scholarly journals Quality Assurance and Continuing Education: A Cyclic Approach for Maintaining High Quality Data in a High Volume Cancer Registry

2020 ◽  
Vol 27 (3) ◽  
pp. 107327482094679
Author(s):  
Vicki Hawhee ◽  
Jennie Jones ◽  
Sandy Stewart ◽  
Kristin Lucia ◽  
Dana E. Rollison
Keyword(s):  
Quality Assurance ◽  
Continuing Education ◽  
Cancer Registry ◽  
High Volume ◽  
Population Based ◽  
Cancer Surveillance ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
High Volume Hospital

Quality Assurance and Education are 2 areas of the Cancer Registry that go hand in hand. High-quality data can only be maintained through routine surveillance of data quality coupled with tailored continuing education of certified tumor registrars (CTRs). However, the magnitude of information a CTR is required to know, the rapid frequency with which standards change, and growing demands on the time of the CTRs can be roadblocks to maintaining quality in the Cancer Registry. Here we describe a robust approach to quality assurance in a high-volume hospital-based Cancer Registry, leveraging a repeated cycle of quality assessment and educational activities targeting identified opportunities for improvement. Establishing such an approach encourages the professional development of CTRs while simultaneously ensuring the highest quality data for use in population-based cancer surveillance, cancer research, and patient care.

scholarly journals Strengthening African Population-Based Cancer Registration Through Regional Mentorship: UICC Fellowship Experience at Zimbabwe National Cancer Registry

2018 ◽  
Vol 4 (Supplement 2) ◽  
pp. 65s-65s ◽  
Author(s):  
L. Motsuku ◽  
E. Chokunonga ◽  
M. Sengayi ◽  
E. Singh ◽  
L. Khoali ◽  
...  
Keyword(s):  
Cancer Registry ◽  
Cancer Control ◽  
Cancer Registries ◽  
Population Based ◽  
Case Finding ◽  
Quality Data ◽  
Cancer Registration ◽  
High Quality ◽  
High Quality Data ◽  
National Cancer Registry

Background: South Africa (SA) recently established an urban population-based cancer registry in Ekurhuleni metropolitan district in Gauteng Province. The Ekurhuleni Population-Based Cancer Registry (EPBCR) aims to inform cancer policy and comprehensive cancer control programs. The registry covers 3.5 million residents including public/private, rural/urban patients and a mix of the multiethnic SA population. The first complete year's data will be published in April 2018. It is crucial that high-quality data collected by newly established registries are comparable regionally and globally. The Union for International Cancer Control (UICC) fellowship provides a practical opportunity for South African National Cancer Registry staff to learn from the Zimbabwe National Cancer Registry (ZNCR), a well-established population-based registry in the region. Aim: To enhance the SA EPBCR through observation and application of methods for population-based cancer registration used at the ZNCR. Methods: A desktop review of published and unpublished articles/reports of the ZNCR was conducted. Semi-structured informal interviews were conducted with registry staff to understand data processes from case finding to reporting. Representative data sources were visited to understand case-finding processes. Results: The ZNCR was established in 1985 through a collaborative research agreement between the Ministry of Health (MoH) and International Agency for Research on Cancer (IARC). Its activities are overseen by a 17-member constituted multidisciplinary advisory committee. The registry staff comprise one registrar, one executive assistant (EA) and four health information assistants (HIA). The process of ensuring quality data are guided by the African Cancer Registry Network and the International Association of Cancer Registries standards for population-based cancer registries. The ZNCR uses a combination of active and passive case-finding methods where HIAs have unrestricted access to patient information in private and public sectors such as hospitals, pathology laboratories, radiotherapy centers and death registries. HIAs conduct patient interviews for accurate demographics and to complete missing information. Cases are coded according to International Classification of Diseases for Oncology-V3 and Canreg software is used for data entry, quality control and analysis. The hard copies are stored in locked cabinets in offices with restricted access. The data are then used for reporting and research. Conclusion: The support of government, commitment of advisory committee volunteers, highly trained and experienced staff are key elements behind the success of ZNCR. Strict adherence to international practices for population-based cancer registration has enabled ZNCR to produce high-quality data for research and cancer programs. The processes used by ZNCR will be customised and implemented at EPBCR.

