scholarly journals COVID-19 surveillance - a descriptive study on data quality issues

2020 ◽  
Author(s):  
Cristina Costa-Santos ◽  
Ana Luísa Neves ◽  
Ricardo Correia ◽  
Paulo Santos ◽  
Matilde Monteiro-Soares ◽  
...  
Keyword(s):  
Data Quality ◽  
Health Care Providers ◽  
Data Entry ◽  
Quality Data ◽  
Care Providers ◽  
Quality Of Data ◽  
Low Degree ◽  
Directorate General ◽  
Quality Issues

AbstractBackgroundHigh-quality data is crucial for guiding decision making and practicing evidence-based healthcare, especially if previous knowledge is lacking. Nevertheless, data quality frailties have been exposed worldwide during the current COVID-19 pandemic. Focusing on a major Portuguese surveillance dataset, our study aims to assess data quality issues and suggest possible solutions.MethodsOn April 27th 2020, the Portuguese Directorate-General of Health (DGS) made available a dataset (DGSApril) for researchers, upon request. On August 4th, an updated dataset (DGSAugust) was also obtained. The quality of data was assessed through analysis of data completeness and consistency between both datasets.ResultsDGSAugust has not followed the data format and variables as DGSApril and a significant number of missing data and inconsistencies were found (e.g. 4,075 cases from the DGSApril were apparently not included in DGSAugust). Several variables also showed a low degree of completeness and/or changed their values from one dataset to another (e.g. the variable ‘underlying conditions’ had more than half of cases showing different information between datasets). There were also significant inconsistencies between the number of cases and deaths due to COVID-19 shown in DGSAugust and by the DGS reports publicly provided daily.ConclusionsThe low quality of COVID-19 surveillance datasets limits its usability to inform good decisions and perform useful research. Major improvements in surveillance datasets are therefore urgently needed - e.g. simplification of data entry processes, constant monitoring of data, and increased training and awareness of health care providers - as low data quality may lead to a deficient pandemic control.

Designing Information Product (IP) Maps On the Process of Data Processing and Academic Information

2017 ◽  
Vol 4 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Diana Effendi
Keyword(s):  
Data Quality ◽  
Data Management ◽  
Information Management ◽  
Information Quality ◽  
Quality Data ◽  
Management Approach ◽  
Quality Of Data ◽  
Information Product ◽  
Academic Activities

Information Product Approach (IP Approach) is an information management approach. It can be used to manage product information and data quality analysis. IP-Map can be used by organizations to facilitate the management of knowledge in collecting, storing, maintaining, and using the data in an organized. The  process of data management of academic activities in X University has not yet used the IP approach. X University has not given attention to the management of information quality of its. During this time X University just concern to system applications used to support the automation of data management in the process of academic activities. IP-Map that made in this paper can be used as a basis for analyzing the quality of data and information. By the IP-MAP, X University is expected to know which parts of the process that need improvement in the quality of data and information management.   Index term: IP Approach, IP-Map, information quality, data quality. REFERENCES[1] H. Zhu, S. Madnick, Y. Lee, and R. Wang, “Data and Information Quality Research: Its Evolution and Future,” Working Paper, MIT, USA, 2012.[2] Lee, Yang W; at al, Journey To Data Quality, MIT Press: Cambridge, 2006.[3] L. Al-Hakim, Information Quality Management: Theory and Applications. Idea Group Inc (IGI), 2007.[4] “Access : A semiotic information quality framework: development and comparative analysis : Journal ofInformation Technology.” [Online]. Available: http://www.palgravejournals.com/jit/journal/v20/n2/full/2000038a.html. [Accessed: 18-Sep-2015].[5] Effendi, Diana, Pengukuran Dan Perbaikan Kualitas Data Dan Informasi Di Perguruan Tinggi MenggunakanCALDEA Dan EVAMECAL (Studi Kasus X University), Proceeding Seminar Nasional RESASTEK, 2012, pp.TIG.1-TI-G.6.

scholarly journals Improving Specimen Labelling and Data Collection in Bio-science Research using Mobile and Web Applications

2020 ◽  
Vol 10 (1) ◽  
pp. 1-16
Author(s):  
Isaac Nyabisa Oteyo ◽  
Mary Esther Muyoka Toili
Keyword(s):  
Data Collection ◽  
Web Applications ◽  
Data Entry ◽  
Science Research ◽  
Quality Data ◽  
Quality Of Data ◽  
Data Entry Form ◽  
Research Activities ◽  
Daunting Task

