Clinical Terminology Research Articles

Web-Based Information Infrastructure Increases the Interrater Reliability of Medical Coders: Quasi-Experimental Study.

BackgroundMedical coding is essential for standardized communication and integration of clinical data. The Unified Medical Language System by the National Library of Medicine is the largest clinical terminology system for medical coders and Natural Language Processing tools. However, the abundance of ambiguous codes leads to low rates of uniform coding among different coders.ObjectiveThe objective of our study was to measure uniform coding among different medical experts in terms of interrater reliability and analyze the effect on interrater reliability using an expert- and Web-based code suggestion system.MethodsWe conducted a quasi-experimental study in which 6 medical experts coded 602 medical items from structured quality assurance forms or free-text eligibility criteria of 20 different clinical trials. The medical item content was selected on the basis of mortality-leading diseases according to World Health Organization data. The intervention comprised using a semiautomatic code suggestion tool that is linked to a European information infrastructure providing a large medical text corpus of >300,000 medical form items with expert-assigned semantic codes. Krippendorff alpha (Kalpha) with bootstrap analysis was used for the interrater reliability analysis, and coding times were measured before and after the intervention.ResultsThe intervention improved interrater reliability in structured quality assurance form items (from Kalpha=0.50, 95% CI 0.43-0.57 to Kalpha=0.62 95% CI 0.55-0.69) and free-text eligibility criteria (from Kalpha=0.19, 95% CI 0.14-0.24 to Kalpha=0.43, 95% CI 0.37-0.50) while preserving or slightly reducing the mean coding time per item for all 6 coders. Regardless of the intervention, precoordination and structured items were associated with significantly high interrater reliability, but the proportion of items that were precoordinated significantly increased after intervention (eligibility criteria: OR 4.92, 95% CI 2.78-8.72; quality assurance: OR 1.96, 95% CI 1.19-3.25).ConclusionsThe Web-based code suggestion mechanism improved interrater reliability toward moderate or even substantial intercoder agreement. Precoordination and the use of structured versus free-text data elements are key drivers of higher interrater reliability.

Pharmacy Students' Knowledge and Attitude toward Registration Trials and Clinical Research: A Survey in a Japanese University Hospital.

Clinical research plays a fundamental role in establishing new treatments. Clinical research coordinators are considered essential in clinical research, and medical professionals such as pharmacists often take on this role. Pharmacy students can be considered future candidates for this task. We used questionnaires to survey the knowledge of and attitudes toward registration trials and clinical research of pharmacy students at Tokushima University Hospital. All pharmacy students (103) to whom questionnaires were sent responded. Almost all respondents were aware of registration trials and clinical research. More than 90% were aware of the existence of clinical research coordinators, and about half (48.6%) understood their role. In clinical research terminology, most respondents were aware of informed consent and related issues, but fewer than 20% were aware of more practical things. In total, 29.1% and 40.8% of the respondents were willing to carry out and coordinate research. These findings suggest that pharmacy students have basic knowledge of clinical research and that many students are willing to carry out and coordinate clinical research. More practical exposure to clinical research may help to strengthen their future contribution. Further studies may help to determine how to provide education on registration trials and clinical research to pharmacy students.

An ontology-aware integration of clinical models, terminologies and guidelines: an exploratory study of the Scale for the Assessment and Rating of Ataxia (SARA)

