Data Cleaning: Detecting, Diagnosing, and Editing Data Abnormalities

Broeck, Jan Van den; Cunningham, Solveig A.; Eeckels, R; Herbst, Kobus

doi:10.1371/journal.pmed.0020267

Cited by 314 publications

(225 citation statements)

References 12 publications

(7 reference statements)

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“…The extent of the data-cleaning helps communicate the quality of the data and the analytic rigour of the investigators. 25 Finally, changing eligibility over time is a component of the recommended limitations, which should be addressed in the study discussion. This can occur when there is a shift in the coding structure or coding practice within the RCD source over time.…”

Section: Current Status Of Reporting Of Rcd Studies In Urologymentioning

confidence: 99%

A review of routinely collected data studies in urology: Methodological considerations, reporting quality, and future directions

Welk

Kwong

2017

CUAJ

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Studies using routinely collected data (RCD) are common in the urological literature; however, there are important considerations in the creation and review of RCD discoveries. A recent reporting guideline (REporting of studies Conducted using Observational Routinely-collected health Data, RECORD) was developed to improve the reporting of these studies. This narrative review examines important considerations for RCD studies. To assess the current level of reporting in the urological literature, we reviewed all the original research articles published in Journal of Urology and European Urology in 2014, and determined the proportion of the RECORD checklist items that were reported for RCD studies. There were 56 RCD studies identified among the 608 articles. When the RECORD items were considered applicable to the specific study, they were reported in 52.5% of cases. Studies most consistently (>80% of them) reported the names of the data sources, the study time frame, the extent to which the authors could access the database source, the patient selection, and discussed missing data. Few studies (<25%) discussed validation of key coding elements, details on data-linkage, data-cleaning, the impact of changing eligibility over time, or provided the complete list of coding elements used to define key study variables. Reporting factors specifically relevant in RCD studies may serve to increase the quality of these studies in the urological literature. With increased technological integration in healthcare and the proliferation of electronic medical records, RCD will continue to be an important source for urological research. IntroductionRoutinely collected data (RCD, also known as administrative data) is increasingly being used to answer clinical research questions. While there are many sources of observational data (for example, population surveys or patient registries), RCD is specifically defined as data that is routinely collected for a purpose other than research. Common examples of RCD sources include physician billing records, insurance company records, or government-mandated healthcare utilization records. 1There are several important strengths that have driven the increased use of RCD research over the last two decades. These include low study costs (especially when compared to prospective clinical studies or randomized, controlled trials), rapid delivery of results, substantial sample sizes, improved generalizability, long-term patient followup (often across different hospitals and physician practices), and the ability to identify very rare patient populations or outcomes.Given the apparent increase in publications using RCD in many urological journals, we sought to review several key issues related to this research area that are relevant for those conducting these studies, and for clinicians who wish to critically read these studies, understand potential methodological limitations, and evaluate the strength of the study's conclusions. Methodological considerations for the use of RCDAny study using RCD ...

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Section: Current Status Of Reporting Of Rcd Studies In Urologymentioning

confidence: 99%

A review of routinely collected data studies in urology: Methodological considerations, reporting quality, and future directions

Welk

Kwong

2017

CUAJ

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“…6 -8 Types of errors included letter and number reversal (e.g., "sh" entered as "hs;" unintentional repeats or deletions of numbers, letters, or decimal points; extraneous characters; simple transcription and reading errors; data entered into the incorrect field; and skipping fields when data were available). 9,10 In this study, the name fields were most prone to noncorrectable errors. Long alphabetic fields such as names and addresses have previously been noted to have error rates 10 to 15 times higher than numeric fields.…”

Section: Discussionmentioning

confidence: 82%

The "Measuring Outcomes of Clinical Connectivity" (MOCC) Trial: Investigating Data Entry Errors in the Electronic Primary Care Research Network (ePCRN)

Fontaine

Mendenhall²,

Peterson

et al. 2007

The Journal of the American Board of Family Medicine

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Introduction:The electronic Primary Care Research Network (ePCRN) enrolled PBRN researchers in a feasibility trial to test the functionality of the network's electronic architecture and investigate error rates associated with two data entry strategies used in clinical trials.Methods: PBRN physicians and research assistants who registered with the ePCRN were eligible to participate. After online consent and randomization, participants viewed simulated patient records, presented as either abstracted data (short form) or progress notes (long form). Participants transcribed 50 data elements onto electronic case report forms (CRFs) without integrated field restrictions. Data errors were analyzed.Results: Ten geographically dispersed PBRNs enrolled 100 members and completed the study in less than 7 weeks. The estimated overall error rate if field restrictions had been applied was 2.3%. Participants entering data from the short form had a higher rate of correctly entered data fields (94.5% vs 90.8%, P ‫؍‬ .004) and significantly more error-free records (P ‫؍‬ .003).

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“…It removes errors, reduces the chance of inaccuracies, and repairs problems that may occur within databases. 15 The data cleaning intervention was performed by a computer systems analyst and programmer from the Perth and Hills Division of General Practice. GPs and practice nurses were surveyed before the register cleansing took place, mid-point during the intervention and post intervention.…”

Section: Methodology Samplementioning

confidence: 99%

Attitudes and practices of recording diabetic patient information within an Australian general practice setting: an exploratory study

Civil

Bulsara

Karner

et al. 2009

Journal of Innovation in Health Informatics

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Background An accurate diabetes register enables a general practice to effectively monitor and manage the services for their patients with diabetes. This pilot project builds on the National Primary Care Collaboratives Program (a quality improvement programme for general practice) as the first change principle for managing chronic diseases. Objectives The main aim of the project was to improve the systems management of electronic registers of people with diabetes in the general practice setting. The pilot project assessed the uptake, awareness and confidence levels amongst practice staff in improving the diabetes register. Method This was completed by conducting a survey of general practitioners and practice nurses

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Data Cleaning: Detecting, Diagnosing, and Editing Data Abnormalities

Abstract: In this policy forum the authors argue that data cleaning is an essential part of the research process, and should be incorporated into study design.

Cited by 314 publications

References 12 publications

A review of routinely collected data studies in urology: Methodological considerations, reporting quality, and future directions

A review of routinely collected data studies in urology: Methodological considerations, reporting quality, and future directions

The "Measuring Outcomes of Clinical Connectivity" (MOCC) Trial: Investigating Data Entry Errors in the Electronic Primary Care Research Network (ePCRN)

Attitudes and practices of recording diabetic patient information within an Australian general practice setting: an exploratory study

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