bams 580b

BAMS 580BBAMS 580B

Class 1

October 27, 2010

www.chcm.ubc.ca1

Canada Health Act - PrinciplesCanada Health Act - Principles

• Public Administration – Health care insurance plans are to be administered and operated on a non-profit basis

by a public authority.• Comprehensiveness

– The health insurance plans of the provinces and territories must insure all hospital, physician and surgical-dental health services.

• Universality – One hundred percent of the insured residents of a province or territory must be entitled

to the insured health services on uniform terms and conditions. • Portability

– Residents moving from one province or territory to another must continue to be covered for insured health care services for up to three months.

• Accessibility– Insured persons have reasonable access to hospital, medical and surgical dental

services unimpeded by charges or discrimination on the basis of age, health status or financial circumstances.

Canadian Health Care BackgroundCanadian Health Care BackgroundCIHI – Health Care in Canada 2006CIHI – Health Care in Canada 2006

• In 2005, Canada spent $142 billion on health care or $4,411 per person– This represents approximately 10% of Canada’s GDP– 30% is spent on hospitals; 17% on retail drugs

• 1.5 million people work in health care– 1 out of 10 Canadians work in health care– Nurses and Physicians are the largest groups– The workforce is rapidly aging

International ComparisonsInternational ComparisonsOECD - 2005 dataOECD - 2005 data

Country Per capita expenditure

(PP adjusted)

Government share of spending

MRIs

(per million

pop’n)

Male life expectancy

(years)

Infant mortality rate (per 1000

births)

US $6,347 45% 26.6 (2004) 75.2 6.9

Canada $3,460 70% 5.7 78.0 5.4

France $3,306 80% 4.7 76.7 3.8

UK $2,580 87% 5.4 77.1 5.1

Source: Stats.oecd.org

Health System ChallengesHealth System Challenges

• Reducing wait times • Meeting the increased health care demands of an aging

population• Replacing an aging and diminishing workforce• Using costly new technologies and therapies

appropriately• Delivering high quality and safe care

Five questionsFive questions

• How do we know whether patients are flowing well?

• Why does it matter? • How can we improve flow? • How do we know that what we did improved

flow?• How do we maintain our improvements?

www.chcm.ubc.ca6

Why is health care OM different than usual OM?

“If you’re not keeping score, you’re just practicing”

Vince Lombardi

MetricsMetrics

• Queue lengths• Waiting times• Percent who meet target• Patient Satisfaction scores• Staff Satisfaction scores

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Marty’s LeversMarty’s Levers

• Manage Demand• Manage Resources• (Manage Efficiency) Use Resources Better

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Derek’s EquationDerek’s Equation

• Variability + Fixed Capacity + High Utilization = Trouble

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Scheduling Unplanned Arrivals:Scheduling Unplanned Arrivals:Appointment SchedulingAppointment Scheduling

TopicsTopics

• The Appointment Scheduling Game• Measuring Wait Times• The “Capacity equals Demand” Fallacy• Levers for Managing Capacity

– Operations Research and Surge Capacity– Intelligent Scheduling

• What We Are Doing Now• Concluding Comments

Appointment Scheduling GameAppointment Scheduling Game

++

The Appointment Scheduling GameThe Appointment Scheduling Game

• Provide a timeline and process diagram for the scheduling clerk’s task.

• What is realistic and what is unrealistic about this game?

• What scheduling rule are you using?• How is it performing?

Metrics

• Demand by color• Lateness

– Percent served in time by color– Days late

• Capacity Utilization• Wait time by color

Scheduling Rules

• First Come First Served• Reservation Policy

Appointment Scheduling Game – More QuestionsAppointment Scheduling Game – More Questions

• What will be tomorrow’s demand? Next week’s?• How should you set capacity?• What levers do you have to regulate this process?• In what situations is this type of scheduling relevant?• What information are you keeping track of?• What does this assume about appointment lengths and start

times?• What if the system is unavailable on a given day?• What if an extremely urgent case has to be slotted in ASAP?

