the development and adoption of standards, and a discussion about desirable future research and development activities.

Health Concept and Knowledge Management: Twenty-five Years of Evolution

OBJECTIVES

The fields of health terminology, classification, ontology, and related information models have evolved dramatically over the past 25 years. Our objective was to review notable trends, described emerging or enabling technologies, and highlight major terminology systems during the interval.

METHODS

We review the progression in health terminology systems informed by our own experiences as part of the community involved in this work, reinforced with literature review and citation.

RESULTS

The transformation in size, scope, complexity, and adoption of health terminological systems and information models has been tremendous, on the scale of orders of magnitude.

CONCLUSION

The present "big science" era of inference and discovery in biomedicine would not have been possible or scalable absent the growth and maturation of health terminology systems and information models over the past 25 years.

Current trends and challenges in health informatics

This paper outlines the roles of health informatics in modern health planning and delivery and defines the key challenges and opportunities for promoting high-quality and cost-effective care. It describes the main information management and technology drivers that improve the generation, use and flow of health information, categorizing these drivers under the headings of healthcare complexity, policy and priorities, clinical support and technology. The discussion draw attention to the ethical and legal issues associated with the drivers and summarizes the general features of these issues as they arise from computer-mediated medicine.

Smart Technology in Lung Disease Clinical Trials

This article describes the use of smart technology by investigators and patients to facilitate lung disease clinical trials and make them less costly and more efficient. By "smart technology" we include various electronic media, such as computer databases, the Internet, and mobile devices. We first describe the use of electronic health records for identifying potential subjects and then discuss electronic informed consent. We give several examples of using the Internet and mobile technology in clinical trials. Interventions have been delivered via the World Wide Web or via mobile devices, and both have been used to collect outcome data. We discuss examples of new electronic devices that recently have been introduced to collect health data. While use of smart technology in clinical trials is an exciting development, comparison with similar interventions applied in a conventional manner is still in its infancy. We discuss advantages and disadvantages of using this omnipresent, powerful tool in clinical trials, as well as directions for future research.

The words of prevention, part I: changing the model

Objective: this part I article explores the different meanings of relevant keywords for General Practice/Family Medicine (GP/FM) in the prevention domain. The aim is to contribute to information process in GP/FM by keeping in line with the main terms used in health care organization. Methods: important keywords for GP/FM in the prevention domain were selected. Then, a search was carried out on the main sources in GP/FM databases, as well as in Medical Subject Heading and major terminological databases available online. Results and Discussion: there is discrepancy between the conceptual contents of major prevention models amongst the usual bibliographic sources of knowledge in GP/FM in particular and medicine in general. Conclusion: For GP/FM, distribution of preventive activities is now firmly established on a new constructivist model, privileging the doctor-patient relationships and introducing a cybernetic thinking on the health care activities with a special commitment to ethics and the positive duty of beneficence.

Chief complaint-based performance measures: a new focus for acute care quality measurement

Performance measures are increasingly important to guide meaningful quality improvement efforts and value-based reimbursement. Populations included in most current hospital performance measures are defined by recorded diagnoses using International Classification of Diseases, Ninth Revision codes in administrative claims data. Although the diagnosis-centric approach allows the assessment of disease-specific quality, it fails to measure one of the primary functions of emergency department (ED) care, which involves diagnosing, risk stratifying, and treating patients' potentially life-threatening conditions according to symptoms (ie, chief complaints). In this article, we propose chief complaint-based quality measures as a means to enhance the evaluation of quality and value in emergency care. We discuss the potential benefits of chief complaint-based measures, describe opportunities to mitigate challenges, propose an example measure set, and present several recommendations to advance this paradigm in ED-based performance measurement.

