Jean-Baptiste Lamy

An iconic language for the graphical representation of medical concepts

BackgroundMany medication errors are encountered in drug prescriptions, which would not occur if practitioners could remember the drug properties. They can refer to drug monographs to find these properties, however drug monographs are long and tedious to read during consultation. We propose a two-step approach for facilitating access to drug monographs. The first step, presented here, is the design of a graphical language, called VCM.MethodsThe VCM graphical language was designed using a small number of graphical primitives and combinatory rules. VCM was evaluated over 11 volunteer general practitioners to assess if the language is easy to learn, to understand and to use. Evaluators were asked to register their VCM training time, to indicate the meaning of VCM icons and sentences, and to answer clinical questions related to randomly generated drug monograph-like documents, supplied in text or VCM format.ResultsVCM can represent the various signs, diseases, physiological states, life habits, drugs and tests described in drug monographs. Grammatical rules make it possible to generate many icons by combining a small number of primitives and reusing simple icons to build more complex ones. Icons can be organized into simple sentences to express drug recommendations. Evaluation showed that VCM was learnt in 2 to 7 hours, that physicians understood 89% of the tested VCM icons, and that they answered correctly to 94% of questions using VCM (versus 88% using text, p = 0.003) and 1.8 times faster (p < 0.001).ConclusionVCM can be learnt in a few hours and appears to be easy to read. It can now be used in a second step: the design of graphical interfaces facilitating access to drug monographs. It could also be used for broader applications, including the design of interfaces for consulting other types of medical document or medical data, or, very simply, to enrich medical texts.

Testing Methods for Decision Support Systems

Decision support systems (DSS) have proved to be efficient for helping humans to make a decision in various domains such as health (Dorr et al., 2007). However, before being used in practice, these systems need to be extensively evaluated to ensure their validity and their efficiency. DSS evaluation usually includes two steps: first, testing the DSS under controlled conditions, and second, evaluating the DSS in real use, during a randomised trial. In this chapter, we will focus on the first step. The test of decision support systems uses various methods aimed at detecting errors in a DSS without having to use the DSS under real use conditions; several of these methods were initially developed in the field of expert systems, or software testing (Meyer, 2008). DSS testing methods are usually classified in two categories (Preece, 1994): •

Using visual analytics for presenting comparative information on new drugs

OBJECTIVE When a new drug is marketed, physicians must decide whether they will consider it for their future practice. However, information about new drugs can be biased or hard to find. In this work, our objective was to study whether visual analytics could be used for comparing drug properties such as contraindications and adverse effects, and whether this visual comparison can help physicians to forge their own well-founded opinions about a new drug. MATERIALS AND METHODS First, an ontology for comparative drug information was designed, based on the expectations expressed during focus groups comprised of physicians. Second, a prototype of a visual drug comparator website was developed. It implements several visualization methods: rainbow boxes (a new technique for overlapping set visualization), dynamic tables, bar charts and icons. Third, the website was evaluated by 22 GPs for four new drugs. We recorded the general satisfaction, the physicians decision whether to consider the new drug for future prescription, both before and after consulting the website, and their arguments to justify their choice. RESULTS The prototype website permits the visual comparison of up to 10 drugs, including efficacy, contraindications, interactions, adverse effects, prices, dosage regimens,…All physicians found that the website allowed them to forge a well-founded opinion on the four new drugs. The physicians changed their decision about using a new drug in their future practice in 29 cases (out of 88) after consulting the website. DISCUSSION AND CONCLUSION Visual analytics is a promising approach for presenting drug information and for comparing drugs. The visual comparison of drug properties allows physicians to forge their opinions on drugs. Since drug properties are available in reference texts, reviewed by public health agencies, it could contribute to the independent of drug information.

Rainbow Boxes: A Technique for Visualizing Overlapping Sets and an Application to the Comparison of Drugs Properties

Overlapping set visualization is a well-known problem in information visualization. This problem considers elements and sets containing all or part of the elements, a given element possibly belonging to more than one set. A typical example is the properties of the 20 amino-acids. A more complex application is the visual comparison of the contraindications or the adverse effects of several similar drugs. The knowledge involved is voluminous, each drug has many contraindications and adverse effects, some of them are shared with other drugs. In this paper, we present rainbow boxes, a novel technique for visualizing overlapping sets, and its application to the properties of amino-acids and to the comparison of drug properties. We also describe a user study comparing rainbow boxes to tables and showing that the former allowed physicians to find information significantly faster. We finally discuss the limits and the perspectives of rainbow boxes.

