Difference between revisions of "Main Page/Featured article of the week/2015"
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| text = If you're looking for the 2014 | | text = If you're looking for other "Article of the Week" archives: [[Main Page/Featured article of the week/2014|2014]] - 2015 - [[Main Page/Featured article of the week/2016|2016]] - [[Main Page/Featured article of the week/2017|2017]] - [[Main Page/Featured article of the week/2018|2018]] - [[Main Page/Featured article of the week/2019|2019]] - [[Main Page/Featured article of the week/2020|2020]] - [[Main Page/Featured article of the week/2021|2021]] - [[Main Page/Featured article of the week/2022|2022]] - [[Main Page/Featured article of the week/2023|2023]] - [[Main Page/Featured article of the week/2024|2024]] | ||
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<h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: January 5–11:</h2> | <h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: December 28—January 3:</h2> | ||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Dander BMCBioinformatics2014 15.jpg|220px]]</div> | |||
'''"[[Journal:Personalized Oncology Suite: Integrating next-generation sequencing data and whole-slide bioimages|Personalized Oncology Suite: Integrating next-generation sequencing data and whole-slide bioimages]]"''' | |||
Cancer immunotherapy has recently entered a remarkable renaissance phase with the approval of several agents for treatment. [[Cancer informatics|Cancer treatment platforms]] have demonstrated profound tumor regressions including complete cure in patients with metastatic cancer. Moreover, technological advances in next-generation sequencing (NGS) as well as the development of devices for scanning whole-slide bioimages from tissue sections and [[Bioimage informatics|image analysis software]] for quantitation of tumor-infiltrating lymphocytes (TILs) allow, for the first time, the development of personalized cancer immunotherapies that target patient specific mutations. However, there is currently no [[bioinformatics]] solution that supports the integration of these heterogeneous datasets. | |||
We have developed a bioinformatics platform – Personalized Oncology Suite (POS) – that integrates clinical data, NGS data and whole-slide bioimages from tissue sections. POS is a web-based platform that is scalable, flexible and expandable. The underlying database is based on a data warehouse schema, which is used to integrate [[information]] from different sources. POS stores clinical data, [[Genomics|genomic]] data (SNPs and INDELs identified from NGS analysis), and scanned whole-slide images. ('''[[Journal:Personalized Oncology Suite: Integrating next-generation sequencing data and whole-slide bioimages|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: December 21—27:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Munkhdalai JCheminformatics2015 7-1.jpg|220px]]</div> | |||
'''"[[Journal:Incorporating domain knowledge in chemical and biomedical named entity recognition with word representations|Incorporating domain knowledge in chemical and biomedical named entity recognition with word representations]]"''' | |||
Chemical and biomedical Named Entity Recognition (NER) is an essential prerequisite task before effective text mining can begin for biochemical-text data. Exploiting unlabeled text data to leverage system performance has been an active and challenging research topic in text mining due to the recent growth in the amount of biomedical literature. | |||
We present a semi-supervised learning method that efficiently exploits unlabeled data in order to incorporate domain knowledge into a named entity recognition model and to leverage system performance. The proposed method includes Natural Language Processing (NLP) tasks for text preprocessing, learning word representation features from a large amount of text data for feature extraction, and conditional random fields for token classification. Other than the free text in the domain, the proposed method does not rely on any lexicon nor any dictionary in order to keep the system applicable to other NER tasks in bio-text data. ('''[[Journal:Incorporating domain knowledge in chemical and biomedical named entity recognition with word representations|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: December 14—20:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 GanzingerPeerJCS2015 3.png|220px]]</div> | |||
'''"[[Journal:Requirements for data integration platforms in biomedical research networks: A reference model|Requirements for data integration platforms in biomedical research networks: A reference model]]"''' | |||
Biomedical research networks need to integrate research data among their members and with external partners. To support such data sharing activities, an adequate information technology infrastructure is necessary. To facilitate the establishment of such an infrastructure, we developed a reference model for the requirements. The reference model consists of five reference goals and 15 reference requirements. Using the Unified Modeling Language, the goals and requirements are set into relation to each other. In addition, all goals and requirements are described textually in tables. This reference model can be used by research networks as a basis for a resource efficient acquisition of their project specific requirements. Furthermore, a concrete instance of the reference model is described for a research network on liver cancer. The reference model is transferred into a requirements model of the specific network. Based on this concrete requirements model, a service-oriented information technology architecture is derived and also described in this paper. ('''[[Journal:Requirements for data integration platforms in biomedical research networks: A reference model|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: December 7—13:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Barker BMCBio2013 14.jpg|220px]]</div> | |||
'''"[[Journal:4273π: Bioinformatics education on low cost ARM hardware|4273π: Bioinformatics education on low cost ARM hardware]]"''' | |||
Teaching [[bioinformatics]] at universities is complicated by typical computer classroom settings. As well as running software locally and online, students should gain experience of systems administration. For a future career in biology or bioinformatics, the installation of software is a useful skill. We propose that this may be taught by running the course on GNU/Linux running on inexpensive Raspberry Pi computer hardware, for which students may be granted full administrator access. | |||
We release 4273''π'', an operating system image for Raspberry Pi based on Raspbian Linux. This includes minor customisations for classroom use and includes our Open Access bioinformatics course, ''4273π Bioinformatics for Biologists''. This is based on the final-year undergraduate module BL4273, run on Raspberry Pi computers at the University of St Andrews, Semester 1, academic year 2012–2013. 4273''π'' is a means to teach bioinformatics, including systems administration tasks, to undergraduates at low cost. ('''[[Journal:4273π: Bioinformatics education on low cost ARM hardware|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: November 30–December 6:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Barker IntJourSTEMEd2015 2.jpg|220px]]</div> | |||
'''"[[Journal:University-level practical activities in bioinformatics benefit voluntary groups of pupils in the last 2 years of school|University-level practical activities in bioinformatics benefit voluntary groups of pupils in the last 2 years of school]]"''' | |||
[[Bioinformatics]] — the use of computers in biology — is of major and increasing importance to biological sciences and medicine. We conducted a preliminary investigation of the value of bringing practical, university-level bioinformatics education to the school level. We conducted voluntary activities for pupils at two schools in Scotland (years S5 and S6; pupils aged 15–17). We used material originally developed for an optional final-year undergraduate module and now incorporated into 4273''π'', a resource for teaching and learning bioinformatics on the low-cost Raspberry Pi computer. | |||
Pupils’ feedback forms suggested our activities were beneficial. During the course of the activity, they provide strong evidence of increase in the following: pupils’ perception of the value of computers within biology; their knowledge of the Linux operating system and the Raspberry Pi; their willingness to use computers rather than phones or tablets; their ability to program a computer and their ability to analyse DNA sequences with a computer. We found no strong evidence of negative effects. ('''[[Journal:University-level practical activities in bioinformatics benefit voluntary groups of pupils in the last 2 years of school|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: November 23–29:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Zheng JPathInfo2015 6.jpg|220px]]</div> | |||
'''"[[Journal:Support patient search on pathology reports with interactive online learning based data extraction|Support patient search on pathology reports with interactive online learning based data extraction]]"''' | |||
