IHI ... : proceedings of the ... ACM SIGHIT International Health Informatics Symposium. ACM SIGHIT International Health Informatics Symposium最新文献

{"title":"Mobile interface design for low-literacy populations","authors":"Beenish Chaudry, Kay Connelly, K. Siek, J. Welch","doi":"10.1145/2110363.2110377","DOIUrl":"https://doi.org/10.1145/2110363.2110377","url":null,"abstract":"Despite the proliferation of mobile health applications, few target low literacy users. This is a matter of concern because 43% of the United States population is functionally illiterate. To empower everyone to be a full participant in the evolving health system and prevent further disparities, we must understand the design needs of low literacy populations. In this paper, we present two complementary studies of four graphical user interface (GUI) widgets and three different cross-page navigation styles in mobile applications with a varying literacy, chronically-ill population. Participant's navigation and interaction styles were documented while they performed search tasks using high fidelity prototypes running on a mobile device. Results indicate that participants could use any non-text based GUI widgets. For navigation structures, users performed best when navigating a linear structure, but preferred the features of cross-linked navigation. Based on these findings, we provide some recommendations for designing accessible mobile applications for varying-literacy populations.","PeriodicalId":90523,"journal":{"name":"IHI ... : proceedings of the ... ACM SIGHIT International Health Informatics Symposium. ACM SIGHIT International Health Informatics Symposium","volume":"22 1","pages":"91-100"},"PeriodicalIF":0.0,"publicationDate":"2012-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87340391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Panel on social media for consumer health","authors":"Yan Zhang, P. Mitra, J. Yen, W. Fu","doi":"10.1145/2110363.2110485","DOIUrl":"https://doi.org/10.1145/2110363.2110485","url":null,"abstract":"Community-based social media, characterized by its ability to glean collective wisdom through supporting communication, information sharing, and collaboration between individual users, is imposing a greater impact on people’s daily information seeking, knowledge construction, and decision making. Unsurprisingly, the impact of social media has extended to the health care domain, as consumers have begun to share healthrelated experiences and knowledge online [4]. Currently, about one-third of Americans who go online to research their health use social networks to find fellow patients and discuss their conditions [1], and 36% of social network users evaluate and leverage other consumers’ knowledge before making healthcare decisions, such as choosing providers, determining a course of treatment, and managing their health risks [3]. In a recent study, Pew Research declares that social media sites are becoming important hubs for health advice [2].","PeriodicalId":90523,"journal":{"name":"IHI ... : proceedings of the ... ACM SIGHIT International Health Informatics Symposium. ACM SIGHIT International Health Informatics Symposium","volume":"18 1","pages":"889-890"},"PeriodicalIF":0.0,"publicationDate":"2012-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89369736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing assigned treatments as labels for supervised machine learning in clinical decision support","authors":"Ruty Rinott, B. Carmeli, C. Kent, Y. Maman, Yoav Rubin, N. Slonim","doi":"10.1145/2110363.2110419","DOIUrl":"https://doi.org/10.1145/2110363.2110419","url":null,"abstract":"Clinical Decision Support (CDS) tools are typically designed to assist physicians in clinical decision making at Point Of Care (POC). Existing CDS tools commonly rely on relatively simple rules, deduced from relevant clinical guidelines. However, the increasing pace by which Health Care Organizations (HCOs) adopt Electronic Health Record technologies suggest great potential for CDS tools that directly mine the massive clinical data collected at the HCO. A natural goal for such tools is to exploit Machine Learning (ML) algorithms in order to predict patient's outcome. However, the technical challenges involved in constructing such a system in practice are quite involved, where in particular treatments outcome are often not available as part of the HCO's data.