Health data use, stewardship, and governance: ongoing gaps and challenges: a report from AMIA's 2012 Health Policy Meeting

Large amounts of personal health data are being collected and made available through existing and emerging technological media and tools. While use of these data has significant potential to facilitate research, improve quality of care for individuals and populations, and reduce healthcare costs, many policy-related issues must be addressed before their full value can be realized. These include the need for widely agreed-on data stewardship principles and effective approaches to reduce or eliminate data silos and protect patient privacy. AMIA's 2012 Health Policy Meeting brought together healthcare academics, policy makers, and system stakeholders (including representatives of patient groups) to consider these topics and formulate recommendations. A review of a set of Proposed Principles of Health Data Use led to a set of findings and recommendations, including the assertions that the use of health data should be viewed as a public good and that achieving the broad benefits of this use will require understanding and support from patients.

Implementing unique device identification in electronic health record systems: organizational, workflow, and technological challenges

BACKGROUND

The United States Food and Drug Administration (FDA) has proposed creating a unique device identification (UDI) system for medical devices to facilitate postmarket surveillance, quality improvement, and other applications. Although a small number of health care institutions have implemented initiatives comparable with the proposed UDI system by capturing data in electronic health record (EHR) systems, it is unknown whether institutions with fewer resources will be able to similarly implement UDI.

OBJECTIVE AND METHODS

This paper calls attention to organizational, workflow, and technological challenges in UDI system implementation by drawing from the literature on EHR and clinical research systems implementation.

FINDINGS

Organizational challenges for UDI system implementation include coordinating multiple stakeholders to define UDI attributes and characteristics for use in EHRs, guiding organizational change within individual institutions for integrating UDI with EHRs, and guiding organizational change for reusing UDI data captured in EHRs. Workflow challenges include capturing UDI data in EHRs using keyboard entry and barcode scanning. Technological challenges involve interfacing UDI data between EHRs and surgical information systems, transforming UDI and related patient data from EHRs for research, and applying data standards to UDI within and beyond EHRs.

DISCUSSION AND CONCLUSIONS

We provide recommendations for regulations, organizational sharing, and professional society engagement to raise awareness of and overcome UDI system implementation challenges. Implementation of the UDI system will require integration of people, process, and technology to achieve benefits envisioned by FDA, including improved postmarket device surveillance and quality of care.

An interface terminology for medical imaging ordering purposes

The need for structured data in electronic health records has not been fully addressed by reference terminologies (RT) due to difficulties of use for end-users. Interface terminologies (IT), built for specific usage and users, and linked to RT, may solve this issue. We propose an IT for medical imaging prescription, based on the French nomenclature for procedure (CCAM), and its qualitative evaluation. The creation and evaluation processes were adapted from published guidelines. Prescription IT is available on the web (http://pts.chu-rouen.fr). It contains 290 orderable terms linked to 249 CCAM codes. The synonymy of prescription IT is significantly richer than the CCAM one and labels are significantly shorter. The main problem came from the CCAM, which is dedicated to billing purposes. We are planning to map prescription IT to other international RT such as RadLex or SNOMED. Prescription IT might quicken the adoption of computerized ordering processes in France.

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 framework for harmonizing terminologies to support representation of nursing practice in electronic records

Nursing terminology development efforts in the United States and globally provide concept coverage across many domains of nursing practice. Efforts to integrate concepts from across terminology systems into a single reference terminology support broad concept coverage but do not provide a means to leverage the full benefits of the individual terminology systems. The purpose of this paper is to explore the feasibility of harmonizing the 198 Clinical Care Classification (CCC) System core intervention concepts with intervention concepts in the International Classification for Nursing Practice (ICNP®) as a means to leverage both the information model components of the CCC system and the broad concept coverage of the ICNP®. Findings suggest that the CCC system and ICNP® are largely interoperable and a common framework underlying the two terminology systems provides a foundation for harmonization.