Structural reporting enables semantic understanding and prompt retrieval of clinical findings about patients. While [[LIS feature#Synoptic reporting|synoptic pathology reporting]] provides templates for data entries, information in [[Clinical pathology|pathology]] reports remains primarily in narrative free text form. Extracting data of interest from narrative pathology reports could significantly improve the representation of the information and enable complex structured queries. However, manual extraction is tedious and error-prone, and automated tools are often constructed with a fixed training dataset and not easily adaptable. Our goal is to extract data from pathology reports to support advanced patient search with a highly adaptable semi-automated data extraction system, which can adjust and self-improve by learning from a user's interaction with minimal human effort. | |||
We have developed an online machine learning based information extraction system called IDEAL-X. With its graphical user interface, the system's data extraction engine automatically annotates values for users to review upon loading each report text. The system analyzes users' corrections regarding these annotations with online machine learning, and incrementally enhances and refines the learning model as reports are processed. ('''[[Journal:Support patient search on pathology reports with interactive online learning based data extraction|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: November 16–22:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Kruse JMIRMedInfo2014 2-1.jpg|220px]]</div> | |||
'''"[[Journal:Factors associated with adoption of health information technology: A conceptual model based on a systematic review|Factors associated with adoption of health information technology: A conceptual model based on a systematic review]]"''' | |||
The purpose of this systematic review is to identify a full-spectrum of both internal organizational and external environmental factors associated with the adoption of [[health information technology]] (HIT), specifically the EHR. The result is a conceptual model that is commensurate with the complexity of with the health care sector. We performed a systematic literature search in PubMed (restricted to English), EBSCO Host, and Google Scholar for both empirical studies and theory-based writing from 1993-2013 that demonstrated association between influential factors and three modes of HIT: EHR, [[electronic medical record]] (EMR), and computerized provider order entry (CPOE). We also looked at published books on organizational theories. We made notes and noted trends on adoption factors. These factors were grouped as adoption factors associated with various versions of EHR adoption. The resulting conceptual model summarizes the diversity of independent variables (IVs) and dependent variables (DVs) used in articles, editorials, books, as well as quantitative and qualitative studies (n=83). ('''[[Journal:Factors associated with adoption of health information technology: A conceptual model based on a systematic review|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: November 2–15:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1_Joyce_2015.png|220px]]</div> | |||
'''"[[Journal:Generalized procedure for screening free software and open-source software applications|Generalized procedure for screening free software and open-source software applications]]"''' | |||
Free software and [[:Category:Open-source software|open-source software projects]] have become a popular alternative tool in both scientific research and other fields. However, selecting the optimal application for use in a project can be a major task in itself, as the list of potential applications must first be identified and screened to determine promising candidates before an in-depth analysis of systems can be performed. To simplify this process, we have initiated a project to generate a library of in-depth reviews of free software and open-source software applications. Preliminary to beginning this project, a review of evaluation methods available in the literature was performed. As we found no one method that stood out, we synthesized a general procedure using a variety of available sources for screening a designated class of applications to determine which ones to evaluate in more depth. In this paper, we examine a number of currently published processes to identify their strengths and weaknesses. By selecting from these processes we synthesize a proposed screening procedure to triage available systems and identify those most promising of pursuit. ('''[[Journal:Generalized procedure for screening free software and open-source software applications|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: October 26–November 1:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Albers Informatics2015 2-2.jpg|220px]]</div> | |||
'''"[[Journal:Human–information interaction with complex information for decision-making|Human–information interaction with complex information for decision-making]]"''' | |||
Human–information interaction (HII) for simple [[information]] and for complex information is different because people's goals and information needs differ between the two cases. With complex information, comprehension comes from understanding the relationships and interactions within the information and factors outside of a design team's control. Yet, a design team must consider all these within an HII design in order to maximize the communication potential. This paper considers how simple and complex information requires different design strategies and how those strategies differ. ('''[[Journal:Human–information interaction with complex information for decision-making|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: October 19–25:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Celi JMIRMedInformatics2014 2-2.jpg|220px]]</div> | |||
'''"[[Journal:Making big data useful for health care: A summary of the inaugural MIT Critical Data Conference|Making big data useful for health care: A summary of the inaugural MIT Critical Data Conference]]"''' | |||
With growing concerns that big data will only augment the problem of unreliable research, the Laboratory of Computational Physiology at the Massachusetts Institute of Technology organized the Critical Data Conference in January 2014. Thought leaders from academia, government, and industry across disciplines — including clinical medicine, computer science, public health, [[informatics]], biomedical research, health technology, statistics, and epidemiology — gathered and discussed the pitfalls and challenges of big data in health care. The key message from the conference is that the value of large amounts of data hinges on the ability of researchers to share data, methodologies, and findings in an open setting. If empirical value is to be from the analysis of retrospective data, groups must continuously work together on similar problems to create more effective peer review. This will lead to improvement in methodology and quality, with each iteration of analysis resulting in more reliability. ('''[[Journal:Making big data useful for health care: A summary of the inaugural MIT Critical Data Conference|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: October 12–18:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab2 Hersh AdvancesMedEdPrac2014 2014-5.jpg|220px]]</div> | |||
'''"[[Journal:Beyond information retrieval and electronic health record use: Competencies in clinical informatics for medical education|Beyond information retrieval and electronic health record use: Competencies in clinical informatics for medical education]]"''' | |||
Physicians in the 21st century will increasingly interact in diverse ways with information systems, requiring competence in many aspects of [[clinical informatics]]. In recent years, many medical school curricula have added content in [[information]] retrieval (search) and basic use of the [[electronic health record]]. However, this omits the growing number of other ways that physicians are interacting with information that includes activities such as [[Clinical decision support system|clinical decision support]], quality measurement and improvement, personal health records, telemedicine, and personalized medicine. We describe a process whereby six faculty members representing different perspectives came together to define competencies in clinical informatics for a curriculum transformation process occurring at Oregon Health & Science University. From the broad competencies, we also developed specific learning objectives and milestones, an implementation schedule, and mapping to general competency domains. We present our work to encourage debate and refinement as well as facilitate evaluation in this area. ('''[[Journal:Beyond information retrieval and electronic health record use: Competencies in clinical informatics for medical education|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: October 5–11:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig5 Deroulers DiagnosticPath2013 8.jpg|220px]]</div> | |||
'''"[[Journal:Analyzing huge pathology images with open source software|Analyzing huge pathology images with open source software]]"''' | |||
Background: Digital pathology images are increasingly used both for diagnosis and research, because slide scanners are nowadays broadly available and because the quantitative study of these images yields new insights in systems biology. However, such virtual slides build up a technical challenge since the images occupy often several gigabytes and cannot be fully opened in a computer’s memory. Moreover, there is no standard format. Therefore, most common open source tools such as ImageJ fail at treating them, and the others require expensive hardware while still being prohibitively slow. | |||
