scholarly journals An Overview of ARM Program Climate Research Facility Data Quality Assurance

2008 ◽  
Vol 2 (1) ◽  
pp. 192-216 ◽  
Author(s):  
R.A. Peppler ◽  
C.N. Long ◽  
D.L. Sisterson ◽  
D.D. Turner ◽  
C.P. Bahrmann ◽  
...  

We present an overview of key aspects of the Atmospheric Radiation Measurement (ARM) Program Climate Research Facility (ACRF) data quality assurance program. Processes described include instrument deployment and calibration; instrument and facility maintenance; data collection and processing infrastructure; data stream inspection and assessment; problem reporting, review and resolution; data archival, display and distribution; data stream reprocessing; engineering and operations management; and the roles of value-added data processing and targeted field campaigns in specifying data quality and characterizing field measurements. The paper also includes a discussion of recent directions in ACRF data quality assurance. A comprehensive, end-to-end data quality assurance program is essential for producing a high-quality data set from measurements made by automated weather and climate networks. The processes developed during the ARM Program offer a possible framework for use by other instrumentation- and geographically-diverse data collection networks and highlight the myriad aspects that go into producing research-quality data.

2001 ◽  
Vol 17 (4) ◽  
pp. 528-541 ◽  
Author(s):  
Carrie N. Klabunde ◽  
Hélène Sancho-Garnier ◽  
Mireille Broeders ◽  
Steinar Thoresen ◽  
Vitor J. L. Rodrigues ◽  
...  

Objectives: To document the mammography data that are gathered by the organized screening programs participating in the International Breast Cancer Screening Network (IBSN), the nature of their procedures for data quality assurance, and the measures used to assess program performance and impact.Methods: A detailed questionnaire covering multiple aspects of quality assurance in screening mammography was mailed to IBSN representatives in 23 countries.Results: Countries collect a wealth of screening mammography data, much of it computerized. Most countries have designated staff for data quality assurance. All provide staff training, and most have documentation requirements for data collection. Nearly all have one or more procedures to maintain data confidentiality. Countries are heterogeneous in collecting and assessing data to monitor screening program performance and impact.Conclusions: Demonstrating that population-based screening mammography reduces breast cancer mortality requires collection of high-quality data on key aspects of the multi-step screening process. Assuring the quality of data collection systems for screening mammography programs is an important and evolving area for IBSN countries.


2021 ◽  
Vol 21 (S1) ◽  
Author(s):  
Harriet Ruysen ◽  
◽  
Ahmed Ehsanur Rahman ◽  
Vladimir Sergeevich Gordeev ◽  
Tanvir Hossain ◽  
...  

Abstract Background Observation of care at birth is challenging with multiple, rapid and potentially concurrent events occurring for mother, newborn and placenta. Design of electronic data (E-data) collection needs to account for these challenges. The Every Newborn Birth Indicators Research Tracking in Hospitals (EN-BIRTH) was an observational study to assess measurement of indicators for priority maternal and newborn interventions and took place in five hospitals in Bangladesh, Nepal and Tanzania (July 2017–July 2018). E-data tools were required to capture individually-linked, timed observation of care, data extraction from hospital register-records or case-notes, and exit-survey data from women. Methods To evaluate this process for EN-BIRTH, we employed a framework organised around five steps for E-data design, data collection and implementation. Using this framework, a mixed methods evaluation synthesised evidence from study documentation, standard operating procedures, stakeholder meetings and design workshops. We undertook focus group discussions with EN-BIRTH researchers to explore experiences from the three different country teams (November–December 2019). Results were organised according to the five a priori steps. Results In accordance with the five-step framework, we found: 1) Selection of data collection approach and software: user-centred design principles were applied to meet the challenges for observation of rapid, concurrent events around the time of birth with time-stamping. 2) Design of data collection tools and programming: required extensive pilot testing of tools to be user-focused and to include in-built error messages and data quality alerts. 3) Recruitment and training of data collectors: standardised with an interactive training package including pre/post-course assessment. 4) Data collection, quality assurance, and management: real-time quality assessments with a tracking dashboard and double observation/data extraction for a 5% case subset, were incorporated as part of quality assurance. Internet-based synchronisation during data collection posed intermittent challenges. 5) Data management, cleaning and analysis: E-data collection was perceived to improve data quality and reduce time cleaning. Conclusions The E-Data system, custom-built for EN-BIRTH, was valued by the site teams, particularly for time-stamped clinical observation of complex multiple simultaneous events at birth, without which the study objectives could not have been met. However before selection of a custom-built E-data tool, the development time, higher training and IT support needs, and connectivity challenges need to be considered against the proposed study or programme’s purpose, and currently available E-data tool options.


