Patient Flow Collaborative Learning Session 3

Department of Human Services

Patient Flow Collaborative Learning Session 3

Welcome

8th February, 2005

Level 12 Conference room , 555 Collins Street,

Melbourne


Patient Flow Collaborative Learning Session 3

Rochelle Condon

Service Improvement Lead

Patient Flow Collaborative

8th February, 2005

WelcomeWelcome

• Dedicated day for Project Coordinators and Data Analysts

• Sessions on;

Measurement for Access

Bed Management

HousekeepingHousekeeping

• Mobile phones/pagers to silent/vibrate

• Rest rooms

• Fire alarms and exits

HousekeepingHousekeeping

• Work in partnership – no one knows all the answers

• Support people – Clinical Innovations Team

AgendaMEASUREMENT FOR ACCESSMEASUREMENT FOR ACCESS

9.10 – 9.30 Statistical Process Control Charts Prue Beams

9.30 – 9.45 Program Measure Interpretation Prue Beams

9.45 – 10.30 Measurement for improvement Prue Beams

and performance

- Southern Health WIES Management System

- HDM Exception report

- Sameday Surgery Basket

AgendaMEASUREMENT FOR ACCESSMEASUREMENT FOR ACCESS

10.30 – 10.45 Morning Tea

10.45 – 11.30 Capacity and Demand Prue Beams

- Variation Mgmt Case Study and - Templating Bernadette - Elective Information Systems McDonald

11.30 – 12.00 Discussion Prue Beams

12.00 – 12.45 Lunch

AgendaBED MANAGEMENTBED MANAGEMENT

12.45 – 1.30 Bed Management Trevor Rixon- Victorian Programs

1.30 – 2.15 Bed Management Penny Pereira- UK Programs

2.15 – 2.30 Afternoon Tea

2.30 – 3.15 Discussion on Bed Penny Pereira

Management Innovations and Trevor Rixon

3.15 – 3.20 Next Steps and Close Rochelle Condon


Learning Session 3Learning Session 3

Measurement for AccessPrue Beams – Data Consultant

Setting the Scene…Setting the Scene…

Sustainability

– PFC data support will cease Jul05• What is the plan for your organisation at this time?

– Health services need to internalise this type of analysis so process improvements can continue to be measured

• Making it Mainstream– Supply resource information for future reference and create

networks


Measurement for Improvement and Performance

– What data do we need to identify and measure process improvements?

– What data do we need to assist us in our performance management?


Capacity and Demand

– What data do we need to identify the variation in our processes?

– What data do we need to help us match capacity to demand?


Statistical Process Control Statistical Process Control ChartsCharts

Revisiting what we have learnt

Outcomes from this sessionOutcomes from this session

• You will:

– Have reinforced your understanding of the two types of variation

– Be able to construct and interpret a simple SPC (XmR) chart

– Know when to recalculate its process limits

– Have planned your next steps in continuing the use of SPC analysis in your organisation post Jul05

• A brief recap on the basics of variation• Introduce the SPC (XmR) chart• Construct an SPC (XmR) chart• Interpret the results• When to change the limits• Managing variation using SPC• Available tools and references

So what are we going to cover?So what are we going to cover?

Variation is inherent in all processesVariation is inherent in all processes

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% D elayed transfers o f C are by Type - source S ITR E P S 7/1 /02-31/08/03

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Reasons for Delayed Transfer August 2003

% AwaitAss<7 days9%

% AwaitAss >7days9%

% Await Public Funding

5%

% Await Further NHS care12%

% Await Residential25%

% Await Domiciliary package

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% Patient Family choice

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% Other Reasons10%

Traditional ways of reporting performance ignore or seek to filter out this variation

Existing reportsExisting reports

Where have we come from?Where have we come from?

• Compare to some arbitrary fixed point in the past– the average (median) waiting time of those on the list, at

2.97 months, fell slightly over the month, and remains lower than at March 1997 (3.04 months).

• Show percentage change this month and to some arbitrary fixed point in the past– the number of over 12 month waiters fell this month by

3,800 (7.4%) to 48,100, and are now 24,000 (33%) below the peak at June 1998

Death by NumbersDeath by Numbers

Every picture tells a story . . . Every picture tells a story . . . Does it!?!Does it!?!

Looks pretty – but what is it telling us?

Reasons for Delayed Transfer August 2003

%AwaitAss<7 days9%

%AwaitAss >7days9%

%Await Public Funding5%

%Await Further NHS care12%

%Await Residential25%

%Await Domiciliary package

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%Patient Family choice18%

%Other Reasons10%

Magic EyeMagic Eye% Delayed transfers of Care by Type - source SITREPS 7/1/02-31/08/03

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Magic World of TrendlinesMagic World of Trendlines

Activity Planning

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Better?Better?