Results: We have developed several cross-platform open source software tools to overcome these limitations. The NDPITools provide a way to transform microscopy images initially in the loosely supported NDPI format into one or several standard TIFF files, and to create mosaics (division of huge images into small ones, with or without overlap) in various TIFF and JPEG formats. They can be driven through ImageJ plugins. The LargeTIFFTools achieve similar functionality for huge TIFF images which do not fit into RAM. We test the performance of these tools on several digital slides and compare them, when applicable, to standard software. A statistical study of the cells in a tissue sample from an oligodendroglioma was performed on an average laptop computer to demonstrate the efficiency of the tools. ('''[[Journal:Analyzing huge pathology images with open source software|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: September 28–October 4:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Stocker BMCBioinformatics2009 10.jpg|220px]]</div> | |||
'''"[[Journal:iLAP: A workflow-driven software for experimental protocol development, data acquisition and analysis|iLAP: A workflow-driven software for experimental protocol development, data acquisition and analysis]]"''' | |||
Background: In recent years, the genome biology community has expended considerable effort to confront the challenges of managing heterogeneous data in a structured and organized way and developed [[laboratory information management system]]s (LIMS) for both raw and processed data. On the other hand, [[Electronic laboratory notebook|electronic notebooks]] were developed to record and manage scientific data, and facilitate data-sharing. Software which enables both, management of large datasets and digital recording of [[laboratory]] procedures would serve a real need in laboratories using medium and high-throughput techniques. | |||
Results: We have developed iLAP (Laboratory data management, Analysis, and Protocol development), a workflow-driven information management system specifically designed to create and manage experimental protocols, and to analyze and share laboratory data. The system combines experimental protocol development, wizard-based data acquisition, and high-throughput data analysis into a single, integrated system. We demonstrate the power and the flexibility of the platform using a microscopy case study based on a combinatorial multiple fluorescence in situ hybridization (m-FISH) protocol and 3D-image reconstruction. ('''[[Journal:iLAP: A workflow-driven software for experimental protocol development, data acquisition and analysis|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: September 21–27:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Pic1 Zieth electronic2014 8-2.jpg|220px]]</div> | |||
'''"[[Journal:The evolution, use, and effects of integrated personal health records: A narrative review|The evolution, use, and effects of integrated personal health records: A narrative review]]"''' | |||
Objective: To present a summarized literature review of the evolution, use, and effects of Personal Health Records (PHRs). | |||
Methods: Medline and PubMed were searched for ‘personal health records’. Seven hundred thirty-three references were initially screened resulting in 230 studies selected as relevant based on initial title and abstract review. After further review, a total of 52 articles provided relevant information and were included in this paper. These articles were reviewed by one author and grouped into the following categories: PHR evolution and adoption, patient user attitudes toward PHRs, patient reported barriers to use, and the role of PHRs in self-management. | |||
Results: Eleven papers described evolution and adoption, 17 papers described PHR user attitudes, 10 papers described barriers to use, and 11 papers described PHR use in self-management. Three papers were not grouped into a category but were used to inform the Discussion. PHRs have evolved from patient-maintained paper health records to provider-linked [[electronic health record]]s. Patients report enthusiasm for the potential of modern PHRs, yet few patients actually use an electronic PHR. Low patient adoption of PHRs is associated with poor interface design and low health and computer literacy on the part of patient users. ('''[[Journal:The evolution, use, and effects of integrated personal health records: A narrative review|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: August 31–September 6:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Cresswell InformaticsPC2014 21-2.jpg|220px]]</div> | |||
'''"[[Journal:Undertaking sociotechnical evaluations of health information technologies|Undertaking sociotechnical evaluations of health information technologies]]"''' | |||
There is an increasing international recognition that the evaluation of health information technologies should involve assessments of both the technology and the social/organisational contexts into which it is deployed. There is, however, a lack of agreement on definitions, published guidance on how such ‘sociotechnical evaluations’ should be undertaken, and how they distinguish themselves from other approaches. We explain what sociotechnical evaluations are, consider the contexts in which these are most usefully undertaken, explain what they entail, reflect on the potential pitfalls associated with such research, and suggest possible ways to avoid these. ('''[[Journal:Undertaking sociotechnical evaluations of health information technologies|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: August 24–30:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1_Bellary_PerspectivesClinRes2014_5-4.jpg|220px]]</div> | |||
'''"[[Journal:Basics of case report form designing in clinical research|Basics of case report form designing in clinical research]]"''' | |||
Case report form (CRF) is a specialized document in clinical research. It should be study protocol driven, robust in content and have material to collect the study specific data. Though paper CRFs are still used largely, use of electronic CRFs (eCRFs) are gaining popularity due to the advantages they offer such as improved data quality, online discrepancy management and faster database lock etc. Main objectives behind CRF development are preserving and maintaining quality and integrity of data. CRF design should be standardized to address the needs of all users such as investigator, site coordinator, study monitor, data entry personnel, medical coder and statistician. Data should be organized in a format that facilitates and simplifies data analysis. Collection of large amount of data will result in wasted resources in collecting and processing it and in many circumstances, will not be utilized for analysis. Apart from that, standard guidelines should be followed while designing the CRF. CRF completion manual should be provided to the site personnel to promote accurate data entry by them. ('''[[Journal:Basics of case report form designing in clinical research|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: August 17–23:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Onega JournalHMInformatics2014 5-6.gif|220px]]</div> | |||
'''"[[Journal:Why health services research needs geoinformatics: Rationale and case example|Why health services research needs geoinformatics: Rationale and case example]]"''' | |||
Delivery of health care in the United States has become increasingly complex over the past 50 years, as health care markets have evolved, technology has diffused, population demographics have shifted, and cultural expectations of health and health care have been transformed. Identifying and understanding important patterns of health care services, accessibility, utilization, and outcomes can best be accomplished by combining data from all of these dimensions in near-real time. The Big Data paradigm provides a new framework to bring together very large volumes of data from a variety of sources and formats, with computing capacity to derive new [[information]], hypotheses, and inferences. The complementary fields of genomics and [[bioinformatics]] have already made great advances only made possible by Big Data approaches. Similar gains can be made by pairing health services research with [[geoinformatics]] –- defined as “the science and technology dealing with the structure and character of spatial information, its capture, its classification and qualification, its storage, processing, portrayal and dissemination, including the infrastructure necessary to secure optimal use of this information”. ('''[[Journal:Why health services research needs geoinformatics: Rationale and case example|Full article...]]''')<br /> | |||
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<h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: August 10–16:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig5 Jang JMIRMedInfo2014 2-2.jpg|220px]]</div> | |||
'''"[[Journal:Return on investment in electronic health records in primary care practices: A mixed-methods study|Return on investment in electronic health records in primary care practices: A mixed-methods study]]"''' | |||