AbstractResearchers in bio-sciences are increasingly harnessing technology to improve processes that were traditionally pegged on pen-and-paper and highly manual. The pen-and-paper approach is used mainly to record and capture data from experiment sites. This method is typically slow and prone to errors. Also, bio-science research activities are often undertaken in remote and distributed locations. Timeliness and quality of data collected are essential. The manual method is slow to collect quality data and relay it in a timely manner. Capturing data manually and relaying it in real time is a daunting task. The data collected has to be associated to respective specimens (objects or plants). In this paper, we seek to improve specimen labelling and data collection guided by the following questions; (1) How can data collection in bio-science research be improved? (2) How can specimen labelling be improved in bio-science research activities? We present WebLog, an application that we prototyped to aid researchers generate specimen labels and collect data from experiment sites. We use the application to convert the object (specimen) identifiers into quick response (QR) codes and use them to label the specimens. Once a specimen label is successfully scanned, the application automatically invokes the data entry form. The collected data is immediately sent to the server in electronic form for analysis.

Evaluating the Data Quality of a National Sample of Young Sexual and Gender Minorities Recruited Using Social Media: The Influence of Different Design Formats

2020 ◽  
pp. 089443932092824 ◽  
Author(s):  
Michael J. Stern ◽  
Erin Fordyce ◽  
Rachel Carpenter ◽  
Melissa Heim Viox ◽  
Stuart Michaels ◽  
...  
Keyword(s):  
Social Media ◽  
Data Quality ◽  
National Sample ◽  
Quality Data ◽  
Quality Of Data ◽  
Gender Minorities ◽  
Population Recruitment ◽  
Youth Population ◽  
And Gender

Social media recruitment is no longer an uncharted avenue for survey research. The results thus far provide evidence of an engaging means of recruiting hard-to-reach populations. Questions remain, however, regarding whether the data collected using this method of recruitment produce quality data. This article assesses one aspect that may influence the quality of data gathered through nonprobability sampling using social media advertisements for a hard-to-reach sexual and gender minority youth population: recruitment design formats. The data come from the Survey of Today’s Adolescent Relationships and Transitions, which used a variety of forms of advertisements as survey recruitment tools on Facebook, Instagram, and Snapchat. Results demonstrate that design decisions such as the format of the advertisement (e.g., video or static) and the use of eligibility language on the advertisements impact the quality of the data as measured by break-off rates and the use of nonsubstantive responses. Additionally, the type of device used affected the measures of data quality.

Analyzing Social Media Research: A Data Quality and Research Reproducibility Perspective

2021 ◽  
pp. 227797522110118
Author(s):  
Amit K. Srivastava ◽  
Rajhans Mishra
Keyword(s):  
Social Media ◽  
Data Quality ◽  
Quality Of Data ◽  
Social Media Data ◽  
National Crisis ◽  
Social Media Platforms ◽  
Quality Issues ◽  
The One ◽  
Media Data

Social media platforms have become very popular these days among individuals and organizations. On the one hand, organizations use social media as a potential tool to create awareness of their products among consumers, and on the other hand, social media data is useful to predict the national crisis, election polls, stock prediction, etc. However, nowadays, a debate is going on about the quality of data generated on social media platforms, whether it is relevant for prediction and generalization. The article discusses the relevance and quality of data obtained from social media in the context of research and development. Social media data quality issues may impact the generalizability and reproducibility of the results of the study. The paper explores possible reasons for quality issues in the data generated over social media platforms along with the suggestive measures to minimize them using the proposed social media data quality framework.

scholarly journals Determinants of Self-referral among Outpatients at Referral Hospitals in East Wollega, Western Ethiopia

2019 ◽  
Author(s):  
Edosa Tesfaye Geta ◽  
Yibeltal Siraneh Belete ◽  
Elias Ali Yesuf
Keyword(s):  
Health Care Providers ◽  
Data Entry ◽  
Severity Of Illness ◽  
Cross Sectional Study ◽  
Health Facilities ◽  
Quality Of Services ◽  
Care Providers ◽  
Cross Sectional ◽  
Referral Hospitals

