Usability testing is recognized as an effective means to improve the usability of medical devices and prevent harm for patients and users. Effectiveness of problem discovery in usability testing strongly depends on size and representativeness of the sample. We introduce the late control strategy, which is to continuously monitor effectiveness of a study towards a preset target. A statistical model, the LNBzt model, is presented, supporting the late control strategy. We report on a case study, where a prototype medical infusion pump underwent a usability test with 34 users. On the data obtained in this study, the LNBzt model is evaluated and compared against earlier prediction models. The LNBzt model fits the data much better than previously suggested approaches and improves prediction. We measure the effectiveness of problem identification, and observe that it is lower than is suggested by much of the literature. Larger sample sizes seem to be in order. In addition, the testing process showed high levels of uncertainty and volatility at small to moderate sample sizes, partly due to users' individual differences. In reaction, we propose the idiosyncrasy score as a means to obtain representative samples. Statistical programs are provided to assist practitioners and researchers in applying the late control strategy.

We describe a clinical research visit scheduling system that can potentially coordinate clinical research visits with patient care visits and increase efficiency at clinical sites where clinical and research activities occur simultaneously. Participatory Design methods were applied to support requirements engineering and to create this software called Integrated Model for Patient Care and Clinical Trials (IMPACT). Using a multi-user constraint satisfaction and resource optimization algorithm, IMPACT automatically synthesizes temporal availability of various research resources and recommends the optimal dates and times for pending research visits. We conducted scenario-based evaluations with 10 clinical research coordinators (CRCs) from diverse clinical research settings to assess the usefulness, feasibility, and user acceptance of IMPACT. We obtained qualitative feedback using semi-structured interviews with the CRCs. Most CRCs acknowledged the usefulness of IMPACT features. Support for collaboration within research teams and interoperability with electronic health records and clinical trial management systems were highly requested features. Overall, IMPACT received satisfactory user acceptance and proves to be potentially useful for a variety of clinical research settings. Our future work includes comparing the effectiveness of IMPACT with that of existing scheduling solutions on the market and conducting field tests to formally assess user adoption.

The potential of plant-based remedies has been documented in both traditional and contemporary biomedical literature. Such types of text sources may thus be sources from which one might identify potential plant-based therapies ("phyto-therapies"). Concept-based analytic approaches have been shown to uncover knowledge embedded within biomedical literature. However, to date there has been limited attention towards leveraging such techniques for the identification of potential phyto-therapies. This study presents concept-based analytic approaches for the retrieval and ranking of associations between plants and human diseases. Focusing on identification of phyto-therapies described in MEDLINE, both MeSH descriptors used for indexing and MetaMap inferred UMLS concepts are considered. Furthermore, the identification and ranking consider both direct (i.e., plant concepts directly correlated with disease concepts) and inferred (i.e., plant concepts associated with disease concepts based on shared signs and symptoms) relationships. Based on the two scoring methodologies used in this study, it was found that a Vector Space Model approach outperformed probabilistic reliability based inferences. An evaluation of the approach is provided based on therapeutic interventions catalogued in both ClinicalTrials.gov and NDF-RT. The promising findings from this feasibility study highlight the challenges and applicability of concept-based analytic strategies for distilling phyto-therapeutic knowledge from text based knowledge sources like MEDLINE.

In this paper we discuss the design and development of TRAK (Taxonomy for RehAbilitation of Knee conditions), an ontology that formally models information relevant for the rehabilitation of knee conditions. TRAK provides the framework that can be used to collect coded data in sufficient detail to support epidemiologic studies so that the most effective treatment components can be identified, new interventions developed and the quality of future randomized control trials improved to incorporate a control intervention that is well defined and reflects clinical practice. TRAK follows design principles recommended by the Open Biomedical Ontologies (OBO) Foundry. TRAK uses the Basic Formal Ontology (BFO) as the upper-level ontology and refers to other relevant ontologies such as Information Artifact Ontology (IAO), Ontology for General Medical Science (OGMS) and Phenotype And Trait Ontology (PATO). TRAK is orthogonal to other bio-ontologies and represents domain-specific knowledge about treatments and modalities used in rehabilitation of knee conditions. Definitions of typical exercises used as treatment modalities are supported with appropriate illustrations, which can be viewed in the OBO-Edit ontology editor. The vast majority of other classes in TRAK are cross-referenced to the Unified Medical Language System (UMLS) to facilitate future integration with other terminological sources. TRAK is implemented in OBO, a format widely used by the OBO community. TRAK is available for download from http://www.cs.cf.ac.uk/trak. In addition, its public release can be accessed through BioPortal, where it can be browsed, searched and visualized.

The paper presents a novel approach to online application of formalized rules for medical treatment procedures when processing data from personal medical devices. The rules are formalized by using a rule-based reasoning approach and are applied in order to enhance patient safety and support physicians in their daily work. The presented approach relies on dividing data processing into two stages: (1) the event processing stage and (2) the knowledge application stage. At the event processing stage raw data produced by personal medical devices is transformed into an aggregated/correlated form, as required by the rules for treatment procedures. At the knowledge application stage formalized rules are applied to transformed data, resulting in execution of various support actions. This paper describes how rules for treatment of patients suffering from cardiovascular diseases can be expressed in terms of an event processing statement set and a rule engine knowledge base. The technical feasibility of the proposed approach is supported by a detailed description of the TeleCARE remote healthcare framework - an implementation of the proposed approach along with evaluation performed using a large number of simulated personal medical devices.