MonthNumber of Delayed

Transfers Difference MonthNumber of Delayed

Transfers 02 vs 03 % ChangeJan-02 151 Jan-03 170 19 13%Feb-02 147 -4 Feb-03 198 51 35%Mar-02 111 -36 Mar-03 159 48 43%Apr-02 167 56 Apr-03 176 9 5%May-02 114 -53 May-03 141 27 24%Jun-02 106 -8 Jun-03 176 70 66%Jul-02 153 47 Jul-03 132 -21 -14%Aug-02 111 -42 Aug-03 132 21 19%Sep-02 150 39Oct-02 123 -27Nov-02 127 4Dec-02 145 18Jan-03 170 25Feb-03 198 28Mar-03 159 -39Apr-03 176 17May-03 141 -35Jun-03 176 35Jul-03 132 -44Aug-03 132 0

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Even better?Even better?Ealing Number of Delayed Transfers

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Or better still?Or better still?Ealing - Number of Delayed Transfers

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Delayed Transfer of Care graph average graph UCL graph LCL

Variation comes in 2 flavoursVariation comes in 2 flavours

• Some processes display controlled variation (common cause)– stable, consistent and predictable – inherent in the process

• While others display uncontrolled variation (special cause)– pattern changes over time– special cause variation/“assignable” causes

• How do we know which is which?

Identifying Controlled Variation…Identifying Controlled Variation…

Stable, consistent pattern of variation “Chance” / constant causes

Identifying Uncontrolled Variation…Identifying Uncontrolled Variation…

Pattern changes over time “Assignable” / special causes

What happened here?

and here?

Common Cause Variation

What type of variation is present in each of these pumpkins?

Special Cause Variation

How about this one?

Special Cause warning…Special Cause warning…

Two dangers to beware of:

1. Reacting to special cause variation by changing the process

2. Ignoring special cause variation by assuming “its part of the process”

• A brief recap on the basics of variation• Introduce the SPC (XmR) chart• Construct an SPC (XmR) chart• Interpret the results• When to change the limits• Available tools and references


The SPC (XmR) chartThe SPC (XmR) chart

• XmR stands for X moving Range

• The ‘X’ represents the data from the process we are monitoring– eg number of delayed discharges, % cancelled operations

• The moving Range describes the way in which we measure the variation in the process

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A typical SPC (XmR) chartA typical SPC (XmR) chart

What Can SPC Do For Me?What Can SPC Do For Me?

• Shows just how much variation is normal• Helps forecast performance• Indicates whether process can meet targets• Shows how to intervene in a process to improve it• Identifies if a process is sustainable• Identifies when an implemented improvement has changed a

process– and it has not just occurred by chance

• Reduces data overload



Constructing the chart…Constructing the chart…

There are 5 steps to creating your chart:

1. Plot the individual values2. Derive the moving range values3. Calculate the mean (X) and plot it4. Calculate the average moving range (R)5. Derive upper and lower limits from this and plot them

The calculations used…The calculations used…

Example data set…Example data set… Table 1 is an example of

what the data should look like

Table 2 is an example of what the formula should look like

Average, Lower limit and Upper limit should only have the formula in the first row and the value pasted for the entire dataset.

Some points to note…Some points to note…

The chart is designed to be applied to one process A minimum of 21 data points is required The moving range describes the way in which we

measure the variation in the process The difference in the Moving Range is always positive

Deriving the process limits– Calculate limits as mean + 3 sigma



Rules for Special Causes…Rules for Special Causes…

Rule 1 Any point outside of the control limits

Rule 2 A run of 7 points all above or below the centre line, or A run of 7 points all increasing or decreasing

Rule 3 Any unusual patterns or trends within the control limits

Rule 4 The number of points within the middle third of the region

between the control limits differs markedly from two-thirds of the total number of points

Special Causes – Rule 1Special Causes – Rule 1Rule 1

Any point outside of the control limits

Point below the Upper Limit

Point above the Upper Limit


A run of 7 points all above or below the centre line

7 points above the line

7 points below the line

Special Causes – Rule 2Special Causes – Rule 2Rule 2 A run of 7 points all increasing or decreasing

7 points in an upward direction

7 points in an downward direction


Any unusual patterns or trends within the control limits

Cyclic pattern Trend pattern


The number of points within the middle third of the region between the control limits differs markedly from two-thirds of the total number of points

Considerably less than 2/3 of the points fall in this zone

Considerably more than 2/3 of the points fall in this zone



When to change the limits…When to change the limits…

If you can answer yes to all of these questions:

When one of the 4 rules has been broken Have you seen the process change significantly – i.e. is there an

assignable (special) cause present?

Do you understand the cause for the change in the process?

Do you have reason to believe that the cause will remain in the process?

Have you observed the changed process long enough to determine if newly-calculated limits will appropriately reflect the behaviour of the process?

