The Health Roundtable

20,000 Days CampaignPresenter: Diana Dowdle

Middlemore Hospital

Innovation Poster SessionHRT1215 – Innovation AwardsSydney 11th and 12th Oct 2012

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Key Problem The increasing demand on resources

across Counties Manukau is driving the need for continuing improvements in the way that we keep our community healthy.

Identified we cannot continue to rely on hospital based healthcare.

Between 2005-2010 the demand for Middlemore Hospital beds was growing 2% faster per year than expected from demographic growth.

Given the predicted 5.5% increase in bed days annually (demographic and non-demographic growth), we need to save 20,000 hospital bed days.

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By 9am on 1 July 2013 the Campaign aims to give back to our community 20,000 healthy & well days, so reducing hospital bed days by 20,000.

To achieve the aim, the Collaborative Improvement programme works across the whole of the health sector and supports the implementation of best practices through five key work streams:

• Living well in community

• Keeping people at risk well in the community

• Rapid response in the community to acute events

• Co-ordinated and rapid care in emergency care

• Safe and timely care for those who need hospital care

20,000 Days Campaign Aim

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Collaborative Structure

Select Topic

Expert Meetings

Identify Change

Concepts

Pre work

LS 1

S

PA D

LS 2Holding

the Gains

Spread

LS 3

Collaborative Teams

Supports: emails/ visits/ reports/ sponsors / meetings/ assessments / conference calls

PA D

S

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Campaign Driver Diagram

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The Health RoundtableVersion: 1.1Dated: 10/09/2012

20,000 Days Campaign Dashboard

August 2012

Campaign Manager : Diana DowdleClinical Leader: David GraysonImprovement Advisor: Ian Hutchby & Prem Kumar

Contacts

Comments: Cumulative bed day saving as at 31st July is 8194 Comments: The graph shows the difference between the Predicted and actual cumulative bed days.

Comments: There were no Dot Days in August

Comments: Admissions are stable and only normal variation exists. Comments: Unplanned readmissions are stable and only normal variation exists.

Dashboard Summary: Cumulative bed day saving of 8194 is a reflection of the difference between actual bed day usage and the predicted growth. This is reflection of the system as whole.

With the exception of occupancy which is showing a change in growth all other measures are stable and exhibiting normal variation.

Comments: ALOS is stable and only normal variation exists. Comments: EC Presentations are growing but stable and only normal variation existsComments: Occupancy is showing special cause variation since September 2011, the data reflects a potential change from a previous pattern of growth to no growth.

Days between Dot Days

0

50

100

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29/0

6/20

09

21/0

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21/0

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04/0

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16/1

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08/0

2/20

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01/0

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25/0

5/20

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31/0

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13/0

6/20

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20/0

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28/0

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15/0

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27/0

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24/0

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Date of Dot Day

Day

s be

wte

en D

ot D

ays

CL = 15 LCL = 0

UCL = 211

EC Presentations-Growth

UCL

CL

LCL

6200

6700

7200

7700

8200

8700

9200

9700

10200

Jul 2

009

Sep

200

9

Nov

200

9

Jan

2010

Mar

201

0

May

201

0

Jul 2

010

Sep

201

0

Nov

201

0

Jan

2011

Mar

201

1

May

201

1

Jul 2

011

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201

1

Nov

201

1

Jan

2012

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201

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May

201

2

Jul 2

012

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201

2

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201

2

Jan

2013

Mar

201

3

Tota

l Mon

thly

Pre

sent

atio

nsCumulative Bed Days Saved Since June 2011

-2,000

-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000Ju

n-11

Aug

-11

Oct

-11

Dec

-11

Feb-

12

Apr

-12

Jun-

12

Aug

-12

Oct

-12

Dec

-12

Feb-

13

Apr

-13

Jun-

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Actual vs Predicted Bed Days