BackgroundElectronic rating scales represent an important resource for standardized data collection. However, the ability to exploit reasoning on rating scale data is still limited. The objective of this work is to facilitate the integration of the semantics required to automatically interpret collections of standardized clinical data. We developed an electronic prototype for the Scale of the Assessment and Rating of Ataxia (SARA), broadly used in neurology. In order to address the modeling challenges of the SARA, we propose to combine the best performances from OpenEHR clinical archetypes, guidelines and ontologies.MethodsA scaled-down version of the Human Phenotype Ontology (HPO) was built, extracting the terms that describe the SARA tests from free-text sources. This version of the HPO was then used as backbone to normalize the content of the SARA through clinical archetypes. The knowledge required to exploit reasoning on the SARA data was modeled as separate information-processing units interconnected via the defined archetypes. Each unit used the most appropriate technology to formally represent the required knowledge.ResultsBased on this approach, we implemented a prototype named SARA Management System, to be used for both the assessment of cerebellar syndrome and the production of a clinical synopsis. For validation purposes, we used recorded SARA data from 28 anonymous subjects affected by Spinocerebellar Ataxia Type 36 (SCA36). When comparing the performance of our prototype with that of two independent experts, weighted kappa scores ranged from 0.62 to 0.86.ConclusionsThe combination of archetypes, phenotype ontologies and electronic information-processing rules can be used to automate the extraction of relevant clinical knowledge from plain scores of rating scales. Our results reveal a substantial degree of agreement between the results achieved by an ontology-aware system and the human experts.

“Kiss myAsthma”: Using a participatory design approach to develop a self-management app with young people with asthma

ABSTRACTObjective: Young people with asthma often lack engagement in self-management. Smartphone apps offer an attractive, immediate method for obtaining asthma information and self-management support. In this research we developed an evidence-based asthma app tailored to young peoples needs, created using a participatory design approach to optimize user engagement. This paper describes the participatory design process. Methods: This multi-phased research included concept generation and ideation of app design by young people with asthma, and development of asthma information by the research team. Clinical review was sought regarding safety and accuracy of app content. Participants suggestions for improvement and any problems with the app were logged throughout. Our young co-designers were invited back to test a high fidelity prototype app using a “think aloud” process and completed a usability questionnaire. Results: Twenty asthma patients aged 15-24 years contributed to the initial app design. Three respiratory specialists and two pharmacists suggested minor corrections to clinical terminology in the app which were all incorporated. Nine co-designers acted as expert reviewers of the prototype app, of whom eight completed a usability questionnaire. Median usability scores (maximum score 6) indicated high satisfaction with app content, usefulness and ease of use [median item score 5.3 (range 4.7-6.0)]. All feedback was incorporated to create an updated prototype app. Conclusions: A clinically sound asthma app has been developed which is considered highly acceptable to the young co-designers. A six-week test of the engagement, acceptability, and usefulness of the app in young people not involved in the participatory design will follow.

Clinical Terminologies in the NHS: SNOMED CT and dm+d

Clinical terminologies are fundamental to the successful recording of clinical data within Electronic Health Record systems and are important in enabling the sharing of data between systems. An overview of the structure and benefits of the international terminology SNOMED CT and the NHS dictionary of medicines and devices (dm+d) are described. Also discussed is a history of clinical terminologies used within the NHS.

SNOMED2HL7: A tool to normalize and bind SNOMED CT concepts to the HL7 Reference Information Model

BackgroundCurrent clinical research and practice requires interoperability among systems in a complex and highly dynamic domain. There has been a significant effort in recent years to develop integrative common data models and domain terminologies. Such efforts have not completely solved the challenges associated with clinical data that are distributed among different and heterogeneous institutions with different systems to encode the information. Currently, when providing homogeneous interfaces to exploit clinical data, certain transformations still involve manual and time-consuming processes that could be automated. ObjectivesThere is a lack of tools to support data experts adopting clinical standards. This absence is especially significant when links between data model and vocabulary are required. The objective of this work is to present SNOMED2HL7, a novel tool to automatically link biomedical concepts from widely used terminologies, and the corresponding clinical context, to the HL7 Reference Information Model (RIM). MethodsBased on the recommendations of the International Health Terminology Standards Development Organisation (IHTSDO), the SNOMED Normal Form has been implemented within SNOMED2HL7 to decompose and provide a method to reduce the number of options to store the same information. The binding of clinical terminologies to HL7 RIM components is the core of SNOMED2HL7, where terminology concepts have been annotated with the corresponding options within the interoperability standard. A web-based tool has been developed to automatically provide information from the normalization mechanisms and the terminology binding. ResultsSNOMED2HL7 binding coverage includes the majority of the concepts used to annotate legacy systems. It follows HL7 recommendations to solve binding overlaps and provides the binding of the normalized version of the concepts. The first version of the tool, available at http://kandel.dia.fi.upm.es:8078, has been validated in EU funded projects to integrate real world data for clinical research with an 88.47% of accuracy. ConclusionsThis paper presents the first initiative to automatically retrieve concept-centered information required to transform legacy data into widely adopted interoperability standards. Although additional functionality will extend capabilities to automate data transformations, SNOMED2HL7 already provides the functionality required for the clinical interoperability community.

