making best use of beds: elective and emergency care modern.nhs.uk/beds
DESCRIPTION
Making Best Use of Beds: elective and emergency care www.modern.nhs.uk/beds. Overview of content. Context and diagnosis: Background to the work A typical picture of flow through beds in the UK Solution ideas: What we found worked and the effect Key messages for implementation - PowerPoint PPT PresentationTRANSCRIPT
Making Best Use of Beds: elective and
emergency care
www.modern.nhs.uk/beds
Overview of content
Context and diagnosis:
➤ Background to the work
➤ A typical picture of flow through beds in the UK
Solution ideas:
➤ What we found worked and the effect
➤ Key messages for implementation
➤ Resources to support work on beds
IMPROVING FLOWTHROUGH BEDS
Capacity & demand
management
Emergency care
Booking
Pre-operative assessment
Waiting list management
Improvement programmes Discharge
planning
Clinical Governance
Theatre Utilisation
Workforce redesign
Leadership development
Diagnostic services Day surgery
Financial flows
Access targets
Staff experience
Patient experience
Performance ratings
Clinical quality
Beds: a key constraint in the system
Background to the work
➤ Beds long seen as a core problem
➤ Emergency Services Collaborative and Improvement Partnership for Hospitals encouraged focus on whole flow
➤ Waiting for a bed the most common cause of breaching emergency waiting time target
➤ Research of best practice across UK
➤ Package of support to Trusts
Our starting point
• The availability of beds within a Trust is a constant problem
• Lack of beds is usually the result of a temporary mismatch between the demand for beds and the time at which they are available (capacity)
• The root cause of this problem is the variation in patient flows through the Trust
IN-PATIENT STAYADMISSION DISCHARGE
Variation in patient pathways and processes.
E.g. in Length of Stay
Variation in Admission Patterns -
particularly for Elective Care
Variation in Discharge - By time of day- By day of week
- Seasonal variations
Bed availability: a problem of variation
IN-PATIENT STAYADMISSION DISCHARGE
Variation in patient pathways and
processes.Variation in Length of
Stay
Variation in Admission Patterns -
particularly for Elective Care
Variation in Discharge - By time of day- By day of week
- Seasonal variations
“We always bring our hips in on Tuesday !”
MRI - Elective & Emergency Inpatient Admissions April 2002 to March 2003
0
20
40
60
80
100
120
140
Date
Num
ber
Emergency Elective
Variation in admissions
Elective Admission and Emergency admission by day of the week ( data excludes weekend)
0
10
20
30
40
50
60
70
80
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
Day
Nu
mb
er
of
ad
mis
sio
ns
Emergency Admissions Elective Admissions
Variation without the weekend effect
IN-PATIENT STAYADMISSION DISCHARGE
Variation in patient pathways and
processes.Variation in Length of
Stay
Variation in Admission Patterns -
particularly for Elective Care
Variation in Discharge - By time of day- By day of week
- Seasonal variations
“Mr Smith’s TURP patients always stay five days but Mr Jones only keeps them in for three days
Length of stay by day of admission
6.56.1
7.6
6.2
7.0
7.8
7.1
0
1
2
3
4
5
6
7
8
9
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Ave
rag
e le
ng
th o
f st
ay (
day
s)
Variation in length of stay
IN-PATIENT STAYADMISSION DISCHARGE
Variation in patient pathways and
processes.Variation in Length of
Stay
Variation in Admission Patterns -
particularly for Elective Care
Variation in Discharge - By time of day- By day of week
- Seasonal variations
“We’re too busy in the morning to think about discharges.
They all get done in the afternoon.
Total Admissions & DischargesMay 2002 - December 2002
0
20
40
60
80
100
120
01
/05
/20
02
15
/05
/20
02
29
/05
/20
02
12
/06
/20
02
26
/06
/20
02
10
/07
/20
02
24
/07
/20
02
07
/08
/20
02
21
/08
/20
02
04
/09
/20
02
18
/09
/20
02
02
/10
/20
02
16
/10
/20
02
30
/10
/20
02
13
/11
/20
02
27
/11
/20
02
11
/12
/20
02
25
/12
/20
02
Admission
Discharges
Discharges vary more than admissions…
Variation within each day
Rate of discharges and admissions by hour of the day
0
2
4
6
8
10
12
14
16
18
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of the day
Nu
mb
er
of
ad
mis
sio
ns
/ d
isc
ha
rge
s
Number of Admissions (Demand) Number of Discharges (Capacity)
Admission Queues
Patient FlowDischargeWeekends
Custom & Practice
Holidays
Managed Flow
Distress Driven Discharge
What drives bed availability in the UK?