BackgroundPatient self-referral is a condition when patients refer themselves to higher level health facilities without having to see anyone else first or without being told to refer themselves by health professional. Despite the expansion in the number of health facilities, it has been seen when patients routinely accessed referral hospitals. The study aims to determine the magnitude and identify determinants of outpatient self-referral at referral hospitals.MethodsFacility based cross sectional study design was used to collect data from December 01- 30; 2017.The sample size was determined by using single population proportion formula. Data entry and analysis were made using SPSS version 20. Descriptive statistics of frequency, bivariate and multivariate logistic regression were performed.ResultsA total of 404 outpatients were included making response rate 96.8%. Among 391 outpatients interviewed 330(84.4%) were self-referred.The factors significantly associated with outpatient self-referral were referral information (AOR and 95%CI=0.324(0.150-0.696), illness severity (AOR and 95% CI=3.496(1.473-8.297), confidence of patients to get providers (AOR and 95 CI=3.027(1.510-6.070), availability of laboratory (AOR and 95%CI=4.966(2.199-11.216) and drugs (AOR and 95%CI=2.366(1.013-5.526) and quality of services (AOR and 95%CI=2.996(1.418-6.328).ConclusionThe proportion of outpatients’ self-referral was high and that associated with referral information, patient confidence to get health care providers, severity of illness, availability of laboratory and drugs, and quality of services. There should be monitoring system of referral linkage of health facilities at all levels and the health facilities should create awareness in the community about referral linkages of health facilities.

scholarly journals Data Quality Issues With Physician-Rating Websites: Systematic Review (Preprint)

2019 ◽  
Author(s):  
Pavankumar Mulgund ◽  
Raj Sharman ◽  
Priya Anand ◽  
Shashank Shekhar ◽  
Priya Karadi
Keyword(s):  
Data Quality ◽  
English Language ◽  
Quality Of Data ◽  
Contextual Data ◽  
Patient Reported ◽  
Physician Rating ◽  
Quality Framework ◽  
Quality Issues ◽  
The Impact

BACKGROUND In recent years, online physician-rating websites have become prominent and exert considerable influence on patients’ decisions. However, the quality of these decisions depends on the quality of data that these systems collect. Thus, there is a need to examine the various data quality issues with physician-rating websites. OBJECTIVE This study’s objective was to identify and categorize the data quality issues afflicting physician-rating websites by reviewing the literature on online patient-reported physician ratings and reviews. METHODS We performed a systematic literature search in ACM Digital Library, EBSCO, Springer, PubMed, and Google Scholar. The search was limited to quantitative, qualitative, and mixed-method papers published in the English language from 2001 to 2020. RESULTS A total of 423 articles were screened. From these, 49 papers describing 18 unique data quality issues afflicting physician-rating websites were included. Using a data quality framework, we classified these issues into the following four categories: intrinsic, contextual, representational, and accessible. Among the papers, 53% (26/49) reported intrinsic data quality errors, 61% (30/49) highlighted contextual data quality issues, 8% (4/49) discussed representational data quality issues, and 27% (13/49) emphasized accessibility data quality. More than half the papers discussed multiple categories of data quality issues. CONCLUSIONS The results from this review demonstrate the presence of a range of data quality issues. While intrinsic and contextual factors have been well-researched, accessibility and representational issues warrant more attention from researchers, as well as practitioners. In particular, representational factors, such as the impact of inline advertisements and the positioning of positive reviews on the first few pages, are usually deliberate and result from the business model of physician-rating websites. The impact of these factors on data quality has not been addressed adequately and requires further investigation.

Evaluating the Quality and Usefulness of Data Breach Information Systems

2013 ◽  
pp. 225-240
Author(s):  
Benjamin Ngugi ◽  
Jafar Mana ◽  
Lydia Segal
Keyword(s):  
Information Systems ◽  
Data Quality ◽  
Quality Data ◽  
Quality Of Data ◽  
Law Enforcement Agencies ◽  
Data Breach ◽  
Security Breaches ◽  
Key Stakeholders ◽  
The U.S

As the nation confronts a growing tide of security breaches, the importance of having quality data breach information systems becomes paramount. Yet too little attention is paid to evaluating these systems. This article draws on data quality scholarship to develop a yardstick that assesses the quality of data breach notification systems in the U.S. at both the state and national levels from the perspective of key stakeholders, who include law enforcement agencies, consumers, shareholders, investors, researchers, and businesses that sell security products. Findings reveal major shortcomings that reduce the value of data breach information to these stakeholders. The study concludes with detailed recommendations for reform.

scholarly journals Data Quality Issues With Physician-Rating Websites: Systematic Review

10.2196/15916 ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. e15916
Author(s):  
Pavankumar Mulgund ◽  
Raj Sharman ◽  
Priya Anand ◽  
Shashank Shekhar ◽  
Priya Karadi
Keyword(s):  
Data Quality ◽  
English Language ◽  
Quality Of Data ◽  
Contextual Data ◽  
Patient Reported ◽  
Physician Rating ◽  
Quality Framework ◽  
Quality Issues ◽  
The Impact

