Assessing data quality and the variability of source data verification auditing methods in clinical research settings

INTRODUCTION Data audits within clinical settings are extensively used as a major strategy to identify errors, monitor study operations and ensure high-quality data. However, clinical trial guidelines are non-specific in regards to recommended frequency, timing and nature of data audits. The absence of a well-defined data quality definition and method to measure error undermines the reliability of data quality assessment. This review aimed to assess the variability of source data verification (SDV) auditing methods to monitor data quality in a clinical research setting. MATERIAL AND METHODS The scientific databases MEDLINE, Scopus and Science Direct were searched for English language publications, with no date limits applied. Studies were considered if they included data from a clinical trial or clinical research setting and measured and/or reported data quality using a SDV auditing method. RESULTS In total 15 publications were included. The nature and extent of SDV audit methods in the articles varied widely, depending upon the complexity of the source document, type of study, variables measured (primary or secondary), data audit proportion (3-100%) and collection frequency (6-24 months). Methods for coding, classifying and calculating error were also inconsistent. Transcription errors and inexperienced personnel were the main source of reported error. Repeated SDV audits using the same dataset demonstrated ∼ 40% improvement in data accuracy and completeness over time. No description was given in regards to what determines poor data quality in clinical trials. CONCLUSIONS A wide range of SDV auditing methods are reported in the published literature though no uniform SDV auditing method could be determined for "best practice" in clinical trials. Published audit methodology articles are warranted for the development of a standardised SDV auditing method to monitor data quality in clinical research settings.

[1]  Michael G. Kahn,et al.  Remote Source Document Verification in Two National Clinical Trials Networks: A Pilot Study , 2013, PloS one.

[2]  J D Horbar,et al.  An assessment of data quality in the Vermont-Oxford Trials Network database. , 1995, Controlled clinical trials.

[3]  J. Higgins,et al.  Cochrane Handbook for Systematic Reviews of Interventions , 2010, International Coaching Psychology Review.

[4]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[5]  Arun Bhatt Quality of clinical trials: A moving target , 2011, Perspectives in clinical research.

[6]  D. Moher,et al.  Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement , 2009, BMJ.

[7]  C. Reid,et al.  Quality control activities associated with registries in interventional cardiology and surgery. , 2011, Heart, lung & circulation.

[8]  Chitra Bargaje Good documentation practice in clinical research , 2011, Perspectives in clinical research.

[9]  R. Califf,et al.  Developing systems for cost-effective auditing of clinical trials. , 1997, Controlled clinical trials.

[10]  S L George,et al.  Guidelines for quality assurance in multicenter trials: a position paper. , 1998, Controlled clinical trials.

[11]  Sheena McCormack,et al.  The potential for central monitoring techniques to replace on-site monitoring: findings from an international multi-centre clinical trial , 2012, Clinical trials.

[12]  Jules Mitchel,et al.  Risk-Based Source Data Verification Approaches: Pros and Cons , 2010 .

[13]  M. Mack,et al.  Are unaudited records from an outcomes registry database accurate? , 2004, The Annals of thoracic surgery.

[14]  Carl F. Pieper,et al.  Quantifying Data Quality for Clinical Trials Using Electronic Data Capture , 2008, PloS one.

[15]  Jane M Blazeby,et al.  A systematic review of on-site monitoring methods for health-care randomised controlled trials , 2013, Clinical trials.

[16]  Catrin Tudur Smith,et al.  The Value of Source Data Verification in a Cancer Clinical Trial , 2012, PloS one.

[17]  Roxanne E. Ward,et al.  Examining Methods and Practices of Source Data Verification in Canadian Critical Care Randomized Controlled Trials , 2013 .

[18]  K. Mate,et al.  Improving public health information: a data quality intervention in KwaZulu-Natal, South Africa. , 2012, Bulletin of the World Health Organization.

[19]  Daniel R. Masys,et al.  Measuring the Quality of Observational Study Data in an International HIV Research Network , 2012, PloS one.

[20]  J. van den Broeck,et al.  Maintaining data integrity in a rural clinical trial , 2007, Clinical trials.

[21]  药学 International Conference on Harmonisation of Technical Requirements , 2013 .

[22]  Meredith Nahm,et al.  What can we learn from a decade of database audits? The Duke Clinical Research Institute experience, 1997—2006 , 2009, Clinical trials.

[23]  Sylvie Chevret,et al.  Validation of a risk-assessment scale and a risk-adapted monitoring plan for academic clinical research studies--the Pre-Optimon study. , 2011, Contemporary clinical trials.

[24]  Ichael,et al.  THE NATIONAL CANCER INSTITUTE AUDIT OF THE NATIONAL SURGICAL ADJUVANT , 2000 .

[25]  Charles Safran,et al.  Toward a national framework for the secondary use of health data: an American Medical Informatics Association White Paper. , 2007, Journal of the American Medical Informatics Association : JAMIA.

[26]  Sue E Bowman Impact of electronic health record systems on information integrity: quality and safety implications. , 2013, Perspectives in health information management.

[27]  D. Moher,et al.  Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement , 2009, BMJ : British Medical Journal.

[28]  D. Sarfati,et al.  An audit of colon cancer data on the New Zealand Cancer Registry. , 2008, The New Zealand medical journal.

[29]  Barbara Castelnuovo,et al.  Implementation of Provider-Based Electronic Medical Records and Improvement of the Quality of Data in a Large HIV Program in Sub-Saharan Africa , 2012, PloS one.

[30]  E van der Schueren,et al.  Quality control of validity of data collected in clinical trials. EORTC Study Group on Data Management (SGDM). , 1989, European journal of cancer & clinical oncology.

[31]  Jonathan R Davis,et al.  Assuring Data Quality and Validity in Clinical Trials for Regulatory Decision Making , 1999 .

[32]  Eric E. Smith,et al.  Data quality in the American Heart Association Get With The Guidelines-Stroke (GWTG-Stroke): results from a national data validation audit. , 2012, American heart journal.

[33]  Christian Ohmann,et al.  Risk analysis and risk adapted on-site monitoring in noncommercial clinical trials , 2009, Clinical trials.

[34]  Samantha J. Togni,et al.  Understanding uptake of continuous quality improvement in Indigenous primary health care: lessons from a multi-site case study of the Audit and Best Practice for Chronic Disease project , 2010, Implementation science : IS.

[35]  R. Franklin,et al.  Verification of data in congenital cardiac surgery , 2008, Cardiology in the Young.

[36]  Jessica D. Tenenbaum,et al.  Practices and perspectives on building integrated data repositories: results from a 2010 CTSA survey , 2012, J. Am. Medical Informatics Assoc..

[37]  Dirk Hasenclever,et al.  Risk-adapted monitoring is not inferior to extensive on-site monitoring: Results of the ADAMON cluster-randomised study , 2017, Clinical trials.

[38]  Eric L Eisenstein,et al.  Reducing the costs of phase III cardiovascular clinical trials. , 2005, American heart journal.