Medical Vocabulary, Terminological Resources and Information Coding in the Health Domain

This chapter explains why it is hard to use medical language in computer applications and why the computer must adopt the human interpretation of medical words to avoid misunderstandings linked to ambiguity, homonymy and synonymy. Terminological resources are specific representations of medical language for dedicated use in particular health domains. We describe here the components of terminology (terms, concepts, relationships between concepts, definitions, constraints). The various artefacts of terminological resources (e.g. thesaurus, classification, nomenclature) are defined. We also provide examples of the dedicated use of terminological resources, such as disease coding, the indexing of biomedical publications, reasoning in decision support systems and data entry into electronic medical records. ICD 10, SNOMED CT, and MeSH are among the terminologies used in the examples. Alignment methods are described, making it possible to identify equivalent terms in different terminologies and to bridge different domains in health. We also present plans for multi-terminological servers, such as the UMLS (Unified Medical Language Systems), which provide a key vocabulary linking heterogeneous health terminologies in different languages.

Improving access to clinical practice guidelines with an interactive graphical interface using an iconic language

BackgroundClinical practice guidelines are useful for physicians, and guidelines are available on the Internet from various websites such as Vidal Recos. However, these guidelines are long and difficult to read, especially during consultation. Similar difficulties have been encountered with drug summaries of product characteristics. In a previous work, we have proposed an iconic language (called VCM, for Visualization of Concepts in Medicine) for representing patient conditions, treatments and laboratory tests, and we have used these icons to design a user interface that graphically indexes summaries of product characteristics. In the current study, our objective was to design and evaluate an iconic user interface for the consultation of clinical practice guidelines by physicians.MethodsFocus groups of physicians were set up to identify the difficulties encountered when reading guidelines. Icons were integrated into Vidal Recos, taking human factors into account. The resulting interface includes a graphical summary and an iconic indexation of the guideline. The new interface was evaluated. We compared the response times and the number of errors recorded when physicians answered questions about two clinical scenarios using the interactive iconic interface or a textual interface. Users’ perceived usability was evaluated with the System Usability Scale.ResultsThe main difficulties encountered by physicians when reading guidelines were obtaining an overview and finding recommendations for patients corresponding to “particular cases”. We designed a graphical interface for guideline consultation, using icons to identify particular cases and providing a graphical summary of the icons organized by anatomy and etiology. The evaluation showed that physicians gave clinical responses more rapidly with the iconic interface than the textual interface (25.2 seconds versus 45.6, p < 0.05). The physicians appreciated the new interface, and the System Usability Scale score value was 75 (between good and excellent).ConclusionAn interactive iconic interface can provide physicians with an overview of clinical practice guidelines, and can decrease the time required to access the content of such guidelines.

Rainbow boxes: A new technique for overlapping set visualization and two applications in the biomedical domain

Overlapping set visualization is a well-known problem in information visualization. This problem considers elements and sets containing all or part of the elements, a given element possibly belonging to more than one set. A typical example is the properties of the 20 amino-acids. A more complex application is the visual comparison of the contraindications or the adverse effects of several similar drugs. The knowledge involved is voluminous, each drug has many contraindications and adverse effects, some of them are shared with other drugs. Another real-life application is the visualization of gene annotation, each gene product being annotated with several annotation terms indicating the associated biological processes, molecular functions and cellular components. In this paper, we present rainbow boxes, a novel technique for visualizing overlapping sets, and its application to the presentation of the properties of amino-acids, the comparison of drug properties, and the visualization of gene annotation. This technique requires solving a combinatorial optimization problem; we propose a specific heuristic and we evaluate and compare it to general optimization algorithms. We also describe a user study comparing rainbow boxes to tables and showing that the former allowed physicians to find information significantly faster. Finally, we discuss the limits and the perspectives of rainbow boxes.

