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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 16–22:</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: September 13–19:</h2> | ||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Navale F1000Research2020 8.gif|240px]]</div> | |||
'''"[[Journal:Development of an informatics system for accelerating biomedical research|Development of an informatics system for accelerating biomedical research]]"''' | |||
The Biomedical Research Informatics Computing System (BRICS) was developed to support multiple disease-focused research programs. Seven service modules are integrated together to provide a collaborative and extensible web-based environment. The modules—Data Dictionary, Account Management, Query Tool, Protocol and Form Research Management System, Meta Study, Data Repository, and Globally Unique Identifier—facilitate the management of research protocols, including the submission, processing, curation, access, and storage of clinical, imaging, and derived [[genomics]] data within the associated data repositories. Multiple instances of BRICS are deployed to support various biomedical research communities focused on accelerating discoveries for rare diseases, traumatic brain injuries, Parkinson’s disease, inherited eye diseases, and symptom science research. No personally identifiable [[information]] is stored within the data repositories. Digital object identifiers (DOIs) are associated with the research studies. ('''[[Journal:Development of an informatics system for accelerating biomedical 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: September 6–12:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Cicek FrontPubHealth2020 8.jpg|240px]]</div> | |||
'''"[[Journal:Mini-review of laboratory operations in biobanking: Building biobanking resources for translational research|Mini-review of laboratory operations in biobanking: Building biobanking resources for translational research]]"''' | |||
[[Biobank]]s have become integral to improving [[Public health|population health]]. We are in a new era in medicine as patients, health professionals, and researchers increasingly collaborate to gain new knowledge and explore new paradigms for diagnosing and treating disease. Many large-scale biobanking efforts are underway worldwide at the institutional, national, and even international level. When linked with subject data from questionnaires and medical records, biobanks serve as valuable resources in [[translational research]]. A biobank must have high-quality biospecimens that meet researcher's needs. Biobank [[laboratory]] operations require an enormous amount of support, from lab and storage space, information technology expertise, and a [[laboratory information management system]] to logistics for biospecimen tracking, [[quality management system]]s, and appropriate facilities. ('''[[Journal:Mini-review of laboratory operations in biobanking: Building biobanking resources for translational 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 30–September 5:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Dixon BMJHealthCareInfo2020 27-1.png|240px]]</div> | |||
'''"[[Journal:Extending an open-source tool to measure data quality: Case report on Observational Health Data Science and Informatics (OHDSI)|Extending an open-source tool to measure data quality: Case report on Observational Health Data Science and Informatics (OHDSI)]]"''' | |||
As the health system seeks to leverage large-scale data to inform population outcomes, the [[Informatics (academic field)|informatics]] community is developing tools for analyzing these data. To support [[data quality]] assessment within such a tool, we extended the open-source software Observational Health Data Sciences and Informatics (OHDSI) to incorporate new functions useful for population health. We developed and tested methods to measure the completeness, timeliness, and entropy of [[information]]. The new data quality methods were applied to over 100 million clinical messages received from emergency department information systems for use in [[Public health informatics|public health syndromic surveillance systems]]. ('''[[Journal:Extending an open-source tool to measure data quality: Case report on Observational Health Data Science and Informatics (OHDSI)|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 23–29:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Raymond BMCMedInfoDecMak2020 20.png|240px]]</div> | |||
'''"[[Journal:Advancing laboratory medicine in hospitals through health information exchange: A survey of specialist physicians in Canada|Advancing laboratory medicine in hospitals through health information exchange: A survey of specialist physicians in Canada]]"''' | |||
[[Laboratory]] testing occupies a prominent place in healthcare. Information technology systems have the potential to empower laboratory experts and to enhance the interpretation of test results in order to better support physicians in their quest for better and safer patient care. This study sought to develop a better understanding of which laboratory information exchange (LIE) systems and features specialist physicians are using in [[hospital]] settings to consult their patients’ laboratory test results, and what benefit they derive from such use. As part of a broader research program on the use of [[health information exchange]] systems for laboratory medicine in Quebec, Canada, this study was designed as on online survey. Our sample is composed of 566 specialist physicians working in hospital settings, out of the 1,512 physicians who responded to the survey (response rate of 17%). Respondents are representative of the targeted population of specialist physicians in terms of gender, age, and hospital location. ('''[[Journal:Advancing laboratory medicine in hospitals through health information exchange: A survey of specialist physicians in Canada|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 16–22:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Krill Metabolites2020 10-7.png|240px]]</div> | <div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Krill Metabolites2020 10-7.png|240px]]</div> | ||
'''"[[Journal:A high-throughput method for the comprehensive analysis of terpenes and terpenoids in medicinal cannabis biomass|A high-throughput method for the comprehensive analysis of terpenes and terpenoids in medicinal cannabis biomass]]"''' | '''"[[Journal:A high-throughput method for the comprehensive analysis of terpenes and terpenoids in medicinal cannabis biomass|A high-throughput method for the comprehensive analysis of terpenes and terpenoids in medicinal cannabis biomass]]"''' |
Revision as of 17:50, 21 September 2021
If you're looking for other "Article of the Week" archives: 2014 - 2015 - 2016 - 2017 - 2018 - 2019 - 2020 - 2021 |
Featured article of the week archive - 2021
Welcome to the LIMSwiki 2021 archive for the Featured Article of the Week.
Featured article of the week: September 13–19:"Development of an informatics system for accelerating biomedical research" The Biomedical Research Informatics Computing System (BRICS) was developed to support multiple disease-focused research programs. Seven service modules are integrated together to provide a collaborative and extensible web-based environment. The modules—Data Dictionary, Account Management, Query Tool, Protocol and Form Research Management System, Meta Study, Data Repository, and Globally Unique Identifier—facilitate the management of research protocols, including the submission, processing, curation, access, and storage of clinical, imaging, and derived genomics data within the associated data repositories. Multiple instances of BRICS are deployed to support various biomedical research communities focused on accelerating discoveries for rare diseases, traumatic brain injuries, Parkinson’s disease, inherited eye diseases, and symptom science research. No personally identifiable information is stored within the data repositories. Digital object identifiers (DOIs) are associated with the research studies. (Full article...)
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