If you can answer Yes…If you can answer Yes…change limitschange limits

Significant points above the mean, these are now used to recalculate the limits

Start of process change

After limit change…After limit change…

Limits now reflect the ‘voice’ of the process. Common cause variation has been minimised.

Upper and Lower limits narrowed.

Beware…Beware…

There is no credit for calculating the right limits, only for taking the right action from what you observe.

The power of the charts is in increasing the organisation’s understanding of it’s processes.

Interpreting SPC charts is:

An Art and not a Science



Managing Variation using SPCManaging Variation using SPC

2 ways to improve a process:

• If controlled variation– process is stable and predictable– variation is inherent to process– therefore, process must be changed / improved

• If uncontrolled variation– process is unstable and unpredictable– variation caused by factor(s) outside process– cause should be identified and “sorted”/”eliminated”

Process Improvement StagesProcess Improvement Stages

Common cause variation reduced

Process improved

Special causes present

Process out of control - unpredictable

Special causes eliminated

Process under control - predictable



Available ToolsAvailable Tools• Tools

– Winchart– BP chart (free!)– SPC Flowmap– Chart Runner

• Website References– Mal Owen - SPC in the office– www.SPCPress.com– qualityamerica.com– isixsigma.com

• Excel Macros can be created– SPC Formula Macro– SPC Chart layout/colour scheme macro

Useful ReferencesUseful References

• Donald Wheeler. Understanding Variation. Knoxville: SPC Press Inc, 1995

• Donald Wheeler. Making sense of data. SPC for the service sector. Knoxville: SPC Press Inc, 2003

• Walter A Shewhart. Economic control of quality of manufactured product. New York: D Van Nostrand 1931.

• American Society for Quality www.asq.org/about/history/shewhart.html

• WE Deming. Out of the crisis. Massachusetts: MIT 1986

• Donald M Berwick. Controlling variation in health care: a consultation from Walter Shewhart. Med Care 1991; 29: 1212-25.

AcknowledgementsAcknowledgements

This presentation draws on the work of:

Martin Silk Information Consultant, IPH

Sally BatelyDeputy Director for Analysis, MA

NHSModernisation Agency


Program Measure InterpretationProgram Measure Interpretation

Knowing what to look for

Program Measure InterpretationProgram Measure Interpretation

As we go through these measures I want us to think about:

• What do you think these charts are telling us?

• What additional data would help us understand this process?

• Who needs to be engaged with this data?

Patient Patient Journey Time in ED Journey Time in ED - All Presentations Chart- All Presentations Chart

Patients presenting to this Emergency Department can expect to have a journey time from arrival to departure between 0 and 1702mins with a mean of 465mins.

-Triage Category’s

-Admitted/Discharged groups

-Diagnosis details of long waiters

Percentage of ED Patients Admitted to Ward in <12 hrsPercentage of ED Patients Admitted to Ward in <12 hrs

For the period Jul03 to Dec04 between 59% and 90% of ED patients waiting for admission to a ward could expect to wait less than 12hrs.

The mean number of patients admitted within 12hrs per week was 74%, with a target of 95%.

-Is this process stable?

-Is the target achievable?

-Should the limits be reset?

Percentage of ED Throughput <6hrs Percentage of ED Throughput <6hrs

For the period Jul03 to Dec04 between 65% and 80% of ED patients could expect to wait less than 6hrs from arrival to departure.

The mean percentage of patients waiting less than 6hrs per week was 72%.

-Watching slide

-Improvements in the admitted 12hr group should not compromise the performance of the overall 6hr group

Patient Journey Time for Admitted Patients Patient Journey Time for Admitted Patients on Waiting Liston Waiting List ( (Cat1)Cat1)

Within the month of Aug04 Category 1 Patients admitted from the waiting list could expect to have a total waiting time (i.e. Ready for Care + Not Ready for Care days) between 0 and 111days with a mean of 38days.

- What is causing this NRFC picture?

- Administrative churn

Patient Journey Time for Admitted Patients Patient Journey Time for Admitted Patients on Waiting Liston Waiting List (Cat3) (Cat3)

Within the month of Aug04 Category 3 Patients admitted from the waiting list could expect to have a total waiting time (i.e. Ready for Care + Not Ready for Care days) between 0 and 787days with a mean of 239days.

- Acceptable NRFC picture

- Why are some Cat3’s getting in so quickly? (que-jumping)

Patient Waiting Times for Admitted Patients Patient Waiting Times for Admitted Patients from Waiting Listfrom Waiting List

80% of admitted patients from the waiting list for the month of Oct04 had a total waiting time (i.e. Ready for Care + Not Ready for Care days) between 0 and 176 days with a maximum waiting time of 1035days.

- Pareto principle

HHospital Initiated Postponementsospital Initiated Postponements per 100 Admissions per 100 Admissions

Hospital Initiated Postponements reporting methodology = The number of cumulated postponements over the entire patient waiting time, reported on the month of admission.