Evaluating alignment quality between iconic language and reference terminologies using similarity metrics.

BackgroundVisualization of Concepts in Medicine (VCM) is a compositional iconic language that aims to ease information retrieval in Electronic Health Records (EHR), clinical guidelines or other medical documents. Using VCM language in medical applications requires alignment with medical reference terminologies. Alignment from Medical Subject Headings (MeSH) thesaurus and International Classification of Diseases – tenth revision (ICD10) to VCM are presented here. This study aim was to evaluate alignment quality between VCM and other terminologies using different measures of inter-alignment agreement before integration in EHR.MethodsFor medical literature retrieval purposes and EHR browsing, the MeSH thesaurus and the ICD10, both organized hierarchically, were aligned to VCM language. Some MeSH to VCM alignments were performed automatically but others were performed manually and validated. ICD10 to VCM alignment was entirely manually performed. Inter-alignment agreement was assessed on ICD10 codes and MeSH descriptors, sharing the same Concept Unique Identifiers in the Unified Medical Language System (UMLS). Three metrics were used to compare two VCM icons: binary comparison, crude Dice Similarity Coefficient (DSCcrude), and semantic Dice Similarity Coefficient (DSCsemantic), based on Lin similarity. An analysis of discrepancies was performed.ResultsMeSH to VCM alignment resulted in 10,783 relations: 1,830 of which were manually performed and 8,953 were automatically inherited. ICD10 to VCM alignment led to 19,852 relations. UMLS gathered 1,887 alignments between ICD10 and MeSH. Only 1,606 of them were used for this study. Inter-alignment agreement using only validated MeSH to VCM alignment was 74.2% [70.5-78.0]CI95%, DSCcrude was 0.93 [0.91-0.94]CI95%, and DSCsemantic was 0.96 [0.95-0.96]CI95%. Discrepancy analysis revealed that even if two thirds of errors came from the reviewers, UMLS was nevertheless responsible for one third.ConclusionsThis study has shown strong overall inter-alignment agreement between MeSH to VCM and ICD10 to VCM manual alignments. VCM icons have now been integrated into a guideline search engine (http://www.cismef.org) and a health terminologies portal (http://www.hetop.eu).

Using preference learning for detecting inconsistencies in clinical practice guidelines: Methods and application to antibiotherapy

Clinical practice guidelines provide evidence-based recommendations. However, many problems are reported, such as contradictions and inconsistencies. For example, guidelines recommend sulfamethoxazole/trimethoprim in child sinusitis, but they also state that there is a high bacteria resistance in this context. In this paper, we propose a method for the semi-automatic detection of inconsistencies in guidelines using preference learning, and we apply this method to antibiotherapy in primary care. The preference model was learned from the recommendations and from a knowledge base describing the domain. We successfully built a generic model suitable for all infectious diseases and patient profiles. This model includes both preferences and necessary features. It allowed the detection of 106 candidate inconsistencies which were analyzed by a medical expert. 55 inconsistencies were validated. We showed that therapeutic strategies of guidelines in antibiotherapy can be formalized by a preference model. In conclusion, we proposed an original approach, based on preferences, for modeling clinical guidelines. This model could be used in future clinical decision support systems for helping physicians to prescribe antibiotics.

Translating Visually the Reasoning of a Perceptron: The Weighted Rainbow Boxes Technique and an Application in Antibiotherapy

In this paper, we propose a technique for translating visually the reasoning of a perceptron. The artificial neuron, or perceptron, is a simplified model of a biological neuron. It can achieve simple reasoning and solve linearly separable problems. Despite its limited reasoning power, it is enough to deal with several real-life problems. The proposed technique is based on rainbow boxes, a technique for overlapping set visualization, which has been applied to the input vectors of the perceptron. We extended this technique, leading to weighted rainbow boxes. It can visualize several input vectors and output values for a single perceptron. We applied this approach to decision support in antibiotherapy, for the determination of the most appropriate antibiotic in urinary infections, by taking into account the properties of each drug (e.g. efficacy, risk of adverse effects, etc). Finally, a user study with 11 physicians showed that most of them found the visualization interesting and easy to read.