The use of [[electronic health record]]s (EHR) in clinical settings is considered pivotal to a patient-centered health care delivery system. However, uncertainty in cost recovery from EHR investments remains a significant concern in primary care practices. | |||
Guided by the question of “When implemented in primary care practices, what will be the return on investment (ROI) from an EHR implementation?”, the objectives of this study are two-fold: (1) to assess ROI from EHR in primary care practices and (2) to identify principal factors affecting the realization of positive ROI from EHR. We used a break-even point, that is, the time required to achieve cost recovery from an EHR investment, as an ROI indicator of an EHR investment. Given the complexity exhibited by most EHR implementation projects, this study adopted a retrospective mixed-method research approach, particularly a multiphase study design approach. For this study, data were collected from community-based primary care clinics using EHR systems. ('''[[Journal:Return on investment in electronic health records in primary care practices: A mixed-methods study|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: August 3–9:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Mickan BMCMedInfoDecMak2014 14.jpg|220px]]</div> | |||
'''"[[Journal:Use of handheld computers in clinical practice: A systematic review|Use of handheld computers in clinical practice: A systematic review]]"''' | |||
Many healthcare professionals use smartphones and tablets to inform patient care. Contemporary research suggests that handheld computers may support aspects of clinical diagnosis and management. This systematic review was designed to synthesise high quality evidence to answer the question; Does healthcare professionals’ use of handheld computers improve their access to [[information]] and support clinical decision making at the point of care? | |||
A detailed search was conducted using Cochrane, MEDLINE, EMBASE, PsycINFO, Science and Social Science Citation Indices since 2001. Interventions promoting healthcare professionals seeking information or making clinical decisions using handheld computers were included. Classroom learning and the use of laptop computers were excluded. Two authors independently selected studies, assessed quality using the Cochrane Risk of Bias tool and extracted data. High levels of data heterogeneity negated statistical synthesis. Instead, evidence for effectiveness was summarised narratively, according to each study’s aim for assessing the impact of handheld computer use. ('''[[Journal:Use of handheld computers in clinical practice: A systematic review|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: July 27–August 2:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Barrett InformaticsPC2014 21-3.jpg|220px]]</div> | |||
'''"[[Journal:Unravelling the tangled taxonomies of health informatics|Unravelling the tangled taxonomies of health informatics]]"''' | |||
Even though [[informatics]] is a term used commonly in healthcare, it can be a confusing and disengaging one. Many definitions exist in the literature, and attempts have been made to develop a clear taxonomy. Despite this, informatics is still a term that lacks clarity in both its scope and the classification of sub-terms that it encompasses. | |||
This paper reviews the importance of an agreed taxonomy and explores the challenges of establishing exactly what is meant by [[health informatics]] (HI). It reviews what a taxonomy should do, summarises previous attempts at categorising and organising HI and suggests the elements to consider when seeking to develop a system of classification. | |||
The paper does not provide all the answers, but it does clarify the questions. By plotting a path towards a taxonomy of HI, it will be possible to enhance understanding and optimise the benefits of embracing technology in clinical practice.('''[[Journal:Unravelling the tangled taxonomies of health informatics|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: July 20–26:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:ANSI logo.gif|220px]]</div> | |||
The '''[[American National Standards Institute]]''' ('''ANSI''') is a private nonprofit organization that oversees the development of voluntary consensus standards for products, services, processes, systems, and personnel in the United States. The organization also coordinates U.S. standards with international standards so that American products can be used worldwide. For example, standards ensure that people who own cameras can find the film they need for that camera anywhere around the globe. | |||
ANSI accredits standards that are developed by representatives of other standards organizations, government agencies, consumer groups, companies, and others. These standards ensure that the characteristics and performance of products are consistent, that people use the same definitions and terms, and that products are tested the same way. ANSI also accredits organizations that carry out product or personnel certification in accordance with requirements defined in international standards. ANSI's operations office is located in New York City. The ANSI annual operating budget is funded by the sale of publications, membership dues and fees, accreditation services, fee-based programs, and international standards programs. ('''[[American National Standards Institute|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: July 13–19:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Drug overdose death rates by state per 100,000 people 2008 US.png|220px]]</div> | |||
'''[[Public health informatics]]''' has been defined as "the systematic application of [[information]] and computer science and technology to public health practice, research, and learning." Like other types of informatics, public health informatics is a multidisciplinary field, involving the studies of [[Informatics (academic field)|informatics]], computer science, psychology, law, statistics, epidemiology, and microbiology. | |||
In 2000, researcher William A. Yasnoff and his colleagues identified four key aspects that differentiate public health informatics from [[Health informatics|medical informatics]] and other informatics specialty areas. Public health informatics focuses on "applications of information science and technology that promote the health of populations as opposed to the health of specific individuals" and that "prevent disease and injury by altering the conditions or the environment that put populations of individuals at risk." It also "explore[s] the potential for prevention at all vulnerable points in the causal chains leading to disease, injury, or disability" and "reflect[s] the governmental context in which public health is practiced." ('''[[Public health informatics|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: July 6–12:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Healthcare Apps for Android Tablets.jpg|220px]]</div> | |||
'''[[Health information technology]] (HIT)''' is the application of "hardware and software in an effort to manage and manipulate health data and information." HIT acts as a framework for the comprehensive management of health information originating from consumers, providers, governments, and insurers in order to improve the overall state of health care. Among those improvements, the Congressional Budget Office (CBO) of the United States believes HIT can reduce or eliminate errors from medical transcription, reduce the number of diagnostic tests that get duplicated, and improve patient outcomes and service efficiency among other things. | |||
The "technology" of "health information technology" represents computers, software, and communications infrastructure that can be networked to create systems for manipulating health information. As such, the science of [[Informatics (academic field)|informatics]] and its focus on information processing and systems engineering is also integral to the development, application, and evaluation of HIT. In particular the subdivision of [[health informatics]], which focuses on the resources, devices, and methods required for optimizing the acquisition, storage, retrieval, and use of information in health and biomedicine, is most relevant. However, other subdivisions of informatics such as [[medical informatics]], [[public health informatics]], [[pharmacoinformatics]], and [[translational research informatics]] are able to inform health informatics from different disciplinary perspectives. ('''[[Health information technology|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: June 29–July 5:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:DSS-Figure-1.PNG|220px]]</div> | |||
A '''[[clinical decision support system]]''' ('''CDSS''') is a "computer [system] designed to impact clinician decision making about individual patients at the point in time these decisions are made." As such, it can be viewed as a knowledge management tool used to further clinical advice for patient care based on multiple items of patient data. In the early days, CDSSs were conceived of as being used to literally make decisions for the clinician. The clinician would input the information and wait for the CDSS to output the "right" choice, and the clinician would simply act on that output. However, the modern methodology involves the clinician interacting with the CDSS at the point of care, utilizing both their own knowledge and the CDSS to produce the best diagnosis from the test data. Typically, a CDSS suggests avenues for the physician to explore, and the physician is expected to use their own knowledge and judgement to narrow down possibilities. | |||