Requirements and validation of a prototype learning health system for clinical diagnosis.

IntroductionDiagnostic error is a major threat to patient safety in the context of family practice. The patient safety implications are severe for both patient and clinician. Traditional approaches to diagnostic decision support have lacked broad acceptance for a number of well‐documented reasons: poor integration with electronic health records and clinician workflow, static evidence that lacks transparency and trust, and use of proprietary technical standards hindering wider interoperability. The learning health system (LHS) provides a suitable infrastructure for development of a new breed of learning decision support tools. These tools exploit the potential for appropriate use of the growing volumes of aggregated sources of electronic health records.MethodsWe describe the experiences of the TRANSFoRm project developing a diagnostic decision support infrastructure consistent with the wider goals of the LHS. We describe an architecture that is model driven, service oriented, constructed using open standards, and supports evidence derived from electronic sources of patient data. We describe the architecture and implementation of 2 critical aspects for a successful LHS: the model representation and translation of clinical evidence into effective practice and the generation of curated clinical evidence that can be used to populate those models, thus closing the LHS loop.Results/ConclusionsSix core design requirements for implementing a diagnostic LHS are identified and successfully implemented as part of this research work. A number of significant technical and policy challenges are identified for the LHS community to consider, and these are discussed in the context of evaluating this work: medico‐legal responsibility for generated diagnostic evidence, developing trust in the LHS (particularly important from the perspective of decision support), and constraints imposed by clinical terminologies on evidence generation.

難解用語の言語問題への具体的対応 : 「外来語」と「病院の言葉」を分かりやすくする提案( 日本社会の変容と言語問題)

難解用語の言語問題への具体的対応 : 「外来語」と「病院の言葉」を分かりやすくする提案( 日本社会の変容と言語問題)

Lexical and semantical paradigm of synonymic pairs of english eponymic terms in clinical terminology

Synonymic forms of English terms - eponyms, which function in parallel in clinical terminology are considered in the article. The most common is the assertion that synonyms are interchangeable terms that are identical in meaning but different forms of expression. Synonymous pairs with a proprietary component can be conditionally divided into equivalents, doublets, and variants. The causes of the appearance of parallel forms are analyzed, taking into account both extralinguistic and intralinguistic factors.

Eponyms in Bulgarian Clinical Terminology

Eponyms in Bulgarian Clinical Terminology

ICNP® R&D Centre Ireland: Defining Requirements for an Intersectoral Digital Landscape

The apparent speed and impact of creating a global digital landscape for health and social care tells us that the health workforce is playing catch-up with eHealth national programmes. Locating how and where the profession of nursing fits with future models of health service delivery is critical to provide focused engagement for the populations they serve. In 2016, Dublin City University (DCU) School of Nursing and Human Sciences (SNHS) created a research and development centre for International Classification for Nursing Practice (ICNP®) in Ireland. This paper provides a summary of the first year of the centre’s research, describing how the initial activities link to the development of global eHealth policy. A key aspect of service delivery relates to defining care requirements, specifically to support sustainable intersectoral healthcare. Considering how nursing-sensitive language (clinical terminology) is best mapped is necessary to articulate the care requirements and processes to achieve optimal patient outcome. The World Health Organisational Framework for Integrated Care provides a pathway for crystallising the steep learning curve that the profession has currently found itself situated in, to deliver on contemporary digital healthcare.

