@article {1212, title = {Bangladesh{\textquoteright}s digital health journey: reflections on a decade of quiet revolution.}, journal = {WHO South East Asia J Public Health}, volume = {8}, year = {2019}, month = {2019 09}, pages = {71-76}, abstract = {

Bangladesh has made remarkable progress in digital health in recent years. Through one of the world{\textquoteright}s largest deployments to date of the open-source District Health Information Software 2 (DHIS2), the country now has a national public sector health data warehouse. Information from previously fragmented data systems is now unified in a common data repository, enabling data exchange for health information systems and decision-making. Work is ongoing to create lifetime electronic health records for all citizens that can be transferred between health facilities. Extensive customization of open-source software has laid the foundations for a national digital networking system. Initiatives have focused on producing digital solutions to aid priorities such as strengthening the health system as a whole as well as supporting specific technical interventions, for example improving the civil registration and vital statistics system. Digital solutions have also supported the Bangladesh health workforce strategy through a set of registries that electronically captures and maintains human resource information for the entire public health sector, including monitoring staff attendance through the use of low-cost biometric fingerprint time-attendance machines. Citizens are encouraged to engage in shaping health services via a web-based complaints and suggestions system, and a new system to raise health awareness via public digital displays has started in Dhaka. Strong support at the highest political level has been critical to the success of efforts to introduce these innovations. The endeavour has also generated a cadre of enthusiastic eHealth proponents, who are focused on further strengthening and expanding the existing systems and on harnessing the vast amount of information amassed at the central data repository through big data analysis, artificial intelligence and machine learning.

}, issn = {2304-5272}, doi = {10.4103/2224-3151.264849}, author = {Khan, Muhammad Abdul Hannan and Cruz, Valeria de Oliveira and Azad, Abul Kalam} } @article {1277, title = {Barriers and facilitators to Electronic Medical Records usage in the Emergency Centre at Komfo Anokye Teaching Hospital, Kumasi-Ghana.}, journal = {Afr J Emerg Med}, volume = {7}, year = {2017}, month = {2017 Dec}, pages = {177-182}, abstract = {

Introduction: The use of paper for record keeping (or a manual system) has been the order of the day in almost all health care facilities in resource poor countries. This system has presented numerous challenges, which the use of Electronic Medical Records (EMR) seeks to address. The objectives of the study were to identify the facilitators and barriers to EMR implementation in Komfo Anokye Teaching Hospital{\textquoteright}s (KATH) Emergency Centre (EC) and to identify lessons learned. These will help in implementation of EMR in ECs in similar settings.

Methods: This was a non-interventional, descriptive cross-sectional and purely qualitative study using a semi-structured interview guide for a study population of 24. The interviews were manually recorded and analysed thematically. EMR implementation was piloted in the EC. Some of the EC staff doubled as EMR personnel. An open source EMR was freely downloaded and customised to meet the needs of the EC. The EMR database created was a hybrid one comprising of digital bio-data of patients and scanned copies of their paper EC records.

Results: The facilitators for utilising the system included providing training to staff, the availability of some logistics, and the commitment of staff. The project barriers were funding, full-time information technology expertise, and automatic data and power backups. It was observed that with the provision of adequate human and financial resources, the challenges were overcome and the adoption of the EMR improved.

Discussion: The EMR has been a partial success. The facilitators identified in this study, namely training, provision of logistics, and staff commitment represent foundations to work from. The barriers identified could be addressed with additional funding, provision of information technology expertise, and data and power back up. It is acknowledged that lack of funding could substantially limit EMR implementation.

