Internet usage trends in medical informatics

Technological growth in the 21st century is exponential. Simultaneously, development of the associated risk, uncertainty and user acceptance are scattered. This required appropriate study to establish people accepting controversial technology (PACT). The Internet and services around it, such as World Wide Web, e-mail, instant messaging and social networking are increasingly becoming important in many aspects of our lives. Information related to medical and personal health sharing using the Internet is controversial and demand validity, usability and acceptance. Whilst literature suggest, Internet enhances patients and physicians’ positive interactions some studies establish opposite of such interaction in particular the associated risk. In recent years Internet has attracted considerable attention as a means to improve health and health care delivery. However, it is not clear how widespread the use of Internet for health care really is or what impact it has on health care utilisation. Estimated impact of Internet usage varies widely from the locations locally and globally. As a result, an estimate (or predication) of Internet use and their effects in Medical Informatics related decision-making is impractical. This open up research issues on validating and accepting Internet usage when designing and developing appropriate policy and processes activities for Medical Informatics, Health Informatics and/or e-Health related protocols. Access and/or availability of data on Internet usage for Medical Informatics related activities are unfeasible. This paper presents a trend analysis of the growth of Internet usage in medical informatics related activities. In order to perform the analysis, data was extracted from ERA (Excellence Research in Australia) ranked “A” and “A*” Journal publications and reports from the authenticated public domain. The study is limited to the analyses of Internet usage trends in United States, Italy, France and Japan. Projected trends and their influence to the field of medical informatics is reviewed and discussed. The study clearly indicates a trend of patients becoming active consumers of health information rather than passive recipients.

The application of medical informatics to the veterinary management programs at companion animal practices in Alberta, Canada: a case study.

Companion animals closely share their domestic environment with people and have the potential to, act as sources of zoonotic diseases. They also have the potential to be sentinels of infectious and noninfectious, diseases. With the exception of rabies, there has been minimal ongoing surveillance of, companion animals in Canada. We developed customized data extraction software, the University of, Calgary Data Extraction Program (UCDEP), to automatically extract and warehouse the electronic, medical records (EMR) from participating private veterinary practices to make them available for, disease surveillance and knowledge creation for evidence-based practice. It was not possible to build, generic data extraction software; the UCDEP required customization to meet the specific software, capabilities of the veterinary practices. The UCDEP, tailored to the participating veterinary practices', management software, was capable of extracting data from the EMR with greater than 99%, completeness and accuracy. The experiences of the people developing and using the UCDEP and the, quality of the extracted data were evaluated. The electronic medical record data stored in the data, warehouse may be a valuable resource for surveillance and evidence-based medical research.

e-MIR2: a public online inventory of medical informatics resources

Background. Over the last years, the number of available informatics resources in medicine has grown exponentially. While specific inventories of such resources have already begun to be developed for Bioinformatics (BI), comparable inventories are as yet not available for Medical Informatics (MI) field, so that locating and accessing them currently remains a hard and time-consuming task. Description. We have created a repository of MI resources from the scientific literature, providing free access to its contents through a web-based service. Relevant information describing the resources is automatically extracted from manuscripts published in top-ranked MI journals. We used a pattern matching approach to detect the resources? names and their main features. Detected resources are classified according to three different criteria: functionality, resource type and domain. To facilitate these tasks, we have built three different taxonomies by following a novel approach based on folksonomies and social tagging. We adopted the terminology most frequently used by MI researchers in their publications to create the concepts and hierarchical relationships belonging to the taxonomies. The classification algorithm identifies the categories associated to resources and annotates them accordingly. The database is then populated with this data after manual curation and validation. Conclusions. We have created an online repository of MI resources to assist researchers in locating and accessing the most suitable resources to perform specific tasks. The database contained 282 resources at the time of writing. We are continuing to expand the number of available resources by taking into account further publications as well as suggestions from users and resource developers.

p-medicine: a medical informatics platform for integrated large scale heterogeneous patient data

Secure access to patient data is becoming of increasing importance, as medical informatics grows in significance, to both assist with population health studies, and patient specific medicine in support of treatment. However, assembling the many different types of data emanating from the clinic is in itself a difficulty, and doing so across national borders compounds the problem. In this paper we present our solution: an easy to use distributed informatics platform embedding a state of the art data warehouse incorporating a secure pseudonymisation system protecting access to personal healthcare data. Using this system, a whole range of patient derived data, from genomics to imaging to clinical records, can be assembled and linked, and then connected with analytics tools that help us to understand the data. Research performed in this environment will have immediate clinical impact for personalised patient healthcare.

