1. Authors: R LITTLEWOOD, S DOUGLAS1, N ANTONIOU2 M LAFFAN3
(1) applied strategic, London, UK
(2) Business Mathematics, London, UK
(3) Imperial College Healthcare NHS Trust, London, UK
INTRODUCTION
The Healthcare Trust
• A major Healthcare NHS Trust provides pathology service at 3 main sites for medicine,
surgery and other specialties. A budget of £25M provides pathology services for 200,000
people amounting to more than 2M tests per year
• Strategic reviews highlight the resources allocated to pathology in UK NHS1 and the
potential to achieve operational improvements2
The Pathology Test service
Analytical approach
OBJECTIVES
• Understand terrain of demand and drivers leading to map of capacity utilization in a
pathology service
• Use an extensive set of data from key points along the pathway to understand
performance within the pathology test process
• Address how one of the laboratories in the Trust network could optimize performance
ANALYSISRESULTS
METHODS
Applying big data analytics and queue theory to identify
opportunity for step change in pathology services activity Insert your
Logos
Activity
review
database
Full blood
count (FBC)
Coagulation
(COAG)
• 1.2M case records
• 21.6M data points
• 204,000 tests• 1M tests
FBC
Hospital 1
100%
Hospital 1 FBC
Hospital 2
Primary Care
Others (n=10) Lab 4
1M 0.6M
Laboratory 1
Laboratory 2
Laboratory 3
• 80% of FBC tests were ordered from
hospitals; they are the primary driver of
demand
1
Hospital 1
Hospital 2
Primary Care
Lab 1
Lab 2
Lab 3
• Considering all 1M FBC tests, 0.6M tests
were ordered in hospital 1. Of this 0.6M test
volume, 239,000 (41%) processed at Lab 1
2
• Analysis of activity in pathology testing shows reasons for delays in achieving results
and more importantly, why full capacity cannot be achieved today
• There are limits to productivity (blood test rate) in blood testing inherent in the current
process: simple solutions exist for some limiting factors
Hospital 1
Hospital 2
Primary Care
Laboratory 1
Laboratory 2
Laboratory 3
• Average FBC turn around time increases as
day progresses; peaking at 4pm
5
Hospital 1
Hospital 2
Primary Care
Laboratory 1
Laboratory 2
Laboratory 3
• As FBC volume received at Lab 1 per hour
falls turn around time increases; delayed
negative impact of FBC arrival on turn
around time
6
Descriptive statistics: Analysis of 1M
FBC records was completed to
understand process delays
0
4000
8000
12000
16000
20000
9 10 11 12 13 14 15 16 17 18 19 20
Hour of day (for Mon-Thur inc.)
Analyses of test by location was
recorded to map the relationship
between performance and demand
• Volume of FBC samples arriving at Lab 1
peaks at 10am, declines as day progresses
• Demand for pathology test service is
greatest in the morning
3
Hospital 1
Hospital 2
Primary Care
Laboratory 1
Laboratory 2
Laboratory 3
• Activity in Lab 1 peaks at 11am; there is a 1
hour delay between peak arrival and peak
reporting of tests
• Reporting rate slows as the day progresses
4
0
40
80
120
160
8 9 1011121314151617181920
Hour of day (for Mon-Thur inc.)
Demand: Assessment of FBC tests
arrival at Lab 1 per hour, Mon-Thurs
0.7
0.9
1.1
1.3
1.5
8 9 10 11 12 13 14 15 16 17 18 19 20
AverageFT/hrs
Test receive hour of day (for Mon-Thur inc.)
Activity: Assessment of FBC tests
reported at Lab 1 per hour, Mon-Thurs
2
2.4
2.8
3.2
3.6
1
6
11
16
21
26
8 9 1011121314151617181920
FBC number arriving
Average turn
around time / hr
Test receive hour of day (for Mon-Thur inc.)
Performance: Assessment of FBC test
turn around time/ hour received at Lab 1
Tests FBC COAG
Installed capacity
Max # tests per machine/
hour
140 28
No. of machines 3 2
Laboratory potential
Max machine capacity/
hour
420 60
Observed performance
Mean tests reported/
hour
60 13
Max tests reported/ hour 95 20
Performance analysis: Assessment of
slowest FBC quartile in Lab 1 - turn
around time v number received per hour
Process map: pathology test to result
Load to
machine, run
test
Collect, ship,
drop off
Sample & local
label
Receive &
label enter
data
Validate results Release results
Process flow limit at step 3: Front of
house sample processing limits to 80
samples/ hour
Potential block in process flow at
step 5: Clinical validation limits
capacity
• Installed capacity in Lab 1 is high
• Activity potential is defined by rate of test
completion by analyzer machines and
test turn around time
• Measurement of true activity highlights
gap between performance and maximum
potential
• Understanding reasons for noted
performance can facilitate a step change
in performance
ACKNOWLEDGEMENTS/ REFERENCES
1 Carter, P. Report of the Second Phase Review of NHS Pathology Services in England.
London: DH, 2008.
2 Carter, P. Operational productivity and performance in English NHS acute hospitals:
Unwarranted variations. London: DH, 2016
• T. Lumley assisted in the preparation of this poster; the team at HH Pathology Service
provided essential input.
• Contact information: richard.littlewood@appliedstrategic.com
FBC pathology
tests
Primary
Care Trusts
Hospitals Others
1M tests
• 0.8M tests
• 2 major
hospitals, 3
laboratories
• 0.2M tests
• 43 PCTs
• 6,900 tests
• 10 organisations
served
Step-by-step process review defines capacity and utilization for FBC pathology tests
• Clinical results and lab
management systems recorded
activity and outcomes of tests
• The data described incidence of
exemplar tests (FBC and
coagulation) over time, with
parameters to show healthcare
professional ordering the test,
location of the patient having the
test and site of the laboratory in
which the test was analysed
• Records did not contain any
information that might identify the
test recipient nor clinical results
Test time
• Time from patient
collection site to
pathology lab
receiving
LaboratoryTransit
• Time from lab
receiving sample to
result reported to
doctor
• Analysis based on turn around time includes
processing and analysis of samples in laboratory as
well as time in transit
• Big data activity analytics, queue modeling and
process mapping were applied
CONCLUSIONS/ RECOMMENDATIONS
• Big data analytics, queue modeling and process mapping shows reasons for under
utilization of installed capacity and options for optimization in an NHS pathology service
• Service advised to deal with blocks in flow by addressing the registration of samples and
introducing smart process for sample validation
• Demand for testing should be controlled and phased by programming standard times for
inpatient routine tests and using off schedule overnight time for primary care service
• NHS UK can make huge operational gains by analyzing big data operational activity
• 1.2M tests ordered per year by 13 different organisations
• 80% FBC; 20% COAG ordered
• Process described as order to action loop to facilitate
analysis
• Service provided by a number of in-house
laboratories operating advanced analyzers capable of
high throughput, automated assessment of samples
• The service tracked performance including volume of
activity and turnaround time to complete a test,
following receipt of samples
Process loop
Blood to
test to
report
Data
influences
action
Service users
order product
21st CONGRESS
JUNE 9-12 2016
E u r o p e a n H e m a t o l o g y A s s o c i a t i o n
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