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The Current State of Validating the Accuracy of Clinical Data Reporting: Lessons to Be Learned from Quality and Process Improvement Scientists

Published online by Cambridge University Press:  02 January 2015

Joseph A. Fortuna ,
William A. Brenneman ,
Sandra Storli ,
David Birnbaum  and
Kay L. Brown
Joseph A. Fortuna*
Affiliation:
American Society for Quality (ASQ) Healthcare Division, Milwaukee, Wisconsin
William A. Brenneman
Affiliation:
Quantitative Sciences, Procter & Gamble Company, Cincinnati, Ohio
Sandra Storli
Affiliation:
Abbott Point of Care and ASQ Audit Division, Princeton, New Jersey
David Birnbaum
Affiliation:
Division of Disease Control and Health Statistics, Washington State Department of Health, Olympia, Washington
Kay L. Brown
Affiliation:
Heartland Kidney Network and Kansas City ASQ Section, Kansas City, Missouri
*
2040 Peniston Street, New Orleans, LA 70115 (josephf41@aol.com).
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Abstract

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Type
Commentary
Information
Infection Control & Hospital Epidemiology , Volume 34 , Issue 6 , June 2013 , pp. 611 - 614
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2013

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References

1. Centers for Disease Control and Prevention. National and state healthcare-associated infections standardized infection ratio report: using data reported to the National Healthcare Safety Network, January–December 2010. http://www.cdc.gov/hai/pdfs/SIR/national-SIR-Report_03_29_2012.pdf. Accessed October 22, 2012.Google Scholar
2. Centers for Disease Control and Prevention. First state-specific healthcare-associated infections summary data report, January–June, 2009. http://www.cdc.gov/hai/pdfs/stateplans/SIR_05_25_2010.pdf. Accessed August 22, 2012.Google Scholar
3. Centers for Disease Control and Prevention. National health-care-associated infections standardized infection ratio report: using data reported to the National Healthcare Safety Network, July 2009 through December 2009. http://www.cdc.gov/HAI/pdfs/stateplans/SIR-2010_JunDec2009.pdf. Accessed August 22, 2012.Google Scholar
4.Muscarella, LF. Assessment of the reported effectiveness of five different quality-improvement initiatives for the prevention of central line-associated bloodstream infections in intensive care units. Open Epidemiol J 2012;5:5–12.Google Scholar
5.Zarate, R, Cummings, J, Birnbaum, D. A practical method to validate the accuracy of state-wide hospital infection surveillance. Fifth Decennial International Conference on Healthcare-Associated Infections, Atlanta, Georgia, 2010. Abstract 842.Google Scholar
6. Maryland Health Care Commission. Maryland central line-associated blood stream infections: data quality review and chart audit, June 2010. http://mhcc.dhmh.maryland.gov/hai/Documents/sp.mhcc.maryland.gov/healthcare_associated_infections/hai/clabsi_final_rpt_20100618.pdf. Accessed August 22, 2012.Google Scholar
7. American Society for Quality. Harold F. Dodge: sampling was his forte, simplicity his creed, http://asq.org/about-asq/who-we-are/bio_dodge.html. Accessed August 22, 2012.Google Scholar
8.Oh, JY, Cunningham, MC, Beldavs, ZG, et al.Statewide validation of hospital-reported central line–associated bloodstream infections: Oregon, 2009. Infect Control Hosp Epidemiol 2012;33(5): 439–445.Google Scholar
9.John Snow, Inc. New Hampshire hospital associated infections validation. http://www.jsi.com/JSIInternetProjects/InternetProjectFactSheet.cfm?dblProjDescID = 8181. Accessed August 22, 2012.Google Scholar
10.John Snow, Inc. Massachusetts healthcare-associated infections prevention and control. http://www.jsi.com/JSIInternetProjects/InternetProjectFactSheet.cfm?dblProjDescID=338. Accessed August 22, 2012.Google Scholar
11.Backman, LA, Melchreit, R, Rodriquez, R. Validation of the surveillance and reporting of central line-associated bloodstream infection data to a state health department. Am J Infect Control 2010;38(10):832–838.Google Scholar
12. American Society for Quality. Quality glossary, http://asq.org/glossary/. Accessed August 22, 2012.Google Scholar
13.Porta, M, ed. Dictionary of epidemiology. 5th ed. New York: Oxford University Press, 2008.Google Scholar
14. International Organization for Standardization. ISO standards. http://www.iso.org/iso/iso_catalogue.htm. Accessed August 22, 2012.Google Scholar
15. Washington State Department of Health. Healthcare Associated Infections Program group administrator instructions for validation of surveillance programs. Version 1.0. Document DOH 200-001. Published September 2010.Google Scholar
16.Wish, JB. The impact of bundling on clinical practice. Renal and Urology News. http://www.renalandurologynews.com/the-impact-of-bundling-on-clinical-practice/article/233271/. Published March 22, 2012. Accessed January 6, 2013.Google Scholar
17.Delva, O. CROWNWeb and its impact on vascular access data collection. Renal and Urology News. http://www.renalandurologynews.com/crownweb-and-its-impact-on-vascular-access-data-collection/article/168922/#. Published April 29, 2010. Accessed January 6, 2013.Google Scholar
18.Uhlig, K, Berns, JS, Kestenbaum, B, et al.KDOQI US commentary on the 2009 KDIGO Clinical Practice Guideline for the Diagnosis, Evaluation, and Treatment of CKD–Mineral and Bone Disorder (CKD-MBD). Am J Kidney Dis 2010;55(5):773–799. http://www.kdigo.org/clinical_practice_guidelines/pdf/KDOQI%20CKD-MBD%20commentary.pdf. Published April 5, 2010. Accessed January 6, 2013.CrossRefGoogle ScholarPubMed
19. Medicare Learning Network. Dialysis adequacy, infection and vascular access reporting. MLN Matters number MM6782. http://www.cms.gov/Outreach-and-Education/Medicare-Learning-Network-MLN/MLNMattersArticles/downloads/MM6782.pdf. Revised December 11, 2012. Accessed January 6, 2013.Google Scholar