Background In recent years, online physician-rating websites have become prominent and exert considerable influence on patients’ decisions. However, the quality of these decisions depends on the quality of data that these systems collect. Thus, there is a need to examine the various data quality issues with physician-rating websites. Objective This study’s objective was to identify and categorize the data quality issues afflicting physician-rating websites by reviewing the literature on online patient-reported physician ratings and reviews. Methods We performed a systematic literature search in ACM Digital Library, EBSCO, Springer, PubMed, and Google Scholar. The search was limited to quantitative, qualitative, and mixed-method papers published in the English language from 2001 to 2020. Results A total of 423 articles were screened. From these, 49 papers describing 18 unique data quality issues afflicting physician-rating websites were included. Using a data quality framework, we classified these issues into the following four categories: intrinsic, contextual, representational, and accessible. Among the papers, 53% (26/49) reported intrinsic data quality errors, 61% (30/49) highlighted contextual data quality issues, 8% (4/49) discussed representational data quality issues, and 27% (13/49) emphasized accessibility data quality. More than half the papers discussed multiple categories of data quality issues. Conclusions The results from this review demonstrate the presence of a range of data quality issues. While intrinsic and contextual factors have been well-researched, accessibility and representational issues warrant more attention from researchers, as well as practitioners. In particular, representational factors, such as the impact of inline advertisements and the positioning of positive reviews on the first few pages, are usually deliberate and result from the business model of physician-rating websites. The impact of these factors on data quality has not been addressed adequately and requires further investigation.

scholarly journals Improving the quality of EHR recording in primary care: a data quality feedback tool

2016 ◽  
Vol 24 (1) ◽  
pp. 81-87 ◽  
Author(s):  
Sjoukje van der Bij ◽  
Nasra Khan ◽  
Petra ten Veen ◽  
Dinny H de Bakker ◽  
Robert A Verheij
Keyword(s):  
Data Quality ◽  
Health Care Providers ◽  
Software Package ◽  
Diagnostic Code ◽  
Care Providers ◽  
Software Packages ◽  
Quality Feedback ◽  
Exchange Information ◽  
Episodes Of Care

Objective: Electronic health record (EHR) data are used to exchange information among health care providers. For this purpose, the quality of the data is essential. We developed a data quality feedback tool that evaluates differences in EHR data quality among practices and software packages as part of a larger intervention. Methods: The tool was applied in 92 practices in the Netherlands using different software packages. Practices received data quality feedback in 2010 and 2012. Results: We observed large differences in the quality of recording. For example, the percentage of episodes of care that had a meaningful diagnostic code ranged from 30% to 100%. Differences were highly related to the software package. A year after the first measurement, the quality of recording had improved significantly and differences decreased, with 67% of the physicians indicating that they had actively changed their recording habits based on the results of the first measurement. About 80% found the feedback helpful in pinpointing recording problems. One of the software vendors made changes in functionality as a result of the feedback. Conclusions: Our EHR data quality feedback tool is capable of highlighting differences among practices and software packages. As such, it also stimulates improvements. As substantial variability in recording is related to the software package, our study strengthens the evidence that data quality can be improved substantially by standardizing the functionalities of EHR software packages.

An Assessment of the Quality of Data From the EPI for Knowledge Management in Healthcare in Ghana

2021 ◽  
pp. 195-216
Author(s):  
Patrick Ohemeng Gyaase ◽  
Joseph Tei Boye-Doe ◽  
Christiana Okantey
Keyword(s):  
Knowledge Management ◽  
Data Quality ◽  
Information Management ◽  
Information Management System ◽  
Quality Data ◽  
Quality Of Data ◽  
District Health ◽  
Self Assessment ◽  
Immunization Programme

Quality data from the Expanded Immunization Programme (EPI), which is pivotal in reducing infant mortalities globally, is critical for knowledge management on the EPI. This chapter assesses the quality of data from the EPI for the six childhood killer diseases from the EPI tally books, monthly reports, and the District Health Information Management System (DHIMS II) using the Data Quality Self-Assessment (DQS) tool of WHO. The study found high availability and completeness of data in the EPI tally books and the monthly EPI reports. The accuracy and currency of data on all antigens from EPI tally books compared to reported number issued were comparatively low. The composite quality index of the data from the EPI is thus low, an indication poor supervision of the EPI programme in the health facilities. There is therefore, the need for effective monitoring and data validation at the point of collection and entry to improve the data quality for knowledge management on the EPI programme.

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