2019 ◽  
Vol 14 (1) ◽  
pp. 10-23 ◽  
Author(s):  
Aynaz Nourani ◽  
Haleh Ayatollahi ◽  
Masoud Solaymani Dodaran

Background:A clinical data management system is a software supporting the data management process in clinical trials. In this system, the effective support of clinical data management dimensions leads to the increased accuracy of results and prevention of diversion in clinical trials. The aim of this review article was to investigate the dimensions of data management in clinical data management systems.Methods:This study was conducted in 2017. The used databases included Web of Science, Scopus, Science Direct, ProQuest, Ovid Medline and PubMed. The search was conducted over a period of 10 years from 2007 to 2017. The initial number of studies was 101 reaching 19 in the final stage. The final studies were described and compared in terms of the year, country and dimensions of the clinical data management process in clinical trials.Results:The research findings indicated that none of the systems completely supported the data management dimensions in clinical trials. Although these systems were developed for supporting the clinical data management process, they were similar to electronic data capture systems in many cases. The most significant dimensions of data management in such systems were data collection or entry, report, validation, and security maintenance.Conclusion:Seemingly, not sufficient attention has been paid to automate all dimensions of the clinical data management process in clinical trials. However, these systems could take positive steps towards changing the manual processes of clinical data management to electronic processes.


Author(s):  
N. Fumai ◽  
C. Collet ◽  
M. Petroni ◽  
K. Roger ◽  
E. Saab ◽  
...  

Abstract A Patient Data Management System (PDMS) is being developed for use in the Intensive Care Unit (ICU) of the Montreal Children’s Hospital. The PDMS acquires real-time patient data from a network of physiological bedside monitors and facilitates the review and interpretation of this data by presenting it as graphical trends, charts and plots on a color video display. Due to the large amounts of data involved, the data storage and data management processes are an important task of the PDMS. The data management structure must integrate varied data types and provide database support for different applications, while preserving the real-time acquisition of network data. This paper outlines a new data management structure which is based primarily on OS/2’s Extended Edition relational database. The relational database design is expected to solve the query shortcomings of the previous data management structure, as well as offer support for security and concurrency. The discussion will also highlight future advantages available from a network implementation.


2022 ◽  
pp. 291-315
Author(s):  
Irfan Siddavatam ◽  
Ashwini Dalvi ◽  
Abhishek Patel ◽  
Aditya Panchal ◽  
Aditya S. Vedpathak ◽  
...  

It is said that every adversity presents the opportunity to grow. The current pandemic is a lesson to all healthcare infrastructure stakeholders to look at existing setups with an open mind. This chapter's proposed solution offers technology assistance to manage patient data effectively and extends the hospital data management system's capability to predict the upcoming need for healthcare resources. Further, the authors intend to supplement the proposed solution with crowdsourcing to meet hospital demand and supply for unprecedented medical emergencies. The proposed approach would demonstrate its need in the current pandemic scenario and prepare the healthcare infrastructure with a more streamlined and cooperative approach than before.


Author(s):  
Sarah Flynn ◽  
Will Meaney ◽  
Adam M. Leadbetter ◽  
Jeffrey P. Fisher ◽  
Caitriona Nic Aonghusa

2007 ◽  
Vol 46 (01) ◽  
pp. 74-79 ◽  
Author(s):  
P. Knaup ◽  
F. Leiner ◽  
R. Haux

Summary Objectives: To summarize background, challenges, objectives, and methods for the usability of patient data, in particular with respect to their multiple use, and to point out how to lecture medical data management. Methods: Analyzing the literature, providing an example based on Simpson’s paradox and summarizing research and education in the field of medical data management, respectively health information management (in German: Medizinische Dokumentation). Results: For the multiple use of patient data, three main categories of use can be identified: patientoriented (or casuistic) analysis, patient-group reporting, and analysis for clinical studies. A so-called documentation protocol, related to study plans in clinical trials, supports the multiple use of data from the electronic health record in order to obtain valid, interpretable results. Lectures on medical data management may contain modules on introduction, basic concepts of clinical data management and coding systems, important medical coding systems (e.g. ICD, SNOMED, TNM, UMLS), typical medical documentation systems (e.g. on patient records, clinical and epidemiological registers), utilization of clinical data management systems, planning of medical coding systems and of clinical data management systems, hospital information systems and the electronic patient record, and on data management in clinical studies. Conclusion: Usability, the ultimate goal of recording and managing patient data, requires, besides technical considerations, in addition appropriate methodology on medical data management, especially if data is intended to be used for multiple purposes, e.g. for patient care and quality management and clinical research. Medical data management should be taught in health and biomedical informatics programs.


1998 ◽  
Vol 24 (2) ◽  
pp. 167-171 ◽  
Author(s):  
N. F. de Keizer ◽  
C. P. Stoutenbeek ◽  
L. A. J. B. W. Hanneman ◽  
E. de Jonge

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