Data Management
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2021 ◽  
Stephan Hachinger ◽  
Jan Martinovič ◽  
Olivier Terzo ◽  
Marc Levrier ◽  
Alberto Scionti ◽  

Sergey Glushakov ◽  
Volodymyr Boichuk

Many specialized positions, even entry-level, in the pharmaceutical industry require training above and beyond standard University degree programs. A shortage of specialized clinical data managers in Ukraine means private sector companies are developing internal resource training programs to deepen their pool of available candidates. Given the strong medical education system and established IT outsourcing industry, we believed developing a pool of talented clinical data managers within Ukraine was a feasible goal.The IT outsourcing industry is the second largest export service industry in Ukraine, and one of the main sectors in the economy. More than 50% of Ukraine's IT services revenue came from the United States, the rest mostly from the EU.[1] Ukraine has built a workforce adapted to IT outsourcing, but the lack of local professionals in the fields of clinical data management and clinical data science hinders similar growth in the clinical research sector. Ukraine has a well-established medical education system that trains its healthcare professionals in accordance with EU regulations. Hospitals are predominantly state-owned; the private medical sector is almost nonexistent. The academic and non-profit clinical research sectors are small in comparison to Western European countries, and opportunities for careers within them limited. This leads to a 'brain drain' of medical professionals from Ukraine to other countries in search of higher wages and professional advancement. With its strong education system and highly educated medical workforce, Ukraine is an attractive but under-utilised location for clinical studies. [2] There are approximately 30 clinical research sites in Ukraine handling preclinical through Phase IV studies. In December 2020 on there were 557 active or recruiting clinical trials listed taking place in Ukraine. Regulatory hurdles and approval timelines have greatly improved in recent years.Currently, when CROs wish to hire data managers to assist with local clinical trials in Ukraine, they have to hire non-specialists who must teach themselves on the job. At present there are no university courses or formal training programs within the country for clinical data managers.Following the success of the Clinical Statistical Programming training program developed by our team and offered since 2013 in partnership V. N. Karazin Kharkiv National University,[3] we recently launched an in-house clinical data management training program in partnership between leading Biometrics CROs Cytel and Intego Group. Upon program completion, students have the opportunity to transition into full-time employment. Ours is the first centralized training program for clinical data managers in the country. We already started a conversation with some of the country's leading universities to help them develop a formal educational program in clinical data management. Our internal training program will serve as a pilot and a proof of concept. We expect that many elements, such as curriculum, admission requirements, quality control, internships, etc., will successfully scale up in an academic environment. Our paper will discuss opportunities for the clinical data management sector in Ukraine, the challenges of recruiting data managers from the existing healthcare workforce, the region’s unique strengths, laws and regulations. We also discuss specifics of the internal training program, development of a course syllabus, and transitioning students from coursework into hands-on data management training.Article length: 8 pages. Article reference count: 9 references.---------------[1] AVentures. Software Development in Ukraine, Poland, Belarus and Romania in 2019.[2] Sinichkina L,  Smolina A,  Svintsitskyi V. Positive Changes for Clinical Trials in Ukraine. Applied Clinical Trials. December 2017.[3] Pirbhai E, Glushakov S. Development of a Clinical SAS University Training Program in Eastern Europe. PharmaSUG. 2015.

2021 ◽  
Vol 18 ◽  
pp. 100263
Jean-Laurent Hippolyte ◽  
Michael Chrubasik ◽  
Frédéric Brochu ◽  
Maurizio Bevilacqua

Ailish Daly ◽  
Seán Paul Teeling ◽  
Marie Ward ◽  
Martin McNamara ◽  
Ciara Robinson

The aim of this study was to redesign an emergency department [ED] data management system to improve the availability of, and access to, data to facilitate patient flow. A pre-/post-intervention design was employed using Lean Six Sigma methodology with a focus on the voice of the customer, Gemba, and 5S to identify areas for improvement in ED data management processes and to inform solutions for improved ED patient flow processes. A multidisciplinary ED team includes medical consultants and registrars, nurses, patient service staff, radiology staff, as well as information technology and hospital management staff. Lean Six Sigma [LSS] diagnostic tools identified areas for improvement in the current process for data availability and access. A set of improvements were implemented to redesign the pathway for data collection in the ED to improve data availability and access. We achieved a reduction in the time taken to access ED patient flow data from a mean of 9 min per patient pre-intervention to immediate post-intervention. This enabled faster decision-making by the ED team related to patient assessment and treatment and informed improvements in patient flow. Optimizing patient flow through a hospital’s ED is a complex task involving collaboration and participation from multiple disciplines. Through the use of LSS methodology, we improved the availability of, and fast access to, accurate, current information regarding ED patient flow. This allows ED and hospital management teams to identify and rapidly respond to actions impacting patient flow.