PURPOSE:

Despite years of effort and millions of dollars spent to create unified electronic communicable disease reporting systems, the goal remains elusive. A major barrier has been a lack of understanding by system designers of communicable disease (CD) work and the public health workers who perform this work. This study reports on the application of user-centered design representations, traditionally used for improving interface design, to translate the complex CD work identified through ethnographic studies to guide designers and developers of CD systems. The purpose of this work is to: (1) better understand public health practitioners and their information workflow with respect to CD monitoring and control at a local health agency, and (2) to develop evidence-based design representations that model this CD work to inform the design of future disease surveillance systems.

METHODS:

We performed extensive onsite semi-structured interviews, targeted work shadowing and a focus group to characterize local health agency CD workflow. Informed by principles of design ethnography and user-centered design we created persona, scenarios and user stories to accurately represent the user to system designers.

RESULTS:

We sought to convey to designers the key findings from ethnographic studies: (1) public health CD work is mobile and episodic, in contrast to current CD reporting systems, which are stationary and fixed, (2) health agency efforts are focused on CD investigation and response rather than reporting and (3) current CD information systems must conform to public health workflow to ensure their usefulness. In an effort to illustrate our findings to designers, we developed three contemporary design-support representations: persona, scenario, and user story.

CONCLUSIONS:

Through application of user-centered design principles, we were able to create design representations that illustrate complex public health communicable disease workflow and key user characteristics to inform the design of CD information systems for public health.

A major goal of Natural Language Processing in the public health informatics domain is the automatic extraction and encoding of data stored in free text patient records. This extracted data can then be utilized by computerized systems to perform syndromic surveillance. In particular, the chief complaint-a short string that describes a patient's symptoms-has come to be a vital resource for syndromic surveillance in the North American context due to its near ubiquity. This paper reviews fifteen systems in North America-at the city, county, state and federal level-that use chief complaints for syndromic surveillance.

A new model of health care is emerging in which individuals can take charge of their health by connecting to online communities and social networks for personalized support and collective knowledge. Web 2.0 technologies expand the traditional notion of online support groups into a broad and evolving range of informational, emotional, as well as community-based concepts of support. In order to apply these technologies to patient-centered care, it is necessary to incorporate more inclusive conceptual frameworks of social support and community-based research methodologies. This paper introduces a conceptualization of online social support, reviews current challenges in online support research, and outlines six recommendations for the design, evaluation, and implementation of social support in online communities, networks, and groups. The six recommendations are illustrated by CanConnect, an online community for cancer survivors in middle Tennessee. These recommendations address the interdependencies between online and real-world support and emphasize an inclusive framework of interpersonal and community-based support. The applications of these six recommendations are illustrated through a discussion of online support for cancer survivors.

Personalized medicine is to deliver the right drug to the right patient in the right dose. Pharmacogenomics (PGx) is to identify genetic variants that may affect drug efficacy and toxicity. The availability of a comprehensive and accurate PGx-specific drug-gene relationship knowledge base is important for personalized medicine. However, building a large-scale PGx-specific drug-gene knowledge base is a difficult task. In this study, we developed a bootstrapping, semi-supervised learning approach to iteratively extract and rank drug-gene pairs according to their relevance to drug pharmacogenomics. Starting with a single PGx-specific seed pair and 20 million MEDLINE abstracts, the extraction algorithm achieved a precision of 0.219, recall of 0.368 and F1 of 0.274 after two iterations, a significant improvement over the results of using non-PGx-specific seeds (precision: 0.011, recall: 0.018, and F1: 0.014) or co-occurrence (precision: 0.015, recall: 1.000, and F1: 0.030). After the extraction step, the ranking algorithm further improved the precision from 0.219 to 0.561 for top ranked pairs. By comparing to a dictionary-based approach with PGx-specific gene lexicon as input, we showed that the bootstrapping approach has better performance in terms of both precision and F1 (precision: 0.251 vs. 0.152, recall: 0.396 vs. 0.856 and F1: 0.292 vs. 0.254). By integrative analysis using a large drug adverse event database, we have shown that the extracted drug-gene pairs strongly correlate with drug adverse events. In conclusion, we developed a novel semi-supervised bootstrapping approach for effective PGx-specific drug-gene pair extraction from large number of MEDLINE articles with minimal human input.

Gene selection is an important task in bioinformatics studies, because the accuracy of cancer classification generally depends upon the genes that have biological relevance to the classifying problems. In this work, randomization test (RT) is used as a gene selection method for dealing with gene expression data. In the method, a statistic derived from the statistics of the regression coefficients in a series of partial least squares discriminant analysis (PLSDA) models is used to evaluate the significance of the genes. Informative genes are selected for classifying the four gene expression datasets of prostate cancer, lung cancer, leukemia and non-small cell lung cancer (NSCLC) and the rationality of the results is validated by multiple linear regression (MLR) modeling and principal component analysis (PCA). With the selected genes, satisfactory results can be obtained.