}, issn = {2211-4203}, doi = {10.1016/j.afjem.2017.05.002}, author = {Gyamfi, Adwoa and Mensah, Kofi A and Oduro, George and Donkor, Peter and Mock, Charles N} } @article {1157, title = {Benefits, {Challenges} and {Potential} {Strategies} of {Open} {Source} {Health} {Economic} {Models}}, journal = {PharmacoEconomics}, year = {2016}, issn = {1179-2027}, doi = {10.1007/s40273-016-0479-8}, author = {Dunlop, William C. N. and Mason, Nicola and Kenworthy, James and Akehurst, Ron L.} } @inbook {1119, title = {BiobankCloud: A Platform for the Secure Storage, Sharing, and Processing of Large Biomedical Data Sets}, booktitle = {Biomedical Data Management and Graph Online Querying: VLDB 2015 Workshops, Big-O(Q) and DMAH, Waikoloa, HI, USA, August 31 {\textendash} September 4, 2015, Revised Selected Papers}, year = {2016}, pages = {89{\textendash}105}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {Biobanks store and catalog human biological material that is increasingly being digitized using next-generation sequencing (NGS). There is, however, a computational bottleneck, as existing software systems are not scalable and secure enough to store and process the incoming wave of genomic data from NGS machines. In the BiobankCloud project, we are building a Hadoop-based platform for the secure storage, sharing, and parallel processing of genomic data. We extended Hadoop to include support for multi-tenant studies, reduced storage requirements with erasure coding, and added support for extensible and consistent metadata. On top of Hadoop, we built a scalable scientific workflow engine featuring a proper workflow definition language focusing on simple integration and chaining of existing tools, adaptive scheduling on Apache Yarn, and support for iterative dataflows. Our platform also supports the secure sharing of data across different, distributed Hadoop clusters. The software is easily installed and comes with a user-friendly web interface for running, managing, and accessing data sets behind a secure 2-factor authentication. Initial tests have shown that the engine scales well to dozens of nodes. The entire system is open-source and includes pre-defined workflows for popular tasks in biomedical data analysis, such as variant identification, differential transcriptome analysis using RNA-Seq, and analysis of miRNA-Seq and ChIP-Seq data.}, isbn = {978-3-319-41576-5}, doi = {10.1007/978-3-319-41576-5_7}, url = {http://dx.doi.org/10.1007/978-3-319-41576-5_7}, author = {Bessani, Alysson and Brandt, J{\"o}rgen and Bux, Marc and Cogo, Vinicius and Dimitrova, Lora and Dowling, Jim and Gholami, Ali and Hakimzadeh, Kamal and Hummel, Micheal and Ismail, Mahmoud and Laure, Erwin and Leser, Ulf and Litton, Jan-Eric and Martinez, Roxanna and Niazi, Salman and Reichel, Jane and Zimmermann, Karin}, editor = {Wang, Fusheng and Luo, Gang and Weng, Chunhua and Khan, Arijit and Mitra, Prasenjit and Yu, Cong} } @article {795, title = {BioImageXD: an open, general-purpose and high-throughput image-processing platform.}, journal = {Nat Methods}, volume = {9}, year = {2012}, month = {2012}, pages = {683-9}, abstract = {BioImageXD puts open-source computer science tools for three-dimensional visualization and analysis into the hands of all researchers, through a user-friendly graphical interface tuned to the needs of biologists. BioImageXD has no restrictive licenses or undisclosed algorithms and enables publication of precise, reproducible and modifiable workflows. It allows simple construction of processing pipelines and should enable biologists to perform challenging analyses of complex processes. We demonstrate its performance in a study of integrin clustering in response to selected inhibitors.}, issn = {1548-7105}, doi = {10.1038/nmeth.2047}, author = {Kankaanp{\"a}{\"a}, Pasi and Paavolainen, Lassi and Tiitta, Silja and Karjalainen, Mikko and P{\"a}iv{\"a}rinne, Joacim and Nieminen, Jonna and Marjom{\"a}ki, Varpu and Heino, Jyrki and White, Daniel J} } @article {776, title = {Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: The SHARPn project.