Systems Approaches to Colorectal Cancer Care

Stephen D. Roberts

Brian Denton

Reha UzsoyEdward P. Fitts Department of Industrial and Systems Engineering

North Carolina State University

Raleigh NC, USA

3rd Annual Cancer Care Engineering Retreat

Colorectal Cancer (CRC)

About 150,000 people diagnosed each yearSecond leading cause of cancer deathsAbout 90M people considered at risk

Key Characteristics

Cancer is a disease of the DNA Usually not symptomatic until advanced Deadly if not found early

Only 8.5% five-year survival if found late Over 90% five-year survival if found early

Risk factors include: Age, race, gender Personal or Family history Other related diseases Lifestyle (?) Diet (?)

Screening for CRC

Endoscopic Tests Colonoscopy Sigmoidoscopy

Non-Endoscopic Tests Fecal Occult Blood Test (FOBT) Double Contract Barium Enema (DCBE) Virtual Colonoscopy Fecal DNA

Treatment and Intervention Treatment

Surgery Resection: removal of “sections” of the colon

Ostomy

Chemotherapy Radiation Combination Therapy

Medical Screening Interventions Accepted Practice

Taught in Medical School (“experts”)

Stated in recognized Medical Literature

Recommended Guidelines American Cancer Society

American Gastroenterological Society

Systems Engineering for CRC Ongoing projects at NCSU Collaborations with

Vanderbilt University Medical Center and Health Services Research

University of North Carolina Chapel Hill ShepsCenter for Health Policy Research

Industry Medical Decision Modeling Inc.

Purdue CCE Project As of May 2008

Mayo Clinic

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Work to date Focused on simulation models Effect of different screening policies on

population Population-level modeling of the natural history Cost-effectiveness of different screening policies

Effects of system design on the number of people seeking preventive screening Broader system including access to primary care

and screening facilities

Performance of endoscopy suites

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Screening Decisions How “population-centric”?

age, gender, race, family history, compliance? What screening method?

Endoscopic and non-endoscopic When to start/stop screening? Protocol if screen is positive?

Verification and treatment Protocol if screen is negative?

Time to next screening When to stop screening

Evaluating medical decisions? Health burden

Mortality – life years Morbidity – quality-adjusted life years (QALY)

Cost burden Cost of intervention Cost of maintenance and surveillance

Value for cost Cost-effectiveness (CE): cost per QALY Cost-benefit (CB): net cost

Modeling Natural History Fundamentally stochastic Focus on the individual Intermediate relevant events

Start of disease (adenoma) Pathway and Progression

“Natural death” without the disease Marginal life expectancies Modify actuarial data (eliminating CRC)

Course of Disease (Natural History)

A1 – undetected first AdenomaA2 – undetected second Adenoma

C1 – invasive Cancer from A1C2 – invasive cancer from A2

CO – Colonoscopy/surgery to remove C1CD – Cancer DeathND – “Natural” Death

A10 A2 C1 CO CD ND C2

Medical TimelineBirth Death

Overall Software Design Strategy

Simulation Engine

User Interface

AccessDatabase

Results inExcel

Data Objects

Scenarios

CRC Variables

Report Writer

MedicalProtocolDesign

CRCExpertise

Sources of Data Cancer

National Cancer Institute (SEER) National Data

Centers for Disease Control (CDC) National Center for Health Statistics (NCHS) US Census Bureau Population Estimates Berkeley Mortality Statistics

Vanderbilt CRC Literature Database Validated based on Minnesota Colon cancer

Control Trial

Results Can be used to study a variety of aspects of

CRC progression and screening Health and cost burdens of CRC by population Effects of parameter uncertainty on the cost-

effectiveness of treatment options Cost-effectiveness of different screening

options based on willingness to pay for an additional QALY

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Result: Health and Cost Burden of CRCGender Race Family history

Life Years Lost

QALYs Lost Costs of CRC

Female BlackNo Family

history10.83 (0.08) 10.24 (0.07) $123,714 (3736)

Female Black Family history 11.23 (0.06) 10.62 (0.05) $114,381 (2408)

Female WhiteNo Family

history11.68 (0.07) 10.99 (0.06) $124,875 (3320)

Female White Family history 12.15 (0.05) 11.45 (0.05) $118,188 (2283)

Male BlackNo Family

history10.19 (0.07) 9.74 (0.07) $110,460 (3188)

Male Black Family history 10.62 (0.05) 10.18 (0.05) $113,317 (2326)

Male WhiteNo Family

history 9.90 (0.06) 9.52 (0.06) $126,345 (3290)

Male White Family history 10.29 (0.05) 9.91 (0.04) $123,590 (2283)

