Ebook: Digital Healthcare Empowering Europeans

Medical terminology appears in the natural language in multiple forms: canonical, derived or inflected form. This research presents an analysis of the form in which medical terminology appears in Romanian and English language. The sources of medical language used for the study are web pages presenting medical information for patients and other lay users. The results show that, in English, medical terminology tends to appear more in canonical form while, in the case of Romanian, it is the opposite. This paper also presents the service that was created to perform this analysis. This tool is available for the general public, and it is designed to be easily extensible, allowing the addition of other languages.

Health-related Web sites have become a primary resource to search for information on diseases, diagnoses or treatment options. Various Web sites offer a great variety of such information. However, lay people might have difficulties to assess whether a certain article or Web site fits their individual level of understandability. Hence, they might get overwhelmed with the delivered complexity of medical information. In this paper, we present a Web browser plugin, Expertizer that supports users in order to easily assess the expert level of textual medical Web content. The plugin communicates with a Web service, which leverages pre-computed classification models based on a Support Vector Machine.

The purpose of this paper is to present the approach and the development of a software application (“lexicons connecting” system) to correlate effectively and unambiguously the correspondence between the specialist medical vocabulary and the familiar medical vocabulary for the cardiovascular domain. To investigate the question, the idea, the design, and the implementation of such system will be described. To this end, firstly, a number of research methodologies will be examined including domain ontologies development, database design and implementation. Then, the following implementation methodology and its results are presented. Finally, an example of the application use will be depicted and future work will be briefly described.

Poor contextual fit is a significant cause of health information technology (HIT) implementation issues. While the need for better fit of HIT and context has been well described there is a shortcoming of approaches for how to do it. While the diversity of the contexts where HIT is used prevents us from designing HIT to fit all contexts, if we had better ways of understanding and modelling contexts we could design and evaluate HIT to better fit contexts of use. This paper addresses the above need by developing a framework consisting of a set of terminology and concepts for modelling contextual structures and behaviours to support HIT design. The framework provides a way of binding contextual considerations to allow us to better model contexts as part of HIT design and evaluation.

Increasing the flexibility from a user-perspective and enabling a workflow based interaction, facilitates an easy user-friendly utilization of EHRs for healthcare professionals' daily work. To offer such versatile EHR-functionality, our approach is based on the execution of clinical workflows by means of a composition of semantic web-services. The backbone of such architecture is an ontology which enables to represent clinical workflows and facilitates the selection of suitable services. In this paper we present the methods and results after running observations of diabetes routine consultations which were conducted in order to identify those workflows and the relation among the included tasks. Mentioned workflows were first modeled by BPMN and then generalized. As a following step in our study, interviews will be conducted with clinical personnel to validate modeled workflows.

The ability to learn specialized languages, such as biomedical language, requires not only specialized knowledge specific to this area, but also linguistic skills. We propose to study this hypothesis on the example of biomedical language as it is learned by advanced paramedical students in Algeria. Two particularities are to be addressed: linguistic specificities of biomedical terms and the fact that learning process is done in French while the native language of students is Arabic. We perform a questionnaire-based study through which students have to work on recognition and production of biomedical terms and of their components. Several difficulties are observed. We propose that terminology learning programs should strongly develop and rely on linguistic skills of students and on their morphological conscience.

Information systems for storing, managing and manipulating electronic medical records must place an emphasis on maintaining the privacy and security of those records. Though the design, development and testing of such systems also requires the use of data, the developers of these systems, rarely also their final end users, are unlikely to have ethical or governance approval to use real data. Alternative test data is commonly either randomly produced or taken from carefully anonymised subsets of records. In both cases there are potential shortcomings that can impact on the quality of the product being developed. We have addressed these shortcomings with a tool and methodology for efficiently simulating large amounts of realistic enough electronic patient records which can underpin the development of data-centric electronic healthcare systems.

Recently Business Intelligence approaches like process mining are applied to the healthcare domain. The goal of process mining is to gain process knowledge, compliance and room for improvement by investigating recorded event data. Previous approaches focused on process discovery by event data from various specific systems. IHE, as a globally recognized basis for healthcare information systems, defines in its ATNA profile how real-world events must be recorded in centralized event logs. The following approach presents how audit trails collected by the means of ATNA can be transformed to enable process mining. Using the standardized audit trails provides the ability to apply these methods to all IHE based information systems.