Bed Occupancy
600
620640
660680
700
720740
760780
800
Mo0
Mo6
Mo12
Mo18
Tu0
Tu6
Tu12
Tu18
We0
We6
We12
We18
Th0
Th6
Th12
Th18
Fr 0Fr 6 Fr12
Fr18
Sa0
Sa6
Sa12
Sa18
Su0
Su6
Su12
Su18
Day/hour Of Week
Bed
s O
ccup
ied
occupied beds estimated beds available
“20 free beds this morning but lots of electives TCI”
“It’s chaos now! 15 DTA’s in A&E& no free beds - we need to get
the wards todischarge ASAP”
“Just about got them all in by the
end of the day - well done!”
“I think we have itall under control
now - lets hope next week is better”
“We need more beds”
What can we do about it?
Solution ideas
Improvements that worked
Short term:➤Gaining operational control of beds➤Moving discharges earlier in the dayLonger term:➤Using prediction and scheduling tools➤Addressing elective flow variation➤Segmenting flows by length of stay ➤Strategic, improvement led, capacity
planning
What would happen if we implemented
a few of the recommendations?
Restricting ourselves to modest changes…
Arrivals and discharges by hour: Monday only
0
5
10
15
20
25
30
Mo 0 Mo 6 Mo 12 Mo 18 24hour of week
nu
mb
er
of
arr
iva
ls o
r d
isc
har
ge
s p
er
ho
ur
Emer Adm A&E Emer Adm direct Elec Adm Disch
Reducing the in day beds mismatch
Cumulative bed state across Monday (from zero at midnight Sunday)
-20
-10
0
10
20
30
40
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
hour of day
cu
m n
et
flo
w in
to b
ed
s
Monday
This trust needs about 35 more beds at midday than it did at midnight
The need for beds during the day
discharges: before and after
0
5
10
15
20
25
30
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
before after
Move 35 (out of 123) discharges from the afternoon to the morning
How moving a few discharges can help
Arrivals and discharges by hour: monday only
0
5
10
15
20
25
30
Mo 0 Mo 6 Mo 12 Mo 18 Tu 0
hour of week
nu
mb
er
of
arr
iva
ls o
r d
isc
har
ge
s p
er
ho
ur
Emer Adm A&E Emer Adm direct Elec Adm Disch
Demand and capacity are more balanced
Cumulative bed state across Monday (from zero at midnight Sunday)
-20
-10
0
10
20
30
40
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
hour of day
cu
m n
et
flo
w in
to b
ed
s
after before
Less of a daily peak in demand for beds
Length of stay by day of admission
6.56.1
7.6
6.2
7.0
7.8
7.1
0
1
2
3
4
5
6
7
8
9
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Ave
rag
e le
ng
th o
f st
ay (
day
s)
Variation in length of stay
Length of stay by day of admission
6.56.1
6.56.2
6.56.56.5
0
1
2
3
4
5
6
7
8
9
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Ave
rag
e le
ng
th o
f st
ay (
day
s)
Aiming for average LOS over the week
0
50
100
150
200
250
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57
Length of stay (days)
Nu
mb
er o
f p
atie
nts
Greatest impact will be seen by concentrating on shorter LOS - usually simple discharges
Target short stay patients for a big impact
20.0
30.0
40.0
50.0
60.0
70.0
80.0
Admissions
Average = 49.7
UPL = 67.9
Beds required each day to give 99.9%
chance of admission
Total Admissions
20.0
30.0
40.0
50.0
60.0
70.0
80.0
Admissions
Average = 49.7
UPL = 78.1
78 beds required each day to give 99.9%
chance of admission
Daily bed requirement reduced from 78 to 68
How to approach implementation:
Key messages
Key messages for implementation
1. Look across the whole system: admission to discharge, electives and emergencies
2. Understand the unique pattern of variation at your hospital
3. Understand the main sources of variation including unnecessary queues / carve out
4. Plan for short and long term improvements and manageable changes
Key messages for implementation
5. Map and measure your main flows
6. Concentrate on the 80% of simple discharges first
7. Aim for real time data analysis
8. Integrate work on beds into existing plans
9. Respond appropriately to common and special cause variation
All MA materials and UK Department of Health Checklists on the website:
www.modern.nhs.uk/beds
Also: toolkits produced by the PFC…
Resources to support work on beds
Department of Human Services
Toolkit
Bed management
Click here to continue
Department of Human Services
Toolkit
Length of stay
A toolkit of the Patient Flow Collaborative
Click here to continue
Department of Human Services
Five innovations to improve length of stay management and whole system patient flow