Note: Dec qtr is current up to Nov04.

-Watching slide

-Improvements in templating should be noticeable in postponement rates

Length of Stay (Surgical) Length of Stay (Surgical)

80% of patients (excluding sameday) at this hospital had a length of stay between 1 and 11 days with the maximum length of stay currently at 505 days.

80% between 1 and 11 days

-What is causing the surgical spike?

-Shaving a few hours or ½ day off the 80% group

Reducing Length of StayReducing Length of Stay

Medical Patients

Note: Average LoS = 7.24 days

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Take ½ day off clinically unnecessary LoS and it has a dramatic impact

? prevent admission

These patients may have more complex support needs

Average Admissions & Discharges by DOW Average Admissions & Discharges by DOW

For the period Jul03-Feb04 the highest number of average admissions for multiday patients was on a Monday (64), and the highest number of average discharges was on a Friday (69).

The lowest number of average admissions (excluding weekends) was on a Friday (53), and the lowest number of average discharges was on a Monday (51).

-This should reflect policy on bed profile

Number of Unplanned Readmissions within Number of Unplanned Readmissions within 28 days by Day28 days by Day

For the period Nov03 to Oct04 we could expect to see between 0 and 44 daily unplanned readmissions within 28days of discharge.

The mean number of patients with an unplanned readmission within 28days is 20 per day.

-Watching slide

-Improvements in discharge planning should not cause an increase in readmission rates


Measurement for Improvement Measurement for Improvement and Performanceand Performance


Southern Health WIES Southern Health WIES Management SystemManagement System

Lesley DwyerDirector Patient Access & Demand Strategy

Where do we currently stand?Where do we currently stand?

Mid year (Dec04)• Approx 2% over WIES• Waiting list of almost 9,000 patients (across all sites)

– Avg waiting time Cat2 = 706 days– Avg waiting time Cat3 = 1491 days– Shortest wait Cat2 = 26 days– Shortest wait Cat3 = 41 days

• Queue-jumping

• A need to implement strategies to improve performance and ensure equity of access

• …And at the same time live within our means!

Elective Theatre Access Management Elective Theatre Access Management – List Construction at MMC– List Construction at MMC

Problem “living within our means”

…Emergency WIES close to target BUT Elective WIES ahead of target in both waiting list electives and non-waiting list (other).

MMC has the following profile:

70% Emergency30% Elective

10% waiting list20% other eg Gastroenterology

Elective Theatre Access Management Elective Theatre Access Management – List Construction at MMC– List Construction at MMC

• Proposal• Develop Strategies that reduce WIES but still deliver

waiting list targets!• Ambitious target• Start date NOW!

• List Construction Project• Went live February 2005• What are the elements of this project?

Elective Theatre Access Management Elective Theatre Access Management – Quasi Mathematics– Quasi Mathematics

Develop a rationale

Formula:Emergency WIES + Cat 1 + Maternity = XLess Target WIES = YWIES Available Cat 2’s, Cat 3’s, Non W/L Z

Z is calculated and distributed equitably across surgical units cognisant of demand pressures and

waiting list targets

Elective Theatre Access Management Elective Theatre Access Management – List Construction– List Construction

Key elements of Project:– Resource appropriately – form a “can-do” group to engage

key stakeholders and monitor– Data, data, data – to the right people– Look for and incorporate “levers” eg ESAS, non-conformers,

capacity at other sites– Understand that there needs to be several components of an

elective surgery strategy• Queuing Equity Project• List construction• ESAS• Waiting list audits• Specialty specific initiatives (Plastics and Varicose Veins)

Elective Theatre Access Management Elective Theatre Access Management – List Construction– List Construction

• What might this look like?– Typical list

– Cat 1 and/or Emergency– Long Wait Cat 2’s – tails to reach target– Long Wait Cat 3

– Additional list– Identifying specialties that require additional resources to

bring them back to an even playing field

• How do we support clinicians?– Develop guidelines for booking patients onto list – work with their

special needs– Give information (such as Exception Report / Unit based reporting)– Monitor progress and report back regularly– Reviewing weekly (with specific focus on Cat1’s)

Queuing Equity ProjectQueuing Equity Project

PROCESS• Based on the volume of Theatre sessions and number of

Category 2 Tail-ending patients.• Even distribution across Weekly Theatre Schedule.• Pre-Admission Clinic Collaboration4. Clear communication with Surgical Registrars & support from

Surgical Heads of Unit.5. Awareness in Bed Bureau/Access Unit of Patient Urgency as to

pt identification on Elective Admission List.

Queuing Equity ProjectQueuing Equity Project

MEASURES

1. Access to Acute Bed2. Cancellation Rate (HIP)3. Visible reduction in average waiting time for Category 2 Patients.4. Patient Satisfaction5. Sustainable change to Monash Medical Centre. 05/06 Financial Year.


