Interprofessional clinical decision-making process in health: A scoping review

AIMS

To describe the key elements of the interprofessional decision-making process in health, based on published scientific studies. To describe the authors, reviews and subject matter of those publications.

DESIGN

Scoping review of the literature.

DATA SOURCES

MEDLINE, APA Psycinfo OpenGrey, Lissa and Cochrane databases were searched in December 2019 and January 2023.

REVIEW METHODS

References were considered eligible if they (i) were written in French or English, (ii) concerned health, (iii) studied a clinical decision-making process, (iv) were performed in an interprofessional context. 'PRISMA-scoping review' guidelines were respected. The eligible studies were analysed and classified by an inductive approach RESULTS: We identified 1429 sources of information, 145 of which were retained for the analysis. Based on these studies, we identified five key elements of interprofessional decision-making in health. The process was found to be influenced by group dynamics, the available information and consideration of the unique characteristics of the patient. An organizational framework and specific training favoured improvements in the process.

CONCLUSION

Decision-making can be based on a willingness of the healthcare organization to promote models based on more shared leadership and to work on professional roles and values. It also requires healthcare professionals trained in the entire continuum of collaborative practices, to meet the unique needs of each patient. Finally, it appears essential to favour the sharing of multiple sources of accessible and structured information. Tools for knowledge formalization should help to optimize interprofessional decision-making in health.

IMPACT

The quality of a team decision-making is critical to the quality of care. Interprofessional decision-making can be structured and improved through different levels of action. These improvements could benefit to patients and healthcare professionals in every settings of care involving care collaboration.

IMPACT STATEMENT

Interprofessional decision-making in health is an essential lever of quality of care, especially for the most complex patients which are a contemporary challenge. This scoping review article offers a synthesis of a large corpus of data published to date about the interprofessional clinical decision-making process in healthcare. It has the potential to provide a global vision, practical data and a list of references to facilitate the work of healthcare teams, organizations and teachers ready to initiate a change.

Knowledge Representation and Management 2022: Findings in Ontology Development and Applications

OBJECTIVES

To select, present, and summarize the best papers in 2022 for the Knowledge Representation and Management (KRM) section of the International Medical Informatics Association (IMIA) Yearbook.

METHODS

We conducted PubMed queries and followed the IMIA Yearbook guidelines for performing biomedical informatics literature review to select the best papers in KRM published in 2022.

RESULTS

We retrieved 1,847 publications from PubMed. We nominated 15 candidate best papers, and two of them were finally selected as the best papers in the KRM section. The topics covered by the candidate papers include ontology and knowledge graph creation, ontology applications, ontology quality assurance, ontology mapping standard, and conceptual model.

CONCLUSIONS

In the KRM best paper selection for 2022, the candidate best papers encompassed a broad range of topics, with ontology and knowledge graph creation remaining a considerable research focus.

An Ontology Design Pattern for Modeling Experimental Paradigms

We present an ontology design pattern for modeling scientific experiments and examinations conducted in a clinical research study. Integrating heterogeneous data into a common ontological model is a challenge, redoubled if we want them to be explored later. In order to facilitate the development of dedicated ontological modules, this design pattern relies on invariants, is centered on the event of the experiment, and keeps the link to the original data.

Ontological Modeling of Clinical Study Forms

The use of eCRFs is now commonplace in clinical research studies. We propose here an ontological model of these forms allowing to describe them, to express their granularity and to link them to the relevant entities of the study in which they are used. It has been developed in a psychiatry project but its generality may allow a wider application.

Ontology-Based Semantic Annotation of French Psychiatric Clinical Documents

Building a timeline of psychiatric patient profiles can answer many valuable questions, such as how important medical events affect the progression of psychosis in patients. However, the majority of text information extraction and semantic annotation tools, as well as domain ontologies, are only available in English and cannot be easily extended to other languages, due to fundamental linguistic differences. In this paper, we describe a semantic annotation system based on an ontology developed in the PsyCARE framework. Our system is being manually evaluated by two annotators on 50 patient discharge summaries, showing promising results.

Knowledge Representation and Management: Notable Contributions in 2021

OBJECTIVES

To select, present, and summarize the best papers in the field of Knowledge Representation and Management (KRM) published in 2021.

METHODS

Following the International Medical Informatics Association (IMIA) Yearbook guidelines, a comprehensive and standardized review of the biomedical informatics literature was performed to select the best KRM papers published in 2021, based on PubMed queries.

RESULTS

A total of 1,231 publications were retrieved from PubMed. We nominated 15 candidate best papers, and four of them were finally selected as the best papers in the KRM section. The topics covered by these papers include knowledge graph, ontology development, ontology alignment, and the International Classification of Diseases.

CONCLUSION

In the KRM best paper selection for 2021, the candidate best papers covered a wider spectrum of topics compared to the last year's significant focus on ontology curation. In particular, ontology development for specific domains (e.g., Alzheimer's disease, infectious diseases, bioethics) has received the most attention.

Qualitative Evaluation of a Drug Terminology Server

BACKGROUND

Although the drug is finished, identifiable, there is no universally accepted standard for naming them. The objective of this work is to evaluate qualitatively the HeTOP drug terminology server by two categories of students: (a) pharmacy students and (b) a control group.

METHODS

A formal evaluation was built to measure the perception of users about the HeTOP drug server, using the three mains questions about "teaching interest", "skill interest" (or competence) and "ergonomics".