Clinical Clarity versus Terminological Order - The Readiness of SNOMED CT Concept Descriptors for Primary Care

As SNOMED usage becomes more ingrained within applications, its range of concept descriptors, and particularly its synonym adequacy, becomes more important. A simulated clinical scenario involving various term-based concept searches is used to assess whether SNOMED's concept descriptors provide sufficient differentiation to enable possible concept selection between similar terms. Four random samples from different SNOMED concept populations are utilized. Of particular interest are concepts mapped duplicately into UMLS concepts due to shared term patterns. While overall synonym problems are rare (1%), some concept populations exhibited a high rate of potential problems for clinical use (17-62%). The vast majority of issues are due to SNOMED's inherent structure and fine granularity. Many findings hint at a lack of clear delineation between reference and interface terminological qualities. Closer attention should be given to practical clinical use-case scenarios. Reducing SNOMED's structural complexity may alleviate many of the described findings and encourage clinical adoption.

An approach to improve LOINC mapping through augmentation of local test names

Mapping medical test names into a standardized vocabulary is a prerequisite to sharing test-related data between health care entities. One major barrier in this process is the inability to describe tests in sufficient detail to assign the appropriate name in Logical Observation Identifiers, Names, and Codes (LOINC®). Approaches to address mapping of test names with incomplete information have not been well described. We developed a process of "enhancing" local test names by incorporating information required for LOINC mapping into the test names themselves. When using the Regenstrief LOINC Mapping Assistant (RELMA) we found that 73/198 (37%) of "enhanced" test names were successfully mapped to LOINC, compared to 41/191 (21%) of original names (p=0.001). Our approach led to a significantly higher proportion of test names with successful mapping to LOINC, but further efforts are required to achieve more satisfactory results.

The SHARPn project on secondary use of Electronic Medical Record data: progress, plans, and possibilities

SHARPn is a collaboration among 16 academic and industry partners committed to the production and distribution of high-quality software artifacts that support the secondary use of EMR data. Areas of emphasis are data normalization, natural language processing, high-throughput phenotyping, and data quality metrics. Our work avails the industrial scalability afforded by the Unstructured Information Management Architecture (UIMA) from IBM Watson Research labs, the same framework which underpins the Watson Jeopardy demonstration. This descriptive paper outlines our present work and achievements, and presages our trajectory for the remainder of the funding period. The project is one of the four Strategic Health IT Advanced Research Projects (SHARP) projects funded by the Office of the National Coordinator in 2010.

Can SNOMED CT fulfill the vision of a compositional terminology? Analyzing the use case for problem list

We analyzed 598 of 63,952 terms employed in problem list entries from seven major healthcare institutions that were not mapped with UMLS to SNOMED CT when preparing the NLM UMLS-CORE problem list subset. We intended to determine whether published or post-coordinated SNOMED concepts could accurately capture the problems as stated by the clinician and to characterize the workload for the local terminology manager. From the terms we analyzed, we estimate that 7.5% of the total terms represent ambiguous statements that require clarification. Of those terms which were unambiguous, we estimate that 38.1% could be encoded using the SNOMED CT January 2011 pre-coordinated (published core) content. 60.4% of unambiguous terms required post-coordination to capture the term meaning within the SNOMED model. Approximately 28.5% of post-coordinated content could not be fully defined and required primitive forms. This left 1.5% of unambiguous terms which were expressed with meaning which could not be represented in SNOMED CT. We estimate from our study that 98.5% of clinical terms unambiguously suggested for the problem list can be equated to published concepts or can be modeled with SNOMED CT but that roughly one in four SNOMED modeled expressions fail to represent the full meaning of the term. Implications for the business model of the local terminology manager and the development of SNOMED CT are discussed.

Normalized names for clinical drugs: RxNorm at 6 years

OBJECTIVE

In the 6 years since the National Library of Medicine began monthly releases of RxNorm, RxNorm has become a central resource for communicating about clinical drugs and supporting interoperation between drug vocabularies.