Elective Surgery Exception Elective Surgery Exception ReportReport

Based on the work of Simon JollyHospital Demand Management


As we go through this report I want us to think about:

• What is the purpose of this data?

• Who should receive this information?

• What actions should come out of this data?


Procedures where the number of patients treated is small in comparison to the number of patients waiting.

There is concern that patients waiting for these procedures will have excessive waits or never be treated. Clearance times calculated on admissions in the past three months. Clearance times and other values specific to the stated urgency.

Number of procedures flagged in this category: 28

Specialty Procedure Urgency

Average admissions per quarter past year

Patients admitted

this quarter Patients waiting

Clearance time

(months)

Statewide clearance

time (months)

Ear, Nose & Throat FESS (Functional Endoscopic Sinus Surgery) 3 1.5 1 15 45 6

Myringotomy 3 1.3 1 11 33 2

Septoplasty 3 3.3 4 66 50 12

Tonsillectomy/Tons & Adenoidectomy 3 6.0 5 44 26 5

Turbinectomy 3 0.5 1 9 27 11


Procedures where admitted patients had shorter waiting times than patients with the same urgency who are waiting for surgery.

This is evidence that recently registered patients are being admitted while long-waiting patients are neglected. Waiting data are for the current month. Admitted data are from the past twelve months. Procedures with low numbers are excluded.

Number of procedures flagged in this category: 13

Specialty Procedure Urgency Same or multi-day Average wait of

admitted patients Average wait of waiting patients

Ear, Nose & Throat Other ENT surgery 3 M 159 372

General surgery Cholecystectomy 3 M 440 470

Other herniorrhaphy 2 M 182 199

Procedures for haemorrhoids 2 M 163 212

Thyroidectomy 2 M 121 125


Non-urgent patients who received treatment after short waits. There is concern that these patients were treated ahead of patients with long waiting times. Patients admitted in the month to 30 November 2004. The average waiting time is the average waiting time of patients on this hospital's list with the same urgency waiting for the same procedure.

Number of patients flagged in this category: 65

Insurance declaration Specialty Procedure UrNo Urgency

Wait of this patient

(days)

Average waiting time

(days)

Other Ophthalmology Repair of cataract X132587 3 3 12

793546 3 16 12

X220681 3 1 12

X25569A 3 20 12

X230661 3 4 12

Orthopaedics Remv of internal fixation device of bone 641535 2 7 60

Other Endoscopic Procs Stomach & Small Intestine SS3333 2 4 154

Plastic surgery Other plastic surgery X93721 2 7 77

X33047 2 6 77

SS5423 3 16 330

Private Cardio-thoracic Other thoracic surgery 624379 2 0 12


Patients who have received three or more hospital-initiated postponements. Hospitals should ensure that numbers of postponements and the inconvenience associated with them are minimised. Data are for patients waiting at the end of 30 November 2004.


Specialty Procedure UrNo Urgency Same or multi-day Number of

postponements

Ear, Nose & Throat Septoplasty 773952 3 M 4

92815 2 M 3

General surgery Lig&stripping of varicose veins of legs 541517 3 M 4

Male sterilisation 451255 2 S 4

Gynaecology Other gynaecological surgery 530742 3 M 3

Orthopaedics Reduct of fracture w internal fixation 553379 3 M 4


Patients who were not admitted on the day of their procedure (Non-DOSA patients). Data are for patients admitted in the month ending 30 November 2004.


UrNo Specialty Procedure Date of admission Date of procedure

802834 Cardio-thoracic Coronary artery bypass graft 21-11-2004 22-11-2004

H433564 14-11-2004 15-11-2004

802756 25-11-2004 26-11-2004

644739 09-11-2004 10-11-2004

800891 11-11-2004 12-11-2004

638388 28-11-2004 29-11-2004

464921 25-11-2004 26-11-2004

638444 Other surgery on the heart 23-11-2004 24-11-2004


Patients with the demographic profile of patients who possibly no longer need surgery. A phone call to these patients asking if they still require surgery may be worthwhile. Data are for patients waiting at the end of 30 November 2004.


UrNo Specialty Procedure Urgency Waiting time

0000115502 General surgery Cholecystectomy 2 314

000H409042 Inguinal herniorrhaphy 2 537

0000796943 2 124

0000625208 2 511

0000329078 Orthopaedics Total hip replacement 2 267

0000637335 2 219

0000631370 Urology Prostatectomy 2 370

0000768470 2 344


Patients who have waited longer than the usual state maximum for this procedure. Efforts should be made to treat these patients, or remove them from the list if they no longer require surgery. Data are for patients waiting at the end of 30 November 2004. The median wait is the time in which 50 per cent of Victorian patients requiring this procedure with the same urgency have been treated in the past. The wait of the 99th percentile is the time in which 99 per cent of Victorian patients requiring this procedure with the same urgency have been treated in the past.