RESULTS

The three pharmacy student subgroups gave the best and the worst score to the same categories.

CONCLUSION

All three criteria are rated above 6.5 out of 10. The HeTOP drug terminology server is freely available to "non drug" specialists (URL: www.hetop.eu/hetop/drugs/).

Temporal Medical Knowledge Representation Using Ontologies

Representing temporal information is a recurrent problem for biomedical ontologies. We propose a foundational ontology that combines the so-called three-dimensional and four-dimensional approaches in order to be able to track changes in an individual and to trace his or her medical history. This requires, on the one hand, associating with any representation of an individual the representation of his or her life course and, on the other hand, distinguishing the properties that characterize this individual from those that characterize his or her life course.

Pattern-Based Logical Definitions of Prenatal Disorders Grounded on Dispositions

Biomedical ontologies define concepts having biomedical significance and the semantic relations among them. Developing high-quality and reusable ontologies in the biomedical domain is a challenging task. Pattern-based ontology design is considered a promising approach to overcome the challenges. Ontology Design Patterns (ODPs) are reusable modeling solutions to facilitate ontology development. This study relies on ODPs to semantically enrich biomedical ontologies by assigning logical definitions to ontological entities. Specifically, pattern-based logical definitions grounded on dispositions are given to prenatal disorders. The proposed approach is performed under the supervision of fetal domain experts.

Knowledge Representation and Management: Interest in New Solutions for Ontology Curation

Objective: To select, present and summarize some of the best papers in the field of Knowledge Representation and Management (KRM) published in 2020.

Methods: A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers of KRM published in 2020, based on PubMed queries. This review was conducted according to the IMIA Yearbook guidelines.

Results: Four best papers were selected among 1,175 publications. In contrast with the papers selected last year, the four best papers of 2020 demonstrated a significant focus on methods and tools for ontology curation and design. The usual KRM application domains (bioinformatics, machine learning, and electronic health records) were also represented.

Conclusion: In 2020, ontology curation emerges as a significant topic of research interest. Bioinformatics, machine learning, and electronics health records remain significant research areas in the KRM community with various applications. Knowledge representations are key to advance machine learning by providing context and to develop novel bioinformatics metrics. As in 2019, representations serve a great variety of applications across many medical domains, with actionable results and now with growing adhesion to the open science initiative.

Design and Use of Semantic Resources: Findings from the Section on Knowledge Representation and Management of the 2020 International Medical Informatics Association Yearbook

OBJECTIVE

To select, present, and summarize the best papers in the field of Knowledge Representation and Management (KRM) published in 2019.

METHODS

A comprehensive and standardized review of the biomedical informatics literature was performed to select the most interesting papers of KRM published in 2019, based on PubMed and ISI Web Of Knowledge queries.

RESULTS

Four best papers were selected among 1,189 publications retrieved, following the usual International Medical Informatics Association Yearbook reviewing process. In 2019, research areas covered by pre-selected papers were represented by the design of semantic resources (methods, visualization, curation) and the application of semantic representations for the integration/enrichment of biomedical data. Besides new ontologies and sound methodological guidance to rethink knowledge bases design, we observed large scale applications, promising results for phenotypes characterization, semantic-aware machine learning solutions for biomedical data analysis, and semantic provenance information representations for scientific reproducibility evaluation.

CONCLUSION

In the KRM selection for 2019, research on knowledge representation demonstrated significant contributions both in the design and in the application of semantic resources. Semantic representations serve a great variety of applications across many medical domains, with actionable results.

Manual Evaluation of the Automatic Mapping of International Classification of Diseases (ICD)-11 (in French)

A lexical method was used to map ICD-11 to the terminologies included in the HeTOP server. About half of ICD-11 codes (47.76%) were mapped to at least one concept. The developed tool reached a global precision of 0.98 and a recall of 0.66. Lexical methods are powerful methods to map health terminologies. Supervised and manual mapping is still necessary to complete the mapping.

Using Equivalent Classes of an Ontology to Understand Care Pathway in Amyotrophic Lateral Sclerosis

To understand the home-based difficulties encountered in the health care pathways of patients with Amyotrophic Lateral Sclerosis (ALS), we must annotate a large amount of textual data, from a database created by the ALS Île de France coordination network. For this purpose, we have developed a modular ontology, consisting of four modules, and a semantic annotation tool integrating the created ontology. The specificity of our approach is the creation of equivalent classes at different levels of the ontology. These equivalent classes represent variables of interest allowing a statistical approach and a clinical analysis of comprehension of care pathways ruptures causing.

Formal Medical Knowledge Representation Supports Deep Learning Algorithms, Bioinformatics Pipelines, Genomics Data Analysis, and Big Data Processes

OBJECTIVE

To select, present, and summarize the best papers published in 2018 in the field of Knowledge Representation and Management (KRM).

METHODS

A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers published in 2018 in KRM, based on PubMed and ISI Web Of Knowledge queries.

RESULTS

Four best papers were selected among the 962 publications retrieved following the Yearbook review process. The research areas in 2018 were mainly related to the ontology-based data integration for phenotype-genotype association mining, the design of ontologies and their application, and the semantic annotation of clinical texts.

CONCLUSION

In the KRM selection for 2018, research on semantic representations demonstrated their added value for enhanced deep learning approaches in text mining and for designing novel bioinformatics pipelines based on graph databases. In addition, the ontology structure can enrich the analyses of whole genome expression data. Finally, semantic representations demonstrated promising results to process phenotypic big data.

