@article {1313, title = {Open-source electronic health record systems: A systematic review of most recent advances.}, journal = {Health Informatics J}, volume = {28}, year = {2022}, month = {2022 Apr-Jun}, pages = {14604582221099828}, abstract = {

Open-source Electronic Health Records (OS-EHRs) are of pivotal importance in the management, operations, and administration of any healthcare organization. With the advancement of health informatics, researchers and healthcare practitioners have proposed various frameworks to assess the maturation of Open-source EHRs. The significance of OS-EHRs stems from the fact that vendor-based EHR implementations are becoming financially burdensome, with some vendors raking in more than $1 billion with one contract. Contrarily, the adoption of OS-EHRs suffers from a lack of systematic evaluation from the standpoint of a standard reference model. To this end, the Healthcare Information and Management Systems Society (HIMSS) has presented a strategic road map called EMR Adoption and Maturity (EMRAM). The HIMSS-EMRAM model proposes a stage-wise model approach that is globally recognized and can be essentially applied as a benchmark evaluation criteria for open-source EHRs. This paper offers an applied descriptive methodology over the frequently studied open-source EHRs currently operational worldwide or has the potential of adoption in healthcare settings. Besides, we also present profiling (User Support, Developer{\textquoteright} Support, Customization Support, Technical details, and Diagnostic help) of studied OS-EHRs from developer{\textquoteright}s and user{\textquoteright}s perspectives using updated standard metrics. We carried out multi-aspect objective analysis of studied systems covering EHR functions, software based features and implementation. This review portrays systematic aspects of electronic medical record standards for open-source software implementations. As we observed in the literature, prevalent research and working prototypes lack systematic review of the HIMSS-EMRAM model and do not present evolving software features. Therefore, after the application of our assessment measures, the results obtained indicate that OS-EHRs are yet to acquire standard compliance and implementation. The findings in this paper can be beneficial in the planning and implementation of OS-EHRs projects in the future.

}, keywords = {electronic health records, Humans, Medical Informatics, Publications, Software}, issn = {1741-2811}, doi = {10.1177/14604582221099828}, author = {Shaikh, Mohsin and Vayani, Arshad Hm and Akram, Sabina and Qamar, Nafees} } @article {1166, title = {Open {Source} {Paradigm}: {A} {Synopsis} of {The} {Cathedral} and the {Bazaar} for {Health} and {Social} {Care}}, journal = {Journal of Innovation in Health Informatics}, volume = {23}, year = {2016}, pages = {488{\textendash}492}, abstract = {BACKGROUND: Open source software (OSS) is becoming more fashionable in health and social care, although the ideas are not new. However progress has been slower than many had expected. OBJECTIVE: The purpose is to summarise the Free/Libre Open Source Software (FLOSS) paradigm in terms of what it is, how it impacts users and software engineers and how it can work as a business model in health and social care sectors. METHOD: Much of this paper is a synopsis of Eric Raymond{\textquoteright}s seminal book The Cathedral and the Bazaar, which was the first comprehensive description of the open source ecosystem, set out in three long essays. Direct quotes from the book are used liberally, without reference to specific passages. The first part contrasts open and closed source approaches to software development and support. The second part describes the culture and practices of the open source movement. The third part considers business models. CONCLUSION: A key benefit of open source is that users can access and collaborate on improving the software if they wish. Closed source code may be regarded as a strategic business risk that that may be unacceptable if there is an open source alternative. The sharing culture of the open source movement fits well with that of health and social care.}, keywords = {Delivery of Health Care., Medical Informatics, Open Access to Information, Software}, issn = {2058-4563}, author = {Benson, Tim} } @article {1292, title = {Innovative Technological Approach to Ebola Virus Disease Outbreak Response in Nigeria Using the Open Data Kit and Form Hub Technology.}, journal = {PLoS One}, volume = {10}, year = {2015}, month = {2015}, pages = {e0131000}, abstract = {

The recent outbreak of Ebola Virus Disease (EVD) in West Africa has ravaged many lives. Effective containment of this outbreak relies on prompt and effective coordination and communication across various interventions; early detection and response being critical to successful control. The use of information and communications technology (ICT) in active surveillance has proved to be effective but its use in Ebola outbreak response has been limited. Due to the need for timeliness in reporting and communication for early discovery of new EVD cases and promptness in response; it became imperative to empower the response team members with technologies and solutions which would enable smooth and rapid data flow. The Open Data Kit and Form Hub technology were used in combination with the Dashboard technology and ArcGIS mapping for follow up of contacts, identification of cases, case investigation and management and also for strategic planning during the response. A remarkable improvement was recorded in the reporting of daily follow-up of contacts after the deployment of the integrated real time technology. The turnaround time between identification of symptomatic contacts and evacuation to the isolation facility and also for receipt of laboratory results was reduced and informed decisions could be taken by all concerned. Accountability in contact tracing was ensured by the use of a GPS enabled device. The use of innovative technologies in the response of the EVD outbreak in Nigeria contributed significantly to the prompt control of the outbreak and containment of the disease by providing a valuable platform for early warning and guiding early actions.