}, journal = {J Biomed Inform}, year = {2012}, month = {2012 Feb 4}, abstract = {The Strategic Health IT Advanced Research Projects (SHARP) Program, established by the Office of the National Coordinator for Health Information Technology in 2010 supports research findings that remove barriers for increased adoption of health IT. The improvements envisioned by the SHARP Area 4 Consortium (SHARPn) will enable the use of the electronic health record (EHR) for secondary purposes, such as care process and outcomes improvement, biomedical research and epidemiologic monitoring of the nation{\textquoteright}s health. One of the primary informatics problem areas in this endeavor is the standardization of disparate health data from the nation{\textquoteright}s many health care organizations and providers. The SHARPn team is developing open source services and components to support the ubiquitous exchange, sharing and reuse or {\textquoteright}liquidity{\textquoteright} of operational clinical data stored in electronic health records. One year into the design and development of the SHARPn framework, we demonstrated end to end data flow and a prototype SHARPn platform, using thousands of patient electronic records sourced from two large healthcare organizations: Mayo Clinic and Intermountain Healthcare. The platform was deployed to (1) receive source EHR data in several formats, (2) generate structured data from EHR narrative text, and (3) normalize the EHR data using common detailed clinical models and Consolidated Health Informatics standard terminologies, which were (4) accessed by a phenotyping service using normalized data specifications. The architecture of this prototype SHARPn platform is presented. The EHR data throughput demonstration showed success in normalizing native EHR data, both structured and narrative, from two independent organizations and EHR systems. Based on the demonstration, observed challenges for standardization of EHR data for interoperable secondary use are discussed.}, issn = {1532-0480}, doi = {10.1016/j.jbi.2012.01.009}, author = {Rea, Susan and Pathak, Jyotishman and Savova, Guergana and Oniki, Thomas A and Westberg, Les and Beebe, Calvin E and Tao, Cui and Parker, Craig G and Haug, Peter J and Huff, Stanley M and Chute, Christopher G} } @article {575, title = {Brainstorm: A User-Friendly Application for MEG/EEG Analysis}, journal = {Computational Intelligence and Neuroscience}, volume = {2011}, year = {2011}, month = {01/2011}, abstract = {Brainstorm is a collaborative open-source application dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data. The primary objective of the software is to connect MEG/EEG neuroscience investigators with both the best-established and cutting-edge methods through a simple and intuitive graphical user interface (GUI).}, doi = {10.1155/2011/879716}, url = {http://www.hindawi.com/journals/cin/2011/879716/}, author = {Fran{\c c}ois Tadel and Sylvain Baillet and John C. Mosher and Dimitrios Pantazis} } @article {531, title = {Building a open source framework for virtual medical training.}, journal = {Journal of digital imaging : the official journal of the Society for Computer Applications in Radiology}, volume = {23}, year = {2010}, month = {2010 Dec}, pages = {706-20}, abstract = {This paper presents a framework to build medical training applications by using virtual reality and a tool that helps the class instantiation of this framework. The main purpose is to make easier the building of virtual reality applications in the medical training area, considering systems to simulate biopsy exams and make available deformation, collision detection, and stereoscopy functionalities. The instantiation of the classes allows quick implementation of the tools for such a purpose, thus reducing errors and offering low cost due to the use of open source tools. Using the instantiation tool, the process of building applications is fast and easy. Therefore, computer programmers can obtain an initial application and adapt it to their needs. This tool allows the user to include, delete, and edit parameters in the functionalities chosen as well as storing these parameters for future use. In order to verify the efficiency of the framework, some case studies are presented.}, issn = {1618-727X}, author = {de Oliveira, Ana Cl{\'a}udia Melo Tiessi Gomes and Nunes, F{\'a}tima de Lourdes dos Santos} } @article {523, title = {Barriers to open source software adoption in Quebec{\textquoteright}s health care organizations.}, journal = {Journal of medical systems}, volume = {33}, year = {2009}, month = {2009 Feb}, pages = {1-7}, abstract = {We conducted in-depth interviews with 15 CIOs to identify the principal impediments to adoption of open source software in the Quebec health sector. We found that key factors for not adopting an open source solution were closely linked to the orientations of ministry level policy makers and a seeming lack of information on the part of operational level IT managers concerning commercially oriented open source providers. We use the case of recent changes in the structure of Quebec{\textquoteright}s health care organizations and a change in the commercial policies of a key vendor to illustrate our conclusions regarding barriers to adoption of open source products.