2021 ◽  
pp. 026666692110484
Muhammad Rafiq ◽  
Kanwal Ameen

This study assesses the research data management (RDM) awareness, attitude, practices, and behaviors of Pakistan's academic researchers. By using an internationally designed structured questionnaire as a data collection instrument. Quantitative survey research method was opted to meet the research objectives and data was collected from academicians and researchers of four premier universities of Pakistan. The study reveals used and produced data file formats, data acquisition sources, data storage patterns, metadata and tagging practices, data sharing patterns, RDM awareness, attitude, and behavior of the respondents by investigating the self-opinion of respondents on extensive sets of structured questionnaire items. It is a comprehensive assessment of the phenomenon from a developing country's perspective where research data management policies are absent at national and institutional level. The findings have theoretical implications for researchers and practical implications for policymakers, university administrators, university library administrators, and educational trainers.

2021 ◽  
Vol 1 (2) ◽  
pp. 49-54
Delviani Kurniawati Djami ◽  
Ferdinandus Lidang Witi ◽  
Anastasia Mude

Benneta Motor Workshop is one of the individual businesses that was founded in 2017, engaged in the business of selling motorcycle spare parts and servicing services. In running its business, Benneta workshop still uses conventional systems so that there are several obstacles found starting from the data management process, purchase transactions, sales and service services, to the process of making reports which are still recorded manually in notes and stored in a ledger so that it is still less efficient. in terms of time and process. Conventional systems cause the data to be inaccurate and not fast in recording so it takes a long time to do it. The purpose of this research is to build an information system for selling spare parts and motorcycle service using Visual Basic.Net programming language and MySQL database. In this study, the authors used a qualitative descriptive research method. The design method used in this application uses the waterfall method and the testing method used by the author is blackbox testing. With the creation of this system, it is hoped that it will provide efficiency and work effectiveness at the Benneta Motor Workshop.

Jose-María Sierra-Fernández ◽  
Olivia Florencias-Oliveros ◽  
Manuel-Jesús Espinosa-Gavira ◽  
Juan-José González-de-la-Rosa ◽  
Agustín Agüera-Pérez ◽  

This article proposes a measurement solution designed to monitor instantaneous frequency in power systems. It uses a data acquisition module and a GPS receiver for time stamping. A program in Python takes care of receiving the data, calculating the frequency, and finally transferring the measurement results to a database. The frequency is calculated with two different methods, which are compared in the article. The stored data is visualized using the Grafana platform, thus demonstrating its potential for comparing scientific data. The system as a whole constitutes an efficient low cost solution as a data acquisition system.

Mary Banach ◽  
Kaye H Fendt ◽  
Johann Proeve ◽  
Dale Plummer ◽  
Samina Qureshi ◽  

With the focus of the COVID-19 pandemic, we wanted to reach all stakeholders representing communities concerned with good clinical data management practices. We wanted to represent not only data managers but bio-statisticians, clinical monitors, data scientists, informaticians, and all those who collect, organize, analyze, and report on clinical research data. In our paper we will discuss the history of clinical data management in the US and its evolution from the early days of FDA guidance. We will explore the role of biomedical research focusing on the similarities and differences in industry and academia clinical research data management and what we can learn from each other. We will talk about our goals for recruitment and training for the CDM community and what we propose for increasing the knowledge and understanding of good clinical data practice to all – particularly our front-line data collectors i.e., nurses, medical assistants, patients, other data collectors. Finally, we will explore the challenges and opportunities to see CDM as the hub for good clinical data research practices in all of our communities.We will also discuss our survey on how the COVID-19 pandemic has affected the work of CDM in clinical research.

2021 ◽  
Vol 18 ◽  
pp. 100206
Matthias Bodenbenner ◽  
Benjamin Montavon ◽  
Robert H. Schmitt

2021 ◽  
Vol 5 (OOPSLA) ◽  
pp. 1-29
Stefan Malewski ◽  
Michael Greenberg ◽  
Éric Tanter

Dynamically-typed languages offer easy interaction with ad hoc data such as JSON and S-expressions; statically-typed languages offer powerful tools for working with structured data, notably algebraic datatypes , which are a core feature of typed languages both functional and otherwise. Gradual typing aims to reconcile dynamic and static typing smoothly. The gradual typing literature has extensively focused on the computational aspect of types, such as type safety, effects, noninterference, or parametricity, but the application of graduality to data structuring mechanisms has been much less explored. While row polymorphism and set-theoretic types have been studied in the context of gradual typing, algebraic datatypes in particular have not, which is surprising considering their wide use in practice. We develop, formalize, and prototype a novel approach to gradually structured data with algebraic datatypes. Gradually structured data bridges the gap between traditional algebraic datatypes and flexible data management mechanisms such as tagged data in dynamic languages, or polymorphic variants in OCaml. We illustrate the key ideas of gradual algebraic datatypes through the evolution of a small server application from dynamic to progressively more static checking, formalize a core functional language with gradually structured data, and establish its metatheory, including the gradual guarantees.

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