Result: Effect of Colonoscopy Screening

ΔCost

ΔLifeYears(0,0)

$500

.100.040

.160

(F,B,N - $8,342)

(F,W,N - $4,008)

(M,B,N - $7,329)

(M,W,N - $2,571)

Higher (Poorer) Cost-Effectiveness

(F,B,F – Cost-Saving)

(F,W,F – Cost-Saving)(M,B,F – Cost-Saving)

(M,W,F – Cost-Saving)

Acceptability Curves

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60 70 80

ThousandsCeiling Ratio

Pro

ba

bili

ty C

os

t-E

ffe

cti

ve

A: No Screening B: FOBT C: Sig D: Sig & FOBT E: Colon 10

A

E

DC

B

No Screening FOBT Colon 10

4.1 25.3

WTP (λ)

A broader view… Effective screening significantly reduces the

risks of CRC However, not all patients who could benefit

from screening receive it Estimate an additional 30,000 lives could be

saved annually if people would get screened How to improve the number of people who get

screened?

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Combined simulation model Combines discrete event and continuous

(system dynamics) aspects Continuous variables change continuously

over time Level of awareness of CRC risk

Other variables change at discrete points in time Number of screening facilities

Used ARENA 7.0 software

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Continuous component Uses causal loop representation to capture

relationships between quantities of interest Sterman(2000); Forester(1962)

Nodes represent variables, arcs causal relationships

Signs on arcs represent positive or negative correlation between variables

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Results

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Simulation of Endoscopy Suites Collaboration with Brian Denton of NCSU-ISE

and Mayo Clinic Examine the operational and design aspects

of endoscopy suites Clear relevance to the level of screening in the

population, cost of providing care

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Endoscopy Process Patient Intake: patient checks-in,

administrative activities, gowning, patient roomed

Procedure: endoscopist enters room, patient sedated, colonoscopy starts, polyps removed, patient extubated

Recovery: post anesthesia care unit (PACU), patient discharged

Intake Procedure Recovery

Complicating Factors

Many types of resources: endoscopists, nurses, equipment, materials

High cost of resources and fixed time to complete activities

Large number of activities to be coordinated in a highly constrained environment

Uncertainty in duration of activities Many competing criteria

ModelP

atie

nt C

hec

k-in

W

aitin

g A

rea

PreoperativeWaiting Area

Procedure Rooms

Recovery Area

Pat

ient

Arr

ival

s

Pat

ient

Dis

char

ge

Intake Area

Dependent (Waiting)

Independent (Process)

Dependent (Waiting)

Independent (Process)

Dependent (Waiting)

Independent (Process)

General Insights

Economies of Scale: No observed efficiencies in patient throughput due to increasing the number of Ors. Some benefits for up and down stream resources.

Turn-over Times: The impact of reducing turnover times for ORs on all performance measures is limited to staffing scenarios is which endoscopists have 1 or 1.5 ORs.

Utilization: The maximum achievable endoscopist utilization is 90% and the maximum achievable OR utilization is 67%.

Some conclusions Systems engineering techniques can be

deployed effectively to support the CRC care cycle (continuum)

Effective collaboration with health care providers and health services researchers is critical

Data collection to parameterize and validate models is a major effort

Increased networking among researchers will allow us to leverage each others’ efforts

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Future directions Continue to develop and enhance simulation

models Treatment pathways?

Extend from simulation to optimization models Determine number and location of CRC

screening facilities Explicitly consider demographics, queueing

effects of capacity on wait times, travel distances affecting demand

Build on work in supply chain network design with lead time aspects

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Validation Overall Characteristics

SEER Data, Life-Table, Prior Model Screening Validation: Minnesota Colon

Cancer Control Study Use FOBT relative to no screening (from 1975

through 1977 and followed until 1991 Randomized trial of three groups: annual

screening, biennial screening, and no screening Simulated population fit to Minnesota trial

population Some parameters had to be modified to be consistent

with the inputs reported

Graphical Interpretation:Cost-Effectiveness Plane

ΔCost

ΔEffect(0,0)

Greater Cost, Greater Effect

Less Cost, Greater Effect

Greater Cost, Less Effect

Less Cost, Less Effect

Unacceptable

Cost Saving

CE > 0

Results

Willingness to Pay (λ) Screening Method

λ≤ $4,100/QALY No Screening

$4,100/QALY <λ

λ≤ $25,300/QALYFOBT

λ> $25,300/QALY Colon 10

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Why Simulation Not readily Markovian

No geometric or exponential state occupancy State explosion to achieve memorylessproperty

(due to age, gender, race, family history)

Concurrent multiple precursors to CRC Multivariate and time-dependent processes

(depend on person and adenoma state) Discrete-Event System (variable time

updating) Object-Oriented