Return to game – measure performanceReturn to game – measure performance

++

Appointment Scheduling Game Issues Appointment Scheduling Game Issues

• Quantifying performance• Forecasting demand• Urgency classes and criteria• Scheduling rules• Surge capacity• Scheduling within a day• Breakdowns/Cancellation by Provider• No Shows by patients• Simulation

Performance MetricsPerformance Metrics

• Patient Wait Times– How to measure?

• Averages• Min, Max; Percentiles• Service levels

• Capacity Utilization• Overtime• ?

Challenges in measuring wait timesChallenges in measuring wait times

• Patients are not homogeneous– Different priority classes of patients face different wait times.

• Wait times, as currently measured, do not tell the whole story. – Wait Time equals the length of time between when request for service

and service delivery.– Ignores upstream process steps and delays.

Challenges in measuring wait timesChallenges in measuring wait times

• Averages do not tell the whole story – Wait times vary between patients, over time and between sites.– Performance measures must account for variability – Wait time distributions are skewed.

• Recommended Metrics– “Proportion of patients of a specific priority class who receive the service within

a specific clinically desirable time” – These provide meaningful guarantees to decision makers and system users.

• Reliable and complete wait time data is often not available– Variability and performance cannot be determined

Scheduling GameSimulation Results

Simulation Output is VariableSimulation Output is Variable

0

5

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15

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25

30

35

40

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97

aver

age

wai

t ti

me

[day

s]

run

Average wait time across simulation runs

PT1

Policy ComparisonPolicy Comparison

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5

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35

401 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97

aver

age

wai

t ti

me

[day

s]

run

First Available Slot vs. Protection Level

FAS

PL

Simulation SummariesSimulation Summaries

Capacity Policy usage PT1 PT2 PT3 PT1 PT2 PT3FAS 3.00 0% 0% 0% 77.94 78.41 78.67PL 3.00 0% 0% 0% 64.48 85.16 85.58BT 3.00 0% 0% 0% 78.66 79.13 79.39

FAS 3.47 94% 100% 100% 0.33 0.53 0.81PL 3.47 96% 100% 100% 0.27 0.54 0.86BT 3.47 95% 99% 100% 0.26 1.12 3.07

FAS 2.97 12% 23% 36% 9.39 9.67 10.02PL 2.97 26% 18% 25% 6.09 11.68 12.40BT 2.97 12% 17% 28% 10.27 10.69 11.39

FAS 19.93 32% 60% 77% 3.16 3.39 3.79PL -- -- -- -- -- -- --BT 19.93 41% 50% 65% 3.43 4.09 5.63

Poisson 20

3

4

on target average wait time

Poisson 3

VGH CT Scanning Study VGH CT Scanning Study

www.chcm.ubc.ca28

www.chcm.ubc.ca29

VGH CT Scanner QuestionsVGH CT Scanner Questions

• Determine whether an additional scanner was needed and if so, where should it be located?

• Define and measure current waiting times for CT scans at VGH

• Identify system bottlenecks and inefficiencies• Identify strategies for eliminating current backlogs and

compare the short term and long term costs and benefits of each

• Propose ways to expand analyses to other sites and explore improvements in booking and centralized planning

Step1: The appointment systemStep1: The appointment system

• Process– Requisition arrives

• Info – scan type and urgency

– Clerk assigns date– Clerk contacts patient– Clerk records date

• We will investigate management of appointment systems like this in depth this afternoon!

www.chcm.ubc.ca30

Data ChallengesData Challenges

• How do we determine if the system is performing well?– Performance Metrics– Urgency Levels

• What data is required?– Time stamps

• Requisition received• Scan completed

– Upstream measures• Where do we get it?

– Databases– Appointment systems– ?

www.chcm.ubc.ca31

More on DataMore on Data

• Perspective vs. Retrospective data– Perspective – from now going forward

• Based on appointment data

– Retrospective – from now going back• Based on scan date

– Historical• Complete records

• What are the strengths and weakness of each type of data?