MATERIALS AND METHODS

Built on the idea of a normalized name for a medication at a given level of abstraction, RxNorm provides a set of names and relationships based on 11 different external source vocabularies. The standard model enables decision support to take place for a variety of uses at the appropriate level of abstraction. With the incorporation of National Drug File Reference Terminology (NDF-RT) from the Veterans Administration, even more sophisticated decision support has become possible.

DISCUSSION

While related products such as RxTerms, RxNav, MyMedicationList, and MyRxPad have been recognized as helpful for various uses, tasks such as identifying exactly what is and is not on the market remain a challenge.

Development of data models for nursing assessment of cancer survivors using concept analysis

Objectives

Sharing of cancer-related information among healthcare professionals is crucial to ensuring the quality of long-term care for cancer survivors. Appropriate distribution of the essential facts can be achieved using data models. The purpose of this study was to develop and validate suitable data models for use in the nursing assessment of cancer survivors.

Methods

The models developed in this study were based on a modification of concept analysis developed by Walker and Avant. Our approach involved determining the purpose of the analysis, identifying data elements, defining these elements and their uses, determining critical attributes, value sets, and cardinalities, and ultimately constructing data models which were examined externally by domain experts.

Results

We developed 112 data models with 112 data elements, 29 critical attributes, 102 value sets, and 6 data types for the assessment of cancer survivors. External validation revealed that the data elements, critical attributes, and value sets proposed were comprehensive, relevant, and sufficiently useful to encompass nursing issues related to cancer survivors.

Conclusions

Data models developed in this study will contribute to ensuring the semantic consistency of data collected from cancer survivors, which will improve the quality of nursing assessments and in turn translate to improved long-term patient care.

A quality improvement model for healthcare terminologies

A number of controlled healthcare terminologies and classification systems have been developed for specific purposes, resulting in variations in content, structure, process management, and quality. A terminology quality improvement (TQI) model or framework would be useful for various stakeholders to guide terminology selection, to assess the quality of healthcare terminologies and to make improvements according to an agreed standard. A TQI model, thus, was formulated based on a review of the literature and existing international standards developed for healthcare terminologies. The TQI model, adapted from Donabedian's approach, encompasses structure, process, and outcome components in relation to a terminology life cycle--change request, editing, and publication. Multi-dimensional quality outcome measures also were identified in the areas of terminology content, modeling structure, mapping, and process management. A case study was developed to validate the TQI model using the International Classification for Nursing Practice (ICNP). The TQI model represented the complexity of activities involved in terminology quality management. The ICNP case study demonstrated both the applicability of the TQI model and the appropriateness of the criteria identified in the TQI model: openness and responsiveness, clarity and reproducibility, understandability, accessibility and usability, interoperability, and quality of documentation. The applicability of the TQI model was validated using ICNP. While ICNP exhibits many of the desirable characteristics of contemporary terminologies, the case study identified a need for further work on ICNP policy and on documentation.

International classification of diseases, 10th edition, clinical modification and procedure coding system: descriptive overview of the next generation HIPAA code sets

Described are the changes to ICD-10-CM and PCS and potential challenges regarding their use in the US for financial and administrative transaction coding under HIPAA in 2013. Using author constructed derivative databases for ICD-10-CM and PCS it was found that ICD-10-CM's overall term content is seven times larger than ICD-9-CM: only 3.2 times larger in those chapters describing disease or symptoms, but 14.1 times larger in injury and cause sections. A new multi-axial approach ICD-10-PCS increased size 18-fold from its prior version. New ICD-10-CM and PCS reflect a corresponding improvement in specificity and content. The forthcoming required national switch to these new administrative codes, coupled with nearly simultaneous widespread introduction of clinical systems and terminologies, requires substantial changes in US administrative systems. Through coordination of terminologies, the systems using them, and healthcare objectives, we can maximize the improvement achieved and engender beneficial data reuse for multiple purposes, with minimal transformations.

