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Informatics work introduces information professionals to taxonomies and other classification systems outside the boundaries of traditional bibliographic systems. This paper aims to provide an overview of the International Statistical Classification of Diseases and Related Health Problems (ICD) for informaticians and information professionals who may not have worked with the system previously.

In this paper, the author reviews the purpose, history, current use and future trends of the ICD classification system.

ICD is used globally as a standard vocabulary for medical diagnoses and, in the USA, for medical procedures in hospitals. Understanding the classification system is vital to working with clinical medical data.

The ICD classification system is not commonly used by information professionals. This paper provides a brief overview that will familiarize the information professional with the standard and its uses related to medical practice.

This paper sets out to examine the degree to which General Finnish Thesaurus (GFT) and FinMeSH cover various semantic expressions of medical concepts in patients' questions and physicians' answers concerning cardiovascular diseases. The former represents lay persons' information needs.

A total of 50 question‐answer pairs were collected in a medical web site. Concepts and their expressions (terms) with their semantic relations were identified in questions and answers.

FinMeSH covered 65 per cent and GFT 41 per cent of all medical terms in texts. The expressions of patients and physicians matched better with FinMeSH than GFT regardless of the type of expression. The difference in favour of FinMeSH was typically about 25 per cent‐units.

The low fit with users' vocabularies makes GFT a poor tool for supporting searching, whereas the relatively high fit of FinMeSH suggests that it is a reasonable tool in assisting searching. Conclusions concerning the bridging of these two thesauri are discussed.

This article discusses the possibility of the automation of sophisticated subject indexing of medical journal articles. Approaches to subject descriptor assignment in information retrieval research are usually either based upon the manual descriptors in the database or generation of search parameters from the text of the article. The principles of the Medline indexing system are described, followed by a summary of a pilot project, based upon the Amed database. The results suggest that a more extended study, based upon Medline, should encompass various components: Extraction of ‘concept strings’ from titles and abstracts of records, based upon linguistic features characteristic of medical literature. Use of the Unified Medical Language System (UMLS) for identification of controlled vocabulary descriptors. Coordination of descriptors, utilising features of the Medline indexing system. The emphasis should be on system manipulation of data, based upon input, available resources and specifically designed rules.

The purpose of this study is to propose a framework for extracting medical information from the Web using domain ontologies. Patient–Doctor conversations have become prevalent on the Web. For instance, solutions like HealthTap or AskTheDoctors allow patients to ask doctors health-related questions. However, most online health-care consumers still struggle to express their questions efficiently due mainly to the expert/layman language and knowledge discrepancy. Extracting information from these layman descriptions, which typically lack expert terminology, is challenging. This hinders the efficiency of the underlying applications such as information retrieval. Herein, an ontology-driven approach is proposed, which aims at extracting information from such sparse descriptions using a meta-model.

A meta-model is designed to bridge the gap between the vocabulary of the medical experts and the consumers of the health services. The meta-model is mapped with SNOMED-CT to access the comprehensive medical vocabulary, as well as with WordNet to improve the coverage of layman terms during information extraction. To assess the potential of the approach, an information extraction prototype based on syntactical patterns is implemented.

The evaluation of the approach on the gold standard corpus defined in Task1 of ShARe CLEF 2013 showed promising results, an F-score of 0.79 for recognizing medical concepts in real-life medical documents.

The originality of the proposed approach lies in the way information is extracted. The context defined through a meta-model proved to be efficient for the task of information extraction, especially from layman descriptions.

The purpose of this research is to examine the role of various management functions within the complex multi‐layered and multi‐faceted history and structure that is the National Health Service (NHS) hospital service and explore the legitimacy of the role of human resource management (HRM).

Semi‐structured interviews with senior medical professionals and HR managers of one particular Hospital Trust are presented within a conceptual framework based upon an interpretivist notion of “vocabularies of motive”.

Qualitative research at an executive level demonstrates vocabularies of motives in action, where it appears that the role and status of HRM is potentially more dominant and influential than has previously been suggested.

Data are limited to a small group of senior managers and medical professionals and, therefore, does not represent generalisable knowledge. It does, however, offer insights from actors employed in senior roles in one particular hospital trust employing a conceptual framework that may have relevance for other studies concerned with the role of HRM and the dynamics of organisational change.

The paper offers insights into the complexities of change in a complex bureaucracy such as the NHS hospital service. It suggests that government policy and management practice can benefit from an understanding of the various vocabularies of motive at play and how these may effect the successful interpretation of policy into practice.

The paper offers original data and a useful conceptual framework which offers the potential for a more nuanced understanding of the implementation and interpretation of government policy in the NHS hospital service

Online medical repositories provide a platform for users to share information and dynamically access abundant electronic health data. It is important to determine whether case report information can assist the general public in appropriately managing their diseases. Therefore, this paper aims to introduce a novel deep learning-based method that allows non-professionals to make inquiries using ordinary vocabulary, retrieving the most relevant case reports for accurate and effective health information.