2008 ◽  
Author(s):  
RA Peppler ◽  
KE Kehoe ◽  
KL Sonntag ◽  
CP Bahrmann ◽  
SJ Richardson ◽  
...  

Stroke ◽  
2016 ◽  
Vol 47 (suppl_1) ◽  
Author(s):  
Elizabeth Linkewich ◽  
Janine Theben ◽  
Amy Maebrae-Waller ◽  
Shelley Huffman ◽  
Jenn Fearn ◽  
...  

Background and Issues: The collection and reporting of Rehabilitation Intensity (RI) in a national rehabilitation database was mandated on April 1, 2015 for all stroke patients within Ontario, to support evaluation of stroke best practice implementation. RI includes minutes of direct task-specific therapy patients experience per day. This requires a shift in thinking from capturing the clinician’s time spent in therapy to the patient perspective. To ensure that high quality data is collected, it was important to understand clinicians’ experiences in collecting RI data. Purpose: To identify enablers and barriers to RI data collection in order to inform the development of resources to support clinicians. Methods: A 12-item electronic survey was developed by an Ontario Stroke Network (OSN) task group to evaluate the clinician experience of RI data collection (including: demographics, barriers, enablers, education, resources, and practice change). The survey was distributed via SurveyMonkey® and sent to clinicians from 48 hospitals, 3 weeks post implementation of RI data collection. Analyses involved descriptive statistics and thematic analysis. Results: Three hundred and twenty-one clinicians from 47 hospitals responded to the survey. Survey results suggest RI data collection is feasible; seventy-one percent of clinicians report it takes 10 minutes or less to enter RI data. Thematic analysis identified: 5 common challenges with most frequently reported relating to data quality, 30% (N=358) and 6 common enablers with most frequently reported relating to ease of collecting RI data through workload measurement systems, 50% (N=46). Suggestions for educational resources included tools for identifying what is included in RI and the provision of education (e.g. webinars). Conclusions: RI data collection is feasible for clinicians. Education and resources developed should support key challenges and enablers identified by clinicians - to enhance data quality and the consistency of RI collection. As RI data fields are available through a national rehabilitation database, this work sets the foundation for other provinces interested in the systematic collection and reporting of RI data.


PEDIATRICS ◽  
1992 ◽  
Vol 90 (6) ◽  
pp. 959-965
Author(s):  
Terri A. Slagle ◽  
Jeffrey B. Gould

The purpose of this national survey was to define the extent and features of database use by 445 tertiary level neonatal intensive care nurseries in the United States. Of the 305 centers responding to our survey, 78% had a database in use in 1989 and 15% planned to develop one in the future. Nurseries varied remarkably in the volume of data collected, the amount of time devoted to completing data collection forms, and the personnel involved in data collection. Although data were used primarily for statistical reports (93% of nurseries), quality assurance (73%) and research activities (61%) were also enhanced by database information. Neonatal databases were used to generate reports for the permanent medical record in 38% of centers. Satisfaction with the database was dependent on how useful the database information was to centers which collected and actually used a large volume of information. Overall, nurseries expressed a high degree of confidence in the data they collected, and 65% felt their neonatal database information could be used directly in publication of research. It was disturbing that accuracy of data was not monitored formally by the majority of nurseries. Only 27% of centers followed a routine schedule of data quality assurance, and only 53% had built in error messages for data entry. We caution all who receive database information in the form of morbidity and mortality statistics, clinical reports on patients cared for in neonatal units, and published manuscripts to be attentive to the quality of the data they consume. We feel that future database design efforts need to better address data quality control. Our findings stress the importance and need for immediate efforts to better address database quality control.


