1. The flexible hospital: myth or reality?
Tom Monks.
CLAHRC Wessex Data Science Hub
IMA Conference. Mathematics of Operational Research. April 2017

2. Are hospitals naturally flexible?
Bed capacity is flexible. But not always in a SAFE way.

3. Overview The slide of doom Rigid hospitals?
Options for hospital ‘flexibility’
Flexibility case studies
queuing 101
buffering admissions
‘short stay’ specialisation
Impact of the work
What next for hospital flexibility research?

4. ED Overcrowding

5. Hospital Flexibility: Challenges
(Extreme) Value for money
Medical Specialization
Delayed Transfers of Care
Space and Layout
Workforce

6. Hospital Flexibility: Options
Flexibility theme
Implementation options
Buffers
Admission units, observation wards and clinical decision making units
Discharge lounges
Pooling and specialization of resources
Carve out capacity and specialization of treatment;
Co-location and pooling of specialism beds;
Carve out a proportion of ward capacity to act as ‘short stay’ admission beds;
Flexible workforce
Advanced Nurse Practitioners (generic servers)
Bank and agency staff
Redeployment of staff
Space and capacity management
Organize wards into bays of beds that can be switched between the genders;
Outlying medical patients to different specialties and surgical wards;
Targets for early ward rounds and discharges;
Chair based emergency care for ambulatory patients;
Early discharge of patients along with home care provided by the hospital;

7. Flexibility Case Studies
Queuing Processes 101
13/02/2015

8. Background
At the beginning of an ED simulation project we were asked to help with some immediate planning issues.
We were given a day to influence decision making
We listened to their issues and used simple models to illustrate some queuing concepts and ‘traps’
Extreme value for money versus queuing time
Specialization versus pooling
Thinking systemically about constraints

9. CS1: Example outputs Specialization Vs. Pooling
Classic Waiting Time Vs Utilization Trade-off
Specialization Vs. Pooling

10. Thinking Systemically
CS1: Example Outputs
Thinking Systemically

11. Flexibility Case Studies
Buffering admissions
13/02/2015

12. Background
Part way through a 3 month ED simulation project we were asked to estimate how ‘big’ a Clinical Decision Making Unit would need to be to be help ED achieve waiting time targets
Our guess was very big.
This was also a concern of the ED and AMU management.
We were given 2 days…
Luckily we had already completed a lot of analysis…

13. CDU Model
The chart illustrates the number of beds needed to achieve 95% of patients with an ED cycle time of < 4 hours for different CDU processing times.

14. Short stay specialization
Flexibility Case Studies
Short stay specialization
13/02/2015

15. Background
We can push a variable % of cases through the dedicated short stay ward
We can vary the number of beds in short stay and specialities
We vary the accuracy/error rate
We vary the LoS and transfers of patients in the specialities 15 15

16. Specialization Example Results
Predicting which patients will benefit from more intense treatment is very difficult!
Hospital given a target of 65% accuracy.
Modelling estimates that this needs to be in the region of 80% for the new system to affect ED.
Explanation:
Bed capacity of the short stay ward
Mean occupancy needs to be <=90%
Transfer delays of ‘incorrect’ patients

17. Short stay specialization
Impact of the work (1)
All that could be done ‘from scratch’ in a single day;
Still influential: shift in focus to downstream process;
NHS research capacity building;
Danger of taking the numbers literally.
Queuing 101
It’s impact was to rule out an option;
Saved effort and morale;
In the right place at the right time with the right data!
Buffering Admissions
Reassessment of the ‘accuracy target’;
Raised the debate about how to identify ‘correct’ patients.
Short stay specialization

18. Impact of the work (2)
At the start of a project I asked a senior manager about the efficiency of downstream processes:
“Those [downstream processes] are out of scope. If we can just get the process right in the ED then we can move on [to them]”
Our experience working to reduce waiting times at ‘underperforming’ EDs has demonstrated that it is difficult to make a quick impact.
Arguably the biggest impact we have had is in changing mindsets
The manager ended up recognizing the constraint was outstide of ED. We ended up modelling the full medical pathway

19. Where next for ED research?
The time needed for detailed modelling (and data collection) is rarely available;
Can we (re)use the published generic models? Not really!
They do not address the ‘flexibility’ questions asked of us!
You need to recode them! You might as well use the time to build a bespoke model!
Maybe a role as educational tools?
It is time to move on from single site research.
We need to take a more implementation science view
“What is needed to get known solutions to work in different contexts”
Recent literature reviews (Gul and Guneri 2015; Saghafian et l. 2015)
Overwhelming focused on micro-departmental flows
Useful, but small potatoes; we need to focus on ED outflow (and inflow = another story)

20. NIHR CLAHRC Wessex in partnership with
University Hospital Southampton NHS Foundation Trust
Portsmouth Hospitals NHS Trust
Hampshire Hospitals NHS Foundation Trust
The Royal Bournemouth and Christchurch Hospitals NHS Foundation Trust
Poole Hospital NHS Foundation Trust
Isle of Wight NHS Trust
Dorset County Hospital NHS Foundation Trust
Salisbury NHS Foundation Trust
Solent NHS Trust
Southern Health NHS Foundation Trust
NHS England South Wessex area team
NHS Dorset CCG
NHS West Hampshire CCG
NHS Southampton City CCG
NHS Portsmouth CCG
NHS North East Hampshire and Farnham CCG
NHS North Hampshire CCG
NHS South Eastern Hampshire CCG
NHS Fareham and Gosport CCG
NHS Isle of Wight CCG
Health Education Wessex