}, keywords = {Disease Outbreaks, Ebolavirus, Epidemiological Monitoring, Female, Hemorrhagic Fever, Ebola, Humans, Male, Medical Informatics, Nigeria}, issn = {1932-6203}, doi = {10.1371/journal.pone.0131000}, author = {Tom-Aba, Daniel and Olaleye, Adeniyi and Olayinka, Adebola Tolulope and Nguku, Patrick and Waziri, Ndadilnasiya and Adewuyi, Peter and Adeoye, Olawunmi and Oladele, Saliu and Adeseye, Aderonke and Oguntimehin, Olukayode and Shuaib, Faisal} } @article {1051, title = {High dimensional biological data retrieval optimization with NoSQL technology.}, journal = {BMC Genomics}, volume = {15 Suppl 8}, year = {2014}, month = {2014}, pages = {S3}, abstract = {

BACKGROUND: High-throughput transcriptomic data generated by microarray experiments is the most abundant and frequently stored kind of data currently used in translational medicine studies. Although microarray data is supported in data warehouses such as tranSMART, when querying relational databases for hundreds of different patient gene expression records queries are slow due to poor performance. Non-relational data models, such as the key-value model implemented in NoSQL databases, hold promise to be more performant solutions. Our motivation is to improve the performance of the tranSMART data warehouse with a view to supporting Next Generation Sequencing data.

RESULTS: In this paper we introduce a new data model better suited for high-dimensional data storage and querying, optimized for database scalability and performance. We have designed a key-value pair data model to support faster queries over large-scale microarray data and implemented the model using HBase, an implementation of Google{\textquoteright}s BigTable storage system. An experimental performance comparison was carried out against the traditional relational data model implemented in both MySQL Cluster and MongoDB, using a large publicly available transcriptomic data set taken from NCBI GEO concerning Multiple Myeloma. Our new key-value data model implemented on HBase exhibits an average 5.24-fold increase in high-dimensional biological data query performance compared to the relational model implemented on MySQL Cluster, and an average 6.47-fold increase on query performance on MongoDB.

CONCLUSIONS: The performance evaluation found that the new key-value data model, in particular its implementation in HBase, outperforms the relational model currently implemented in tranSMART. We propose that NoSQL technology holds great promise for large-scale data management, in particular for high-dimensional biological data such as that demonstrated in the performance evaluation described in this paper. We aim to use this new data model as a basis for migrating tranSMART{\textquoteright}s implementation to a more scalable solution for Big Data.

}, keywords = {Database Management Systems, Databases, Genetic, High-Throughput Nucleotide Sequencing, Humans, Information Storage and Retrieval, Medical Informatics, Multiple Myeloma, Oligonucleotide Array Sequence Analysis, Transcriptome}, issn = {1471-2164}, doi = {10.1186/1471-2164-15-S8-S3}, author = {Wang, Shicai and Pandis, Ioannis and Wu, Chao and He, Sijin and Johnson, David and Emam, Ibrahim and Guitton, Florian and Guo, Yike} } @article {1055, title = {The NIH National Center for Integrative Biomedical Informatics (NCIBI).}, journal = {J Am Med Inform Assoc}, volume = {19}, year = {2012}, month = {2012 Mar-Apr}, pages = {166-70}, abstract = {

The National Center for Integrative and Biomedical Informatics (NCIBI) is one of the eight NCBCs. NCIBI supports information access and data analysis for biomedical researchers, enabling them to build computational and knowledge models of biological systems to address the Driving Biological Problems (DBPs). The NCIBI DBPs have included prostate cancer progression, organ-specific complications of type 1 and 2 diabetes, bipolar disorder, and metabolic analysis of obesity syndrome. Collaborating with these and other partners, NCIBI has developed a series of software tools for exploratory analysis, concept visualization, and literature searches, as well as core database and web services resources. Many of our training and outreach initiatives have been in collaboration with the Research Centers at Minority Institutions (RCMI), integrating NCIBI and RCMI faculty and students, culminating each year in an annual workshop. Our future directions include focusing on the TranSMART data sharing and analysis initiative.

}, keywords = {Biomedical Research, Databases as Topic, Forecasting, Goals, Information Dissemination, Integrative Medicine, Medical Informatics, National Institutes of Health (U.S.), United States}, issn = {1527-974X}, doi = {10.1136/amiajnl-2011-000552}, author = {Athey, Brian D and Cavalcoli, James D and Jagadish, H V and Omenn, Gilbert S and Mirel, Barbara and Kretzler, Matthias and Burant, Charles and Isokpehi, Raphael D and DeLisi, Charles} } @article {1029, title = {A partnership approach for Electronic Data Capture in small-scale clinical trials.}, journal = {J Biomed Inform}, volume = {44 Suppl 1}, year = {2011}, month = {2011 Dec}, pages = {S103-8}, abstract = {

Amid researchers{\textquoteright} growing need for study data management, the CTSA-funded Institute for Translational Health Sciences developed an approach to combine technical and scientific resources with small-scale clinical trials researchers in order to make Electronic Data Capture more efficient. In a 2-year qualitative evaluation we found that the importance of ease of use and training materials outweighed number of features and functionality. EDC systems we evaluated were Catalyst Web Tools, OpenClinica and REDCap. We also found that two other systems, Caisis and LabKey, did not meet the specific user needs of the study group.

