Quality Assurance Aspects of Environmental (Aerometric) Data Management

2008 ◽  
pp. 65-65-11
Author(s):  
RC Beebe ◽  
AS McDaniel ◽  
DW Clement
Keyword(s):  
Quality Assurance ◽  
Data Management

scholarly journals Patient-generated health data management and quality challenges in remote patient monitoring

JAMIA Open ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 471-478 ◽  
Author(s):  
Robab Abdolkhani ◽  
Kathleen Gray ◽  
Ann Borda ◽  
Ruth DeSouza
Keyword(s):  
Quality Assurance ◽  
Data Management ◽  
Remote Monitoring ◽  
Patient Monitoring ◽  
Health Data ◽  
Clinical Settings ◽  
Remote Patient Monitoring ◽  
Monitoring Programs ◽  
Remote Patient ◽  
Promising Source

Abstract Background Patient-Generated Health Data (PGHD) in remote monitoring programs is a promising source of precise, personalized data, encouraged by expanding growth in the health technologies market. However, PGHD utilization in clinical settings is low. One of the critical challenges that impedes confident clinical use of PGHD is that these data are not managed according to any recognized approach for data quality assurance. Objective This article aims to identify the PGHD management and quality challenges that such an approach must address, as these are expressed by key PGHD stakeholder groups. Materials and Methods In-depth interviews were conducted with 20 experts who have experience in the use of PGHD in remote patient monitoring, including: healthcare providers, health information professionals within clinical settings, and commercial providers of remote monitoring solutions. Participants were asked to describe PGHD management processes in the remote monitoring programs in which they are involved, and to express their perspectives on PGHD quality challenges during the data management stages. Results The remote monitoring programs in the study did not follow clear PGHD management or quality assurance approach. Participants were not fully aware of all the considerations of PGHD quality. Digital health literacy, wearable accuracy, difficulty in data interpretation, and lack of PGHD integration with electronic medical record systems were among the key challenges identified that impact PGHD quality. Conclusion Co-development of PGHD quality guidelines with relevant stakeholders, including patients, is needed to ensure that quality remote monitoring data from wearables is available for use in more precise and personalized patient care.

Quality assurance review center: Role in multi-institutional breast cancer trials.

2011 ◽  
Vol 29 (27_suppl) ◽  
pp. 205-205
Author(s):  
J. Moni ◽  
H. T. Le-Petross ◽  
J. C. Boughey ◽  
F. Laurie ◽  
R. Hanusik ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Quality Assurance ◽  
Data Acquisition ◽  
Data Management ◽  
Remote Access ◽  
Data Management System ◽  
Data Systems ◽  
Neoadjuvant Endocrine Therapy ◽  
Trial Management ◽  
Cancer Trials

205 Background: Central review is an important trial management and validation tool. Multisite trials require data systems to accommodate diverse image acquisition and review. Breast cancer is challenging as its imaging objects are not always easily shared across departments. Quality Assurance Review Center (QARC) has a diverse portfolio of facile data acquisition and powerful informatics support that meets these needs. We report on the role of QARC in three of the current breast cancer trials from the American College of Surgeons Oncology Group. Methods: QARC data management system includes secure network infrastructure and a validated relational operating database (MAX). QARC has data acquisition/imaging re-distribution expertise for real time response reviews, response measuring tools and corresponding data archive for secondary reviews. MAX includes query ability; records link to digital information. QARC underwent systematic information systems validation process for 21 CFR Part 11. DICOM/non-DICOM files are stored in the QARC PACS. On-site/remote reviewers use MAX to retrieve, view, annotate and save images. Data extracted is securely sent to partner statistical centers. Results: In Z1031, a neoadjuvant endocrine therapy trial, all of the mammographic exams are archived at QARC. For Z1071, a sentinel lymph node trial, pre- and post-treatment ultrasound (US) images of 321 patients were remotely reviewed. For Z1072, the cryoablation US, pre and post-cryoablation MRI for 40 of 47 cases were remotely archived, retrieved and transferred to investigator workstation for post-processing and review. The other 7 patients did not have evaluable imaging studies. In all three trials, the costs of shipping hardcopies of exams and travel to QARC for central review functions were eliminated. Conclusions: QARC data management systems provide diverse informatics supports for multi-institutional trials, ranging from archives of images, provide remote access and download of data, and central review. The vibrant informatics supports meet the growing needs of clinical trials. Future directions include radiation field review for breast cancer trials and the incorporation of pathology microarray analysis as DICOM objects.

scholarly journals Developing a modern data workflow for evolving data

2018 ◽  
Author(s):  
Glenda M. Yenni ◽  
Erica M. Christensen ◽  
Ellen K. Bledsoe ◽  
Sarah R. Supp ◽  
Renata M. Diaz ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Data Management ◽  
Research Process ◽  
Data Management System ◽  
Limited Attention ◽  
Data Pipeline ◽  
Evolving Data ◽  
Access To Data ◽  
And Control ◽  
Quality Assurance And Control

AbstractData management and publication are core components of the research process. An emerging challenge that has received limited attention in biology is managing, working with, and providing access to data under continual active collection. “Evolving data” present unique challenges in quality assurance and control, data publication, archiving, and reproducibility. We developed a evolving data workflow for a long-term ecological study that addresses many of the challenges associated with managing this type of data. We do this by leveraging existing tools to: 1) perform quality assurance and control; 2) import, restructure, version, and archive data; 3) rapidly publish new data in ways that ensure appropriate credit to all contributors; and 4) automate most steps in the data pipeline to reduce the time and effort required by researchers. The workflow uses two tools from software development, version control and continuous integration, to create a modern data management system that automates the pipeline.

P56 Using data management statistics and system resources statistics for quality assurance

1993 ◽  
Vol 14 (5) ◽  
pp. 450-451
Author(s):  
Nancy H. Remaley ◽  
William P. Amoroso ◽  
Kimberly C. Beringer ◽  
Gerald L. Swanson ◽  
Jeffrey P. Martin ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Data Management ◽  
System Resources ◽  
Using Data
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