A Novel Intelligent Scan Assistant System for Early Pregnancy Diagnosis by Ultrasound: Clinical Decision Support System Evaluation Study

Background

Early pregnancy ultrasound scans are usually performed by nonexpert examiners in obstetrics/gynecology (OB/GYN) emergency departments. Establishing the precise diagnosis of pregnancy location is key for appropriate management of early pregnancies, and experts are usually able to locate a pregnancy in the first scan. A decision-support system based on a semantic, expert-validated knowledge base may improve the diagnostic performance of nonexpert examiners for early pregnancy transvaginal ultrasound.

Objective

This study aims to evaluate a novel Intelligent Scan Assistant System for early pregnancy ultrasound to diagnose the pregnancy location and determine the image quality.

Methods

Two trainees performed virtual transvaginal ultrasound examinations of early pregnancy cases with and without the system. The ultrasound images and reports were blindly reviewed by two experts using scoring methods. A diagnosis of pregnancy location and ultrasound image quality were compared between scans performed with and without the system.

Results

Each trainee performed a virtual vaginal examination for all 32 cases with and without use of the system. The analysis of the 128 resulting scans showed higher quality of the images (quality score: +23%; P<.001), less images per scan (4.6 vs 6.3 [without the CDSS]; P<.001), and higher confidence in reporting conclusions (trust score: +20%; P<.001) with use of the system. Further, use of the system cost an additional 8 minutes per scan. We observed a correct diagnosis of pregnancy location in 39 (61%) and 52 (81%) of 64 scans in the nonassisted mode and assisted mode, respectively. Additionally, an exact diagnosis (with precise ectopic location) was made in 30 (47%) and 49 (73%) of the 64 scans without and with use of the system, respectively. These differences in diagnostic performance (+20% for correct location diagnosis and +30% for exact diagnosis) were both statistically significant (P=.002 and P<.001, respectively).

Conclusions

The Intelligent Scan Assistant System is based on an expert-validated knowledge base and demonstrates significant improvement in early pregnancy scanning, both in diagnostic performance (pregnancy location and precise diagnosis) and scan quality (selection of images, confidence, and image quality).

As Ontologies Reach Maturity, Artificial Intelligence Starts Being Fully Efficient: Findings from the Section on Knowledge Representation and Management for the Yearbook 2018

Objectives:  To select, present, and summarize the best papers published in 2017 in the field of Knowledge Representation and Management (KRM).

Methods:  A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers of KRM published in 2017, based on a PubMed query.

Results:  In direct line with the research on data integration presented in the KRM section of the 2017 edition of the International Medical Informatics Association (IMIA) Yearbook, the five best papers for 2018 demonstrate even further the added-value of ontology-based integration approaches for phenotype-genotype association mining. Additionally, among the 15 preselected papers, two aspects of KRM are in the spotlight: the design of knowledge bases and new challenges in using ontologies.

Conclusions:  Ontologies are demonstrating their maturity to integrate medical data and begin to support clinical practices. New challenges have emerged: the query on distributed semantically annotated datasets, the efficiency of semantic annotation processes, the semantic representation of large textual datasets, the control of biases associated with semantic annotations, and the computation of Bayesian indicators on data annotated with ontologies.

Semantic interoperability challenges to process large amount of data perspectives in forensic and legal medicine

This article is a position paper dealing with semantic interoperability challenges. It addresses the Variety and Veracity dimensions when integrating, sharing and reusing large amount of heterogeneous data for data analysis and decision making applications in the healthcare domain. Many issues are raised by the necessity to conform Big Data to interoperability standards. We discuss how semantics can contribute to the improvement of information sharing and address the problem of data mediation with domain ontologies. We then introduce the main steps for building domain ontologies as they could be implemented in the context of Forensic and Legal medicine. We conclude with a particular emphasis on the current limitations in standardisation and the importance of knowledge formalization.

From Text to Knowledge: a Unifying Document-Centered View of Analyzed Medical Language

Although medical language processing (MLP) has achieved some success, the actual use and dissemination of data extracted from free text by MLP systems is still very limited. We claim that the adoption of an ‘enricheddocument’ paradigm (or ‘document-centered’ view) can help to address this issue. We present this paradigm and explain how it can be implemented, then discuss its expected benefits both for end-users and MLP researchers.

Issues in the Structuring and Acquisition of an Ontology for Medical Language Understanding

Medical natural language understanding basically aims at representing the contents of medical texts in a formal, conceptual representation. The understanding process itself increasingly relies on a body of domain knowledge, generally expressed in the same conceptual formalism. The design of such a conceptual representation is a key knowledge-acquisition issue. When representing knowledge, the most important point is to ensure that the formal exploitation of the knowledge representation conforms to its meaning in the domain. We examined some methodological and theoretical principles to enforce this conformity. These principles result from our experience in MENELAS, a medical language understanding project.

A Modular Ontology for Modeling Service Provision in a Communication Network for Coordination of Care

This paper presents a modular ontology of health care in the context in Amyotrophic Lateral Sclerosis. 4 modules cover socio-environmental, medical, and care coordination aspects of the domain. They are organized by a core module. Its goal is to understand interruptions in health care provision in the context of a neurodegenerative disease.