The dataset of case reports was collected from both the patient-generated research network and the digital medical journal repository. To enhance the accuracy of obtaining relevant case reports, the authors propose a retrieval approach that combines BERT and BiLSTM methods. The authors identified representative health-related case reports and analyzed the retrieval performance, as well as user judgments.

This study aims to provide the necessary functionalities to deliver relevant health case reports based on input from ordinary terms. The proposed framework includes features for health management, user feedback acquisition and ranking by weights to obtain the most pertinent case reports.

This study contributes to health information systems by analyzing patients' experiences and treatments with the case report retrieval model. The results of this study can provide immense benefit to the general public who intend to find treatment decisions and experiences from relevant case reports.

Clinical notes typically contain medical jargons and specialized words and phrases that are complicated and technical to most people, which is one of the most challenging obstacles in health information dissemination to consumers by healthcare providers. The authors aim to investigate how to leverage machine learning techniques to transform clinical notes of interest into understandable expressions.

The authors propose a natural language processing pipeline that is capable of extracting relevant information from long unstructured clinical notes and simplifying lexicons by replacing medical jargons and technical terms. Particularly, the authors develop an unsupervised keywords matching method to extract relevant information from clinical notes. To automatically evaluate completeness of the extracted information, the authors perform a multi-label classification task on the relevant texts. To simplify lexicons in the relevant text, the authors identify complex words using a sequence labeler and leverage transformer models to generate candidate words for substitution. The authors validate the proposed pipeline using 58,167 discharge summaries from critical care services.

The results show that the proposed pipeline can identify relevant information with high completeness and simplify complex expressions in clinical notes so that the converted notes have a high level of readability but a low degree of meaning change.

The proposed pipeline can help healthcare consumers well understand their medical information and therefore strengthen communications between healthcare providers and consumers for better care.

An innovative pipeline approach is developed to address the health literacy problem confronted by healthcare providers and consumers in the ongoing digital transformation process in the healthcare industry.

This chapter aims to discuss the issues associated with social indexing as a solution to the challenges of current information organization systems by investigating the quality and efficacy of social indexing.

The chapter focuses on the study which compared indexing similarity between two professional groups and also compared social tagging and professional indexing. The study employed the method of the modified vector-based Indexing Consistency Density (ICD) with three different similarity measures: cosine similarity, dot product similarity, and Euclidean distance metric.

The investigation of social indexing in comparison of professional indexing demonstrates that social tags are more accurate descriptions of resources and reflection of more current terminology than controlled vocabulary. Through the characteristics of social tagging discussed in this chapter, we have a clearer understanding of the extent to which social indexing can be used to replace and improve upon professional indexing.

As investment in professionally developed web directories diminishes, it becomes even more critical to understand the characteristics of social tagging and to obtain benefit from it. In future research, the examination of subjective tags needs to be conducted. A survey or user study on tagging behavior also would help to extend understanding of social indexing practices.

Purpose – The purpose of this chapter is to explore whether and how social tagging can be useful in an information web site for cancer patients and their relatives.

Methodology/approach – Three studies have been carried out in order to investigate the research questions. First, we reviewed and analyzed literature about cancer patients’ information needs and seeking behavior, and about social tagging and patient terminology. Second, we analyzed tags applied to blog postings at Blogomkraeft.dk, a blog site at the Danish information web site Cancer.dk. The tags were compared with the formal browsing structure of Cancer.dk. Results from the two studies were used to develop a prototype for social tagging at Cancer.dk. Thus third, we evaluated the prototype in a usability study.

Findings – We found that tags have the potential to describe and provide access to web site content from the users’ perspective and language use. Social tags may be a means to bridge between scientific viewpoints and terminology and everyday problems and vocabulary. Tags at Blogomkraeft.dk are mainly factual, often detailed, and do not cover as many functions as tags in more general bookmarking systems. An important finding is that some tags seemed to add to and supplement the content instead of factually describing the content of a blog posting. The usability test showed that our test persons liked the tagging feature.

Social implications – Tagging features give the public an opportunity to apply their own terms to documents, reflecting their own model of the current topic. Tags may furthermore function as colloquial lead-in terms from users’ search formulations at search engines such as Google to the domain-specific, tailored cancer web site.

Originality/value – Unlike most research on social tagging so far, we investigate tagging in a domain-specific setting, how tags can improve the interaction and communication between layman users and domain experts in an information web site within health care.

Design and construction of indexing languages require thorough knowledge and understanding of the information environment. This empirical study investigated a mixed set of methods (group interviews, recollection of information needs and word association tests to collect data; content analysis and discourse analysis to analyse data) to evaluate whether these methods collected the data needed for work domain oriented thesaurus design. The findings showed that the study methods together provided the domain knowledge needed to define the role of the thesaurus and design its content and structure. The study was carried out from a person‐insituation perspective. The findings reflected the information environment and made it possible to develop a thesaurus according to the characteristics of the work domain. It seemed more difficult to capture the needs of the individual user and adapt the thesaurus to individual characteristics.