“Facebook Official,” “Hooking Up,” “Friends with Benefits”: Decoding the Terminology, Relationship Status, and Sexual Behaviors of Female College Students to Best Target Health Needs

ABSTRACTObjective: This study aimed to assist college healthcare providers with patient communication by determining the relationship terminology commonly used by college students.Participants: A total of 17 female college students participated in four focus groups across two Universities. Participants were primarily white (71%) and heterosexual (88%). Data collection was completed in April 2013.Methods: Focus groups were audio recorded and transcribed verbatim. Transcriptions, notes, and additional discussion summaries were reviewed, coded, and analyzed using NVivo software (v10).Results: Four over-arching themes emerged from the data, each with specific attachment characteristics, sexual behavior attributes, and terminology: No Commitment, Casual Commitment, Partial Commitment, and Full Commitment.Conclusions: The results of this study support other research that identify young adult sexual terminology as vastly differing from the clinical terminology used by healthcare professionals. However, this was the first study to discuss specific terminology across the relationship spectrum.

Histological, immunohistological and molecular characteristics of intraductal precursor of carcinoma ex pleomorphic adenoma support a multistep carcinogenic process.

In recent years, multistep carcinogenesis of carcinoma ex pleomorphic adenoma (CEPA) has been identified, starting with intraductal neoplasia within pre-existent pleomorphic adenoma (PA). However, as yet there is no consensus regarding clinical relevance and appropriate terminology of precursor lesions in CEPA. We therefore decided to investigate precursor lesions, especially intraductal carcinoma, in a series of 85 cases of CEPA. Intraductal carcinoma confined by benign myoepithelial cells was found in 60 cases and mostly exhibited high-grade cellular atypia, increased cellular proliferation and frequent genetic alterations (TP53, Her2-neu, androgen receptor). Intraductal carcinoma was absent only in the myoepithelial type of CEPA. In 26 cases, purely intraductal CEPA with extensive intraductal expansion was found. This suggests that there is a long period of intraductal growth before extraductal intracapsular infiltration of the PA. We identified two different histomorphological types of intraductal carcinoma, which we call 'clinging' and 'solid' types. In summary, combined histological, immunohistological and molecular data strongly support multistep carcinogenesis starting with intraductal carcinoma for all non-myoepithelial types of CEPA. The clinical significance of our finding of two histomorphological types of intraductal carcinoma (clinging and solid) is not yet clear. Intraductal carcinoma, intracapsular invasive CEPA and minor extracapsular invasive CEPA (up to about 6mm) all show favourable prognosis and together comprise half of the cases in our study.

The role of physical therapy and rehabilitation after lumbar fusion surgery for degenerative disease: a systematic review.

OBJECTIVE The purpose of this study was to provide a systematic and comprehensive review of the existing literature regarding postfusion rehabilitation. METHODS Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the authors conducted an exhaustive review of multiple electronic databases. Potential articles were screened using inclusion/exclusion criteria. Two authors independently analyzed these studies using predefined data fields, including study quality indicators such as level of evidence and availability of accepted patient-reported outcomes measures. These findings were synthesized in a narrative format. A third author resolved disagreements regarding the inclusion of a study. RESULTS Twenty-one articles with I or II levels of evidence were included in the review. The authors divided the findings of the literature review into several groups: rehabilitation terminology, timing and duration of postfusion rehabilitation, the need for rehabilitation relative to surgery-related morbidity, rehabilitation's relationship to outcomes, and cognitive and psychosocial aspects of postsurgical rehabilitation. Current evidence generally supports formal rehabilitation after lumbar fusion surgery. Starting physical therapy at the 12-week postoperative mark results in better outcomes at lower cost than an earlier, 6-week start. Where available, psychosocial support improves outcomes. However, a number of the questions could not be answered with high-grade evidence. In these cases, the authors used "best evidence available" to make recommendations. There are many cases in which different types of caregivers use clinical terminology differently. The data supporting an optimal protocol for postfusion rehabilitation remains elusive but, using the data available, the authors have crafted recommendations and a model protocol, which is currently undergoing prospective study. CONCLUSIONS Rehabilitation has long been a common feature in the postoperative management of patients undergoing spinal fusion. Although caregivers from multiple disciplines agree that the majority of their patients will benefit from this effort, the supporting data remain sparse. In creating a model protocol for postlumbar fusion rehabilitation, the authors hope to share a starting point for future postoperative lumbar fusion rehabilitation research.