CDSSs can be roughly divided into two types: those with knowledge bases and those without. The knowledge-based approach typically covers the diagnosis of many different diseases, while the non-knowledge-based approach often focuses on a narrow list of symptoms, such as symptoms for a single disease. ('''[[Clinical decision support system|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: June 22–28:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Doctor reviewing pdq.jpg|220px]]</div> | |||
A '''[[medical practice management system]]''' (also '''practice management system''' or '''PMS''') is a software-based information and enterprise management tool for physician offices that offers a set of key features that support an individual or group medical practice's operations. Those key features include — but are not limited to — appointment scheduling, patient registration, procedure posting, insurance billing, patient billing, payment posting, data and file maintenance, and reporting. | |||
The PMS has traditionally been a stand-alone application, installed on computers in the physician office. But like [[laboratory information management system]]s, [[hospital information system]]s, and other informatics software, trends have shifted to both web-based and cloud-based access to PMS applications. Cloud-based PMSs have been around at least since 2011, and they have become more attractive for several reasons, including the ease of letting the vendor maintain and update the technology from their end, the need for less hardware, and the convenience of accessing the system from anywhere. ('''[[Medical practice management system|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: June 15–21:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Phanerozoic Biodiversity.png|220px]]</div> | |||
'''[[Biodiversity informatics]]''' is the application of informatics techniques to biodiversity [[information]] for improved management, presentation, discovery, exploration, and analysis. It typically builds on a foundation of taxonomic, biogeographic, and synecologic information stored in digital form, which, with the application of modern computer techniques, can yield new ways to view and analyze existing information, as well as predictive models for information that does not yet exist. Biodiversity informatics has also been described by others as "the creation, integration, analysis, and understanding of information regarding biological diversity" and a field of science "that brings information science and technologies to bear on the data and information generated by the study of organisms, their genes, and their interactions." | |||
According to correspondence reproduced by Walter Berendsohn, the term "biodiversity informatics" was coined by John Whiting in 1992 to cover the activities of an entity known as the Canadian Biodiversity Informatics Consortium (CBIC), a group involved with fusing basic biodiversity information with environmental economics and geospatial information. Subsequently it appears to have lost at least some connection with the geospatial world, becoming more closely associated with the computerized management of biodiversity information. ('''[[Biodiversity informatics|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: June 8–14:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Day 253 - West Midlands Police - Forensic Science Lab (7969822920).jpg|220px]]</div> | |||
'''The [[American Society of Crime Laboratory Directors/Laboratory Accreditation Board]]''' ('''ASCLD/LAB''') is a Missouri-based not-for-profit that "offers voluntary accreditation to public and private crime laboratories" around the world. | |||
The main objectives of the ASCLD/LAB are: | |||
- to improve the quality of laboratory services provided to the criminal justice system. | |||
- to adopt, develop and maintain criteria which may be used by a laboratory to assess its level of performance and to strengthen its operation. | |||
- to provide an independent, impartial, and objective system by which laboratories can benefit from a total operational review. | |||
- to offer to the general public and to users of laboratory services a means of identifying those laboratories which have demonstrated that they meet established standards. ('''[[The American Society of Crime Laboratory Directors/Laboratory Accreditation Board|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: June 1–7:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:SSTm.png|220px]]</div> | |||
'''[[Environmental informatics]]''' ('''EI''') is a developing field of science that applies [[information]] processing, management, and sharing strategies to the interdisciplinary field of environmental science. Applications include the integration of information and knowledge, the application of computational intelligence to environmental data, and the identification of the environmental impacts of information technology. EI helps scientists define information processing requirements, analyze real-world problems, and solve those problems using informatics methodologies and tools. | |||
As EI has continued to evolve, several other definitions have been offered over the years. Some consider it "an emerging field centering around the development of standards and protocols, both technical and institutional, for sharing and integrating environmental data and information." Others consider it the application of "[r]esearch and system development focusing on the environmental sciences relating to the creation, collection, storage, processing, modelling, interpretation, display and dissemination of data and information." ('''[[Environmental informatics|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: May 25–31:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Linux kernel API.svg|220px]]</div> | |||
An '''[[application programming interface]]''' ('''API''') is a particular set of rules and specifications that software programs can follow to communicate with each other. It serves as an interface between different software programs and facilitates their interaction, similar to the way the user interface facilitates interaction between humans and computers. An API can be created for applications, libraries, operating systems, etc. as a way of defining their "vocabularies" and resource request conventions (e.g. function-calling conventions). It may include specifications for routines, data structures, object classes, and protocols used to communicate between the consumer program and the implementer program of the API. | |||
An API can be generalized for many functions, appearing bundled in the libraries of a programming language, e.g. the Standard Template Library in C++ or the Java API. It can also be function-specific, meant to address a specific problem, e.g. the Google Maps API or the Java API for XML Web Services. Some APIs are language-dependent, meaning they can only use the syntax and elements of a particular language, which makes the API more convenient to use. Of course, an API may also be language-independent, written so that it can be called from several programming languages. This is a desirable feature for a service-oriented API that is not bound to a specific process or system and may be provided as remote procedure calls or web services. ('''[[Application programming interface|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: May 18–24:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:1FYT T-cell receptor and HLA class II complex.png|120px]]</div> | |||
'''[[Immunoinformatics]]''' (sometimes referred to as '''computational immunology''') is a sub-branch of [[bioinformatics]] that focuses on the use of data management and computational tools to improve immunological research. The scope of immunoinformatics covers a wide variety of territory, from genomic and proteomic study of the immune system to molecular- and organism-level modeling, putting it in close ties with [[genome informatics]]. | |||
Immunology researchers like Hans-Georg Rammensee trace the history of immunoinformatics back to the study of theoretical immunology. In June 1987, the Theoretical Immunology Workshop was hosted in Santa Fe, New Mexico to discuss "the topics of immune surveillance, mathematical models of HIV infection, complexities of antigen-antibody systems, immune suppression and tolerance, and idiotypie networks." One of the first immunoinformatics efforts to result in a long-term informatics solution was the construction of the IMGT information system in 1989 by the Laboratoire d'ImmunoGénétique Moléculaire (LIGM). Created to "standardize and manage the complexity of the immunogenetics data" coming out of the lab, the information system went on to become an international public reference for genetic and proteomic data related to immunology. ('''[[Immunoinformatics|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: May 11–17:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Molecular diagnostics qia symphony.jpg|120px]]</div> | |||