\u0000 Here, we propose a different strategy in which we use the assigned treatments as the labels in the learning process of the supervised ML algorithms. We present two different use-cases in which our approach could be used. First, in order to highlight the clinical features most associated with the assigned treatments; and second, in order to predict the customary treatment for a patient at POC in a statistically data-driven manner. Altogether, our approach represents a novel strategy that is complementary to the classical paradigm of rule-based clinical guidelines adherence. Experimental results over hypertension clinical data demonstrate the validity of our approach.","PeriodicalId":90523,"journal":{"name":"IHI ... : proceedings of the ... ACM SIGHIT International Health Informatics Symposium. ACM SIGHIT International Health Informatics Symposium","volume":"219 1","pages":"493-502"},"PeriodicalIF":0.0,"publicationDate":"2012-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76434322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An intelligent web-based decision support tool for enhancing asthma guideline adherence","authors":"J. Bi, A. Abraham","doi":"10.1145/2110363.2110373","DOIUrl":"https://doi.org/10.1145/2110363.2110373","url":null,"abstract":"Asthma is a leading chronic disease of children and currently affects about 6.2 million (8.5%) children in the United States. National Asthma Education and Prevention Program (NAEPP) Guidelines recommend a multimodal chronic care approach. However, both of the 1997 and 2007 NAEPP guidelines have not been widely adopted by primary care clinicians because implementing the guidelines in primary care setting requires changes and redesign in clinical practice and the use of Health Information Technology (HIT) to facilitate the changes. Existing electronic asthma care systems are either inefficient to be used in real clinical encounter or provide only after-fact feedback. This paper introduces a web-based clinical decision support system that integrates intelligent computer technology, complex guideline guidance and knowledge necessary for operationalizing guidelines to improve decision making for asthma care. This system is capable of collecting guideline-suggested diagnostic measures, automatically assessing asthmatic severity and control categories based on collected measures as well as providing guideline-appropriate medication regimen. The system is implemented in a model-view-controller principle with three tiers: web-server, core reasoning algorithms and back-end database. We develop the system in conjunction with University of Connecticut Health Center to enable preliminary evaluation of the decision support system and to design an efficient and user-friendly interface that requires minimum changes in the clinical routine for primary care physicians to adapt to the system when deployed into clinical settings.","PeriodicalId":90523,"journal":{"name":"IHI ... : proceedings of the ... ACM SIGHIT International Health Informatics Symposium. ACM SIGHIT International Health Informatics Symposium","volume":"53 1","pages":"51-60"},"PeriodicalIF":0.0,"publicationDate":"2012-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74578312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sentiment lexicons for health-related opinion mining","authors":"L. Goeuriot, Jin-Cheon Na, Wai Yan Min Kyaing, Christopher S. G. Khoo, Yun-Ke Chang, Y. Theng, Jung-jae Kim","doi":"10.1145/2110363.2110390","DOIUrl":"https://doi.org/10.1145/2110363.2110390","url":null,"abstract":"Opinion mining consists in extracting from a text opinions expressed by its author and their polarity. Lexical resources, such as polarized lexicons, are needed for this task. Opinion mining in the medical domain has not been well explored, partly because little credence is given to patients and their opinions (although more and more of them are using social media). We are interested in opinion mining of user-generated content on drugs/medication. We present in this paper the creation of our lexical resources and their adaptation to the medical domain. We first describe the creation of a general lexicon, containing opinion words from the general domain and their polarity. Then we present the creation of a medical opinion lexicon, based on a corpus of drug reviews. We show that some words have a different polarity in the general domain and in the medical one. Some words considered generally as neutral are opinionated in medical texts. We finally evaluate the lexicons and show with a simple algorithm that using our general lexicon gives better results than other well-known ones on our corpus and that adding the domain lexicon improves them as well.","PeriodicalId":90523,"journal":{"name":"IHI ... : proceedings of the ... ACM SIGHIT International Health Informatics Symposium. ACM SIGHIT International Health Informatics Symposium","volume":"7 1","pages":"219-226"},"PeriodicalIF":0.0,"publicationDate":"2012-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85622422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating barriers to electronic medical record use during collaborative information seeking activities","authors":"Arvind Karunakaran, Young Hee-Nam, Madhu C. Reddy","doi":"10.1145/2110363.2110451","DOIUrl":"https://doi.org/10.1145/2110363.2110451","url":null,"abstract":"Collaborative information seeking (CIS) is an intrinsic