Using the Bigtown Simulation Model to Predict the Impact of Enhanced Seven Day Services on Hospital Performance and Patient Outcomes Poster from the 'Delivering NHS services, seven days a week' event held in Birmingham on 16 November 2013 More information about this event can be found at http://www.nhsiq.nhs.uk/news-events/events/nhs-services-seven-days-a-week.aspx

1. Using the Bigtown Simulation Model to Predict the Impact of Enhanced Seven Day Services on
Hospital Performance and Patient Outcomes
David Halsall, Laura Bown & Katrina Walker
Summary – A hospital is a complex system. Predicting the impact that individual changes have on system design can be difficult. The BigTown simulation model has been used
to explore the impact that increasing senior decision maker presence and additional diagnostic capacity at the weekend might have. We have found that individual changes
have minimal impact on overall hospital performance. However if diagnostic and decision making capacity are increased together at the weekend this can reduce the peaks in
demand over the weekly cycle of emergency admissions and lower average length of stay.
Background – At the weekend both the services offered to non-elective patients and the case mix of patients are different compared to a weekday. Recent studies have
shown that even after corrections are made for the differences in case mix patients, they are more likely to die if admitted to hospital as an emergency at a weekend
compared to a weekday. This effect is most noticeable in a range of medical rather than surgical conditions (Aylin et al 2010, Freemantle et al 2012).
An emergency admission in hospital can be characterised by three phases (i) diagnosis and treatment plan (ii) treatment phase and (iii) discharge. The hypothesis is that stage
one of an emergency spell, diagnosis and treatment planning, is more protracted at the weekend because of fewer senior decision makers being present in the hospital. This
then leads to poorer patient outcomes due to the “ failure to rescue”. Caring to the End? (NCEPOD 2009) reviewed the care of patients who died in hospital within four days
of admission. Reviewers took the view that there were clinically important delays in first review by a consultant in a quarter of the cases. However simply adding additional
senior decision making capability at the weekend may by itself not be sufficient to improve outcomes if support services such as diagnostics are not also increased. There
may be a need for a general rising tide for services on Saturday and Sunday if improvements in outcomes is to go hand-in-hand with increased costs.
Populating the Model with Data –The model was populated with a patient flow which
represents a large DGH by time of day and day of the week. The entry point of the
model was construed using a simplified version of the A&E model used when the 4
hour target was introduced (Fletcher et al, 2006). The values for the outcome of patient
review once admitted were synthesised from current literature (Seven Day Consultant
present care, Academy of Medical Royal Colleges, 2012), clinical advice and modified
during model testing and calibration.
Figure 1 Screenshot of the structure of the BigTown simulation showing the three
main stages of the patient journey A&E, MAU and in-patient care.
Approach taken to modelling the situation – The differences in mortality outcomes
from those admitted Monday to Friday compared to those admitted at the
weekend are small and subtle. Complex analysis of admissions over a year have
shown statistically significant results and identified hospitals whose performance is
worse than average. Case studies can be helpful to poor performing trusts by
suggesting approaches which may improve outcomes. However translating what
has happened in the past into a different setting is not straightforward .
Discrete event simulation considers a complete system as a linked series of
processors and buffers in which entities can flow. The characteristics of individual
stages in the process can be described and rules can be applied to the flow of
entities (which in our case are acute medical patients) through the system. The
power of the approach is that a computer simulation can represent a complete
system such as a hospital over many years in a couple of minutes. Performance
measures such as length of stay and death rate can be measured and tracked as
proposed changes are made.
“What-if” experimentation can be performed on a system quickly and without risk
to the real world. Both these have potential benefit for re-designing hospital care to
improve services seven days a week. The BigTown simulation is built around a high
level model of an acute hospital representing the key clinical decision making
pathways for admitted emergency patients. It has three main stages: A&E, medical
assessment unit and ward based care (broken down into medical and surgical).
Figure 2 Model calibration using length of stay by day of admission of medical and surgical
patients. (Los data NHS Analytical services analysis of HES data 2010/11)
Model calibration – The Dr Foster Guide (Dr Foster Intelligence, 2011) assessed trusts
using the HSMR metric to identify trusts which had a higher than expected death rates
for patients admitted at the weekend compared to a weekday. Two trusts with higher
than expected death rate were used as model calibration.
Given a known pattern of arrivals the model is permitted to “warm up” for 6 months
and the data is then collected for a subsequent 12 months. If the model is a good
representation of the internal processes with a hospital the average length of stay
should correspond to observed results. Derived measures such as bed usage should
then correspond to the real world.
Average Length of stay by day of admissionSimulation results
Base Case
6.2
Addtional weekend resources
6.0
5.8
5.6
5.4
First Review
Discharged dead
Tests ordered
Wait and review
Discharge alive
No change
Lower (60% v 70%)
No change (10%)
Higher (30% v 20%)
First follow-up
review
Subsequent review
Lower (0.5%)
Lower (30% v 60%)
Higher(20% v 10%)
Higher (50% v 30%)
Lower (1%)
Lower (0% v 40%)
Higher (20% v 10%)
Higher (80% v 50%
5.2
5.0
4.8
Table 1 A sample outcome table used in the simulation model of outcome clinical review
showing the difference that a senior decision maker compared to a junior decision maker.
4.6
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Sunday
Figure 5 Average length of stay by day of the week with and without additional clinical and diagnostic
capacity on Saturday and Sunday
Testing the BigTown model for enhanced Seven Day Services –The main test reported
here is to increase the senior decision making capacity at the weekend, as this has
been suggested by Dr Foster as being associated with poor relative mortality rates.
By incrementally increasing resources at the weekend the impact that the changes
have on key metrics can be observed. For example, as shown in Figure 3, we can
measure the number of patients awaiting clinical review at any one time. Adding
resources at the weekend has an effect of reducing the number of patients awaiting
clinical review over the whole week.
Figure 3 and 4 Additional senior decision makers at the weekend reduces the number of
patients awaiting review but increases the pressure on diagnostics early in the week.
Figure 5 above shows that the simulation model suggests that additional senior
decision making capacity at the weekend may reduce the length of stay for patients
admitted at the weekend but may increase the pressure early in the week
Conclusion - The BigTown is a relatively simple simulation model which aims to explore how changes to the complex emergency care pathway may best benefit patient
outcomes and reduce costs. Very early results show how changes to weekend capacity may have less impact than originally anticipated unless the whole system is “tuned” to
ensure bottlenecks are eliminated rather than just moved to a different part of the week.
Aylin P, Yunnis A, Bottle A, Majeed A, Bell D. Weekend mortality for emergency admissions. A large, multi centre study. Qual Saf Health Care 2010; 19: 213-217, http://qualitysafety.bmj.com/content/19/3/213.abstract
Freemantle, N. Et al (2012) Weekend hospitalization and additional risk of death: An analysis of inpatient data J R Soc Med February 2012 vol. 105no. 2 74-84, http://jrs.sagepub.com/content/105/2/74.full
NCEPOD, Deaths in Acute Hospitals: Caring to the End? (2009), http://www.ncepod.org.uk/2009dah.htm
A Fletcher, D Halsall, S Huxham and D Worthington, The DH Accident and Emergency Department model: a national generic model used locally, Journal of the Operational Research Society 58, 1554-1562 (December 2007)
Dr Foster Intelligence, Inside Your Hospital 2001-2011, http://drfosterintelligence.co.uk
Academy of Medical Royal Colleges, The benefits of consultant delivered care, (January 2012) http://aomrc.org.uk/item/benefits-of-consultant-delivered-care.html