Knowledge Representation and Management, It's Time to Integrate!

Objectives: To select, present, and summarize the best papers published in 2016 in the field of Knowledge Representation and Management (KRM). Methods: A comprehensive and standardized review of the medical informatics literature was performed based on a PubMed query. Results: Among the 1,421 retrieved papers, the review process resulted in the selection of four best papers focused on the integration of heterogeneous data via the development and the alignment of terminological resources. In the first article, the authors provide a curated and standardized version of the publicly available US FDA Adverse Event Reporting System. Such a resource will improve the quality of the underlying data, and enable standardized analyses using common vocabularies. The second article describes a project developed in order to facilitate heterogeneous data integration in the i2b2 framework. The originality is to allow users integrate the data described in different terminologies and to build a new repository, with a unique model able to support the representation of the various data. The third paper is dedicated to model the association between multiple phenotypic traits described within the Human Phenotype Ontology (HPO) and the corresponding genotype in the specific context of rare diseases (rare variants). Finally, the fourth paper presents solutions to annotation-ontology mapping in genome-scale data. Of particular interest in this work is the Experimental Factor Ontology (EFO) and its generic association model, the Ontology of Biomedical AssociatioN (OBAN). Conclusion: Ontologies have started to show their efficiency to integrate medical data for various tasks in medical informatics: electronic health records data management, clinical research, and knowledge-based systems development.

Towards ontology-based decision support systems for complex ultrasound diagnosis in obstetrics and gynecology

INTRODUCTION

We have developed a new knowledge base intelligent system for obstetrics and gynecology ultrasound imaging, based on an ontology and a reference image collection. This study evaluates the new system to support accurate annotations of ultrasound images. We have used the early ultrasound diagnosis of ectopic pregnancies as a model clinical issue.

MATERIAL AND METHODS

The ectopic pregnancy ontology was derived from medical texts (4260 ultrasound reports of ectopic pregnancy from a specialist center in the UK and 2795 Pubmed abstracts indexed with the MeSH term "Pregnancy, Ectopic") and the reference image collection was built on a selection from 106 publications. We conducted a retrospective analysis of the signs in 35 scans of ectopic pregnancy by six observers using the new system.

RESULTS

The resulting ectopic pregnancy ontology consisted of 1395 terms, and 80 images were collected for the reference collection. The observers used the knowledge base intelligent system to provide a total of 1486 sign annotations. The precision, recall and F-measure for the annotations were 0.83, 0.62 and 0.71, respectively. The global proportion of agreement was 40.35% 95% CI [38.64-42.05].

DISCUSSION

The ontology-based intelligent system provides accurate annotations of ultrasound images and suggests that it may benefit non-expert operators. The precision rate is appropriate for accurate input of a computer-based clinical decision support and could be used to support medical imaging diagnosis of complex conditions in obstetrics and gynecology.

Developing a knowledge base to support the annotation of ultrasound images of ectopic pregnancy

Background

Ectopic pregnancy is a frequent early complication of pregnancy associated with significant rates of morbidly and mortality. The positive diagnosis of this condition is established through transvaginal ultrasound scanning. The timing of diagnosis depends on the operator expertise in identifying the signs of ectopic pregnancy, which varies dramatically among medical staff with heterogeneous training. Developing decision support systems in this context is expected to improve the identification of these signs and subsequently improve the quality of care. In this article, we present a new knowledge base for ectopic pregnancy, and we demonstrate its use on the annotation of clinical images.

Results

The knowledge base is supported by an application ontology, which provides the taxonomy, the vocabulary and definitions for 24 types and 81 signs of ectopic pregnancy, 484 anatomical structures and 32 technical elements for image acquisition. The knowledge base provides a sign-centric model of the domain, with the relations of signs to ectopic pregnancy types, anatomical structures and the technical elements. The evaluation of the ontology and knowledge base demonstrated a positive feedback from a panel of 17 medical users. Leveraging these semantic resources, we developed an application for the annotation of ultrasound images. Using this application, 6 operators achieved a precision of 0.83 for the identification of signs in 208 ultrasound images corresponding to 35 clinical cases of ectopic pregnancy.

Conclusions

We developed a new ectopic pregnancy knowledge base for the annotation of ultrasound images. The use of this knowledge base for the annotation of ultrasound images of ectopic pregnancy showed promising results from the perspective of clinical decision support system development. Other gynecological disorders and fetal anomalies may benefit from our approach.

From Patient Discharge Summaries to an Ontology for Psychiatry

Psychiatry aims at detecting symptoms, providing diagnoses and treating mental disorders. We developed ONTOPSYCHIA, an ontology for psychiatry in three modules: social and environmental factors of mental disorders, mental disorders, and treatments. The use of ONTOPSYCHIA, associated with dedicated tools, will facilitate semantic research in Patient Discharge Summaries (PDS). To develop the first module of the ontology we propose a PDS text analysis in order to explicit psychiatry concepts. We decided to set aside classifications during the construction of the modu le, to focus only on the information contained in PDS (bottom-up approach) and to return to domain classifications solely for the enrichment phase (top-down approach). Then, we focused our work on the development of the LOVMI methodology (Les Ontologies Validées par Méthode Interactive - Ontologies Validated by Interactive Method), which aims to provide a methodological framework to validate the structure and the semantic of an ontology.

