Conquery: an Open Source Application to analyze High Content Healthcare Data (Preprint)

2021 ◽  
Author(s):  
Fabian Kovacs ◽  
Max Thonagel ◽  
Marion Ludwig ◽  
Alexander Albrecht ◽  
Manuel Hegner ◽  
...  
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Medical Records ◽  
Healthcare Sector ◽  
Study Cohort ◽  
Decision Making Processes ◽  
Analytical Approaches

BACKGROUND Big data in healthcare must be exploited to achieve a substantial increase in efficiency and competitiveness. Especially the analysis of patient-related data possesses huge potential to improve decision-making processes. However, most analytical approaches used today are highly time- and resource-consuming. OBJECTIVE The presented software solution Conquery is an open-source software tool providing advanced, but intuitive data analysis without the need for specialized statistical training. Conquery aims to simplify big data analysis for novice database users in the medical sector. METHODS Conquery is a document-oriented distributed timeseries database and analysis platform. Its main application is the analysis of per-person medical records by non-technical medical professionals. Complex analyses are realized in the Conquery frontend by dragging tree nodes into the query editor. Queries are evaluated by a bespoke distributed query-engine for medical records in a column-oriented fashion. We present a custom compression scheme to facilitate low response times that uses online calculated as well as precomputed metadata and data statistics. RESULTS Conquery allows for easy navigation through the hierarchy and enables complex study cohort construction whilst reducing the demand on time and resources. The UI of Conquery and a query output is exemplified by the construction of a relevant clinical cohort. CONCLUSIONS Conquery is an efficient and intuitive open-source software for performant and secure data analysis and aims at supporting decision-making processes in the healthcare sector.

scholarly journals Better Antimicrobial Resistance Data Analysis & Reporting in Less Time

2021 ◽  
Author(s):  
Christian F Luz ◽  
Matthijs S Berends ◽  
Xuewei W Zhou ◽  
Mariette Lokate ◽  
Alexander W Friedrich ◽  
...  
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Antimicrobial Resistance ◽  
Open Source ◽  
Open Source Software ◽  
Clinical Setting ◽  
Software Tools ◽  
Care Hospital ◽  
Test Results ◽  
Decision Making Processes

Background: Insights and knowledge about local antimicrobial resistance (AMR) levels and epidemiology are essential to guide optimal decision-making processes in antimicrobial use. However, dedicated tools for reliable and reproducible AMR data analysis and reporting are often lacking. In this study, we aimed at comparing the effectiveness and efficiency of traditional analysis and reporting versus a new approach for reliable and reproducible AMR data analysis in a clinical setting. Methods: Ten professionals that routinely work with AMR data were recruited and provided with one year's blood culture test results from a tertiary care hospital results including antimicrobial susceptibility test results. Participants were asked to perform a detailed AMR data analysis in a two-round process: first using their analysis software of choice and next using previously developed open-source software tools. Accuracy of the results and time spent were compared between the both rounds. Finally, participants rated the usability of the tools using the systems usability scale (SUS). Results: The mean time spent on creating a comprehensive AMR report reduced from 93.7 (SD ±21.6) minutes to 22.4 (SD ±13.7) minutes (p < 0.001). Average task completion per round changed from 56% (SD: ±23%) to 96% (SD: ±5.5%) (p<0.05). The proportion of correct answers in the available results increased from 37.9% in the first to 97.9% in the second round (p < 0.001). The usability of the new tools was rated with a median of 83.8 (out of 100) on the SUS. Conclusion: This study demonstrated the significant improvement in efficiency and accuracy in standard AMR data analysis and reporting workflows through open-source software tools in a clinical setting. Integrating these tools in clinical settings can democratise the access to fast and reliable insights about local microbial epidemiology and associated AMR levels. Thereby, our approach can support evidence-based decision-making processes in the use of antimicrobials.

Commercial and Open Source Business Intelligence Platforms for Big Data Warehousing

2019 ◽  
pp. 158-181 ◽  
Author(s):  
Jorge Bernardino ◽  
Joaquim Lapa ◽  
Ana Almeida
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Comparative Analysis ◽  
Open Source ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Selection Process ◽  
Decision Making Processes ◽  
Big Data Warehouse

A big data warehouse enables the analysis of large amounts of information that typically comes from the organization's transactional systems (OLTP). However, today's data warehouse systems do not have the capacity to handle the massive amount of data that is currently produced. Business intelligence (BI) is a collection of decision support technologies that enable executives, managers, and analysts to make better and faster decisions. Organizations must make good use of business intelligence platforms to quickly acquire desirable information from the huge volume of data to reduce the time and increase the efficiency of decision-making processes. In this chapter, the authors present a comparative analysis of commercial and open source BI tools capabilities, in order to aid organizations in the selection process of the most suitable BI platform. They also evaluated and compared six major open source BI platforms: Actuate, Jaspersoft, Jedox/Palo, Pentaho, SpagoBI, and Vanilla; and six major commercial BI platforms: IBM Cognos, Microsoft BI, MicroStrategy, Oracle BI, SAP BI, and SAS BI & Analytics.