scholarly journals Credentialing Proton Centers for Clinical Trials

2021 ◽  
Author(s):  
Paige A. Taylor
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Quality Assurance ◽  
Patient Outcomes ◽  
Proton Therapy ◽  
Quality Data ◽  
Trial Participation ◽  
Clinical Trial Participation ◽  
High Quality ◽  
High Quality Data

This chapter will provide an overview of quality assurance processes to credential proton therapy centers for clinical trial participation. There are a number of credentialing audit steps, including independent output verification, anthropomorphic phantom audits, image guidance credentialing, knowledge assessments, and on-site dosimetry review. The purpose of these credentialing steps is to ensure consistency across proton centers participating in clinical trials, and well as comparability with photon centers for randomized trials. This uniformity ensures high quality data for measuring patient outcomes, which are pivotal at a time when proton therapy is being assessed for superior outcomes.

Collecting High-Quality Data

2018 ◽  
Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan
Keyword(s):  
Data Collection ◽  
Social Desirability ◽  
Measurement Errors ◽  
Monitoring And Evaluation ◽  
Quality Data ◽  
Social Desirability Bias ◽  
High Quality ◽  
High Quality Data ◽  
Evaluation Systems ◽  
Demand Effect

Without high-quality data, even the best-designed monitoring and evaluation systems will collapse. Chapter 7 introduces some the basics of collecting high-quality data and discusses how to address challenges that frequently arise. High-quality data must be clearly defined and have an indicator that validly and reliably measures the intended concept. The chapter then explains how to avoid common biases and measurement errors like anchoring, social desirability bias, the experimenter demand effect, unclear wording, long recall periods, and translation context. It then guides organizations on how to find indicators, test data collection instruments, manage surveys, and train staff appropriately for data collection and entry.

Developmental Impacts of Remittances on Migrant-Sending Households: Micro-Level Evidence from Punjab, Pakistan

2019 ◽  
Vol 14 (3) ◽  
pp. 338-366
Author(s):  
Kashif Imran ◽  
Evelyn S. Devadason ◽  
Cheong Kee Cheok
Keyword(s):  
Human Development ◽  
Human Development Index ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Development Index ◽  
Policy Interventions ◽  
Education And Health ◽  
Micro Level ◽  
The Government

This article analyzes the overall and type of developmental impacts of remittances for migrant-sending households (HHs) in districts of Punjab, Pakistan. For this purpose, an HH-based human development index is constructed based on the dimensions of education, health and housing, with a view to enrich insights into interactions between remittances and HH development. Using high-quality data from a HH micro-survey for Punjab, the study finds that most migrant-sending HHs are better off than the HHs without this stream of income. More importantly, migrant HHs have significantly higher development in terms of housing in most districts of Punjab relative to non-migrant HHs. Thus, the government would need policy interventions focusing on housing to address inequalities in human development at the district-HH level, and subsequently balance its current focus on the provision of education and health.

Capturing sexual assault data: An information system designed by forensic clinicians and healthcare researchers

2017 ◽  
Vol 47 (1) ◽  
pp. 46-55 ◽  
Author(s):  
S Aqif Mukhtar ◽  
Debbie A Smith ◽  
Maureen A Phillips ◽  
Maire C Kelly ◽  
Renate R Zilkens ◽  
...  
Keyword(s):  
Information System ◽  
Sexual Assault ◽  
Data Management ◽  
Sexual Violence ◽  
Western Australia ◽  
Management System ◽  
Data Management System ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data