part of medical work. Patient care teams increasingly rely upon various systems, such as Electronic Medical Records (EMRs), to support collaborative information seeking across hierarchical, functional and occupational boundaries of the organization in order to enhance the quality of medical care. However, despite their proven benefits, there still are several challenges to using EMRs for CIS, in specific, and for collaborative work, in general. The objective of this research study, therefore, is to identify some of the barriers associated with using EMRs during CIS activities. We employed qualitative research methods to understand how individuals within patient care teams use EMRs during CIS and what challenges they confront in the process. We conducted this study in the Emergency Department of a 500-bed teaching hospital. We used non-participant observations and semi-structured interviews of patient care team members for collecting our data and for capturing the practices. Through our data analysis, we identified a set of barriers to using EMRs during CIS. These include a) clash of \"technological frames\", b) lack of collective affordances, c) fear of deviations and d) alert fatigues. From the findings of our study, we highlight implications for designing EMR systems that could augment and facilitate better CIS.","PeriodicalId":90523,"journal":{"name":"IHI ... : proceedings of the ... ACM SIGHIT International Health Informatics Symposium. ACM SIGHIT International Health Informatics Symposium","volume":"27 1","pages":"743-748"},"PeriodicalIF":0.0,"publicationDate":"2012-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73159794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IFC soft: visual comparison of flow cytometry data using self-organizing maps","authors":"Kyle Thayer, Li Xiong, V. Hertzberg","doi":"10.1145/2110363.2110474","DOIUrl":"https://doi.org/10.1145/2110363.2110474","url":null,"abstract":"Using flow cytometry (FCM) technology, multi-faceted measurements can be taken of many individual particles, and thus is often used in tissue sample analysis. As the resulting data set can have over ten dimensions and millions of points, analysis can be complicated. Visualizing the data requires significantly reducing the number of dimensions or condensing the volume of the data. In studies using FCM data from multiple patients at multiple times, difficulties are compounded. We demonstrate IFC Soft, a program that uses Self-Organizing Maps (SOMs) to visually analyze and compare FCM data. Taking pre-processed FCM data from a transplant study, we show how to use SOMs to select clusters of cells and then to find trends among different classes of patients.","PeriodicalId":90523,"journal":{"name":"IHI ... : proceedings of the ... ACM SIGHIT International Health Informatics Symposium. ACM SIGHIT International Health Informatics Symposium","volume":"75 1","pages":"863-866"},"PeriodicalIF":0.0,"publicationDate":"2012-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74183238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semi-supervised image classification for automatic construction of a health image library","authors":"Yang Chen, Guo-Qiang Zhang, Rong Xu","doi":"10.1145/2110363.2110379","DOIUrl":"https://doi.org/10.1145/2110363.2110379","url":null,"abstract":"Images represent an important aspect of the entire medical knowledge. Health illustrations and photos are essential for patient education and self-care. Currently there is few effort in automatically building comprehensive image bases for general health information consumers. We present an approach to automatically construct a consumer-oriented image library containing images of human organs, diseases, drugs and other medical entities. Such an image library can then be annotated with standard medical ontologies such as the FMA [26] and RxNorm [20], creating a rich information resource for illustration and educational purposes. As a first step in constructing such an image library, we present a semi-supervised bootstrapping classification method to retrieve relevant images from the web for given medical terms. Our method uses one positive image (for each term) as a seed and iteratively searches for images that are visually similar to the seed. We have conducted experiments in classifying web images for seven organs, four diseases and four drug tablets. We achieved average precisions of 62.4% and 66.8% for organ and drug images, which are comparable to those of a supervised classification method based on standard support vector machine (SVM) (precisions of 59.5% and 56.7%), while requiring minimum human intervention.","PeriodicalId":90523,"journal":{"name":"IHI ... : proceedings of the ... ACM SIGHIT International Health Informatics Symposium. ACM SIGHIT International Health Informatics Symposium","volume":"20 