Use of Networked Westgard Advisor Quality Control Software and Dashboard to Reduce QC Rejections in a Regional Laboratory System Jay B. Jones, D. Young, K. Smeal, D Kimmich, M. Lopatka, D. Sargent, R. Nowak, E. Yu, M. Wilkerson, D. Kremitske, H. HarrisonDivision of Laboratory Medicine, Geisinger Health System, Danville PA

METHODS1. Common lot number of BioRad Levels 1,2 liquid QC material used on

various harmonized chemistry instruments in the Geisinger Health System (GHS) (Figure 1).

2. All QC data extracted real time from Data Innovations (DI) interface and sent to a BioRad virtual server on a wide area network (WAN).

3. Sigmas calculated in BioRad Unity RealTime (URT) software on a GHS BioRad virtual server using Westgard Advisor module in URT. Sigmas calculated from monthly peer data aggregated in URT employing CLIA total allowable error (TEa).

4. Monthly Sigmas plotted from each GHS lab site (10 total) on a shared network dashboard using MS Excel (Figure 2).

5. Those analytes with each GHS location’s Sigma consistently above 4 allowed less stringent Westgard 1-3s rule to replace 1-2s rule in the Lab Information System (LIS).

6. System Sigma dashboard was monitored after the Westgard rule change to judge if Sigmas were decreasing significantly (e.g. to less than 4).

7. Decreased number of QC rejections were tabulated for each lab site and the entire system in toto after a single baseline month (March 2011).

8. CAP Proficiency testing was closely monitored for any trend of failed challenge to any of these analytes.

CONCLUSIONS1. Standardizing QC with identical a) lot

numbers (i.e. BioRad liquid QC material stored under identical conditions), b) data capture (i.e. URT transmission to a virtual shared network server), c) data comparison (i.e. URT monthly peer summaries electronically transmitted to a BioRad web site), and d) Sigma statistics calculation (i.e Westgard Advisor formula transferred to an Excel dashboard) was highly automated and efficient.

2. Use of the Sigma dashboard allowed consistent application of parametrically defined quality standards to all labs harmonized in a regional health system.

3. Sigma statistics decreased “unnecessary” QC run rejection (by approximately 75%) thus avoiding repeat QC analysis and disruption to auto-verification.

4. Changing Westgard rules from 1-2s to 1-3s for analytes which consistently performed above 4 Sigma did not diminish quality significantly as judged by continued good peer comparison, continued Sigmas above 4, and CAP proficiency testing scores at or near 100%.

5. A similar Sigma approach is being planned for immunochemistry testing performed with BioRad QC material on a fewer number of GHS instruments and at GHS affiliated hospitals using chemistry instruments from a different vendor.

ABSTRACT

Enterprise quality control (QC) data in the Geisinger Medical Laboratories is generated from 1) eight rapid response labs each using Roche mid sized chemistry instruments 2) two hospital laboratories using four Roche c501 instruments and 3) a core laboratory using five “P” Roche modular instruments. Enterprise chemistry instruments utilize the same lot number of calibrator and BioRad unassayed serum QC material and are integrated on a wide area network (WAN). All instruments are interfaced via Data Innovations (DI) v 8.10 “middleware” to a single SunQuest laboratory information system (LIS). The DI middleware is integrated on the WAN with BioRad Unity Realtime (URT) software containing the Westgard Advisor sigma statistic module. QC data from all locations are captured on the URT server and sigma statistics calculated for all chemistry tests using CLIA and CAP total allowable error. A sigma dashboard is plotted monthly for all chemistry tests in the enterprise. Those tests that consistently produce sigmas above 4 are selected for use of less stringent 1-3s Westgard rules in daily QC. QC run frequency remains at q 4 hours at the rapid response and hospital labs and q 2 hours at the core lab. The number of monthly QC rejections for selected tests dropped significantly at all three sizes of labs as shown in toto in the following table (baseline month, March 2011; most recent month, January6 2012):See Table 1.While the reduction of “false” QC rejections with associated QC repeats and troubleshooting averaged 75% for 5 months, peer comparison data from URT did not show significant trends of increased SDIs or CVRs. Hence it is concluded that identifying tests performing well within total allowable error by Westgard sigma statistics and increasing ranges of acceptability for daily QC from 1-2s to 1-3s will reduce workload by approximately 75% without degradation of peer compared quality.This reduction of unnecessary workload also creates less disruption to automated processes such as autoverification. False QC triggers turn off autoverification (“by the test or by the specimen”) and create the need to manually verify those tests that are performed during the time required for QC rerun and recovery. Time studies should be performed to quantitate these savings associated with the “leaning” of automated chemistry testing.

Figure 2. A Representative Sigma Dashboard Showing Monthly Trends of Sigma for Level 2 BioRad Control by GHS Lab Location.

Table 1. Entire Geisinger Health System (GHS) Run Rejection Rate Pre- and Post- Application of Westgard Advisor Sigma Analysis and Rule Change from 1-2s to 1-3s to Reject Quality Control (QC) Run.

Figure 1. Enterprise Analytics - ConnectivityRoche / DI/ BioRad June 2012 Update