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Unscheduled Care Collaborative Programme 27th January 2006 Understanding Demand & Planning Appropriate Capacity. Gary Thompson Policy Lead for Emergency Care Trent Strategic Health Authority. Why do queues form?. because demand exceeds capacity? mismatch between demand & capacity?

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slide1
Unscheduled Care Collaborative Programme27th January 2006Understanding Demand & Planning Appropriate Capacity

Gary Thompson

Policy Lead for Emergency Care

Trent Strategic Health Authority

why do queues form
Why do queues form?
  • because demand exceeds capacity?
  • mismatch between demand & capacity?
  • Do we want queues to keep us busy - utilised?
mismatch between demand and capacity
Mismatch between demand and capacity
  • Variation in demand + variation in capacity

= queue

  • Occasionally demand > capacity
variation mismatch queue

Queue

Demand

Capacity

time

Variation mismatch = queue

Can’t pass

unused capacity

forward to next week

slide5

Demand

Waste

More

Capacity

Capacity

time

How do we work out required capacity?

lean thinking i

2. Setaverage capacityat80%

of variation in demand

1. Why iscapacity varying?

Queue

Queue

Capacity

Capacity

Demand

Demand

time

time

Lean Thinking I
lean thinking ii

4. Reduce variation in demand

3. Why isdemand varying?

Queue

Queue

Capacity

Capacity

Demand

Demand

time

time

Lean Thinking II
manage constraints
Manage constraints
  • Manage and match variability
    • Reduce variation in capacity
    • reduce carve outs
    • (demand)
  • Increase capacity
    • redesign (releasing resources)
    • actual increase
  • Reduce demand ?
    • reduce variation in demand
    • agree thresholds and protocols
slide11

A Trust Near You ?

“It’s chaos now !

15 DTA’s in A&E

& no free beds -

we need to get

the wards to

discharge ASAP”

Bed Occupancy

“I think we have it

all under control now

-lets hope next week

is better”

“We need more beds”

“20 free beds this

morning but lots of

electives TCI”

“Just about got them

all in by the end of the

day - well done!”

800

780

760

740

720

Beds Occupied

700

680

660

640

620

600

Mo

Mo

Mo

Mo

Tu

Tu

Tu

Tu

We

We

We

We

Th

Th

Th

Th

Fr 0

Fr 6

Fr

Fr

Sa

Sa

Sa

Sa

Su

Su

Su

Su

0

6

12

18

0

6

12

18

0

6

12

18

0

6

12

18

12

18

0

6

12

18

0

6

12

18

Day/hour Of Week

occupied beds

estimated beds available

for this trust monday was a bad day they ran out of beds before lunchtime
For this trust Monday was a bad day: they ran out of beds before lunchtime

Bed occupancy reached 100% in the middle of Monday

with hourly information on arrival and discharges we can see why
With hourly information on arrival and discharges, we can see why

Arrivals and discharges by hour: Monday only

Elective admissions and discharges are poorly co-ordinated with arrivals starting early morning and discharges not peaking until mid afternoon.

30

25

20

number of arrivals or discharges per hour

15

10

5

0

Mo 0

Mo 6

Mo 12

Mo 18

24

hour of week

Emer Adm A&E

Emer Adm direct

Elec Adm

Disch

slide18
We can use the hourly information to calculate the change in the number of occupied beds across the day

This trust needs about 35 more beds at midday than it did at midnight

slide19

Variation in Discharge - By time of day

- By day of week

- Seasonal variations

Variation in patient pathways and processes.

E.g. in Length of Stay

Variation in Admission Patterns - particularly for Elective Care

Bed Availability: A problem of variation

ADMISSION

IN-PATIENT STAY

DISCHARGE

slide20

Variation in Discharge - By time of day

- By day of week

- Seasonal variations

Variation in Admission Patterns - particularly for Elective Care

“We always bring

our hips in on Tuesday !”

ADMISSION

IN-PATIENT STAY

DISCHARGE

Variation in patient pathways and processes.

E.g. in Length of Stay

slide21

Variation in Discharge - By time of day

- By day of week

- Seasonal variations

Variation in patient pathways and processes.

Variation in Length of Stay

Variation in Admission Patterns - particularly for Elective Care

“Mr Smith’s TURP patients always stay five days

but Mr Jones only keeps them in for three days

ADMISSION

IN-PATIENT STAY

DISCHARGE

slide22

Variation in Discharge - By time of day

- By day of week

- Seasonal variations

Variation in patient pathways and processes.