The utilization of decision support systems, in the point of care, to alert drug-drug interactions has been shown to improve quality of care. Still, the use of these systems has not been as expected, it is believed, because of the difficulties in their knowledge databases; errors in the generation of the alerts and the lack of a suitable design. This study expands on the development of alerts using participatory design techniques based on user centered design process. This work was undertaken in three stages (inquiry, participatory design and usability testing) it showed that the use of these techniques improves satisfaction, effectiveness and efficiency in an alert system for drug-drug interactions, a fact that was evident in specific situations such as the decrease of errors to meet the specified task, the time, the workload optimization and users overall satisfaction in the system.

Clinical data recorded in modern EHRs are very rich, although their secondary use research and medical decision may be complicated (eg, missing and incorrect data, data spread over several clinical databases, information available only within unstructured narrative documents). We propose to address the issue related to the processing of narrative documents in order to detect and extract numerical values and to associate them with the corresponding concepts (or themes) and units. We propose to use a CRF supervised categorisation for the detection of segments (themes, numerical sequences and units) and a rules-based system for the association of these segments among them in order to build semantically meaningful sequences. The average results obtained are competitive (0.96 precision, 0.78 recall, and 0.86 F-measure) and we plan to use the system with larger clinical data.

Twitter has been proposed by several studies as a means to track public health trends such as influenza and Ebola outbreaks by analyzing user messages in order to measure different population features and interests. In this work we analyze the number and features of mentions on Twitter of drug brand names in order to explore the potential usefulness of the automated detection of drug side effects and drug-drug interactions on social media platforms such as Twitter. This information can be used for the development of predictive models for drug toxicity, drug-drug interactions or drug resistance. Taking into account the large number of drug brand mentions that we found on Twitter, it is promising as a tool for the detection, understanding and monitoring the way people manage prescribed drugs.

Sonic display of HR changes during exercise helps both the patient and the investigator to better identify the transition from rest values to “exercise” zone. then crossing the “attention” threshold and reaching the “alert” threshold. Three types of sonic display were tested, based on combinations of saccadic sounds, different intensities and different pitch. The results indicate a clear preference towards a display marking each of the four zones by a specific pitch.

Adverse events are detected by monitoring the patient's status, including blood test results. However, it is difficult to identify all adverse events based on recognition by individual doctors. We developed a system that can be used to detect hematotoxicity adverse events according to blood test results recorded in an electronic medical record system. The blood test results were graded based on Common Terminology Criteria for Adverse Events (CTCAE) and changes in the blood test results (Up, Down, Flat) were assessed according to the variation in the grade. The changes in the blood test and injection data were stored in a database. By comparing the date of injection and start and end dates of the change in the blood test results, adverse events related to a designated drug were detected. Using this method, we searched for the occurrence of serious adverse events (CTCAE Grades 3 or 4) concerning WBC, ALT and creatinine related to paclitaxel at Osaka University Hospital. The rate of occurrence of a decreased WBC count, increased ALT level and increased creatinine level was 36.0%, 0.6% and 0.4%, respectively. This method is useful for detecting and estimating the rate of occurrence of hematotoxicity adverse drug events.

Care pathways play significant roles in delivering evidence-based and coordinated care to patients with specific conditions. In order to put care pathways into practice, clinical institutions always need to adapt them based on local care settings so that the best local practices can be incorporated and used to develop refined pathways. However, it is knowledge-intensive and error-prone to incorporate various analytic insights from local data sets. In order to assist care pathway developers in working effectively and efficiently, we propose to automatically synthesize the analytical evidences derived from multiple analysis methods, and recommend modelling operations accordingly to derive a refined care pathway for a specific patient cohort. We validated our method by adapting a Congestive Heart Failure (CHF) Ambulatory Care Pathway for patients with additional condition of COPD through synthesizing the results of variation analysis and frequent pattern mining against patient records.

Content based image retrieval (CBIR) concerns the retrieval of similar images from image databases, using feature vectors extracted from images. These feature vectors globally define the visual content present in an image, defined by e.g., texture, colour, shape, and spatial relations between vectors. Herein, we propose the definition of feature vectors using the Local Binary Pattern (LBP) operator. A study was performed in order to determine the optimum LBP variant for the general definition of image feature vectors. The chosen LBP variant is then subsequently used to build an ultrasound image database, and a database with images obtained from Wireless Capsule Endoscopy. The image indexing process is optimized using data clustering techniques for images belonging to the same class. Finally, the proposed indexing method is compared to the classical indexing technique, which is nowadays widely used.