The impact of health information technology on the quality of medical and health care : a systematic review

Objective: To systematically review the published evidence of the impact of health information technology (HIT) on the quality of medical and health care specifically clinicians’ adherence to evidence-based guidelines and the corresponding impact this had on patient clinical outcomes. In order to be as inclusive as possible the research examined literature discussing the use of health information technologies and systems in both medical care such as clinical and surgical, and other health care such as allied health and preventive services.----- Design: Systematic review----- Data Sources: Relevant literature was systematically searched on English language studies indexed in MEDLINE and CINAHL(1998 to 2008), Cochrane Library, PubMed, Database of Abstracts of Review of Effectiveness (DARE), Google scholar and other relevant electronic databases. A search for eligible studies (matching the inclusion criteria) was also performed by searching relevant conference proceedings available through internet and electronic databases, as well as using reference lists identified from cited papers.----- Selection criteria: Studies were included in the review if they examined the impact of Electronic Health Record (EHR), Computerised Provider Order-Entry (CPOE), or Decision Support System (DS); and if the primary outcomes of the studies were focused on the level of compliance with evidence-based guidelines among clinicians. Measures could be either changes in clinical processes resulting from a change of the providers’ behaviour or specific patient outcomes that demonstrated the effectiveness of a particular treatment given by providers. ----- Methods: Studies were reviewed and summarised in tabular and text form. Due to heterogeneity between studies, meta-analysis was not performed.----- Results: Out of 17 studies that assessed the impact of health information technology on health care practitioners’ performance, 14 studies revealed a positive improvement in relation to their compliance with evidence-based guidelines. The primary domain of improvement was evident from preventive care and drug ordering studies. Results from the studies that included an assessment for patient outcomes however, were insufficient to detect either clinically or statistically important improvements as only a small proportion of these studies found benefits. For instance, only 3 studies had shown positive improvement, while 5 studies revealed either no change or adverse outcomes.----- Conclusion: Although the number of included studies was relatively small for reaching a conclusive statement about the effectiveness of health information technologies and systems on clinical care, the results demonstrated consistency with other systematic reviews previously undertaken. Widescale use of HIT has been shown to increase clinician’s adherence to guidelines in this review. Therefore, it presents ongoing opportunities to maximise the uptake of research evidence into practice for health care organisations, policy makers and stakeholders.

Semantic judgement of medical concepts : combining syntagmatic and paradigmatic information with the tensor encoding model

This paper outlines a novel approach for modelling semantic relationships within medical documents. Medical terminologies contain a rich source of semantic information critical to a number of techniques in medical informatics, including medical information retrieval. Recent research suggests that corpus-driven approaches are effective at automatically capturing semantic similarities between medical concepts, thus making them an attractive option for accessing semantic information. Most previous corpus-driven methods only considered syntagmatic associations. In this paper, we adapt a recent approach that explicitly models both syntagmatic and paradigmatic associations. We show that the implicit similarity between certain medical concepts can only be modelled using paradigmatic associations. In addition, the inclusion of both types of associations overcomes the sensitivity to the training corpus experienced by previous approaches, making our method both more effective and more robust. This finding may have implications for researchers in the area of medical information retrieval.

An evaluation of corpus-driven measures of medical concept similarity for information retrieval

Measures of semantic similarity between medical concepts are central to a number of techniques in medical informatics, including query expansion in medical information retrieval. Previous work has mainly considered thesaurus-based path measures of semantic similarity and has not compared different corpus-driven approaches in depth. We evaluate the effectiveness of eight common corpus-driven measures in capturing semantic relatedness and compare these against human judged concept pairs assessed by medical professionals. Our results show that certain corpus-driven measures correlate strongly (approx 0.8) with human judgements. An important finding is that performance was significantly affected by the choice of corpus used in priming the measure, i.e., used as evidence from which corpus-driven similarities are drawn. This paper provides guidelines for the implementation of semantic similarity measures for medical informatics and concludes with implications for medical information retrieval.