Background: The Sexual Assault Resource Center (SARC) in Perth, Western Australia provides free 24-hour medical, forensic, and counseling services to persons aged over 13 years following sexual assault. Objective: The aim of this research was to design a data management system that maintains accurate quality information on all sexual assault cases referred to SARC, facilitating audit and peer-reviewed research. Methods: The work to develop SARC Medical Services Clinical Information System (SARC-MSCIS) took place during 2007–2009 as a collaboration between SARC and Curtin University, Perth, Western Australia. Patient demographics, assault details, including injury documentation, and counseling sessions were identified as core data sections. A user authentication system was set up for data security. Data quality checks were incorporated to ensure high-quality data. Results: An SARC-MSCIS was developed containing three core data sections having 427 data elements to capture patient’s data. Development of the SARC-MSCIS has resulted in comprehensive capacity to support sexual assault research. Four additional projects are underway to explore both the public health and criminal justice considerations in responding to sexual violence. The data showed that 1,933 sexual assault episodes had occurred among 1881 patients between January 1, 2009 and December 31, 2015. Sexual assault patients knew the assailant as a friend, carer, acquaintance, relative, partner, or ex-partner in 70% of cases, with 16% assailants being a stranger to the patient. Conclusion: This project has resulted in the development of a high-quality data management system to maintain information for medical and forensic services offered by SARC. This system has also proven to be a reliable resource enabling research in the area of sexual violence.

scholarly journals Fooled by Performance Randomness: Overrewarding Luck

2019 ◽  
Vol 101 (4) ◽  
pp. 658-666 ◽  
Author(s):  
Romain Gauriot ◽  
Lionel Page
Keyword(s):  
Causal Effect ◽  
Experimental Situation ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Economic Organizations ◽  
Quasi Experimental ◽  
Similar Location ◽  
Do So

We provide evidence of a violation of the informativeness principle whereby lucky successes are overly rewarded. We isolate a quasi-experimental situation where the success of an agent is as good as random. To do so, we use high-quality data on football (soccer) matches and select shots on goal that landed on the goal posts. Using nonscoring shots, taken from a similar location on the pitch, as counterfactuals to scoring shots, we estimate the causal effect of a lucky success (goal) on the evaluation of the player's performance. We find clear evidence that luck is overly influencing managers' decisions and evaluators' ratings. Our results suggest that this phenomenon is likely to be widespread in economic organizations.

scholarly journals R factors in Rietveld analysis: How good is good enough?

2006 ◽  
Vol 21 (1) ◽  
pp. 67-70 ◽  
Author(s):  
Brian H. Toby
Keyword(s):  
Rietveld Analysis ◽  
Poor Quality ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Poor Quality Data ◽  
Error Index ◽  
R Factors ◽  
Very High

The definitions for important Rietveld error indices are defined and discussed. It is shown that while smaller error index values indicate a better fit of a model to the data, wrong models with poor quality data may exhibit smaller values error index values than some superb models with very high quality data.

scholarly journals AGRIBALYSE®, the French LCI Database for agricultural products: high quality data for producers and environmental labelling

OCL ◽  
2014 ◽  
Vol 22 (1) ◽  
pp. D104 ◽  
Author(s):  
Vincent Colomb ◽  
Samy Ait Amar ◽  
Claudine Basset Mens ◽  
Armelle Gac ◽  
Gérard Gaillard ◽  
...  
Keyword(s):  
Agricultural Products ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Environmental Labelling

scholarly journals A Study on Pattern Recognition with the Histograms of Oriented Gradients in Distorted and Noisy Images

2020 ◽  
Vol 26 (4) ◽  
pp. 454-478
Author(s):  
Andrzej Bukała ◽  
Michał Koziarski ◽  
Bogusław Cyganek ◽  
Osman Koç ◽  
Alperen Kara
Keyword(s):  
Pattern Recognition ◽  
Negative Impact ◽  
Quality Data ◽  
High Quality ◽  
Denoising Method ◽  
High Quality Data ◽  
Histograms Of Oriented Gradients ◽  
Different Types ◽  
Simple Implementation ◽  
Salt And Pepper

Histograms of oriented gradients (HOG) are still one of the most frequently used low-level features for pattern recognition in images. Despite their great popularity and simple implementation performance of the HOG features almost always has been measured on relatively high quality data which are far from real conditions. To fill this gap we experimentally evaluate their performance in the more realistic conditions, based on images affected by different types of noise, such as Gaussian, quantization, and salt-and-pepper, as well on images distorted by occlusions. Different noise scenarios were tested such anti-distortions during training as well as application of a proper denoising method in the recognition stage. As underpinned with experimental results, the negative impact of distortions and noise on object recognition with HOG features can be significantly reduced by employment of a proper denoising strategy.

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