Efficient Results in Semantic Interoperability for Health Care. Findings from the Section on Knowledge Representation and Management

OBJECTIVES

To summarize excellent current research in the field of Knowledge Representation and Management (KRM) within the health and medical care domain.

METHOD

We provide a synopsis of the 2016 IMIA selected articles as well as a related synthetic overview of the current and future field activities. A first step of the selection was performed through MEDLINE querying with a list of MeSH descriptors completed by a list of terms adapted to the KRM section. The second step of the selection was completed by the two section editors who separately evaluated the set of 1,432 articles. The third step of the selection consisted of a collective work that merged the evaluation results to retain 15 articles for peer-review.

RESULTS

The selection and evaluation process of this Yearbook's section on Knowledge Representation and Management has yielded four excellent and interesting articles regarding semantic interoperability for health care by gathering heterogeneous sources (knowledge and data) and auditing ontologies. In the first article, the authors present a solution based on standards and Semantic Web technologies to access distributed and heterogeneous datasets in the domain of breast cancer clinical trials. The second article describes a knowledge-based recommendation system that relies on ontologies and Semantic Web rules in the context of chronic diseases dietary. The third article is related to concept-recognition and text-mining to derive common human diseases model and a phenotypic network of common diseases. In the fourth article, the authors highlight the need for auditing the SNOMED CT. They propose to use a crowdbased method for ontology engineering.

CONCLUSIONS

The current research activities further illustrate the continuous convergence of Knowledge Representation and Medical Informatics, with a focus this year on dedicated tools and methods to advance clinical care by proposing solutions to cope with the problem of semantic interoperability. Indeed, there is a need for powerful tools able to manage and interpret complex, large-scale and distributed datasets and knowledge bases, but also a need for user-friendly tools developed for the clinicians in their daily practice.

Knowledge Representation and Management. From Ontology to Annotation. Findings from the Yearbook 2015 Section on Knowledge Representation and Management

OBJECTIVE

To summarize the best papers in the field of Knowledge Representation and Management (KRM).

METHODS

A comprehensive review of medical informatics literature was performed to select some of the most interesting papers of KRM published in 2014.

RESULTS

Four articles were selected, two focused on annotation and information retrieval using an ontology. The two others focused mainly on ontologies, one dealing with the usage of a temporal ontology in order to analyze the content of narrative document, one describing a methodology for building multilingual ontologies.

CONCLUSION

Semantic models began to show their efficiency, coupled with annotation tools.

[Artificial intelligence meeting neuropsychology. Semantic memory in normal and pathological aging]

Artificial intelligence (IA) is the subject of much research, but also many fantasies. It aims to reproduce human intelligence in its learning capacity, knowledge storage and computation. In 2014, the Defense Advanced Research Projects Agency (DARPA) started the restoring active memory (RAM) program that attempt to develop implantable technology to bridge gaps in the injured brain and restore normal memory function to people with memory loss caused by injury or disease. In another IA's field, computational ontologies (a formal and shared conceptualization) try to model knowledge in order to represent a structured and unambiguous meaning of the concepts of a target domain. The aim of these structures is to ensure a consensual understanding of their meaning and a univariant use (the same concept is used by all to categorize the same individuals). The first representations of knowledge in the AI's domain are largely based on model tests of semantic memory. This one, as a component of long-term memory is the memory of words, ideas, concepts. It is the only declarative memory system that resists so remarkably to the effects of age. In contrast, non-specific cognitive changes may decrease the performance of elderly in various events and instead report difficulties of access to semantic representations that affect the semantics stock itself. Some dementias, like semantic dementia and Alzheimer's disease, are linked to alteration of semantic memory. We propose in this paper, using the computational ontologies model, a formal and relatively thin modeling, in the service of neuropsychology: 1) for the practitioner with decision support systems, 2) for the patient as cognitive prosthesis outsourced, and 3) for the researcher to study semantic memory.

Enrich classifications in psychiatry with textual data: an ontology for psychiatry including social concepts

We propose a modular approach to develop an ontology of psychiatry, ONTOPSYCHIA, based on Patient Discharges Summaries (PDS) and divided into three modules (i.e. social, mental disorders and treatments). We decided to take into account the social aspects of the patient life described in PDS to consider information such as family history, social environment or education.

A "pivot" Model to set up Large Scale Rare Diseases Information Systems: Application to the Fibromuscular Dysplasia Registry

The SIR-FMD project is a partnership between the Department of Genetics and Reference Centre for Rare Vascular Diseases at the Georges Pompidou European Hospital in Paris and the Medical Informatics and Knowledge Engineering Laboratory of Inserm. Its aim is to use an ontological approach to implement an information system for the French Fibromuscular Dysplasia Registry. The existing data was dispersed in numerous databases, which had been created independently. These databases have different structures and contain data of diverse quality. The project aims to provide generic solutions for the management of the communication of medical data. The secondary objective is to demonstrate the applicability of these generic solutions in the field of rare diseases (RD) in an operational context. The construction of the French FMD registry was a multistep process. A secure platform has been available since the beginning of November 2013. The medical records of 471 patients from the initial dataset provided by the HEGP-Paris, France have been included, and are accessible from a secure user account. Users are organized into a collaborative group, and can access patient groups. Each electronic patient record contains more than 2,200 items. The problem of semantic interoperability has become one of the major challenges for the development of applications requiring the sharing and reuse of data. The information system component of the SIR-FMD project has a direct impact on the standardisation of coding of rare diseases and thereby contributes to the development of e-Health.