scholarly journals Towards Implementation of Big Data Concepts in a Pharmaceutical Company

2020 ◽  
Vol 10 (1) ◽  
pp. 343-356
Author(s):  
Snezana Savoska ◽  
Blagoj Ristevski
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Data Analysis ◽  
Pharmaceutical Company ◽  
Big Data Analytics ◽  
Structured Interviews ◽  
Holistic View ◽  
Analysis Methodology ◽  
Decision Making Processes ◽  
Research Demands

AbstractNowadays, big data is a widely utilized concept that has been spreading quickly in almost every domain. For pharmaceutical companies, using this concept is a challenging task because of the permanent pressure and business demands created through the legal requirements, research demands and standardization that have to be adopted. These legal and standards’ demands are associated with human healthcare safety and drug control that demands continuous and deep data analysis. Companies update their procedures to the particular laws, standards, market demands and regulations all the time by using contemporary information technology. This paper highlights some important aspects of the experience and change methodology used in one Macedonian pharmaceutical company, which has employed information technology solutions that successfully tackle legal and business pressures when dealing with a large amount of data. We used a holistic view and deliverables analysis methodology to gain top-down insights into the possibilities of big data analytics. Also, structured interviews with the company’s managers were used for information collection and proactive methodology with workshops was used in data integration toward the implementation of big data concepts. The paper emphasizes the information and knowledge used in this domain to improve awareness for the needs of big data analysis to achieve a competitive advantage. The main results are focused on systematizing the whole company’s data, information and knowledge and propose a solution that integrates big data to support managers’ decision-making processes.

scholarly journals Open Plot Project: an open-source toolkit for 3-D structural data analysis

Solid Earth ◽  
2011 ◽  
Vol 2 (1) ◽  
pp. 53-63 ◽  
Author(s):  
S. Tavani ◽  
P. Arbues ◽  
M. Snidero ◽  
N. Carrera ◽  
J. A. Muñoz
Keyword(s):  
Spatial Distribution ◽  
Data Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Source Code ◽  
Structural Data ◽  
Geological Modelling ◽  
Analysis Tools ◽  
Transect Analysis ◽  
Selection Of

Abstract. In this work we present the Open Plot Project, an open-source software for structural data analysis, including a 3-D environment. The software includes many classical functionalities of structural data analysis tools, like stereoplot, contouring, tensorial regression, scatterplots, histograms and transect analysis. In addition, efficient filtering tools are present allowing the selection of data according to their attributes, including spatial distribution and orientation. This first alpha release represents a stand-alone toolkit for structural data analysis. The presence of a 3-D environment with digitalising tools allows the integration of structural data with information extracted from georeferenced images to produce structurally validated dip domains. This, coupled with many import/export facilities, allows easy incorporation of structural analyses in workflows for 3-D geological modelling. Accordingly, Open Plot Project also candidates as a structural add-on for 3-D geological modelling software. The software (for both Windows and Linux O.S.), the User Manual, a set of example movies (complementary to the User Manual), and the source code are provided as Supplement. We intend the publication of the source code to set the foundation for free, public software that, hopefully, the structural geologists' community will use, modify, and implement. The creation of additional public controls/tools is strongly encouraged.

Evolution of e-participation in Greek local government

2021 ◽  
pp. 1-15
Author(s):  
Constantina Costopoulou ◽  
Maria Ntaliani ◽  
Filotheos Ntalianis
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Local Government ◽  
Local Governments ◽  
Local Community ◽  
New Technologies ◽  
Mobile Apps ◽  
Digital Technologies ◽  
Decision Making Processes ◽  
The Impact

Local governments are increasingly developing electronic participation initiatives, expecting citizen involvement in local community affairs. Our objective was to assess e-participation and the extent of its change in local government in Greece. Using content analysis for 325 Greek municipal websites, we assessed e-participation status in 2017 and 2018 and examined the impact of change between these years. The assessment regards two consecutive years since the adoption of digital technologies by municipalities has been rapid. The main findings show that Greek local governments have made significant small- to medium-scale changes, in order to engage citizens and local societies electronically. We conclude that the integration of advanced digital technologies in municipalities remains underdeveloped. We propose that Greek municipalities need to consider incorporating new technologies, such as mobile apps, social media and big data, as well as e-decision making processes, in order to eliminate those obstacles that hinder citizen engagement in local government. Moreover, the COVID-19 outbreak has highlighted the need for enhancing e-participation and policymakers’ coordination through advanced digital technologies.

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