The biological and '''[[life sciences industry]]''' is concerned with many aspects of physiological and medical sciences, covering the entire range of plants, bacteria, and animals. As such, there are significant crossover opportunities, such as between fermentation based companies such as beer producers and genetically engineered protein pharmaceutical companies, or between genetic engineering and biofuels. Several types of activities can be grouped under the heading of life sciences, including biorepositories, molecular diagnostics, and pharmaceutical research. | |||
Biorepositories, as their name implies, are essentially libraries of biological specimens. Frequently, biorepositories are focused on cancer research, as the type and variety of cancers require a significant bank of available tumor, tissue, and body fluid samples. Within the U.S. the National Cancer Institute (NCI) has established the Office of Biorepositories and Biospecimen research(OBBR), whose main objective is "developing a common biorepository infrastructure that promotes resource sharing and team science, in order to facilitate multi-institutional, high throughput genomic and proteomic studies." ('''[[Life sciences industry|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: May 4–10:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Silicon single crystal.jpg|120px]]</div> | |||
The [[chemical industry]] is comprised of numerous sectors, with no fewer than 45 different subdivisions, including glass manufacturing, petrochemical manufacturing, electronics chemicals, ceramics, and dye and pigment manufacturing to name a few. | |||
Glass manufacturing is a process that is largely dependent on raw material quality control, as in process controls are limited by the fact that the process for manufacturing glass is high temperature, and does not lend itself to any sort of sampling process. That being said, temperature is actually a key control measure, and multivariate process control methods have been developed to maximize finished product quality. Primary petrochemicals such as ethylene, methanol, and benzene are used to produce a variety of intermediate and derivative products which ultimately are used to produce an amazing array of materials of great importance to the modern industrial world, such as plastics, tires, solvents, and the like. ('''[[Chemical industry|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: April 27–May 3:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:USMC-100921-M-2155E-002.jpg|220px]]</div> | |||
A '''[[skilled nursing facility]]''' ('''SNF''', pronounced like the English word "sniff"), as defined by the U.S. Social Security Act, is an institution or distinct part of an institution that provides skilled nursing care to residents or physical rehabilitation care to the injured, disabled, or ill. The skilled nursing facility is primarily a designation driven by the [[Centers for Medicare and Medicaid Services]] (CMS) and its associated billing. To qualify, a SNF must meet certain requirements, including considerations for quality of life, scope, assessment, and training. | |||
The Federal Nursing Home Reform Act in 1987 set new requirements for nursing facilities, including the SNF. These facilities would have to emphasize quality of life and care to residents, create and assess an individual's care plan, provide the right to remain in care even after a hospital stay and the right to choose a personal physician, provide additional opportunities to residents with mental retardation or illness, and function under minimum federal standards or face even stricter penalties. ('''[[Skilled nursing facility|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: April 20–26:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Water Stress Around 2000 A.D. By WaterGAP.jpg|220px]]</div> | |||
'''[[Hydroinformatics]]''' is the multidisciplinary application of information and decision support systems to address the equitable and efficient management and use of water for many different purposes. Hydroinformatics draws on and integrates hydraulics, hydrology, environmental engineering, and many other disciplines. It sees application at all points in the water cycle, from atmosphere to ocean, and in artificial interventions in that cycle such as urban drainage and water supply systems. It provides support for decision making at all levels, from governance and policy through to management and operations. | |||
Hydroinformatics also recogniszs the inherently social nature of the problems of water management and of decision making processes, and it includes mechanisms towards understanding the social processes by which technologies are brought into use and how they change the water system. Since the resources to obtain and develop technological solutions affecting water collection, purification, and distribution continue to be concentrated in the hands of the minority, the need to examine these social processes are particularly acute. Hydroinformatics can help tackle problems and tasks such as improving shallow-water flow models, optimizing damn breaks, and constructing bridges across bodies of water. ('''[[Hydroinformatics|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: April 13–19:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Облачные_вычисления.jpg|220px]]</div> | |||
'''[[Software as a service]]''' ('''SaaS''') — sometimes referred to as "on-demand software" — is a software delivery model in which software and its associated data are hosted centrally (on the [[Cloud computing|cloud]], for example) and are typically accessed by users using a thin client, normally using a web browser over the Internet. The customer subscribes to this "service" rather than requiring a software license, and the software doesn't require an implementation on customer premises. | |||
A SaaS solution is typically a "multi-tenant solution," meaning more than one entity is sharing the server and database resource(s) hosted by the vendor, though in the process potentially limiting customer customization. With this model, a single version of the application with a single configuration (hardware, network, operating system, etc.) is used for all customers. To support scalability, the application is installed on multiple machines. In some cases, a second version of the application may be set up to offer a select group of customers a separate instance of the software environment, better enabling customers to customize their configuration. (This could be accomplished with platform as a service (PaaS), for example. This is contrasted with traditional software, where multiple physical copies of the software — each potentially of a different version, with a potentially different configuration, and often customized — are installed across various customer sites. ('''[[Software as a service |Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: April 6–12:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Doctor review brain images.jpg|220px]]</div> | |||
'''[[Health informatics]]''' (also called '''health care informatics''', '''healthcare informatics''', '''medical informatics''', '''nursing informatics''', '''clinical informatics''', or '''biomedical informatics''') is a discipline at the intersection of [[information science]], computer science, and health care. It deals with the resources, devices, and methods required to optimize the "collection, storage, retrieval, [and] communication ... of health-related data, [[information]], and knowledge." Health informatics is applied to the areas of nursing, clinical care, dentistry, pharmacy, public health, occupational therapy, and biomedical research. Health informatics resources include not only computers but also clinical guidelines, formal medical terminologies, and information and communication systems. | |||
Worldwide use of technology in medicine began in the early 1950s with the rise of computers. Medical informatics research units began to appear during the 1970s in Poland and in the U.S., with medical informatics conferences springing up as early as 1974. Since then the development of high-quality health informatics research, education, and infrastructure has been the goal of the U.S. and the European Union. Hundreds of datasets, publications, guidelines, specifications, meetings, conferences, and organizations around the world continue to shape what health informatics is today. ('''[[Health informatics |Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: March 30–April 5:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:NCDN - CDN.png|260px]]</div> | |||
A '''[[content delivery network]]''' or '''content distribution network''' ('''CDN''') is a large distributed system of servers deployed in multiple data centers or "nodes" across the Internet. The goal of a CDN is to serve content to end-users with the intended benefit of reducing bandwidth costs, improving page load times, and/or increasing global availability of content. This is done by hosting the content on several servers, and when a user makes a request to CDN-hosted content, the domain name server (DNS) will resolve to an optimized server based on location, availability, cost, and other metrics. | |||
Content providers such as media companies and e-commerce vendors pay CDN operators to deliver their content to their audience of end-users. In turn, a CDN pays Internet service providers (ISPs), carriers, and network operators for hosting its servers in their data centers. Besides better performance and availability, CDNs also offload the traffic served directly from the content provider's origin infrastructure, resulting in possible cost savings for the content provider. In addition, CDNs provide the content provider a degree of protection from denial-of-service (DoS) attacks by using their large distributed server infrastructure to absorb the traffic. | |||