140,000

142,000

144,000

146,000

148,000

150,000

152,000

154,000

156,000

158,000

160,000

162,000

164,000

166,000

Jun-

11

Jul-1

1

Aug-

11

Sep-

11

Oct

-11

Nov

-11

Dec-

11

Jan-

12

Feb-

12

Mar

-12

Apr-

12

May

-12

Jun-

12

Jul-1

2

Aug-

12

Sep-

12

Oct

-12

Nov

-12

Dec-

12

Jan-

13

Feb-

13

Mar

-13

Apr-

13

May

-13

Tota

l Bed

day

util

isatio

n ov

er a

rolli

ng 1

2 m

onth

per

iod

Actual bedday cumulative total Predicted bedday cumulative total

Admissions

UCL =105.4

CL = 96.0

LCL = 86.7

80

90

100

110

Jul 2

009

Sep

200

9

Nov

200

9

Jan

2010

Mar

201

0

May

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0

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010

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201

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201

0

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2011

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201

1

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011

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201

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2012

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201

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Jul 2

012

Sep

201

2

Ave

rage

Dai

ly A

dmis

sion

s

Readmission rate

5.0%CL =

6.2%UCL =

3.8%LCL =

3%

4%

5%

6%

7%

Jul 2

009

Sep

200

9

Nov

200

9

Jan

2010

Mar

201

0

May

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010

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201

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2011

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2012

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Jul 2

012

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2

Rea

dmis

sion

Rat

e

Average Length of Stay

UCL = 4.72

CL = 4.14

LCL = 3.57

3.40

3.60

3.80

4.00

4.20

4.40

4.60

4.80

5.00

Jul 2

009

Sep

200

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Nov

200

9

Jan

2010

Mar

201

0

May

201

0

Jul 2

010

Sep

201

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Nov

201

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Jan

2011

Mar

201

1

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201

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Sep

201

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201

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Jan

2012

Mar

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May

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Jul 2

012

Sep

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2

ALO

S

Occupancy

UCL

CL

LCL

10000105001100011500120001250013000135001400014500

Jul 2

009

Oct

200

9

Jan

2010

Apr

201

0

Jul 2

010

Oct

201

0

Jan

2011

Apr

201

1

Jul 2

011

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201

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012

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201

2Tota

l Mon

thly

Bed

Day

s O

ccup

ied

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Bed day Saving

Unplanned Re admission

Operational DefinitionBed Days: Actual patient time on bedSavings: Cumulative savings is the difference between the forecasted bed required and the actual bed used since June 2011.Savings can be a positive or negative figure.

Average Length of Stay (ALOS)

This graph shows the cumulative bed saving on a monthly basis.

CriteriaMiddlemore, Age >-15 years, Surgical/Medical specialty (incl Gynae), Acute and Elective

This graph shows the readmission rate over a period of time.

Operational DefinitionRe-admission: An unplanned acute readmission to same speciality as discharged within 7 days

CriteriaMiddlemore, Age >-15 years, Surgical/Medical specialty (incl Gynae), Data extracted based on Inpatient discharged location

Operational DefinitionLOS: Days between admission to discharge

Criteria Middlemore, Age >-15 years, Surgical/Medical specialty (incl Gynae)

CriteriaMiddlemore, Age >-15 years, Surgical/Medical specialty (incl Gynae)

This graph reflects the ALOS over a period of time.

Trigger /Dot Days

Admission

OccupancyEC Presentation

Bed day Predicted Vs Actual

20,000 Days Campaign Dashboard Definitions

Operational DefinitionDot Days: A day is referred as “Dot Day” when Middlemore central send an email when the Hospital is full. Date of Dot Days: The actual date when the email was sent.

Operational DefinitionAdmission: Patient admitted to MMH wards for more than 3 hours from the 1st seen by time

This graph shows the admission of acute adult patient admitted to Middlemore over a period of time.

This Graph chart shows the days on which date the hospital was full and also the days between two Dot days. Hospital full days are also termed as Dot days. One of the aim is to minimise the Dot days and increase the time between Dot days. One of the contributing factor to achieve this is bed day saving

CriteriaAll emails sent by Middlemore central with a subject “Hospital full”

This graph represents the Average daily presentation to MMH emergency care.

CriteriaAll presentation to MMH Emergency departmentThis figures include adult and Paediatrics

Operational Definition

This graph reflects the total monthly occupancy of Surgical, Medical and Gyne specialty combined on a monthly basis

CriteriaMiddlemore, Age >-15 years, Surgical/Medical specialty (incl Gynae). Occupancy includes: MSSU and Observation

Operational DefinitionOccupancy: Actual patient time on bedC.L in the graph represents Median

This graph shows the Actual bed day usage compared to the predicted usage. If the actual is less than predicated then we will have bed day gain.

Operational DefinitionBed Days: Actual patient time on bedPredicted bed day: Cumulative bed required calculated based on bed modellingCumulative: Previous 12 months of data from the current month

CriteriaMiddlemore, Age >-15 years, Surgical/Medical specialty (incl Gynae), Acute and Elective

UCL: Upper control Limit is automatically calculated by the software it selves.CL: Centre Line can also be called as Average. LCL: Upper control Limit is automatically calculated by the software it selves.

Note: The graphs will help us to detect Shifts, Trends and variations. The lines within control limits indicate that the data is stable and in Statistical control.

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Key Changes Implemented

13 Collaborative teams established for intervention areas that will make an impact on reducing bed days

Better Breathing: - set up Community based Pulmonary rehabilitation - early diagnosis bundle in GP practices with screening smokers /ex

smokers 40 years and over Cellulitis & Skin Infections:

- simple abscess cases managed via TADU with no admission St John Ambulance services:

- increased numbers of status 3 & 4 people transported to community Accident & Medical Centres.

Hip Fracture Management:- 7 day rehabilitation with Physiotherapy available at the weekends

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Outcomes So Far

13 Collaborative teams established with Aims / Charters / Measurement dashboards / designing and testing for change through PDSA cycles

Ability to report on high level measures for whole system 8,194 days saved since June 2011 Active engagement across the health sector Strengthening of networks Successful Learning Sessions: 4-5 May & 6-7 September 2012 Building capability for sustaining model of improvement

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Lessons Learnt

Involvement of all key clinician leaders, managers, frontline staff, community, primary care at the beginning buiolds the will and maintains engagement.

Having multidisciplinary health professionals involved in each Collaborative team provides co-ordinated care

Telling patient experiences of their journey through the health system told either by video/ interviews or photos is very powerful reminder of the need to make improvements.

Strong support from the CEO for the Campaign provides a mandate for the work and links with Counties Manukau Health strategic direction

Baseline measurements are important to demonstrate improvements.

www.koawateablog.co.nz

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