}, keywords = {Attitude of Health Personnel, Delivery of Health Care, Health Facility Administrators, Health Policy, Humans, Medical Informatics, Organizational Innovation, Quebec, Software, Systems Integration, Technology Transfer}, issn = {0148-5598}, author = {Par{\'e}, Guy and Wybo, Michael D and Delannoy, Charles} } @article {499, title = {Bayesian analysis of neuroimaging data in FSL.}, journal = {NeuroImage}, volume = {45}, year = {2009}, month = {2009 Mar}, pages = {S173-86}, abstract = {Typically in neuroimaging we are looking to extract some pertinent information from imperfect, noisy images of the brain. This might be the inference of percent changes in blood flow in perfusion FMRI data, segmentation of subcortical structures from structural MRI, or inference of the probability of an anatomical connection between an area of cortex and a subthalamic nucleus using diffusion MRI. In this article we will describe how Bayesian techniques have made a significant impact in tackling problems such as these, particularly in regards to the analysis tools in the FMRIB Software Library (FSL). We shall see how Bayes provides a framework within which we can attempt to infer on models of neuroimaging data, while allowing us to incorporate our prior belief about the brain and the neuroimaging equipment in the form of biophysically informed or regularising priors. It allows us to extract probabilistic information from the data, and to probabilistically combine information from multiple modalities. Bayes can also be used to not only compare and select between models of different complexity, but also to infer on data using committees of models. Finally, we mention some analysis scenarios where Bayesian methods are impractical, and briefly discuss some practical approaches that we have taken in these cases.}, keywords = {Bayes Theorem, Brain, Diffusion Magnetic Resonance Imaging, Humans, Image Interpretation, Computer-Assisted, Software}, issn = {1095-9572}, author = {Woolrich, Mark W and Jbabdi, Saad and Patenaude, Brian and Chappell, Michael and Makni, Salima and Behrens, Timothy and Beckmann, Christian and Jenkinson, Mark and Smith, Stephen M} } @conference {1084, title = {Building the Free/Libre Open Source Health Care (FLOSS-HC) Community: A Strategy for Pushing Free/Libre Open Source in European Health Care}, booktitle = {Proceedings of the 1st International Workshop on Open Source in European Health Care: The Time is Ripe (BIOSTEC 2009)}, year = {2009}, isbn = {978-989-8111-79-1}, doi = {10.5220/0001828100030007}, author = {Thomas Karopka} } @article {17917780, title = {Benefits of using the DCM4CHE DICOM archive.}, journal = {Journal of digital imaging : the official journal of the Society for Computer Applications in Radiology}, volume = {20 Suppl 1}, year = {2007}, month = {2007 Nov}, pages = {125-9}, url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2039778}, author = {Warnock, Max J and Toland, Christopher and Evans, Damien and Wallace, Bill and Nagy, Paul} } @article {18238234, title = {BioSig: an imaging bioinformatics system for phenotypic analysis.}, journal = {IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society}, volume = {33}, year = {2003}, month = {2003}, pages = {814-24}, abstract = {Organisms express their genomes in a cell-specific manner, resulting in a variety of cellular phenotypes or phenomes. Mapping cell phenomes under a variety of experimental conditions is necessary in order to understand the responses of organisms to stimuli. Representing such data requires an integrated view of experimental and informatic protocols. The proposed system, named BioSig, provides the foundation for cataloging cellular responses as a function of specific conditioning, treatment, staining, etc., for either fixed tissue or living cell studies. A data model has been developed to capture experimental variables and map them to image collections and their computed representation. This representation is hierarchical and spans across sample tissues, cells, and organelles, which are imaged with light microscopy. At each layer, content is represented with an attributed graph, which contains information about cellular morphology, protein localization, and cellular organization in tissue or cell culture. The Web-based multilayer informatics architecture uses the data model to provide guided workflow access for content exploration.}, author = {Parvin, B and Yang, Qing and Fontenay, G and Barcellos-Hoff, M H} }