AB1131 STATISTICAL PROCESS CONTROL AND PROCESS MAPPING QUANTIFY THE EFFECTS OF HISTORICAL CHANGES TO THE CONNECTIVE TISSUE DISEASE TESTING ALGORITHM AND IDENTIFY AREAS FOR FUTURE IMPROVEMENT IN A LARGE DIAGNOSTIC IMMUNOLOGY SERVICE
Authors: M. Stevens, N. Proudlove, J. Ball, C. Scott
Journal: Annals of the Rheumatic Diseases (2020)

Background: Pathology test turnaround times (TATs) are a limiting factor in patient flow through rheumatology services. Quality improvement (QI) methodologies such as Lean use tools including statistical process control (SPC) and process mapping to study the performance of the whole of a clinical pipeline, expose unnecessary complexity (non-value-adding activity), and streamline processes and staff roles. Objectives: Understand effects of changes made to CTD testing algorithm over last 12 years by measuring some of the effects on TATs. Model current processes and suggest changes to workflow to improve TAT. Methods: High-level flow diagrams of the current testing algorithm, and low-level process maps of analyser and staff processes were drawn. Activity and TATs (working days between report and booking date) for ANA, ENA, DNA and CCP tests were plotted as XmR control charts. Results: Finding 1 : Largest referral laboratory does not currently operate a separate DNA monitoring workstream, resulting in unnecessary ANA and ENA testing (figure 1). Figure 1. Current testing strategy (left) and suggested improvement (right) Finding 2: Samples are handed off between 3 different lab benches, each of which may be staffed by a different staff member on a different day, and results processing involves handoff to a further 2 different staff members. Finding 3: ANA demand is close to capacity, ENA demand exceeds current capacity (table 1). Table 1. Demand for ANA, ENA and DNA tests, compared to capacity Test Median Demand (tests/ day) Approx. Capacity (tests/ day) Notes ANA 74 100 Close to 80% recommended by the ILGs ENA 38 36 *Less capacity than demand!! DNA 34 100 Plenty Finding 4: Stopping screening DNA requests on ANA result increased the number of DNA tests performed by about 10 samples per day (30%), but decreased turnaround time by a similar proportion (3.3 to 2.3 days, figure 2). It also reduced turnaround times of ANA and ENA tests. Figure 2. Control chart of average TAT of dsDNA antibodies by request date Conclusion: Typically for a QI project, the initially simple CTD testing pipeline has accumulated many changes made without consideration of whole system performance, and is now a struggle to run. Improvement ideas to be explored from this work include: Liaising with main referral lab to develop a DNA monitoring workstream to reduce unnecessary ANA and ENA testing Reduce handoffs, sample journey around lab analysers, and staff hands-on time by: changing ANA test methodology to same as DNA creating new staff roles (analyser operators to perform validation/ authorisation steps) Create more capacity for ENA testing by increasing the frequency of this test on the weekly rota Create more capacity for service expansion by running analysers at weekends (staff consultation required) Reduce demand on service by engaging and educating requestors Improve TAT for DNA by: processing samples the day they are booked in, instead of 1 day later auto-validating runs …using control charts to measure improvement Disclosure of Interests: None declared