Managing free text for secondary use of health data

OBJECTIVE

To summarize the best papers in the field of Knowledge Representation and Management (KRM).

METHODS

A comprehensive review of medical informatics literature was performed to select some of the most interesting papers of KRM and natural language processing (NLP) published in 2013.

RESULTS

Four articles were selected, one focuses on Electronic Health Record (EHR) interoperability for clinical pathway personalization based on structured data. The other three focus on NLP (corpus creation, de-identification, and co-reference resolution) and highlight the increase in NLP tools performances.

CONCLUSION

NLP tools are close to being seriously concurrent to humans in some annotation tasks. Their use could increase drastically the amount of data usable for meaningful use of EHR.

Standard-based EHR-enabled applications for clinical research and patient safety: CDISC - IHE QRPH - EHR4CR & SALUS collaboration

Integration profiles collaboratively developed by CDISC and IHE for integrating data from Electronic Health Records (EHRs) with clinical research and pharmacovigilance are limited to resolving lexical/syntactic data integration issues and do not address semantic barriers. This paper describes the collaboration between two European projects - EHR4CR and SALUS - in implementing ISO/IEC 11179-based metadata registries (MDRs) and semantically integrated cross-platform data access. A common "semantic MDR" provides a framework for bidirectional/cross-MDR mapping and federated queries are enabled using the newly-defined IHE Data Exchange (DEX) profile. In the pilot implementation, mappings for 178 EHR4CR and 199 SALUS metadata elements were persisted in the semantic MDR. The DEX profile was then used to access semantically equivalent data elements in SALUS or EHR4CR participating EHR systems. ISO/IEC 11179-based MDRs and DEX integration profile address the goal of developing pan-EU computable semantic integration of data from clinical care, clinical research, and patient safety platforms.

Towards an automatic harmonization of the representation of medical reports to assess their similarities

Numerous hospitals contain unexploited knowledge deposits. These often take the form of unstructured records with heterogeneous content, which, at various levels of those organizations, register past cases. Those records are for instance patient medical records. Accessing the knowledge and experience they gather would help us to handle present cases. We present here a method to normalize textual reports in foetopathology in order to constitute a proper case base that will be the target of case-based reasoning techniques. Statistics of noise and silence generated by this method on 10 cases are presented.

Describing localized diseases in medical ontology: an FMA-based algorithm

OntolUrgences is a termino-ontological resource developed to index and retrieve information in electronic Emergency Medical Record. In this project, we improved the ontology coverage to accommodate both anatomical and pathophysiological concepts in emergency medicine. This work lead to the automatic addition of 3,470 concepts and their underlying semantic formalization. In our method, we reuse and select the anatomical concepts relevant to emergency from FMA: To capture the anatomical specific concepts, (i) we involved Emergency practitioners and identified the key concepts from this domain; (ii) we applied an automatic algorithm to define the semantic relationships and integrated the result in the existing ontology.

Knowledge Representation and Management: Towards an Integration of a Semantic Web in Daily Health Practice

Objective: To summarize the best papers in the field of Knowledge Representation and Management (KRM).

Methods: A synopsis of the four selected articles for the IMIA Yearbook 2013 KRM section is provided, as well as highlights of current KRM trends, in particular, of the semantic web in daily health practice. The manual selection was performed in three stages: first a set of 3,106 articles, then a second set of 86 articles followed by a third set of 15 articles, and finally the last set of four chosen articles.

Results: Among the four selected articles (see Table 1), one focuses on knowledge engineering to prevent adverse drug events; the objective of the second is to propose mappings between clinical archetypes and SNOMED CT in the context of clinical practice; the third presents an ontology to create a question-answering system; the fourth describes a biomonitoring network based on semantic web technologies.

Conclusion: These four articles clearly indicate that the health semantic web has become a part of daily practice of health professionals since 2012. In the review of the second set of 86 articles, the same topics included in the previous IMIA yearbook remain active research fields: Knowledge extraction, automatic indexing, information retrieval, natural language processing, management of health terminologies and ontologies.

Implementation and management of a biomedical observation dictionary in a large healthcare information system

OBJECTIVE

This study shows the evolution of a biomedical observation dictionary within the Assistance Publique Hôpitaux Paris (AP-HP), the largest European university hospital group. The different steps are detailed as follows: the dictionary creation, the mapping to logical observation identifier names and codes (LOINC), the integration into a multiterminological management platform and, finally, the implementation in the health information system.

METHODS

AP-HP decided to create a biomedical observation dictionary named AnaBio, to map it to LOINC and to maintain the mapping. A management platform based on methods used for knowledge engineering has been put in place. It aims at integrating AnaBio within the health information system and improving both the quality and stability of the dictionary.

RESULTS

This new management platform is now active in AP-HP. The AnaBio dictionary is shared by 120 laboratories and currently includes 50 000 codes. The mapping implementation to LOINC reaches 40% of the AnaBio entries and uses 26% of LOINC records. The results of our work validate the choice made to develop a local dictionary aligned with LOINC.

DISCUSSION AND CONCLUSIONS

This work constitutes a first step towards a wider use of the platform. The next step will support the entire biomedical production chain, from the clinician prescription, through laboratory tests tracking in the laboratory information system to the communication of results and the use for decision support and biomedical research. In addition, the increase in the mapping implementation to LOINC ensures the interoperability allowing communication with other international health institutions.