• What we did - Obtain booked requisitions at the end of each day – Copy them

• What are the shortcomings of the approach we used?

www.chcm.ubc.ca32

33

Sample Wait Time DataSample Wait Time Data

Nov-0

3

Nov-0

3

Jan-

04

Jan-

04

Mar

-04

Mar

-04

May

-04

May

-04

Jul-0

4

Jul-0

4

Sep-0

4

Sep-0

4

Nov-0

4

Nov-0

4

Jan-

05

Jan-

05

Mar

-05

Mar

-05

May

-05

May

-05

Jul-0

5

Jul-0

5

Sep-0

5

Sep-0

5

Nov-0

5

Nov-0

5

Jan-

06

Jan-

06

Mar

-06

Mar

-06

May

-06

May

-06

Jul-0

6

Jul-0

6

Sep-0

6

Sep-0

6

Nov-0

6

Nov-0

6

Jan-

07

Jan-

07

Mar

-07

Mar

-07

Calendar TimeCalendar Time

Clie

nts

Clie

nts

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Data Summary - Outpatient Waiting TimeData Summary - Outpatient Waiting TimeOutpatient Categories OP1 OP2 OP3

Recommended WT (RWT) < 1 wk < 2 wks < 4 wks

Actual WT

Average (wks) 1.6 3.6 6.3

Max 6.6 10.4 13.9

Min 0.0 0.0 0.1

Sample Size 42 86 103 % scanned after

RWT 50.0% 68.6% 74.8%

CT Wait Times for Outpatients at VGH (Priority OP1: < 1 week)

0

5

10

15

20

0 1 2 3 4 5 6 7

Weeks

Fre

qu

en

cy Scheduled after 1 week: 50.0%

CT Wait Times for Outpatients at VGH (Priority OP2: < 2 weeks)

0

5

10

15

20

0 1 2 3 4 5 6 7 8 9 10 11Weeks

Fre

qu

en

cy

Scheduled after 2 weeks: 68.6%

CT Wait Times for Outpatients at VGH

(Priority OP3: < 4 weeks)

0

5

10

15

20

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14Weeks

Fre

qu

en

cy Scheduled after 4 weeks: 74.8%

Step 2: the scanning processStep 2: the scanning process

• Key steps– Check in– Get ready– Enter room– Scan– Check scan– Out of room

• How do we assess its performance?• What data do we need?• How do we get data?

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On Site Observations - VGHOn Site Observations - VGH

Scanners are not being used efficiently; capacity is wasted

Date Scanner % time spent scanning % time room empty09-Jan-04 1 43% 17%16-Jan-04 1 24% 49%23-Jan-04 1 31% 21%25-Jan-04 1 18% 37%09-Jan-04 2 13% 58%21-Jan-04 2 24% 41%23-Jan-04 2 25% 38%25-Jan-04 2 12% 66%16-Jan-04 4 12% 52%23-Jan-04 4 24% 61%

Total* 1 33% 28%2 21% 44%4 19% 57%

all 25% 41%*Totals do not include Sunday

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Possible impediments to flowPossible impediments to flow

• System starved• No patient available • Why?

• Porter delays• No exams scheduled during tech lunch breaks• Excessive time scheduled for an exam• Outpatients arrive late • Outpatients do not arrive (no shows)l

• System congested• Exam times too short• Patients not picked up

• Reformatting of images • Maintenance • Difficult IVs

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Outcomes of study

• Added dedicated porter to CT area• Management accepted idea of overbooking• Hired lunch time technologist• Commissioned in-depth study of porter services at VGH• VCHA hired several grads• Generated further research

– Allocation of capacity between outpatients and inpatients– Jonathan Patrick’s PhD Dissertation on scheduling

Scheduling RulesScheduling Rules

Scheduling RulesScheduling Rules

• Earliest available slot– a.k.a. first come first served

• Reserve capacity– For most urgent cases– For each class

• Patrick - Puterman rule– Fill tomorrow– Then book as late as possible without exceeding target– If exceed target, use overtime or surge capacity– Based on complex stochastic optimization model

• Markov decision processes

““Current Practice” – Simulation resultsCurrent Practice” – Simulation results

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30000

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29

Day

Nu

mb

er o

f O

P

OP1 OP2 OP3

53% of OP1,

36% of OP2,

25% of OP3

booked late!

Reservation Policy – Simulation Results Reservation Policy – Simulation Results

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10000

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700001 3 5 7 9 11

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OP1 OP2 OP3

Cost: 21% of OP3 demand overtime, 50% of booked OP3 late.

