AGENT BASED MODELING AND SYSTEM DYNAMICS

IN HEALTHCARE

Modeling Two Stage Preventive Medical Checkup Systems

Andreas Martischnig, Siegfried Voessner

Department of Engineering and Business Informatics, Graz University of Technology

Kopernikusgasse 24, 8010 Graz, Austria

Gerhard Stark

Department of Internal Medicine, LKH Deutschlandsberg, 8530 Deutschlandsberg, Austria

Keywords: Agent based modeling, System dynamics, Healthcare, Preventive medical checkup, Preventive cancer

checkup.

Abstract: Modeling preventive medical checkup systems (PMCS) is an important part of predicting future healthcare

coverage. In this paper we show how to model a two stage interdependent System as it applies to basic

cancer prevention. Starting with a short introduction of the two used modeling techniques we show the basic

principle of the preventive cancer checkup process (PCCP) and how it was modeled with these opposing

approaches. We then extract the key benefits from each technique and also their shortcomings when

applying it onto the PCCP. Furthermore we show at what level of detail which method should be used to

gain the most valuable insight into those complex checkup systems.

1 INTRODUCTION

In medical science, especially health care, computer

simulation is still a relatively young field. In contrast

to that social sciences use computer simulation as a

well-established domain of research, to gain insight

to a system and make predictions for the future.

Troitzsch (1997) divided prediction into two parts:

(1) qualitative prediction, which is prediction of

behavior modes, and (2) quantitative prediction,

which is to predict a certain system state in timeline.

Currently there are two major schools, System

Dynamics and Agent Based Modeling, which use

computer simulation to gain insight into non-linear

social and socio-economic systems (Milling and

Schieritz, 2003). Both approaches have a broad

overlap in research topics, but have been quite

unnoticed by each other. (Phelan, 1999)

There are only a few publications about health

care systems concerning prevention frameworks.

The health care system itself is complex and large

and it is quite hard to understand all the

dependencies and influences in this system. Because

of the constantly growing demand for preventive

cancer checkups the main purpose of this paper is to

show how to model those systems with both

approaches.

Western industrial countries are facing an over

aging of their population. This makes it necessary to

model future health care scenarios to get valid

answers to problems arising from these systems

because media seems to continuously bombard us

with one horror scenario of health care issues after

the other. For example the amount of people in

Austria above the age of 60 will grow till 2030 by

54% although the whole population will just grow

by 8% (Statistik Austria, 2007). Is this significant

increase in older people an indication requiring 50%

more medical specialists to cope the demand of

preventive medical checkup in this age group? This

is just one pressing question concerning preventive

medical checkups for the future. In this paper we

will discuss the main modeling differences of the

two approaches based on the preventive cancer

checkup process (PCCP) and give a first short

answer to the question above.

416

Martischnig A., Voessner S. and Stark G. (2009).

AGENT BASED MODELING AND SYSTEM DYNAMICS IN HEALTHCARE - Modeling Two Stage Preventive Medical Checkup Systems .

In Proceedings of the International Conference on Agents and Artiﬁcial Intelligence, pages 416-421

DOI: 10.5220/0001659404160421

 SciTePress

1.1 The System Dynamics Approach

System Dynamics is an approach that has been

developed by Jay W. Forrester, an electrical

engineer, in the mid 1950s and was originally called

Industrial Dynamics since the initial applications,

which he described in the book of the same title,

were all in private industry (Forrester, 1961). Later

works focused on urban dynamics (Forrester, 1969)

and on social systems, with the probably most

popular publication “Limits to growth”. (Meadows

et al). In 1983 the International System Dynamics

Society (SDS) has been established, and within it a

special interest group on health issues was organized

in 2003 (Homer and Hirsch, 2006). Although many

papers dealing with health care systems have been

published, in a variety of journals worldwide, since

then very few of them focused on prevention

frameworks. (Koelling and Schwandt, 2005)

The basic concept behind System Dynamics is

that the complex behaviors of organizational and

social systems are the result of both reinforcing and

balancing feedback mechanisms. The central

observation point when modeling a system in SD is

to describe its feedback loops, which consist of the

real-world processes, called stocks, and the flows

between these stocks. These generated computerized

models can then be used to test alternative scenarios

and policies in a systematic way to answer both

“what if” and “why” questions. (Borshchev and

Filippov, 2004), (Sterman, 2001).

1.2 The Agent Based Modeling

Approach

Agent Based Modeling (ABM) is a relatively new

computational modeling paradigm. Although it had

been developed in the late 1940s, it did not become

widespread until the 1990s, because compared to SD

significantly more computational power is required.

The increase of available and powerful

computational resources in the last years and the

inherent parallel nature of ABM approaches

contributed to their popularity. There are three

different fields of research for ABM: (1) artificial

intelligence, (2) object oriented programming and

concurrent object-based systems, and (3) human-

computer interface design (Jennings and

Wooldridge, 1998). The concept of agents can be

tracked through many different disciplines, but using

agents on designing simulation models is mainly

applied in complexity science and game theory

(Milling and Schieritz, 2003). In contrast to SD there

is no universally accepted definition of ABM and

this makes it much more difficult to identify the

basic concept and assumptions underlying this

paradigm. An Agent is basically an independent

component that has individual rules and is able to

interact with its environment or not. The behavior

can range from primitive reactive decision rules to

complex adaptive intelligence (Macal and North,

2005). The global System behavior emerges as a

result of the agents following their rules and doesn’t

need to be known at the beginning of the modeling

session.

That’s why ABM is often called bottom-up

modeling (Borshchev and Filippov, 2004). Agent

Based Modeling is used in a wide range in medical

health care but mostly to simulate patient scheduling

and workflow management (Nealon and Moreno,

2004). Estimating the medical demand of equipment

and specialists for the future is quite a new area for

ABM.