Integrating the human phenotype ontology into HeTOP terminology-ontology server

The Human Phenotype Ontology (HPO) is a controlled vocabulary which provides phenotype data related to genes or diseases. The Health Terminology/Ontology Portal (HeTOP) is a tool dedicated to both human beings and computers to access and browse biomedical terminologies or ontologies (T/O). The objective of this work was to integrate the HPO into HeTOP in order to enhance both works. This integration is a success and allows users to search and browse the HPO with a dedicated interface. Furthermore, the HPO has been enhanced with the addition of content such as new synonyms, translations, mappings. Integrating T/O such as the HPO into HeTOP is a benefit to vocabularies because it allows enrichment of them and it is also a benefit for HeTOP which provides a better service to both humans and machines.

Knowledge representation and management: towards an integration of a semantic web in daily health practice

OBJECTIVE

To summarize the best papers in the field of Knowledge Representation and Management (KRM).

METHODS

A synopsis of the four selected articles for the IMIA Yearbook 2013 KRM section is provided, as well as highlights of current KRM trends, in particular, of the semantic web in daily health practice. The manual selection was performed in three stages: first a set of 3,106 articles, then a second set of 86 articles followed by a third set of 15 articles, and finally the last set of four chosen articles.

RESULTS

Among the four selected articles (see Table 1), one focuses on knowledge engineering to prevent adverse drug events; the objective of the second is to propose mappings between clinical archetypes and SNOMED CT in the context of clinical practice; the third presents an ontology to create a question-answering system; the fourth describes a biomonitoring network based on semantic web technologies.

CONCLUSION

These four articles clearly indicate that the health semantic web has become a part of daily practice of health professionals since 2012. In the review of the second set of 86 articles, the same topics included in the previous IMIA yearbook remain active research fields: Knowledge extraction, automatic indexing, information retrieval, natural language processing, management of health terminologies and ontologies.

An ontological approach for the exploitation of clinical data

Clinical data captured in hospital information systems may be unusable in their original format due to missing information or knowledge. The use of external resources (e.g. domain ontology) could be a way of dealing with this lack of knowledge. Our study thus aimed to develop a framework allowing a user to perform medical queries in the context of infectious diseases. By creating an interaction between a knowledge source and clinical data, using semantic and semantic web tools and methods, the users are able to perform queries on a database to obtain results about antibiotic resistance. This work has been performed in the context of the DebugIT European project that aims to control and monitor the antibioresistance growth via a semantic interoperability platform. The results obtained by the use of different semantic web tools were quantitatively evaluated by comparison of the number of results and the query execution time. We have compared our approach with classic business intelligence approaches in terms of usability and functionality.

Rare diseases knowledge management: the contribution of proximity measurements in OntoOrpha and OMIM

In this paper, we introduce an application of Proxima and define a new measure of proximity between two concepts present in an ontology. The approach is based on the three dimensions of a conceptualization: intention with relations between concepts, expression with terms denoting concepts, and extension with instances of concepts. This preliminary work, in the field of rare diseases, involved the Orphanet Ontology of Rare Diseases (OntoOrpha) and corpus of texts extracted from Online Inheritance in Man (OMIM). The proximity measurements are consistent with an appropriate representation of groups of diseases in the ontology, which are derived from the Orphanet classifications of rare diseases. Other semantic relations are explored and new perspectives in medical knowledge curation are proposed.

Pharmaceutical validation of medication orders using an OWL Ontology and Business Rules

In this paper we present an application of pharmaceutical validation of medication based on an OWL ontology and business rules or more specifically clinical decision rules. This application has been developed based on a prototype that enables business users to author, execute and manage their Business Rules over OWL Ontology. This prototype is based on the Business Rule Management System (BRMS) IBM WebSphere ILOG JRules.

Implementation of a platform dedicated to the biomedical analysis terminologies management

BACKGROUND AND OBJECTIVES

Assistance Publique - Hôpitaux de Paris (AP-HP) is implementing a new laboratory management system (LMS) common to the 12 hospital groups. First step to this process was to acquire a biological analysis dictionary. This dictionary is interfaced with the international nomenclature LOINC, and has been developed in collaboration with experts from all biological disciplines. In this paper we describe in three steps (modeling, data migration and integration/verification) the implementation of a platform for publishing and maintaining the AP-HP laboratory data dictionary (AnaBio).

MATERIAL AND METHODS

Due to data complexity and volume, setting up a platform dedicated to the terminology management was a key requirement. This is an enhancement tackling identified weaknesses of previous spreadsheet tool. Our core model allows interoperability regarding data exchange standards and dictionary evolution.

RESULTS

We completed our goals within one year. In addition, structuring data representation has lead to a significant data quality improvement (impacting more than 10% of data). The platform is active in the 21 hospitals of the institution spread into 165 laboratories.

[Representation of knowledge in respiratory medicine: ontology should help the coding process]

INTRODUCTION

Access to medical knowledge is a major issue for health professionals and requires the development of terminologies. The objective of the reported work was to construct an ontology of respiratory medicine, i.e. an organized and formalized terminology composed by specific knowledge. The purpose is to help the medico-economical coding process and to represent the relevant knowledge about the patient.

METHODS

Our researches cover the whole life cycle of an ontology, from the development of a methodology, to building it from texts, to its use in an operational system. A computerized tool, based on the ontology, allows both a medico-economical coding and a graphical medical one. This second one will be used to index hospital reports.