Specialty Procedure UrNo Urgency Readiness Same or multi-day

Waiting time

Statewide median wait

Statewide wait 99th percentile

Ear, Nose & Throat Excision of lesion / tissue of lip

H318279 3 R S 860 27 579

Mastoidectomy 600425 3 N M 1,147 115 866

Rhinoplasty 386225 3 R S 1,001 118 974

511934 3 R S 1,387 118 974

766699 3 R S 1,089 118 974

747122 3 R M 1,153 118 974

75824 3 R M 1,204 118 974


The top 50 longest waiting patients. Removing patients within this group will help reduce the waiting time of the 95th percentile. Data are for patients waiting for included procedures at the end of 30 November 2004. Less than 50 patients are listed for some high-performing hospitals.

Rank Specialty Procedure UrNo Same or multi-day

Waiting time (days)

Waiting time (years)

1 General surgery Procedures for morbid obesity 480184 M 1,900 5.2


3 General surgery Fundoplication/fundoplasty 485510 M 1,839 5.0

4 General surgery Cholecystectomy 580350 M 1,780 4.9


6 Plastic surgery Reduction of nasal fracture 556332 M 1,743 4.8

7 General surgery Lig&stripping of varicose veins of legs 40704 M 1,743 4.8


9 General surgery Lig&stripping of varicose veins of legs 749856 S 1,727 4.7

10 General surgery Cholecystectomy 574270 M 1,614 4.4

11 Plastic surgery Other plastic surgery 66551 M 1,587 4.3

12 General surgery Inguinal herniorrhaphy 581642 M 1,544 4.2

13 Vascular surgery Lig&stripping of varicose veins of legs 342957 M 1,541 4.2





18 Plastic surgery Rhinoplasty 511934 S 1,387 3.8

Elective Surgery Exception Elective Surgery Exception ReportReport Report Summary

Procedures where the number of patients treated is inadequate in comparison to the number of patients waiting: 28 Procedures where admitted patients had shorter waiting times than patients with the same urgency who are still waiting for surgery: 13 Non-urgent patients who received treatment after extremely short waits: 65 Patients who have received three or more hospital-initiated postponements: 18 Patients who were not admitted on the day of their procedure (Non-DOSA patients): 18 Patients who have waited much longer than would be expected from state averages for this procedure: 146

Also…

Elective Surgery Outcome report


Sameday Surgery BasketSameday Surgery Basket

Where do we start - LocalWhere do we start - Local

Victorian Sameday Basket criteria…

1) Proven• Currently done as a sameday procedure within Victoria

2) Room for improvement3) Common procedures

• Target high volume procedures

VIC Sameday Surgery BasketVIC Sameday Surgery Basket- General- General

VIC Sameday Surgery BasketVIC Sameday Surgery Basket- Other- Other

• Hospitals for Women• Royal Children’s Hospital• Royal Victorian Eye and Ear Hospital• Peter MacCallum Cancer Institute

Where do we start – Where do we start – Interstate/InternationalInterstate/International

Interstate models• Sameday Laparoscopic Cholecystectomy at The Royal Hobart

Hospital, Mr Stuart Walker - Staff Specialist Vascular Surgery• http://www.health.vic.gov.au/hdms/presentations/

lap_chole_presentation_hobart.pdf

International models• NHS Basket of 25

NHS Basket of 25NHS Basket of 25

The Procedures Orchidopexy Arthroscopy Circumcision Bunion operations Inguinal hernia repair Removal of metalware Excision of breast lump Extraction of cataract Anal fissure dilation or excision Correction of squint Haemorrhoidectomy Myringotomy Laparoscopic cholecystectomy Tonsillectomy Varicose vein stripping or ligation Sub mucous resection Transurethral resection of baldder tumour Reduction of nasal fracture Excision of Dupuytren's contracture Operation for bat ears Carpal tunnel decompression Dilation & curretage hysteroscopy Excision of ganglion Laparoscopy Termination of pregnancy

NHS Basket of 25 link…

http://www.modern.nhs.uk/scripts/default.asp?site_id=36&id=13905

NHS TrolleyNHS Trolley

Maintaining the supermarket analogy, the British Association of Day Surgery proposed a 'trolley' of procedures which are suitable for day surgery in some cases. Laproscopic hernia repair Thoracoscopic sympathectomy Submandibular gland excision Partial thyroidectomy Superficial parotidectomy Wide excision of breast lump with axillary clearance Urethrotomy Bladder neck incision Laser prostatectomy Trans cervical resection of endometrium Eyelid surgery Arthroscopic menisectomy

What data should we analyseWhat data should we analyse

• What is our current performance?– HDM Sameday Surgery reports– Based on VAED activity data– Source: VAED (ICD-10 in combination with specified DRG’s –

i.e. Not all DRG’s will be included in the basket procedure)

• What scope do we have to improve our performance?– Analysis of what is on the waiting list– Based on Principle Prescribed Procedure codes within ESIS

Other considerationsOther considerations• Calculating bedday savings

– May generate multiday bed savings but are there sufficient day beds to cope with the change in profile

• Equipment– Is more laparoscopic equipment required

• Recovery/Staffing– Hours of operation and patient flow

• WIES– What will be the impact on WIES and costings

• Clinician buy-in and Training– Is the move to increased sameday surgery being lead by

management or a clinical champion?