A review of auditing methods applied to the content of controlled biomedical terminologies

Although controlled biomedical terminologies have been with us for centuries, it is only in the last couple of decades that close attention has been paid to the quality of these terminologies. The result of this attention has been the development of auditing methods that apply formal methods to assessing whether terminologies are complete and accurate. We have performed an extensive literature review to identify published descriptions of these methods and have created a framework for characterizing them. The framework considers manual, systematic and heuristic methods that use knowledge (within or external to the terminology) to measure quality factors of different aspects of the terminology content (terms, semantic classification, and semantic relationships). The quality factors examined included concept orientation, consistency, non-redundancy, soundness and comprehensive coverage. We reviewed 130 studies that were retrieved based on keyword search on publications in PubMed, and present our assessment of how they fit into our framework. We also identify which terminologies have been audited with the methods and provide examples to illustrate each part of the framework.

The adequacy of ICNP version 1.0 as a representational model for electronic nursing assessment documentation

OBJECTIVES

The purpose of this study was to evaluate the adequacy of the International Classification of Nursing Practice (1) (ICPN) Version 1.0 as a representational model for nursing assessment documentation.

DESIGN AND MEASUREMENTS

To identify representational requirements of nursing assessments, the authors mapped key concepts and semantic relations extracted from standardized and local nursing admission assessment documentation forms/templates and inpatient admission assessment records to the ICNP. Next, they expanded the list of ICNP semantic relations with those obtained from the admission assessment forms/templates. The expanded ICNP semantic relations were then validated against the semantic relations identified from an additional set of admission assessment records and a set of 300 randomly selected North American Nursing Diagnosis Association defining characteristic phrases. The concept coverage of the ICNP was evaluated by mapping the concepts extracted from these sources to the ICNP concepts. The UMLS Methathesaurus was then used to map concepts without exact matches to other American Nursing Association (ANA) recognized terminologies.

RESULTS

The authors found that along with the 30 existing ICNP semantic relations, an additional 17 are required for the ICNP to function as a representational model for nursing assessment documentation. Eight hundred and five unique assessment concepts were extracted from all sources. Forty-three percent of these unique assessment concepts had exact matches in the ICNP. An additional 20% had matches in the ICNP classified as narrower, broader, or "other." Of the concepts without exact matches in the ICNP, 81% had exact matches found in other ANA recognized terminologies.

CONCLUSIONS

The broad concept coverage and the logic-based structure of the ICNP make it a flexible and robust standard. The ICNP provides a framework from which to capture and reuse atomic level data to facilitate evidence-based practice.

Development and evaluation of a clinical note section header terminology

Clinical documentation is often expressed in natural language text, yet providers often use common organizations that segment these notes in sections, such as history of present illness or physical examination. We developed a hierarchical section header terminology, supporting mappings to LOINC and other vocabularies; it contained 1109 concepts and 4332 synonyms. Physicians evaluated it compared to LOINC and the Evaluation and Management billing schema using a randomly selected corpus of history and physical notes. Evaluated documents contained a median of 54 sections and 27 major sections. There were 16,196 total sections in the evaluation note corpus. The terminology contained 99.9% of the clinical sections; LOINC matched 77% of section header concepts and 20% of section header strings in those documents. The section terminology may enable better clinical note understanding and interoperability. Future development and integration into natural language processing systems is needed.

Auditing the semantic completeness of SNOMED CT using formal concept analysis

OBJECTIVE

This study sought to develop and evaluate an approach for auditing the semantic completeness of the SNOMED CT contents using a formal concept analysis (FCA)-based model.

DESIGN

We developed a model for formalizing the normal forms of SNOMED CT expressions using FCA. Anonymous nodes, identified through the analyses, were retrieved from the model for evaluation. Two quasi-Poisson regression models were developed to test whether anonymous nodes can evaluate the semantic completeness of SNOMED CT contents (Model 1), and for testing whether such completeness differs between 2 clinical domains (Model 2). The data were randomly sampled from all the contexts that could be formed in the 2 largest domains: Procedure and Clinical Finding. Case studies (n = 4) were performed on randomly selected anonymous node samples for validation.