RESULTS

Our ontology counts 1913 concepts and contains all the knowledge included in the PMSI part of the SPLF thesaurus. Our tool has been evaluated and showed a recall of 80% and an accuracy of 85% regarding the medico-economical coding.

CONCLUSION

The work presented in this paper justifies the approach that has been used. It must be continued on a large scale to validate our coding principles and the possibility of making enquiries on patient reports concerning clinical research.

Functional requirements of terminology services for coupling interface terminologies to reference terminologies

Desiderata for interface terminologies (IT), designed to support interactions between humans and structured medical information, differ from desiderata for reference terminologies (RT). Terminology experts have recommended that IT be mapped to RT. The interface terminology of the Georges Pompidou European Hospital (GPEH-IT) contains more than 5,000 concepts, sometimes linked to ICD-10 but not yet to the SNOMED 3.5 VF, now available in France. Our objective was to use a formal characterization framework to compare GPEH-IT to SNOMED 3.5 VF and to define the functionalities of terminology services for managing both IT and RT and the mapping between them. We discuss the role of IT and RT in representing the meaning of clinical data.

Informal notes to support the asynchronous collaborative activities

BACKGROUND

Health care professionals' collaboration is highly important for the medical practice. Efficient exchange of information improves good cooperation, but remains complex, due to the diversity of the medical activities. Currently, the health record is mainly used to manage structured medical information. On the one hand, such structure supports treatment that requires the documented information. On the other hand, however, the structure also imposes constraints on narrative and conversational practices of health care professionals. They use other collaboration means through phone, mail, annotations and free texts for informal strategies of communication. We focussed on informal written documents. Two different studies provided us some materials: home care charts in the context of home care and annotations in the context of the hospital health records.

PURPOSES

We wanted to design a model of the Communication Notes to computerize the written notes so as to improve the communication and the coordination of the practitioners.

METHODS

We compared the results of the two studies about the various writing strategies used by the health care professionals to keep traces of their exchanges and of their acts. The first study deals with the information mentioned by the nurses in a chart during home care situations. We analysed the distribution of cooperation activities in action and in planning. The second study deals with the annotations which are written by all the practitioners to complete the documents of the health record in a paediatric ward. We analysed how annotations take part in their collaborations.

RESULTS

We found some invariable items in these two situations and we proposed a model for these Communication Notes which can be used to describe and to index them according to different points of view. Some indications on the way such descriptions are used in current computerized systems are also reported. The originality of this model comes from the way it takes into account a collaborative perspective which is not often used in the electronic medical settings.

CONCLUSIONS

With our model of Communication Notes, we now dispose of a promising setting for managing all the informal and unforeseeable information produced by the health care professionals during care.

Maintenance of a computerized medical record form

Structured entry forms for clinical records should be updated to take into account the physicians' needs during consultation and advances in medical knowledge and practice. We updated the computerized medical record form of a hypertension clinic, based on its previous use and clinical guidelines. A statistical analysis of previously completed forms identified several unnecessary items rarely used by clinicians. A terminological analysis of guidelines and of free-text answers on completed forms identified several new topics relevant to current clinical practice. We therefore added new items to the form and some topics previously recorded as free text were itemized. We collaborated with clinicians in interpretation of the results of the statistical and terminological analyses used as the starting point and guide for this updating process.

Building an ontology of pulmonary diseases with natural language processing tools using textual corpora

Pathologies and acts are classified in thesauri to help physicians to code their activity. In practice, the use of thesauri is not sufficient to reduce variability in coding and thesauri are not suitable for computer processing. We think the automation of the coding task requires a conceptual modeling of medical items: an ontology. Our task is to help lung specialists code acts and diagnoses with software that represents medical knowledge of this concerned specialty by an ontology. The objective of the reported work was to build an ontology of pulmonary diseases dedicated to the coding process. To carry out this objective, we develop a precise methodological process for the knowledge engineer in order to build various types of medical ontologies. This process is based on the need to express precisely in natural language the meaning of each concept using differential semantics principles. A differential ontology is a hierarchy of concepts and relationships organized according to their similarities and differences. Our main research hypothesis is to apply natural language processing tools to corpora to develop the resources needed to build the ontology. We consider two corpora, one composed of patient discharge summaries and the other being a teaching book. We propose to combine two approaches to enrich the ontology building: (i) a method which consists of building terminological resources through distributional analysis and (ii) a method based on the observation of corpus sequences in order to reveal semantic relationships. Our ontology currently includes 1550 concepts and the software implementing the coding process is still under development. Results show that the proposed approach is operational and indicates that the combination of these methods and the comparison of the resulting terminological structures give interesting clues to a knowledge engineer for the building of an ontology.

Methodology to build medical ontology from textual resources

In the medical field, it is now established that the maintenance of unambiguous thesauri goes through ontologies. Our research task is to help pneumologists code acts and diagnoses with a software that represents medical knowledge through a domain ontology. In this paper, we describe our general methodology aimed at knowledge engineers in order to build various types of medical ontologies based on terminology extraction from texts. The hypothesis is to apply natural language processing tools to textual patient discharge summaries to develop the resources needed to build an ontology in pneumology. Results indicate that the joint use of distributional analysis and lexico-syntactic patterns performed satisfactorily for building such ontologies.