Dosimetric parameters correlate with duodenal histopathologic damage after stereotactic body radiotherapy for pancreatic cancer: Secondary analysis of a prospective clinical trial

PurposeProspectively assess relationships between dosimetric parameters and histopathologic/clinical duodenal toxicities in patients on a phase I trial for pancreatic cancer. MethodsForty-six borderline resectable/unresectable patients were enrolled on a prospective trial testing neoadjuvant gemcitabine/5-fluorouracil followed by SBRT (5 daily fractions of 5–8Gy) and concurrent nelfinavir. Post-SBRT surgery was performed in 13 resectable patients, which constituted the patient population herein. Pathologic duodenal damage was assessed using predetermined criteria: 1, no/minimal; 2, moderate; and 3, marked damage. Clinical toxicities were assessed per the Clinical Terminology Criteria for Adverse Events (CTCAE). Duodenal dosimetric parameters included V5–V40 and mean/maximum doses. Spearman correlation and linear regression evaluated associations between dosimetric parameters and clinical/pathologic duodenal toxicity. ResultsThe median duodenal mean and maximum doses were 20 and 37Gy. Median duodenal V5–V40 were 64, 62, 52, 39, 27, 14, 5 and 0cc, respectively. The median duodenal damage score was 2 (four 1, eight 2, and one 3). Higher duodenal damage scores correlated with higher duodenal mean doses (r=0.75, p=0.003), V35 (r=0.61, p=0.03), V30 (r=0.67, p=0.01), V25 (r=0.68, p=0.01), V20 (r=0.56, p=0.05), and the planning target volume (PTV) mean (r=0.59, p=0.03) and maximum (r=0.61, p=0.03) doses. Clinical toxicities did not correlate with dosimetric parameters or duodenal pathologic damage. ConclusionsDuodenal histologic damage correlates with mean duodenal dose, V20-V35, and PTV mean/maximum doses.

Semantic Inference on Clinical Documents: Combining Machine Learning Algorithms With an Inference Engine for Effective Clinical Diagnosis and Treatment

Clinical practice calls for reliable diagnosis and optimized treatment. However, human errors in health care remain a severe issue even in industrialized countries. The application of clinical decision support systems (CDSS) casts light on this problem. However, given the great improvement in CDSS over the past several years, challenges to their wide-scale application are still present, including: 1) decision making of CDSS is complicated by the complexity of the data regarding human physiology and pathology, which could render the whole process more time-consuming by loading big data related to patients; and 2) information incompatibility among different health information systems (HIS) makes CDSS an information island, i.e., additional input work on patient information might be required, which would further increase the burden on clinicians. One popular strategy is the integration of CDSS in HIS to directly read electronic health records (EHRs) for analysis. However, gathering data from EHRs could constitute another problem, because EHR document standards are not unified. In addition, HIS could use different default clinical terminologies to define input data, which could cause additional misinterpretation. Several proposals have been published thus far to allow CDSS access to EHRs via the redefinition of data terminologies according to the standards used by the recipients of the data flow, but they mostly aim at specific versions of CDSS guidelines. This paper views these problems in a different way. Compared with conventional approaches, we suggest more fundamental changes; specifically, uniform and updatable clinical terminology and document syntax should be used by EHRs, HIS, and their integrated CDSS. Facilitated data exchange will increase the overall data loading efficacy, enabling CDSS to read more information for analysis at a given time. Furthermore, a proposed CDSS should be based on self-learning, which dynamically updates a knowledge model according to the data-stream-based upcoming data set. The experiment results show that our system increases the accuracy of the diagnosis and treatment strategy designs.

