Online from: 1988
Subject Area: Health Care Management/Healthcare
|Title:||Improving laboratory data entry quality using Six Sigma|
|Author(s):||Ali Elbireer, (Clinical Core Laboratory, Infectious Diseases Institute, Makerere University-Johns Hopkins University (MU-JHU), Kampala, Uganda), Julie Le Chasseur, (Pharmaceutical Sciences Analytical Research & Development, Pfizer Worldwide Research & Development, Sandwich, UK), Brooks Jackson, (Clinical Core Laboratory, Infectious Diseases Institute, Makerere University-Johns Hopkins University (MU-JHU), Kampala, Uganda)|
|Citation:||Ali Elbireer, Julie Le Chasseur, Brooks Jackson, (2013) "Improving laboratory data entry quality using Six Sigma", International Journal of Health Care Quality Assurance, Vol. 26 Iss: 6, pp.496 - 509|
|Keywords:||Cost reduction, Data handling, Laboratories, Quality improvement, Six Sigma, Uganda|
|Article type:||Conceptual paper|
|DOI:||10.1108/IJHCQA-08-2011-0050 (Permanent URL)|
|Publisher:||Emerald Group Publishing Limited|
|Acknowledgements:||Special thanks are given to the MU-JHU Core Laboratory at the Infectious Diseases Institute (IDI) processing team and the Six Sigma Quality Improvement Team including: Paula Namayanja, Sam Acellam, Rogers Kisame, Timothy Matovu, Emmanuel Okiring, Alice Nansamba, Wilberforce Bisoborwa, Bosco Sabila, Charles Nyende, Esther Nakyala, Ellen Mukokoma, Emmanuel Hakiza and Patrick Karugaba.|
Purpose – The Uganda Makerere University provides clinical laboratory support to over 70 clients in Uganda. With increased volume, manual data entry errors have steadily increased, prompting laboratory managers to employ the Six Sigma method to evaluate and reduce their problems. The purpose of this paper is to describe how laboratory data entry quality was improved by using Six Sigma.
Design/methodology/approach – The Six Sigma Quality Improvement (QI) project team followed a sequence of steps, starting with defining project goals, measuring data entry errors to assess current performance, analyzing data and determining data-entry error root causes. Finally the team implemented changes and control measures to address the root causes and to maintain improvements. Establishing the Six Sigma project required considerable resources and maintaining the gains requires additional personnel time and dedicated resources.
Findings – After initiating the Six Sigma project, there was a 60.5 percent reduction in data entry errors from 423 errors a month (i.e. 4.34 Six Sigma) in the first month, down to an average 166 errors/month (i.e. 4.65 Six Sigma) over 12 months. The team estimated the average cost of identifying and fixing a data entry error to be $16.25 per error. Thus, reducing errors by an average of 257 errors per month over one year has saved the laboratory an estimated $50,115 a year.
Practical implications – The Six Sigma QI project provides a replicable framework for Ugandan laboratory staff and other resource-limited organizations to promote quality environment. Laboratory staff can deliver excellent care at a lower cost, by applying QI principles.
Originality/value – This innovative QI method of reducing data entry errors in medical laboratories may improve the clinical workflow processes and make cost savings across the health care continuum.
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