E.g. in Length of Stay

Variation in Admission Patterns - particularly for Elective Care

“We’re too busy in the morning and

haven’t time to think about discharges.

They all get done in the afternoon.

ADMISSION

IN-PATIENT STAY

DISCHARGE

slide23
Where do you start?

Where there is greatest variation

slide24
In patient variation

Usually indicated by Length of stay (LOS)

slide25

Total Admissions & Discharges

May 2002 - December 2002

120

Admission

Discharges

100

80

60

40

20

0

21/08/2002

01/05/2002

29/05/2002

04/09/2002

18/09/2002

02/10/2002

11/12/2002

15/05/2002

12/06/2002

26/06/2002

10/07/2002

24/07/2002

07/08/2002

16/10/2002

30/10/2002

13/11/2002

27/11/2002

25/12/2002

slide26

Length of stay by day of admission

9

7.8

7.6

8

7.1

7.0

6.5

7

6.1

6.2

6

Average length of stay (days)

5

4

3

2

1

0

Monday

Tuesday

Wednesday

Thursday

Friday

Saturday

Sunday

Variation in in-patient LOS

slide27

Length of stay by day of admission

9

8

6.5

6.5

6.5

6.5

6.5

7

6.1

6.2

6

5

Average length of stay (days)

4

3

2

1

0

Monday

Tuesday

Wednesday

Thursday

Friday

Saturday

Sunday

slide28

Greatest impact will be seen by concentrating on shorter LOS - usually simple discharges

Length of stay

250

200

150

Number of patients

100

50

0

0

3

6

9

12

15

18

21

24

27

30

33

36

39

42

45

48

51

54

57

Length of stay (days)

solutions
Solutions
  • Estimated Date of Discharge

Every patient has an EDD that drives their patient pathway.

Patient pathways are actively managed

solutions1
Solutions
  • Earlier in Day Discharge

Morning discharge should be the default position

Patients are discharged in the afternoon only as the exception

with hourly information on arrival and discharges we can see why1
With hourly information on arrival and discharges, we can see why

Arrivals and discharges by hour: Monday only

Elective admissions and discharges are poorly co-ordinated with arrivals starting early morning and discharges not peaking until mid afternoon.

30

25

20

number of arrivals or discharges per hour

15

10

5

0

Mo 0

Mo 6

Mo 12

Mo 18

24

hour of week

Emer Adm A&E

Emer Adm direct

Elec Adm

Disch

but what would their situation have looked like with a different pattern of discharges

We moved 35 (out of 123) discharges from the afternoon to the morning

But what would their situation have looked like with a different pattern of discharges?

discharges: before and after

30

25

20

15

10

5

0

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

the arrivals and discharges are now much better balanced
The arrivals and discharges are now much better balanced...

Arrivals and discharges by hour: monday only

30

25

20

number of arrivals or discharges per hour

15

10

5

0

Mo 0

Mo 6

Mo 12

Mo 18

Tu 0

hour of week

Emer Adm A&E

Emer Adm direct

Elec Adm

Disch

slide36

Emergency & Elective Admissions

April-November 2002

60

50

40

Emergency

Number of Admissions

30

Admissions

20

Elective

Admissions

10

0

10/06/2002

08/07/2002

16/09/2002

14/10/2002

11/11/2002

01/04/2002

15/04/2002

29/04/2002

13/05/2002

27/05/2002

24/06/2002

22/07/2002

05/08/2002

19/08/2002

02/09/2002

30/09/2002

28/10/2002

elective emergency profile
Elective / emergency profile

Note the high elective demand peaks Mon - Wednesday.

short term improvements
Short-Term Improvements
  • Gain operational control of beds
  • Identify the system variations causing problems with bed availability
  • Redesign systems and processes to reduce variation, thereby improving flow
  • Implement the Wait for a Bed Checklist
medium term improvements
Medium-Term Improvements
  • Address variation in elective flows
  • Develop predictive and scheduling tools to manage patient flows across the whole Trust
  • Segment patient flows to maximise the use of capacity
long term improvements
Long-Term Improvements
  • Gain strategic control of bed management
  • Bed configuration
  • Integrate service improvement work into strategic planning
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