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.

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.

Ontology for Data Quality and Chronic Disease Management

2015 ◽  
pp. 303-331
Author(s):  
Alireza Rahimi ◽  
Siaw-Teng Liaw ◽  
Pradeep Kumar Ray ◽  
Jane Taggart ◽  
Hairong Yu
Keyword(s):  
Chronic Disease ◽  
Data Quality ◽  
English Language ◽  
Assessment Tools ◽  
Fitness For Purpose ◽  
Language Studies ◽  
Full Text Review ◽  
Automated Tools ◽  
The Impact

Improved Data Quality (DQ) can improve the quality of decisions and lead to better policy in health organizations. Ontologies can support automated tools to assess DQ. This chapter examines ontology-based approaches to conceptualization and specification of DQ based on “fitness for purpose” within the health context. English language studies that addressed DQ, fitness for purpose, ontology-based approaches, and implementations were included. The authors screened 315 papers; excluded 36 duplicates, 182 on abstract review, and 46 on full-text review; leaving 52 papers. These were appraised with a realist “context-mechanism-impacts/outcomes” template. The authors found a lack of consensus frameworks or definitions for DQ and comprehensive ontological approaches to DQ or fitness for purpose. The majority of papers described the processes of the development of DQ tools. Some assessed the impact of implementing ontology-based specifications for DQ. There were few evaluative studies of the performance of DQ assessment tools developed; none compared ontological with non-ontological approaches.

scholarly journals A systematic review of patient-reported outcome measures in paediatric otolaryngology

2017 ◽  
Vol 132 (1) ◽  
pp. 2-7 ◽  
Author(s):  
J Powell ◽  
S Powell ◽  
A Robson
Keyword(s):  
Outcome Measures ◽  
English Language ◽  
Health Assessment ◽  
Paediatric Population ◽  
Patient Reported Outcome Measures ◽  
Patient Reported Outcome ◽  
Specific Patient ◽  
Patient Reported ◽  
The Impact

AbstractBackground:Recently, there has been increased emphasis on the development and application of patient-reported outcome measures. This drive to assess the impact of illness or interventions, from the patient's perspective, has resulted in a greater number of available questionnaires. The importance of selecting an appropriate patient-reported outcome measure is specifically emphasised in the paediatric population. The literature on patient-reported outcome measures used in paediatric otolaryngology was reviewed.Methods:A comprehensive literature search was conducted using the databases Medline, Embase, Cumulative Index to Nursing and Allied Health Literature, and PsycInfo, using the terms: ‘health assessment questionnaire’, ‘structured questionnaire’, ‘questionnaire’, ‘patient reported outcome measures’, ‘PROM’, ‘quality of life’ or ‘survey’, and ‘children’ or ‘otolaryngology’. The search was limited to English-language articles published between 1996 and 2016.Results:The search yielded 656 articles, of which 63 were considered relevant. This included general paediatric patient-reported outcome measures applied to otolaryngology, and paediatric otolaryngology disease-specific patient-reported outcome measures.Conclusion:A large collection of patient-reported outcome measures are described in the paediatric otolaryngology literature. Greater standardisation of the patient-reported outcome measures used in paediatric otolaryngology would assist in pooling of data and increase the validation of tools used.

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 IMPACT OF LOTTERY INCENTIVE ON RESPONSE RATE AND DATA QUALITY: EVIDENCE FROM ORGANIC FOOD CONSUMPTION SURVEY OF CONVENTIONAL SHOPPERS

2021 ◽  
Vol 2 (2) ◽  
pp. 68-74
Author(s):  
Shahidul Islam
Keyword(s):  
Data Quality ◽  
Food Consumption ◽  
Response Rate ◽  
Organic Food ◽  
Grocery Stores ◽  
Increase Response Rate ◽  
Quality Of Data ◽  
Organic Food Consumption ◽  
The Impact

Incentives of different forms and at different stages are used for motivating people to participate in human subject research. Although it is widely accepted that incentives, in general, play a positive role in increasing participation rate and are widely used, there are exceptions that they may not increase response rate and may even contaminate the quality of data resulting in poor research findings. This study examines the impact of pre- and post-disclosed committed lottery incentives on response rate and data quality in a face-to-face survey of conventional consumers for organic food consumption. A survey was conducted at the premises of four conventional grocery stores in Edmonton, Alberta, Canada. Half of the randomly approached and agreed upon respondents were disclosed the lottery incentives at the beginning, and the rest half were told at the end. Data quality was measured using three indicators – edit occurrences, imputation occurrences, and proportion of incomplete answers. Our study finds little difference in response rate between pre- and post-disclosed committed lottery payments. However, the useability of incomplete questionnaires among post-disclosed lottery was significantly higher than those of pre-disclosed. Our study also shows that people with likings of organic food and buying organic food more frequently are likely to offer a better quality of information.