An increasing number of ISPs have built their own CDNs to improve on-net content delivery, reduce demand on their own infrastructure, and generate revenues from content customers. Additionally, some companies such as Microsoft, Amazon, and Netflix have built their own CDNs to tie in with their own products. ('''[[Content delivery network |Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: March 23–29:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:School Based Health Clinic.jpg|140px]]</div> | |||
A '''[[federally qualified health center]]''' ('''FQHC''') is a reimbursement designation from the [[Centers for Medicare and Medicaid Services]] (CMS) of the [[United States Department of Health and Human Services]] (HHS). This designation is significant for several health programs funded under Section 330 of the Public Health Service Act, as part of the Health Center Consolidation Act. The FQHC program is designed "to enhance the provision of primary care services in underserved urban and rural communities." | |||
FQHCs are community-based organizations that provide comprehensive primary and preventive care, including health, oral, and mental health services to persons of all ages, regardless of their ability to pay or health insurance status. Thus, they are a critical component of the health care safety net. As of 2011 over 1,100 FQHCs operate approximately 6,000 sites throughout the United States and territories, serving an estimated 20 million patients. That number is expected to go up to 40 million people by 2015 thanks to extra grant funding to the program. FQHCs may also be referred to as community/migrant health centers (C/MHC), community health centers (CHC), and 330 funded clinics. ('''[[Federally qualified health center|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: March 16–22:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:USMC-03785.jpg|140px]]</div> | |||
A '''[[home health agency]]''' ('''HHA''') is a public agency, private organization, or a subdivision of such dedicated to providing health care services to people in their residence or in another non-institutional setting. Care may be provided by licensed healthcare professionals who provide medical care needs or by professional caregivers who provide daily care to help to ensure the activities of daily living (ADL's) are met. Often, the term "home health care" is used to distinguish a home health agency's services from personal, non-medical, custodial, or private-duty care services, which are provided by persons who are not nurses, doctors, or other licensed medical personnel. | |||
In 2010, over 10,800 Medicare-certified home health agencies operated throughout the United States, serving 3,446,057 beneficiaries over 122,578,603 visits. Services at home health agencies vary widely. Common categories of services include taking and recording vital signs, turning and positioning bed-bound patients, assisting in the self-administration of medication, conducting physical and occupational therapy, and providing medical social work, among other activities. ('''[[Home health agency|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: March 9–15:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Janesville GM Assembly Plant - ISO 9001 Certified sign (3549915451).jpg|260px]]</div> | |||
'''[[ISO 9000]]''' is a family of standards related to quality management systems and designed to help organizations ensure that they meet the needs of customers and other stakeholders. The standards are published by the [[International Organization for Standardization]] (ISO) and are available through national standards bodies. ISO 9000 deals with the fundamentals of quality management systems, including the eight management principles on which the family of standards is based. | |||
The global adoption of the ISO 9000 family of standards may be attributable to a number of factors. Many major purchasers require their suppliers to hold ISO 9001 certification. In addition to several stakeholders' benefits, a number of studies have identified significant financial benefits for organizations certified to ISO 9001. Over a million organizations worldwide are independently certified, making ISO 9001 one of the most widely used management tools in the world today. Despite widespread use, however, the ISO certification process has been criticized as being wasteful and not being useful for all organizations. ('''[[ISO 9000|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: March 2–8:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:HL7 Reference Information Model.jpg|260px]]</div> | |||
'''[[Health Level Seven]]''' ('''HL7''') is an international non-profit volunteer-based organization involved with the development of international health care informatics interoperability standards. The HL7 community consists of health care experts and information scientists collaborating to create standards for the exchange, management, and integration of electronic health care information. The term "HL7" is also used to refer to some of the specific standards created by the organization (e.g., HL7 v2.x, v3.0, HL7 RIM). HL7 and its members provide a framework (and related standards) for the exchange, integration, sharing, and retrieval of electronic health information. v2.x of the standards, which support clinical practice and the management, delivery, and evaluation of health services, are the most commonly used in the world. | |||
In total HL7 develops conceptual standards (e.g., HL7 RIM), document standards (e.g., HL7 CDA), application standards (e.g., HL7 CCOW), and messaging standards (e.g., HL7 v2.x and v3.0). The HL7 messaging standards v2.x and 3.0 are the primary standards from the organization. They provide a framework for data exchange among clinical and healthcare systems in an ideal format. The 2.x standards are flexible, with several implementation options, loosely geared towards "clinical interface specialists" working to move clinical data in the application space. The 3.0 standards are designed to be more fixed, precise, and international, geared towards governments and end users of clinical applications. ('''[[Health Level Seven|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: February 22–March 1:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:US Navy 100714-N-3852A-118 Hull Systems Technician monitors nitrogen tank.jpg|260px]]</div> | |||
'''[[ISO/IEC 17025]]''' is an [[International Organization for Standardization]] (ISO) standard used by testing and calibration laboratories to provide a basis for accreditation of laboratory quality systems. There are many commonalities with the [[ISO 9000]] family of standards, but ISO/IEC 17025 adds in the concept of competence to the equation, applying directly to those organizations that produce testing and calibration results. | |||
ISO/IEC 17025:1999 was issued by the ISO in late 1999 and was internationally adopted in 2000. A second release was made on May 12, 2005 after it was agreed that it needed to have its wording more closely aligned with the 2000 version of ISO 9001. The most significant changes introduced greater emphasis on the responsibilities of senior management, as well as explicit requirements for continual improvement of the management system itself, particularly communication with the customer. | |||
The ISO/IEC 17025 standard itself comprises five elements: scope, normative references, terms and definitions, management requirements, and technical requirements. In particular the management and technical requirements are the most important sections, with the management requirement section detailing the operation and effectiveness of the quality management system within the laboratory and the technical requirements section detailing the factors which determine the correctness and reliability of the tests and calibrations performed in laboratory. ('''[[ISO/IEC 17025|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: February 16–22:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Pharma-production.jpg|160px]]</div> | |||
'''[[Good Automated Manufacturing Practice]]''' ('''GAMP''') is both a technical subcommittee of the International Society for Pharmaceutical Engineering (ISPE) and a set of guidelines for manufacturers and users of automated systems in the pharmaceutical industry. One of the core principles of GAMP is that quality cannot be tested into a batch of product but must be built into each stage of the manufacturing process. As a result, GAMP covers all aspects of production; from the raw materials, facility and equipment to the training and hygiene of staff. | |||
GAMP is largely about automated system validation. In October 2014, Irish tech company Dataworks Ltd. described it as such: | |||