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Days

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OP1 OP2 OP3

Patrick Puterman Rule – Simulation ResultsPatrick Puterman Rule – Simulation Results

Cost: 1.5% of OP1 demand removed from the queue but only at

special times

Comparative ResultsComparative Results

% of Patients with late scans % of Patients served through OT

OP1 OP2 OP3 Total OP1 OP2 OP3 Total

PP Rule 0 0 0 0 1.44 0 0 0.72

Reservation Policy

0 0.02 49.52 9.94 0 0 21 4.13

Current Practice

53.17 35.77 24.85 42.29 0.08 0.43 0 0.17

Policy InsightsPolicy Insights

• In a system where demand is close to capacity, the judicious use of a small amount of overtime coupled with intelligent patient scheduling can meet wait time targets– Overtime gives the resource manager the ability to deal with spikes

in demand– Without this ability, once the system is behind, it can’t catch up– This reduces the need for excess base capacity

• Booking demand later and later merely compounds the problem– Best to address the problem directly through the judicious use of

overtime

Forecasting DemandForecasting Demand

Forecasting DemandForecasting Demand

• What is the arrival rate per day?• How variable is it?• Two ways to find this

– Theory (at least for dice)– Empirical or data driven

• Means or medians• Standard Deviations and Quantiles

• What data to use

Forecasting Forecasting

• Forecasts are necessary for effective decision making– Forecasting, planning and control are interrelated

• Forecasts are usually (almost always) wrong– Quantifying forecast variability is as important as

determining the forecast; it is the basis for decision making.– Rare events happen and can have significant impact on

forecasts

• Scientific methods improve forecasting• “Don’t predict the future, invent it!”

– Alan Kay

Quantitative Forecasting methodsQuantitative Forecasting methods

• Naïve: Last Period or Same Period Last Year• Regression

– Extrapolation

– Causal

• Exponential Smoothing– Simple

– Trend / Damped Trend

– Holt-Winters

• ARIMA models• Simulations• These require software and analyst expertise

Data PatternsData Patterns

Diagram 1.2: Seasonal - more or less regular movements

w ithin a year

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120

Year 5 10

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Diagram 1.1: Trend - long-term growth or decline occuring

w ithin a series

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30

Diagram 1.3: Cycle - alternating upswings of varied length

and intensity

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45

Diagram 1.4: Irregular - random movements and those which

reflect unusual events

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350

1 10

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28

37

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64

73

82

VariabilityVariability

• Two types– Systematic – occurs at calendar intervals

• Time of day• Day of week• Week (or month) of year• We can forecast this

– Random• Unexplained variation

– Might be understandable in retrospect– Not predictable

• This is described through standard deviations and helps us determine service levels

Sample dataSample data

Number of Inpatient CT scans performed at VGH (Daily volume, Jan-Sept 2003)

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01/01/03 31/01/03 02/03/03 01/04/03 01/05/03 31/05/03 30/06/03 30/07/03 29/08/03 28/09/03

Date

Nu

mb

er

of

Sc

an

s

Weekdays

Weekends

Do we want to forecast this?

Scan data by monthScan data by month

Average number of CT scans performed at VGH (Daily average by month, Jan-Sept 2003)

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IP-Weekday

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IP-Weekend

Sample DataSample Data

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30

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Mid

nig

ht

ce

ns

us

Day

Ward Occupancy

How can we forecast ward occupancy?Should we forecast ward occupancy?What impacts ward occupancy?

Another way to look at dataAnother way to look at data

200000.0

450000.0

700000.0

950000.0

200000.0

0.9 55.4 109.9 164.4 218.9

Monthly Ferry Traffic Vancouver Victoria - 1980- 1998

Month

Tra

ffic

200000.0

533333.3

866666.7

200000.0

1 2 3 4 5 6 7 8 9 10 11 12

Monthly Ferry Traffic - Box Plots

Month

Tra

ffic

What is the advantage of each type of data display?Why would we want to forecast this?

Ferry Traffic Forecast Ferry Traffic Forecast

200000.0

450000.0

700000.0

950000.0

200000.0

1979.9 1984.9 1989.9 1994.9 1999.9

Traffic Forecast Plot

Time

Tr

af

fic

Holt-Winters model – Multiplicative seasonal factors