Questions

?

Morning TeaMorning Tea

Meet us backMeet us back here at 10.45 here at 10.45


Capacity & DemandCapacity & Demand

Whole Health Service Variation Management Whole Health Service Variation Management Case StudyCase Study

Bernadette McDonald / Lee MartinBernadette McDonald / Lee Martin

Whole health service variation Whole health service variation managementmanagement

Theory-• Predict emergencies • Schedule electives around emergency prediction• Manage admissions and discharges -emergency

prediction and elective schedule• Smooths demand and increases capacity

Variation in Admission PatternsVariation in Admission Patterns

Variation in Inpatient ProcessesVariation in Inpatient Processes

Variation in Admissions and DischargesVariation in Admissions and Discharges

Average Separations vs Admissions – Average Separations vs Admissions – Acute Non Emergency by DOWAcute Non Emergency by DOW

Average Separations vs Admissions - by DOW(All) ACUTE Non-Emergency Episodes

Total Hospital

29 29

27

22

19

1 2

12

21 20

25

27

17

5

-

5

10

15

20

25

30

35

Mon Tue Wed Thu Fri Sat Sun

Sepa

ratio

ns /

Adm

issi

ons

Ave Admissions

Ave Separations

Non same day episodes onlyData for 52 week period ended 30 Nov 2004

Average Separations vs. Admissions –Average Separations vs. Admissions –Acute Emergency by DOWAcute Emergency by DOW

Average Separations vs Admissions - by DOW(All) ACUTE Emergency Episodes

Total Hospital

48 4846 44 45

36 36

49

53

4950

52

31

21

-

10

20

30

40

50

60


Sep

arati

on

s /

Ad

mis

sio

ns

Av e Admissions

Av e Separations


Average Separations vs Admissions – Average Separations vs Admissions – (All) acute by Day of week(All) acute by Day of week

Average Separations vs Admissions - by DOW(All) ACUTE (All) Episodes

Total Hospital

77 7772

6663

37 38

61

74

69

7579

48

26

-

10

20

30

40

50

60

70

80

90


Sep

arati

on

s /

Ad

mis

sio

ns

Av e Admissions

Av e Separations


Separations minus Admissions – Nett Separations minus Admissions – Nett difference by DOWdifference by DOW

Separations minus Admissions - Nett Difference by DOW(All) ACUTE (All) Episodes

Total Hospital

-16

-3 -4

8

15

10

-12

-20

-15

-10

-5

0

5

10

15

20


Se

pa

ra

tio

ns

- A

dm

iss

ion

s


ConsiderationsConsiderations

• Total non emergency bookings• Cancellations• Seasonal variation• Direct Admits

Average Separations vs Admissions – Average Separations vs Admissions – Acute Non Emergency by DOW General SurgeryAcute Non Emergency by DOW General Surgery

Average Separations vs Admissions - by DOWGeneral Surgery ACUTE Emergency Episodes

General Surgery Specialty

3

33 3

4

3

3

4

4

3

4 4

2

2

-

1

1

2

2

3

3

4

4

5


Sep

arati

on

s /

Ad

mis

sio

ns

Av e Admissions

Av e Separations

Average Separations vs Admissions – Average Separations vs Admissions – Acute Non Emergency by DOW – General SurgeryAcute Non Emergency by DOW – General Surgery

Average Separations vs Admissions - by DOWGeneral Surgery ACUTE Non-Emergency Episodes


3

5

3

2

2

0 0

22

3

3

2

2

1

-

1

1

2

2

3

3

4

4

5

5


Sep

arati

on

s /

Ad

mis

sio

ns

Av e Admissions

Av e Separations

Average Separations vs Admissions – Average Separations vs Admissions – (All) acute by Day of week – General (All) acute by Day of week – General SurgerySurgery

Average Separations vs Admissions - by DOWGeneral Surgery ACUTE (All) Episodes


6

8

66

5

33

6 6 6

7

6

4

2

-

1

2

3

4

5

6

7

8

9


Se

pa

ra

tio

ns

/ A

dm

iss

ion

s

Av e Admissions

Av e Separations

Separations minus Admissions – Nett Separations minus Admissions – Nett difference by DOW – General Surgerydifference by DOW – General Surgery