"It is a formal process of thorough documentation, testing, and logical process steps that validate clients' required specifications. The process begins with a user requirements specification for the machine, from which a functional requirement and a design specification are created. These documents then form the basis for the traceability matrix and for the formal testing of internal acceptance, factory acceptance, and site acceptance. Categorising software is used to support the approach to validation based on the difficulty and individuality of the computerised system." ('''[[Good Automated Manufacturing Practice|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: February 9–15:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Dialysis machines by irvin calicut.jpg|250px]]</div> | |||
An '''[[end-stage renal disease facility]]''' ('''ESRD facility''', '''dialysis facility''', or '''dialysis center''') is a medical facility that operates to assist people with irreversible loss of kidney function (stage five), requiring a regular course of dialysis or a kidney transplant to survive. The facility may operate independently, as part of a [[hospital]]-based unit, or as a self-care unit that furnishes only self-dialysis services. | |||
The U.S. [[Centers for Medicare and Medicaid Services]] (CMS) describes four types of ESRD facilities, including the renal transplantation center, for ESRD transplant patients; the renal dialysis center, a hospital unit for ESRD dialysis patients; a renal dialysis facility, a direct or stand-alone dialysis unit for ESRD patients; and a self-dialysis unit attached to one of the previous three, providing self-dialysis services. Patients undergoing dialysis at these facilities require two important documentation steps: the patient assessment and the patient plan of care. U.S. Federal regulation requires a comprehensive 13-point assessment, including current health status, laboratory profile, and nutritional status. ('''[[End-stage renal disease facility|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: February 2–8:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Genome sequencing costs 2011.jpg|250px]]</div> | |||
'''[[Genome informatics]]''' is a field of computational molecular biology and branch of [[Informatics (academic field)|informatics]] that uses computers, software, and computational solution techniques to make observations, resolve problems, and manage data related to the genomic function of DNA sequences, comparison of gene structures, determination of the tertiary structure of all proteins, and other molecular biological activities. The informatics side of genomics has largely focused on analytical tools and methodologies. DNA-microarray and sequencing technology helped researchers for the Human Genome Project, for example, analyze and understand thousands of genes and their expressions. By 2000, artificial neural networks were being theorized as a possible informatics tools to aid with data analysis and the problem of "high dimensionality" of the outputted data; by 2014 artificial neural networks were being proposed for cancer genomic research. | |||
Genome informatics can help tackle problems and tasks such as analyzing DNA sequences, recognizing genes and proteins and predicting their structures, and predicting the biochemical function of new genes or fragments, as well as molecular profiling. ('''[[Genome informatics|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: January 26–February 1:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:In-Silico-Gene-Prioritization-by-Integrating-Multiple-Data-Sources-pone.0021137.g005.jpg|250px]]</div> | |||
'''[[Cancer informatics]]''' is a multidisciplinary field of science that "deals with the resources, devices, and methods required to optimize the acquisition, storage, retrieval, and use of [[information]] in cancer" research and treatment. Like many other fields of science, researchers in cancer biology have seen a dramatic increase in the amount of clinical and research data, in particular with genomic and molecular cancer data. While this data can benefit researchers' understanding of cancer behavior and development of better therapies, new and improved data management and analysis tools are needed. Cancer informatics attempts to provide those tools "that interconnect research, clinical activities, and data in an organized and efficient manner, with as broad a database as possible." For many, the coupling of cancer informatics and other bioinformatics tools with computational modeling and statistical analysis will accelerate the goal of making cancer a more treatable if not curable disease. | |||
Cancer informatics can help tackle problems and tasks such as the development of computational diagnosis, prognosis, and predictive models; the development of standards for the entry, annotation, and sharing of clinical cancer data; and the management and distribution of annotated molecular data for further research. ('''[[Cancer informatics|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: January 19–25:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tree, plant and animal stem. Wellcome M0001295.jpg|130px]]</div> | |||
'''[[Evolutionary informatics]]''' is a sub-branch of [[informatics]] that addresses the algorithmic and technological tools (like information and analytical systems) needed to better manage data from research in ecology and evolutionary biology and answer evolutionary questions. As in [[bioinformatics]] and [[genomics]], scientists studying biological evolution have gathered an increasingly large volume of information, resulting in information management problems. Additionally, as bioinformatics and genomics are pertinent to the study of evolution, utilization of information from those areas is of concern in evolutionary informatics. | |||
Evolutionary informatics has evolved out of a wide variety of scientific, mathematical, and computational endeavors, including evolutionary biology, evolutionary computation, algorithmic and evolutionary algorithmic research, and software development. It can help tackle problems and tasks such as reducing "the growing number of lineages that lack formal taxonomic names," digitizing and semantically enhancing legacy biodiversity data while also making it more portable, and building "sustainable digital community repositories that provide access to rich data and metadata" in the field of evolutionary biology. ('''[[Evolutionary informatics|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: January 12–18:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:NIST Testing standard interfaces.jpg|200px]]</div> | |||
A '''[[scientific data management system]]''' (SDMS) is a piece or package of software that acts as a document management system (DMS), capturing, cataloging, and archiving data generated by [[laboratory]] instruments ([[HPLC]], [[mass spectrometry]]) and applications ([[LIMS]], analytical applications, [[electronic laboratory notebook]]s) in a compliant manner. The SDMS also acts as a gatekeeper, serving platform-independent data to informatics applications and/or other consumers. | |||
As with many other [[laboratory informatics]] tools, the lines between a [[LIMS]], [[ELN]], and an SDMS are at times blurred. However, there are some essential qualities that an SDMS owns that distinguishes it from other informatics systems. It's built to handle unstructured, mostly heterogeneous data; it typically acts as a seamless "wrapper" for other data systems like LIMS and ELN in the laboratory; and it is designed primarily for data consolidation, knowledge management, and knowledge asset realization. An SDMS also must be focused on increasing research productivity without sacrificing data sharing and collaboration efforts. ('''[[Scientific data management system|Full article...]]''')<br /> | |||
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: January 5–11:</h2> | |||
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:US CDC logo.svg|220px]]</div> | <div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:US CDC logo.svg|220px]]</div> |
Latest revision as of 18:55, 12 April 2024
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Featured article of the week archive - 2015
Welcome to the LIMSwiki 2015 archive for the Featured Article of the Week.
Featured article of the week: December 28—January 3:"Personalized Oncology Suite: Integrating next-generation sequencing data and whole-slide bioimages" Cancer immunotherapy has recently entered a remarkable renaissance phase with the approval of several agents for treatment. Cancer treatment platforms have demonstrated profound tumor regressions including complete cure in patients with metastatic cancer. Moreover, technological advances in next-generation sequencing (NGS) as well as the development of devices for scanning whole-slide bioimages from tissue sections and image analysis software for quantitation of tumor-infiltrating lymphocytes (TILs) allow, for the first time, the development of personalized cancer immunotherapies that target patient specific mutations. However, there is currently no bioinformatics solution that supports the integration of these heterogeneous datasets. We have developed a bioinformatics platform – Personalized Oncology Suite (POS) – that integrates clinical data, NGS data and whole-slide bioimages from tissue sections. POS is a web-based platform that is scalable, flexible and expandable. The underlying database is based on a data warehouse schema, which is used to integrate information from different sources. POS stores clinical data, genomic data (SNPs and INDELs identified from NGS analysis), and scanned whole-slide images. (Full article...)
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