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.

scholarly journals Assessment of the Nursing Quality Indicators for Reporting and Evaluation (NQuIRE) database using a data quality index

2020 ◽  
Vol 27 (5) ◽  
pp. 776-782 ◽  
Author(s):  
Shanoja Naik ◽  
Stephanie Voong ◽  
Megan Bamford ◽  
Kyle Smith ◽  
Angela Joyce ◽  
...  
Keyword(s):  
Case Report ◽  
Data Quality ◽  
Quality Indicators ◽  
Quality Index ◽  
Best Practice ◽  
Quality Framework ◽  
The Impact ◽  
Nursing Quality Indicators ◽  
Nursing Quality

Abstract A comprehensive data quality assessment is necessary to expand a nursing database that is designed for evaluating the impact of implementing Best Practice Guidelines (BPG) developed by the Registered Nurses’ Association of Ontario (RNAO). This case report presents a method to standardize data quality assessments of the Nursing Quality Indicators for Reporting and Evaluation (NQuIRE) database by developing a data quality framework (DQF) and assessing key dimensions of the framework using a data quality index (DQI). The data quality index is a single key performance metric for assessing the quality of the database. The aims of sharing this case report are 2-fold: (1) to promote best practices for assessing data quality by developing and implementing a data quality framework and (2) to demonstrate an unprecedented method of assessing the data quality of a nursing database.

scholarly journals Beyond Data Validation – Advanced Strategies for Assessing Data Quality for Oil Spill Investigations

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Linda Cook ◽  
Laurie Benton ◽  
Melanie Edwards
Keyword(s):  
Data Quality ◽  
Oil Spill ◽  
Analytical Data ◽  
Data Validation ◽  
Quality Of Data ◽  
Validation Process ◽  
Chemistry Data ◽  
Quality Issues ◽  
The Impact ◽  
Analytical Approaches

ABSTRACT Field sampling investigations in response to oil spill incidents are growing increasingly more complex with analytical data collected by a variety of interested parties over many years and with different investigative purposes. For the Deepwater Horizon (DWH) Oil Spill, the analytical chemistry data and toxicity study data were required to be validated in accordance with U.S. Environmental Protection Agency's (EPA's) data validation for Superfund program methods. The process of validating data according to EPA guidelines is a manual and time-consuming process focused on chemistry results for individual samples within a single data package to assess if data meet quality control criteria. In hindsight, the burden of validating all of the chemistry data appears to be excessive, and for some parameters unnecessary, which was costly and slowed the process of disseminating data. Depending on the data use (e.g., assessing human and ecological risk, qualitative oil tracking, or forensic fingerprinting), data validation may not be needed in every circumstance or for every data type. Publicly available water column, sediment, and oil chemistry analytical data associated with the DWH Oil Spill, obtained from the Gulf of Mexico Research Initiative Information and Data Cooperative data portal were evaluated to understand the impact, effort, accuracy, and benefit of the data validation process. Questions explored include: What data changed based on data validation reviews?How would these changes affect the associated data evaluation findings?Did data validation introduce additional errors?What data quality issues did the data validation process miss?What statistical and data analytical approaches would more efficiently identify potential data quality issues? Based on our evaluation of the chemical data associated with the DWH Oil Spill, new strategies to assess the quality of data associated with oil spill investigations will be presented.

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.

The effect of surgery in tenosynovial giant cell tumours as measured by patient-reported outcomes on quality of life and joint function

2019 ◽  
Vol 101-B (3) ◽  
pp. 272-280 ◽  
Author(s):  
F. G. M. Verspoor ◽  
M. J. L. Mastboom ◽  
G. Hannink ◽  
W. T. A. van der Graaf ◽  
M. A. J. van de Sande ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Giant Cell ◽  
Diffuse Type ◽  
Joint Function ◽  
Population Means ◽  
Sf 36 ◽  
Patient Reported ◽  
The Impact

Aims The aim of this study was to evaluate health-related quality of life (HRQoL) and joint function in tenosynovial giant cell tumour (TGCT) patients before and after surgical treatment. Patients and Methods This prospective cohort study run in two Dutch referral centres assessed patient-reported outcome measures (PROMs; 36-Item Short-Form Health Survey (SF-36), visual analogue scale (VAS) for pain, and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC)) in 359 consecutive patients with localized- and diffuse-type TGCT of large joints. Patients with recurrent disease (n = 121) and a wait-and-see policy (n = 32) were excluded. Collected data were analyzed at specified time intervals preoperatively (baseline) and/or postoperatively up to five years. Results A total of 206 TGCT patients, 108 localized- and 98 diffuse-type, were analyzed. Median age at diagnosis of localized- and diffuse-type was 41 years (interquartile range (IQR) 29 to 49) and 37 years (IQR 27 to 47), respectively. SF-36 analyses showed statistically significant and clinically relevant deteriorated preoperative and immediate postoperative scores compared with general Dutch population means, depending on subscale and TGCT subtype. After three to six months of follow-up, these scores improved to general population means and continued to be fairly stable over the following years. VAS scores, for both subtypes, showed no statistically significant or clinically relevant differences pre- or postoperatively. In diffuse-type patients, the improvement in median WOMAC score was statistically significant and clinically relevant preoperatively versus six to 24 months postoperatively, and remained up to five years’ follow-up. Conclusion Patients with TGCT report a better HRQoL and joint function after surgery. Pain scores, which vary hugely between patients and in patients over time, did not improve. A disease-specific PROM would help to decipher the impact of TGCT on patients’ daily life and functioning in more detail. Cite this article: Bone Joint J 2019;101-B:272–280.

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