MEASUREMENTS

In Model 1, the outcome variable is the number of fully defined concepts within a context, while the explanatory variables are the number of lattice nodes and the number of anonymous nodes. In Model 2, the outcome variable is the number of anonymous nodes and the explanatory variables are the number of lattice nodes and a binary category for domain (Procedure/Clinical Finding).

RESULTS

A total of 5,450 contexts from the 2 domains were collected for analyses. Our findings revealed that the number of anonymous nodes had a significant negative correlation with the number of fully defined concepts within a context (p < 0.001). Further, the Clinical Finding domain had fewer anonymous nodes than the Procedure domain (p < 0.001). Case studies demonstrated that the anonymous nodes are an effective index for auditing SNOMED CT.

CONCLUSION

The anonymous nodes retrieved from FCA-based analyses are a candidate proxy for the semantic completeness of the SNOMED CT contents. Our novel FCA-based approach can be useful for auditing the semantic completeness of SNOMED CT contents, or any large ontology, within or across domains.

The first step toward data reuse: disambiguating concept representation of the locally developed ICU nursing flowsheets

Although an unambiguous and consistent representation is the foundation of data reuse, a locally developed documentation system such as nursing flowsheets often fails to meet the requirement. This article presents the domain modeling process of the ICU nursing flowsheet to clarify the meaning that its contents represent and the lessons learned during the activity. This study has been done as a first step toward reusing the data documented in a computerized nursing flowsheet for an algorithmic decision making. Following the ontology development processes proposed by other researchers, a conceptual model was developed using Protégé. Then, the existing information model was refined by fully specifying the embedded information structures and by establishing linkages to the conceptual model at the finest-grained concept level. Domain knowledge that the experienced nurses provided was critical to correctly interpret the meaning of the flowsheet contents as well as to verify the newly developed models. This study reassured the importance of the roles of a nurse informaticist to develop a computerized nursing documentation system that accurately represents the information needs in nursing practice.

Development and application of a framework for maintenance of medical terminological systems

OBJECTIVE

Terminological Systems (TSs) need to be maintained in order to sustain their utility. This paper describes a study aiming at the standardization of the maintenance processes of medical TSs by capturing the criteria for the management of the maintenance processes into a framework. Furthermore, this paper describes application of the framework, which sheds light on the current practice of TS maintenance.

DESIGN

Observational study.

MEASUREMENTS

By means of a literature study, criteria for the maintenance of TSs were obtained and categorized into a framework. The current practice of TS maintenance was explored by a survey among organizations that maintain a TS. Results were stratified by the size of the TS being maintained.

RESULTS

From Sixty-three relevant articles, criteria for the maintenance processes of TSs were extracted and organized into four components. The primary component "Execution" concerns the core activities of the maintenance process. The other three components "Process management," "Change specifications," and "Editing tools" support the core activities of the component "Execution." The survey had a response rate of 40% (37 of 93). The answers reflect the large variation in the number of criteria that are satisfied for the participating organizations. Overall, maintenance of larger TSs seems to satisfy more criteria.

CONCLUSIONS

The framework is an important step towards standardization of the maintenance of medical TSs and can be used to eliminate shortcomings in this process. Surveying the current practice showed that there is ample room to improve the maintenance processes of medical TSs, especially for the smaller TSs.

An electronic health record based on structured narrative

OBJECTIVE

To develop an electronic health record that facilitates rapid capture of detailed narrative observations from clinicians, with partial structuring of narrative information for integration and reuse.

DESIGN

We propose a design in which unstructured text and coded data are fused into a single model called structured narrative. Each major clinical event (e.g., encounter or procedure) is represented as a document that is marked up to identify gross structure (sections, fields, paragraphs, lists) as well as fine structure within sentences (concepts, modifiers, relationships). Marked up items are associated with standardized codes that enable linkage to other events, as well as efficient reuse of information, which can speed up data entry by clinicians. Natural language processing is used to identify fine structure, which can reduce the need for form-based entry.