2009 ◽  
Vol 19 (1) ◽  
pp. 94-102 ◽  
Author(s):  
Christian Marth ◽  
Sonja Hiebl ◽  
Willi Oberaigner ◽  
Raimund Winter ◽  
Sepp Leodolter ◽  
...  

Objective:The Austrian Association for Gynecologic Oncology initiated in 1998 a prospective quality assurance program for patients with ovarian cancer. The aim of this study was to evaluate factors predicting overall survival especially under consideration of department volume.Methods:All Austrian gynecological departments were invited to participate in the quality assurance program. A questionnaire was sent out that included birth date, histology, date of diagnosis, stage, and basic information on primary treatment. Description of comorbidity was not requested. Patient life status was assessed in a passive way. We did record linkage between each patient's name and birth date and the official mortality data set collected by Statistics Austria. No data were available on progression-free survival. Patients treated between January 1, 1999 and December 31, 2004 were included in the analysis. Mortality dates were available to December 31, 2006. Data were analyzed by means of classical statistical methods. Cut-off point for departments was 24 patients per year.Results:A total of 1948 patients were evaluable. Approximately 75% of them were treated at institutions with fewer than 24 new patients per year. Patient characteristics were grossly similar for both department types. Multivariate analysis confirmed established prognostic factors such as International Federation of Gynecologists and Obstetricians (FIGO) stage, lymphadenectomy, age, grading, and residual disease. In addition, we found small departments (<24 patients per year) to have a negative effect on overall survival (hazards ratio, 1.38: 95% confidence interval, 1.2-1.7; and P < 0.001).Conclusions:The results indicate that in Austria, rules prescribing minimum department case load can further improve survival for patients with ovarian cancer.


2020 ◽  
Vol 40 (3) ◽  
pp. 259-272
Author(s):  
danah boyd

This paper is based upon the closing keynote presentation that was given by danah boyd at the inaugural NISO Plus conference held from February 23–25, 2020 in Baltimore, MD (USA). It focuses on how data are used, and how they can be manipulated to meet specific objectives – both good and bad. The paper reinforces the importance of understanding the biases and limitations of any data set. Topics covered include data quality, data voids, data infrastructures, alternative facts, and agnotology. The paper stresses that data become legitimate because we collectively believe that those data are sound, valid, and fit for use. This not only means that there is power in collecting and disseminating data, but also that there is power in interpreting and manipulating the data. The struggle over data’s legitimacy says more about our society – and our values – than it says about the data itself.


2018 ◽  
Vol 77 (4) ◽  
pp. 476-479 ◽  
Author(s):  
Helga Radner ◽  
Katerina Chatzidionysiou ◽  
Elena Nikiphorou ◽  
Laure Gossec ◽  
Kimme L Hyrich ◽  
...  

Personalised medicine, new discoveries and studies on rare exposures or outcomes require large samples that are increasingly difficult for any single investigator to obtain. Collaborative work is limited by heterogeneities, both what is being collected and how it is defined. To develop a core set for data collection in rheumatoid arthritis (RA) research which (1) allows harmonisation of data collection in future observational studies, (2) acts as a common data model against which existing databases can be mapped and (3) serves as a template for standardised data collection in routine clinical practice to support generation of research-quality data. A multistep, international multistakeholder consensus process was carried out involving voting via online surveys and two face-to-face meetings. A core set of 21 items (‘what to collect’) and their instruments (‘how to collect’) was agreed: age, gender, disease duration, diagnosis of RA, body mass index, smoking, swollen/tender joints, patient/evaluator global, pain, quality of life, function, composite scores, acute phase reactants, serology, structural damage, treatment and comorbidities. The core set should facilitate collaborative research, allow for comparisons across studies and harmonise future data from clinical practice via electronic medical record systems.


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