Health informatics domain knowledge analysis : an information technology perspective

Health Informatics is an intersection of information technology, several disciplines of medicine and health care. It sits at the common frontiers of health care services including patient centric, processes driven and procedural centric care. From the information technology perspective it can be viewed as computer application in medical and/or health processes for delivering better health care solutions. In spite of the exaggerated hype, this field is having a major impact in health care solutions, in particular health care deliveries, decision making, medical devices and allied health care industries. It also affords enormous research opportunities for new methodological development. Despite the obvious connections between Medical Informatics, Nursing Informatics and Health Informatics, most of the methodologies and approaches used in Health Informatics have so far originated from health system management, care aspects and medical diagnostic. This paper explores reasoning for domain knowledge analysis that would establish Health Informatics as a domain and recognised as an intellectual discipline in its own right.

Medical semantic similarity with a neural language model

Advances in neural network language models have demonstrated that these models can effectively learn representations of words meaning. In this paper, we explore a variation of neural language models that can learn on concepts taken from structured ontologies and extracted from free-text, rather than directly from terms in free-text. This model is employed for the task of measuring semantic similarity between medical concepts, a task that is central to a number of techniques in medical informatics and information retrieval. The model is built with two medical corpora (journal abstracts and patient records) and empirically validated on two ground-truth datasets of human-judged concept pairs assessed by medical professionals. Empirically, our approach correlates closely with expert human assessors ($\approx$ 0.9) and outperforms a number of state-of-the-art benchmarks for medical semantic similarity. The demonstrated superiority of this model for providing an effective semantic similarity measure is promising in that this may translate into effectiveness gains for techniques in medical information retrieval and medical informatics (e.g., query expansion and literature-based discovery).

Active learning: A step towards automating medical concept extraction

Objective This paper presents an automatic active learning-based system for the extraction of medical concepts from clinical free-text reports. Specifically, (1) the contribution of active learning in reducing the annotation effort, and (2) the robustness of incremental active learning framework across different selection criteria and datasets is determined. Materials and methods The comparative performance of an active learning framework and a fully supervised approach were investigated to study how active learning reduces the annotation effort while achieving the same effectiveness as a supervised approach. Conditional Random Fields as the supervised method, and least confidence and information density as two selection criteria for active learning framework were used. The effect of incremental learning vs. standard learning on the robustness of the models within the active learning framework with different selection criteria was also investigated. Two clinical datasets were used for evaluation: the i2b2/VA 2010 NLP challenge and the ShARe/CLEF 2013 eHealth Evaluation Lab. Results The annotation effort saved by active learning to achieve the same effectiveness as supervised learning is up to 77%, 57%, and 46% of the total number of sequences, tokens, and concepts, respectively. Compared to the Random sampling baseline, the saving is at least doubled. Discussion Incremental active learning guarantees robustness across all selection criteria and datasets. The reduction of annotation effort is always above random sampling and longest sequence baselines. Conclusion Incremental active learning is a promising approach for building effective and robust medical concept extraction models, while significantly reducing the burden of manual annotation.

Randomized comparisons among health informatics students identify hypertutorial features as improving web-based instruction.

Hypertutorials optimize five features - presentation, learner control, practice, feedback, and elaborative learning resources. Previous research showed graduate students significantly and overwhelmingly preferred Web-based hypertutorials to conventional "Book-on-the-Web" statistics or research design lessons. The current report shows that the source of hypertutorials' superiority in student evaluations of instruction lies in their hypertutorial features. Randomized comparisons between the two methodologies were conducted in two successive iterations of a graduate level health informatics research design and evaluation course. The two versions contained the same text and graphics, but differed in the presence or absence of hypertutorial features: Elaborative learning resources, practice, feedback, and amount of learner control. Students gave high evaluations to both Web-based methodologies, but consistently rated the hypertutorial lessons as superior. Significant differences localized in the hypertutorial subscale that measured student responses to hypertutorial features.