While patients can freely access their Electronic Health Records or online health information, they may not be able to correctly understand the content of these documents. One of the challenges is related to the difference between expert and non-expert languages. We propose to investigate this issue within the Information Retrieval field. The patient queries have to be associated with the corresponding expert documents, that provide trustworthy information. Our approach relies on a state-of-the-art IR system called Indri and on semantic resources. Different query expansion strategies are explored. Our system shows up to 0.6740 P@10, up to 0.7610 R@10, and up to 0.6793 NDCG@10.

Data mining of electronic health records (eHRs) allows us to identify patterns of patient data that characterize diseases and their progress and learn best practices for treatment and diagnosis. Clinical Prediction Rules (CPRs) are a form of clinical evidence that quantifies the contribution of different clinical data to a particular clinical outcome and help clinicians to decide the diagnosis, prognosis or therapeutic conduct for any given patient. The TRANSFoRm diagnostic support system (DSS) is based on the construction of an ontological repository of CPRs for diagnosis prediction in which clinical evidence is expressed using a unified vocabulary. This paper explains the proposed methodology for constructing this CPR repository, addressing algorithms and quality measures for filtering relevant rules. Some preliminary application results are also presented.

Achieving a better understanding of the clinical reasoning process is an important approach to improve patient management and patient safety. Although clinical psychologists have used talk-aloud or stimulated recall approaches, these methods have biases. Recently, researchers have been exploring eye-tracking technology to gain “live” insight into clinicians' reasoning processes in certain fields of medicine (radiology, dermatology, etc.). We present a systematic review of eye-tracking literature used for clinical reasoning. We performed a literature search using the terms “eye” or “gaze tracking”, “clinical” or “diagnostic reasoning”, and “physician” in Pubmed, Embase, Psychinfo, Web of Science and ACM databases. Two investigators screened the abstracts, then full-text articles to select 10 pertinent studies. The studies evaluated medical decision making in four different medical domains using mostly experimental, observational approaches. A total of 208 participants were enrolled for the selected experiments. Paths for further studies are discussed that may extend the use of eye trackers in order to improve understanding of medical decision making.

The medical curriculum is the main tool representing the entire undergraduate medical education. Due to its complexity and multilayered structure it is of limited use to teachers in medical education for quality improvement purposes. In this study we evaluated three visualizations of curriculum data from a pilot course, using teachers from an undergraduate medical program and applying visual analytics methods. We found that visual analytics can be used to positively impacting analytical reasoning and decision making in medical education through the realization of variables capable to enhance human perception and cognition on complex curriculum data. The positive results derived from our evaluation of a medical curriculum and in a small scale, signify the need to expand this method to an entire medical curriculum. As our approach sustains low levels of complexity it opens a new promising direction in medical education informatics research.

Like no other area, health and social care are characterized by a multi-disciplinary nature. This development gets even stronger by the move towards a personalized, predictive, preventive and participative care paradigm as well as by organizational and technological changes leading to highly distributed care setting realized by multiple stakeholder communities from different policy domains. Those paradigm changes result in growing interoperability challenges when enabling communication and cooperation of all the different actors based on shared knowledge and skills. For meeting those challenges, a systems-oriented, architecture-centric, ontology-based and policy-driven approach in health informatics education, but also in modeling, implementing and maintaining health informatics interoperability is inevitable. The paper introduces the aforementioned concepts.

In the 21^st century technology has rapidly evolved and has managed to eliminate distances, and especially in the health sector. The purpose of this study is to investigate the families' satisfaction and effectiveness resulted from the use of telehealth services that are provided to children with diabetes mellitus type 1. The evaluation at the individual level (user) has been done by completing two questionnaires. The study involved 100 parents whose children have diabetes mellitus type 1 (50 using telemedicine and 50 without). The majority of parents involved in the study, thinks that their knowledge level on telemedicine system is sufficient (96%) and would recommend its use to other parents whose children have diabetes (82%). Meanwhile, 80% evaluate the telemedicine system as adequate.

Systematic quality improvement (QI) interventions are increasingly used to change complex health care systems. Results of randomized clinical trials can provide quantitative evidence whether QI interventions were effective but they do not teach us why and how QI was (not) achieved. Qualitative research methods can answer these questions but typically involve only a small group of respondents against high resources. Concept mapping methodology overcomes these drawbacks by integrating results from qualitative group sessions with multivariate statistical analysis to represent ideas of diverse stakeholders visually on maps in an efficient way. This paper aims to describe how to use concept mapping to qualitatively gain insight into barriers and facilitators of an electronic QI intervention and presents experiences with the method from an ongoing case study to evaluate a QI system in the field of cardiac rehabilitation in the Netherlands.