Six Sigma project reduces analytical errors in an automated lab

Medical Laboratory Observer, June, 2005 by Nancy Riebling, Laurel Tria

The North Shore-LIJ Health System is the third-largest nonsectarian health system in the country. Created in 1998, the health system's laboratory model consists of a strategically located core lab that uses total laboratory automation and offers consolidated testing, a rapid response lab in each of the system's 18 hospitals, a standardized LIS, and standardized laboratory instrumentation. The core laboratory performs over 3.5 million tests annually for a client base comprised of hospitals, long-term care facilities, clinical trials, physician offices, and reference testing. The lab performs approximately 65% of the routine testing for the network as well as all microbiology, esoteric, molecular diagnostic, and reference testing.

As part of the laboratory's ongoing performance-improvement process, changed results had been measured for years. Although the average percentage of changed results was consistently below 1% in the three main areas of the laboratory--hematology, coagulation, and chemistry--the administration had noted that eight analytical process failures had occurred in the first half of 2003, resulting in the correction of reported values that affected multiple patients at one time.

The problem was sporadic; there was no clear solution; and correcting the issue would help achieve the core lab's goal of improving patient care, increasing customer satisfaction, and boosting staff morale. The core lab's administration believed that reducing the number of failed analytical processes was a worthy goal for a Six Sigma project. A multidisciplinary team of technical management, information systems staff, and physicians assembled to tackle the problem using the Six Sigma define, measure, analyze, improve, and control (DMAIC) approach.

Six Sigma methodology

Six Sigma, a focused, high-impact process, uses proven quality principles and techniques to reduce process variance, and seeks to confine errors to 3.4 defects per million opportunities (DPMO). Six Sigma relies on rigorous statistical methods and implements control mechanisms in order to tie together quality, cost, process, people, and accountability, and begins with an understanding of customer requirements and values (referred to as voice of the customer). Once these are defined, Six Sigma's process enables the identification of factors critical to customer satisfaction. The processes involved in these critical factors are then analyzed and measured. Improvement strategies are focused on the vital "X." The Six Sigma goal is to reduce both variance and control processes in order to assure compliance with the critical specifications.

Defining and measuring the process

During the define phase, the Six Sigma team developed a high-level process map (see Figure 1), with the initial step being preparation of the analyzers for use and the final step being release of the result. The project's scope covered the process from sample placement on the analyzer to the point at which the result was released in the LIS. A defect was defined as the need to change a result for any reason after verification.

Also during the define phase, the Six Sigma team needed to convince lab employees that further reduction of changed results was necessary, even though the average changed-result rate was already less than 1%. To accomplish this, the team used change acceleration process tools, such as the threat/opportunity matrix, to demonstrate the benefits of reducing changed results and the disadvantages of maintaining the current changed-result rate. For instance, reducing changed results would increase lab efficiency, improve staff performance and morale, and boost market share. Maintaining the current rate of changed results would ultimately diminish the core lab's reputation, leading to a loss of revenue and decreased staff morale.

[FIGURE 1 OMITTED]

In the measure phase, the Six Sigma team used operational definitions and the lab supervisory staff to perform measurement-system analysis. Because the lab already operated at a high sigma level, the measurement system had to be 100% accurate for reproducibility and repeatability. The team had to ensure that any variations were due to the process, not the measurement system. In order to obtain this type of accuracy, the team developed operational definitions to classify errors: procedural, autoverification, sample, clerical, mechanical, and unknown. With the aid of logic trees, the team refined these definitions five times to ensure all errors were classified consistently so that repeatability and reproducibility were 100%. Statistical analysis using the Six Sigma methodology revealed that the lab operated at a 4.8 sigma level.

For the period of May 2003 through July 2003, the laboratory corrected 585 test results out of 1,645,975 results reported. The DPMO was 355. One of the Six Sigma tools--the stakeholder analysis--aided in developing a strategy to gain support for the project from moderately opposed individuals and helped identify those individuals likely to be involved in the process who could serve as resources for the team.