}, keywords = {Clinical Trials as Topic, Data Collection, Humans, Internet, Medical Informatics}, issn = {1532-0480}, doi = {10.1016/j.jbi.2011.05.008}, author = {Franklin, Joshua D and Guidry, Alicia and Brinkley, James F} } @article {1028, title = {On selecting a clinical trial management system for large scale, multi-centre, multi-modal clinical research study.}, journal = {Stud Health Technol Inform}, volume = {168}, year = {2011}, month = {2011}, pages = {89-95}, abstract = {

Clinical research studies offer many challenges for their supporting information systems. AIBL assembled 1112 participants who volunteered crucial information for a comprehensive study on neurodegenerative diseases. This paper discusses the shortcomings of the clinical trial management system chosen to record the results of the study. A set of guidelines was devised and a critique of five systems ensued. OpenClinica was selected as the most appropriate option. The main contribution of this paper is: (i) proposing a set of guidelines to determine the appropriateness of Clinical Trial Management Systems (CTMS) solution; (ii) providing a brief critique of existing commercial and open-sourced CTMS; and (iii) alluding to some data migration issues and providing cues on how to address them. We conclude that open-source CTMS are viable alternatives to the more expensive commercial systems to conduct, record and manage clinical studies.

}, keywords = {Australia, Checklist, Choice Behavior, Clinical Trials as Topic, Decision Support Systems, Clinical, Medical Informatics, Multicenter Studies as Topic}, issn = {0926-9630}, author = {Leroux, Hugo and McBride, Simon and Gibson, Simon} } @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 {873, title = {The caBIG terminology review process.}, journal = {J Biomed Inform}, volume = {42}, year = {2009}, month = {2009 Jun}, pages = {571-80}, abstract = {

The National Cancer Institute (NCI) is developing an integrated biomedical informatics infrastructure, the cancer Biomedical Informatics Grid (caBIG), to support collaboration within the cancer research community. A key part of the caBIG architecture is the establishment of terminology standards for representing data. In order to evaluate the suitability of existing controlled terminologies, the caBIG Vocabulary and Data Elements Workspace (VCDE WS) working group has developed a set of criteria that serve to assess a terminology{\textquoteright}s structure, content, documentation, and editorial process. This paper describes the evolution of these criteria and the results of their use in evaluating four standard terminologies: the Gene Ontology (GO), the NCI Thesaurus (NCIt), the Common Terminology for Adverse Events (known as CTCAE), and the laboratory portion of the Logical Objects, Identifiers, Names and Codes (LOINC). The resulting caBIG criteria are presented as a matrix that may be applicable to any terminology standardization effort.

}, keywords = {Medical Informatics, National Institutes of Health (U.S.), Terminology as Topic, United States}, issn = {1532-0480}, doi = {10.1016/j.jbi.2008.12.003}, author = {Cimino, James J and Hayamizu, Terry F and Bodenreider, Olivier and Davis, Brian and Stafford, Grace A and Ringwald, Martin} } @article {872, title = {Metadata mapping and reuse in caBIG.}, journal = {BMC Bioinformatics}, volume = {10 Suppl 2}, year = {2009}, month = {2009}, pages = {S4}, abstract = {

BACKGROUND: This paper proposes that interoperability across biomedical databases can be improved by utilizing a repository of Common Data Elements (CDEs), UML model class-attributes and simple lexical algorithms to facilitate the building domain models. This is examined in the context of an existing system, the National Cancer Institute (NCI){\textquoteright}s cancer Biomedical Informatics Grid (caBIG). The goal is to demonstrate the deployment of open source tools that can be used to effectively map models and enable the reuse of existing information objects and CDEs in the development of new models for translational research applications. This effort is intended to help developers reuse appropriate CDEs to enable interoperability of their systems when developing within the caBIG framework or other frameworks that use metadata repositories.

RESULTS: The Dice (di-grams) and Dynamic algorithms are compared and both algorithms have similar performance matching UML model class-attributes to CDE class object-property pairs. With algorithms used, the baselines for automatically finding the matches are reasonable for the data models examined. It suggests that automatic mapping of UML models and CDEs is feasible within the caBIG framework and potentially any framework that uses a metadata repository.

CONCLUSION: This work opens up the possibility of using mapping algorithms to reduce cost and time required to map local data models to a reference data model such as those used within caBIG. This effort contributes to facilitating the development of interoperable systems within caBIG as well as other metadata frameworks. Such efforts are critical to address the need to develop systems to handle enormous amounts of diverse data that can be leveraged from new biomedical methodologies.

}, keywords = {Algorithms, Computational Biology, Database Management Systems, Databases, Factual, Medical Informatics, Software, User-Computer Interface}, issn = {1471-2105}, doi = {10.1186/1471-2105-10-S2-S4}, author = {Kunz, Isaac and Lin, Ming-Chin and Frey, Lewis} }