Abstract
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).
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
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).
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
| Original language | English |
|---|---|
| Pages (from-to) | 1851-1851 |
| Journal | Annals of the rheumatic diseases |
| Volume | 79 |
| Issue number | Supplement 1 |
| DOIs | |
| Publication status | Published - Jun 2020 |