Increasing Patient Findability of Medical Research: Annotating Clinical Trials Using Standard Vocabularies

EDITOR'S SUMMARYMultiple groups at Mayo Clinic organize knowledge with the aid of metadata for a variety of purposes. The ontology group focuses on consumer‐oriented health information using several controlled vocabularies to support and coordinate care providers, consumers, clinical knowledge and, as part of its research management, information on clinical trials. Poor findability, inconsistent indexing and specialized language undermined the goal of increasing trial participation. The ontology group designed a metadata framework addressing disorders and procedures, investigational drugs and clinical departments, adopted and translated the clinical terminology of SNOMED CT and RxNorm vocabularies to consumer language and coordinated terminology with Mayo's Consumer Health Vocabulary. The result enables retrieval of clinical trial information from multiple access points including conditions, procedures, drug names, organizations involved and trial phase. The jump in inquiries since the search site was revised and vocabularies were modified show evidence of success.

Febrile Infection-Related Epilepsy Syndrome: Clinical Review and Hypotheses of Epileptogenesis.

Febrile infection-related epilepsy syndrome (FIRES, AERRPS, or DESC) is one of the most severe, mostly irreversible, and presumably immune-mediated epileptic encephalopathies affecting healthy children. Refractory status epilepticus or a cluster of seizures start a few days after the onset of an acute febrile illness; however, encephalitis cannot be proved. Sequelae of FIRES are drug-resistant epilepsy and neuropsychological impairments occurring without latency. Clinical knowledge is limited because FIRES is sporadic and extremely rare. Therefore, based on literature and our data, this review includes clinical features, terminology, epidemiology, diagnostic criteria and procedures, differential diagnoses, acute and chronic therapeutic options, and outcome data. Particular attention is paid to the epileptogenesis. We hypothesize that FIRES is an immune but not an autoimmune disease and discuss GABAergic therapy at high doses, avoidance of burst-suppression coma, and early introduction of enteral or even parenteral ketogenic diet as the most promising treatment. The lack of evidence requires both a network and a multinational web-based clinical registry to define the clinical spectrum for improving diagnosis and treatment and at the very least, to clarify the cause of FIRES. We conclude that the term "fulminant inflammatory response epilepsy syndrome" may be more appropriate.

Guideline Implementation: Patient Information Management

Clinical documentation captured in a patient’s record provides health care personnel with information that can be used to guide patient care. Data collected in electronic health records can be accessed and aggregated across the health care delivery system to enhance the safety, quality, and efficacy of care. The updated AORN “Guideline for patient information management” provides guidance to perioperative personnel on documenting and managing patient information. This article focuses on key points of the guideline, which address data capture that supports the clinical workflow, incorporation of professional guidelines and standards as well as regulatory and mandatory reporting elements, use of standardized clinical terminologies, data aggregation for use in research and analytics, considerations for patient care orders, and safeguards for the patient’s security and confidentiality. Perioperative RNs should review the complete guideline for additional information and for guidance when writing and updating policies and procedures.

Integrating Bio-ontologies and Controlled Clinical Terminologies: From Base Pairs to Bedside Phenotypes.

Electronic Health Records (EHR) are inherently complex and diverse and cannot be readily integrated and analyzed. Analogous to the Gene Ontology, controlled clinical terminologies were created to facilitate the standardization and integration of medical concepts and knowledge and enable their subsequent use for translational research, official statistics and medical billing. This chapter will introduce several of the main controlled clinical terminologies used to record diagnoses, surgical procedures, laboratory results and medications. The discovery of novel therapeutic agents and treatments for rare or common diseases increasingly requires the integration of genotypic and phenotypic knowledge across different biomedical data sources. Mechanisms that facilitate this linkage, such as the Human Phenotype Ontology, are also discussed.