VALIDATION

The model is validated through an example of use by a clinician, with discussion of relevant aspects of the user interface, data structures and processing rules.

DISCUSSION

The proposed model represents all patient information as documents with standardized gross structure (templates). Clinicians enter their data as free text, which is coded by natural language processing in real time making it immediately usable for other computation, such as alerts or critiques. In addition, the narrative data annotates and augments structured data with temporal relations, severity and degree modifiers, causal connections, clinical explanations and rationale.

CONCLUSION

Structured narrative has potential to facilitate capture of data directly from clinicians by allowing freedom of expression, giving immediate feedback, supporting reuse of clinical information and structuring data for subsequent processing, such as quality assurance and clinical research.

A model for evaluating interface terminologies

OBJECTIVE

Evaluations of individual terminology systems should be driven in part by the intended usages of such systems. Clinical interface terminologies support interactions between healthcare providers and computer-based applications. They aid practitioners in converting clinical "free text" thoughts into the structured, formal data representations used internally by application programs. Interface terminologies also serve the important role of presenting existing stored, encoded data to end users in human-understandable and actionable formats. The authors present a model for evaluating functional utility of interface terminologies based on these intended uses.

DESIGN

Specific parameters defined in the manuscript comprise the metrics for the evaluation model.

MEASUREMENTS

Parameters include concept accuracy, term expressivity, degree of semantic consistency for term construction and selection, adequacy of assertional knowledge supporting concepts, degree of complexity of pre-coordinated concepts, and the "human readability" of the terminology. The fundamental metric is how well the interface terminology performs in supporting correct, complete, and efficient data encoding or review by humans.

RESULTS

Authors provide examples demonstrating performance of the proposed evaluation model in selected instances.

CONCLUSION

A formal evaluation model will permit investigators to evaluate interface terminologies using a consistent and principled approach. Terminology developers and evaluators can apply the proposed model to identify areas for improving interface terminologies.

Concept dictionary creation and maintenance under resource constraints: lessons from the AMPATH Medical Record System

The challenges of creating and maintaining concept dictionaries are compounded in resource-limited settings. Approaches to alleviate this burden need to be based on information derived in these settings. We created a concept dictionary and evaluated new concept proposals for an open source EMR in a resource-limited setting. Overall, 87% of the concepts in the initial dictionary were used. There were 5137 new concepts proposed, with 77% of these proposed only once. Further characterization of new concept proposals revealed that 41% were due to deficiency in the existing dictionary, and 19% were synonyms to existing concepts. 25% of the requests contained misspellings, 41% were complex terms, and 17% were ambiguous. Given the resource-intensive nature of dictionary creation and maintenance, there should be considerations for centralizing the concept dictionary service, using standards, prioritizing concept proposals, and redesigning the user-interface to reduce this burden in settings with limited resources.

Integrating preventive care and nursing standardized terminologies in nursing education: a case study

This study investigated the development of a community-focused curriculum integrating primary, secondary, and tertiary prevention and nursing standardized terminologies as an organizing infrastructure. This is a case study of the curriculum redesign of the Marcella Niehoff School of Nursing, Loyola University Chicago. Faculty developed a conceptual framework integrating core concepts into curriculum design, course content, and clinical applications. A coherent curriculum was designed using a community-focused approach; primary, secondary, and tertiary prevention strategies; and standardized terminologies as the organizing infrastructure to teach and apply nursing practice. The curriculum provides a meaningful correlation between the classroom and clinical experience. Students journey with their patients throughout the health care experience, applying nursing concepts using standardized terminologies. Clinical experiences provide students with the opportunity to transfer knowledge to the health experiences of patients in their care. Patient encounters, whether at the primary, secondary, or tertiary level of prevention, are used to assist students in developing critical thinking skills through the use of standardized nursing terminologies.