Separations minus Admissions - Nett Difference by DOWGeneral Surgery ACUTE (All) Episodes


0

-2

-0

1

1

1

-1

-3

-2

-2

-1

-1

0

1

1

2


Se

pa

ra

tio

ns

- A

dm

iss

ion

s

Average LOS by Day of AdmissionAverage LOS by Day of Admission

Average LOS by (All)Day of Admission ACUTE Episodes

Total Hospital

8.4

7.3 7.5

8.37.7

7.0 6.9

6.0 5.75.0

5.7 5.5

14.1

8.7

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0


Averag

e L

OS

0

500

1,000

1,500

2,000

2,500

3,000

Nu

mb

er o

f E

pis

od

es

Emergency Av e LOS

Non-Emergency Av e LOS

Emergency Episodes

Non-Emergency Episodes

Patient Movements by Hour - Patient Movements by Hour - wardward

Patient Movements by Hour - Ward A08AB20-Dec-2004 to 16-Jan-2005

0

5

10

15

20

25

00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour of Day

Nu

mb

er

of

Pa

tie

nt

Mo

ve

me

nts

Patients in

Patients out

Further AnalysisFurther Analysis

Cross check• Previous Bypass by DOW / TOD• Cancellations for Electives by DOW / TOD• Delays within patient streams


TemplatingTemplating

A Radiology Example

Process steps examples

Step Time per step

Patient checks in 2

Patient gets undressed 4

Consent taken 3

Patient positioned 2Scan 2

Patient get dressed 4

Patient waits 3

Post scan check 3

Patient leaves 2

Report on scan 10

Introduction

Goals of the toolkit

Overview and strategy

Health service team

Processes

Data

Resources

Diagnostics and tools

Click to continuePage 7 of 25

Back to menu


Process steps examples

Step Time per step

Colour code

Patient checks in 2

Patient gets undressed 4

Consent taken 3

Patient positioned 2Scan 2

Patient get dressed 4

Patient waits 3

Post scan check 3

Patient leaves 2

Report on scan 10

Introduction



Health service team

Processes

Data

Resources



Back to menu


Build your schedule

Use graph paper with one square per minute to sequence time scales per procedure.

1 MINUTE35 MINUTES

Introduction



Health service team

Processes

Data

Resources



Back to menu


Build your schedule

Transfer graph sequence timescales to chart clinic time.

9.00 am start12.00 pm end

Align steps to maximise use of equipment/radiology room/staff.

Introduction



Health service team

Processes

Data

Resources



Back to menu



Also check out:

Rowena Clift (Ballarat Health Services)Breakout Session 1

‘Using templating for clinical system redesign’


Elective Information Systems

Current WorkCurrent Work

• Western Health experience• Patient Flow Information Systems

– Wendy Tomlinson (Travelling Fellow) presenting at Breakout Session 3


Elective Information Systems

Western Health/Simon Jolly Waiting List Scheduling System

Current IssuesCurrent Issues

• Duplication of work• No knowledge transfer• Missed equipment/prosthesis needs• Difficult to pull pts in waiting order• Difficult to fully utilise lists• Patients booked minimal consultation

Interim Improvement PlanInterim Improvement Plan

Microsoft Outlook Diaries– Off site access to schedule for Surgeons– Access from NUM to theatre schedule– Still duplication

Outlook Scheduling Outlook Scheduling

Long-term SolutionsLong-term Solutions

• DHS secondment – Simon Jolly • Development of IT based scheduling tool

IT Based Scheduling toolIT Based Scheduling tool

Predicted ImprovementsPredicted Improvements

• New Schedule will “talk” to PAS• Upper level schedule for Theatres• Individual Surgeon lists available off site• Ready reckoner for Equipment/ Prosthesis

requirements

Questions

?


Emergency DepartmentData Analysis

Prue BeamsData Consultant

Time of Presentation to ED by Hour of ArrivalTime of Presentation to ED by Hour of Arrival

ED Median Length of StayED Median Length of Stay- Admitted v Discharged streams- Admitted v Discharged streams


Discussion

Planning your next steps in continuing the use of process improvement analysis in your organisation post July 05

LunchLunch

Meet us backMeet us back here at 12.45 here at 12.45

Bed Management Bed Management Innovations DiscussionInnovations Discussion

Will the current system help your organisation manage beds now

and in the next 5 years?

Next StepsNext Steps

• Take some time to consider your teams use of measurement for improvement and how you will mainstream it.

Next StepsNext Steps

• Assess the current bed management system..– analyse your information systems

• Engage stakeholders and work towards a whole system approach to patient flow

• Consider the NHS and PFC learnings

• Share / Debate/ Challenge/ Engage and Improve

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Patient Flow Collaborative Learning Session 3

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