1","pages":"111-120"},"PeriodicalIF":0.0,"publicationDate":"2012-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74809144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards data: a human/machine-oriented approach of medical data collection","authors":"S. Tsumoto, M. Otake, H. Nakajima, T. Fujita, Y. Chow","doi":"10.1145/2110363.2110484","DOIUrl":"https://doi.org/10.1145/2110363.2110484","url":null,"abstract":"More than twenty years have passed since clinical data were computerized as a hospital information system, whose stored data include all the histories of clinical activities in a hospital, including accounting information, medical image, laboratory data and electronic patient records. Due to the traceability of all the information, a hospital cannot function without its information system. Furthermore, if it is extended into electronic healthcare records, it may not be a dream for each patient to benefit from their personal database with all the healthcare information. Recent advances in data mining will support this trend: analysis of such large scale databases enable us to visualize and capture what we have not seen only from clinical sites. However, it is notable that conventional computerized data are described by medical staff or measured by various kinds of medical instruments. They can be viewed as summaries of clinical processes, which remove background information behind the observations. Thus, analysis based on these data cannot overcome their limitations. One of the reasons why data mining is not successful for medical risk is that it takes a long time even for medical staff to interpret the results obtained and fill the gap between their knowledge and extracted patterns.\u0000 If we want to go beyond these summaries and to get more information in order to capture the whole clinical actions or patients' behavior, we have to monitor and store their details through more sophisticated methods, such as sensor networks. For example, if we want to prevent medical incidents, in-hospital infections we have to monitor the behavior of medical staff, and if we want to prevent chronic diseases (metabolic syndromes) in an efficient way, we have to monitor the measurements of behavior of patients. Thus, although \"On data\" approaches are important for future medicine and healthcare, Ones of \"Towards data\" are more important for IT-oriented future development of these fields, which has been proposed by the organizer [1]. These data collection and their analysis will be new challenges for healthcare IT and thanks to the recent developments of sensors and devices: it will not be a dream or science fiction.\u0000 This panel gives recent advances in IT technology for data collection, their problems and their future vision: not only hardware-based or sensor-based, but also human-oriented, or human-agent-interaction based will be discussed by the panelists who play important roles in building up these fields.","PeriodicalId":90523,"journal":{"name":"IHI ... : proceedings of the ... ACM SIGHIT International Health Informatics Symposium. ACM SIGHIT International Health Informatics Symposium","volume":"14 1","pages":"887-888"},"PeriodicalIF":0.0,"publicationDate":"2012-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73945564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MNFL: the monitoring and notification flow language for assistive monitoring","authors":"Alex D. Edgcomb, F. Vahid","doi":"10.1145/2110363.2110387","DOIUrl":"https://doi.org/10.1145/2110363.2110387","url":null,"abstract":"Assistive monitoring analyzes data from sensors and cameras to detect situations of interest, and notifies appropriate persons in response. Customization of assistive technology by end-users is necessary for technology adoption and retention. We introduce MNFL, the Monitoring and Notification Flow Language, developed over the past several years to allow lay people without programming experience, but with some technical acumen, to effectively program customized monitoring and notification systems. MNFL is a graphical flow language having intuitive yet sufficiently powerful execution semantics and built-in constructs for assistive monitoring. We describe the language's semantics and built-in constructs, demonstrate the language's use for customizing several common assistive monitoring tasks, and provide results of initial usability trials showing that lay people with almost no training on MNFL can more than 50% of the time and in just a few minutes select and connect the right 1-2 blocks to complete basic applications that have 4-5 blocks total.","PeriodicalId":90523,"journal":{"name":"IHI ... : proceedings of the ... ACM SIGHIT International Health Informatics Symposium. ACM SIGHIT International Health Informatics Symposium","volume":"16 1","pages":"191-200"},"PeriodicalIF":0.0,"publicationDate":"2012-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77128474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}