1.3 Short Comparison of the

Approaches

To characterize both approaches, the major

differences are summarized in Table 1 and described

below (Milling and Schieritz, 2003) (Stotz and

Größler, 2004).

Table 1: System Dynamics versus Agent Based Modeling.

System

Dynamics

Agent Based

Modeling

Basic building

Block

Feedback loop Agents

Level of

modeling

Macro Micro

Mathematical

formulation

Differential

equations

Logic,

Differential

equations

Perspective Top-down Bottom-up

Unit of analysis Structure Rules

The core building blocks:

The main behavior of a System Dynamics model is

generated by its interacting feedback loops that

consist of Stocks and Flows. In Agent Based Models

the behavior emerges from the interaction rules of

the Agents. These elements can therefore be

considered as the basic building blocks of their

approaches.

Level of modeling:

In macro simulations, individuals are viewed as a

structure that can be characterized by a number of

variables, whereas in micro simulations the structure

AGENT BASED MODELING AND SYSTEM DYNAMICS IN HEALTHCARE - Modeling Two Stage Preventive

Medical Checkup Systems

417

is viewed as emergent from the rules and the

interacting individuals. (Davidson, 2002)

Mathematical formulation:

The basic principle behind SD is to couple non-

linear first-order differential equations. This is done

by Levels that accumulate the difference between

the Flows (in- and outflows). In ABM there are

many diverse methodologies from logic-based to

emergent equations and that’s why no universally

accepted formalism for the mathematical description

of a model exists. (Milling and Schieritz, 2003)

Perspective:

In SD the structure of the basic system phenomenon

is modeled and in ABM this evolves in the

simulation.

Unit of analysis:

SD models behavior is determined by the structure

that is fix and has to be defined before simulation. In

ABM the focus lies on the rules an agent obeys to, to

interact with other ones.

2 THE BASIC PREVENTIVE

CANCER CHECKUP PROCESS

(PCCP)

Modern preventive cancer checkups can diagnose

cancer risks at a very early stage making necessary

treatment easier, more effective, and more efficient.

Most of the common malignant diseases, if detected

in an early stage, can successfully be cured, due to

tremendous progress in treatment possibilities.

That’s why regular checkups can prolong a healthy

life.

The basic preventive cancer checkup process that

is shown in Figure 1 can be applied to all of the

malignant diseases for example (colon cancer,

prostate cancer, gynecological tumors, skin tumors,

etc.). There is always a risk group in a population,

normally being addressed by age and gender. This

group can then be divided into two parts (percentage

R1 and R2): the ones that will never go to a

preventive medical checkup and the other ones that

go to a preventive medical checkup at least once in

their lifetime after entering the specific risk group.

Once entering the prevention path there will be a

medical checkup. If an indication for the specific

cancer is found during the checkup an intervention

will be performed and the patient will be send back

to regular preventive medical checkup after some

years (indicated by X2). If no indication is detected

the patient will also be sent back to regular

preventive medical checkup after some years

(indicated by X1). Once being in the prevention

cycle the normal mortality for the specific cancer

will decreases with a given percentage (indicated by

PI). The basic PCCP will now be applied onto the

colon carcinoma one of the most common cancer

type of men and women.

Figure 1: Basic principle of a preventive cancer checkup

process (PCCP).

To demonstrate both principles we assumed the

following standard values, taken from literature

(Citarda et al. 2000) (Barclay et al. 1993) (Barclay et

al. 2006) for the colon carcinoma prevention:

Table 2: System parameters for simulating a preventive

cancer checkup process (PCCP).

R1 R2 F1 F2 X1 X2 PI X

40% 10% 90% 7 3 80% 0,45

* 10

-3

With this given values the average year a patient

comes to the preventive medical checkup is 6.6

according to equation (1).

average year = X1 * F2 + X2 * F1 (1)

2.1 Modeling PCCP with System

Dynamics

Based on the basic PCCP process we designed a first

Causal Loop Diagram (CLD) of the system and

simulated it in Powersim Studio 2005. We split up

populations age groups into those within the risk

group and those outside. Because of the intuitive

user Interface of Powersim the model was quickly

built but the output did not quite match real systems

data because SD averaged all the Stocks

representing the age groups. Population distributions

in Western industrial countries are more like bulbs

or apples than rectangles and because of the two

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418

world wars and the baby boom generation Austria’s

population distribution has two abnormal spikes.

And these two spikes are completely filtered in the

standard SD model.

So we split up the age groups into one year

groups and added both prevention cycles to the

simulation to get a more detailed output. A

simplified version of the extended basic Causal

Loop Diagram (CLD) is shown in Figure 2.

The implemented model now was an “Array

Model” with all the different probabilities for each

group and the output was qualitatively quite near to

real data.

To look at the consequences of another cancer

prevention model, for example prostate cancer, we

added a second cycle for this disease. This was

really a challenging problem because of the arrays

and global death rates and at the end we weren’t able

to complete it because of cyclic references. Both

prevention models affect the death rate of the

population and are also affected by this rate. When

you think in stock and flows you get cyclic

references between these rates. Our basic SD model

can only capture the qualitative behavior well but

lacks realistic quantitative output. The extended

model is able to produce a realistic quantitative

output but is due to the specialization not able to

handle more than one prevention model.

Figure 2: Extended basic Causal Loop Diagram (CLD).

2.2 Modeling PCCP with Agent based

Modeling

In the ABM solution we first had to decide what

defines an agent to produce an output like the real

data. So we decided to model an agent with the

basic attributes like number, age and gender and

some medical attributes we needed for the

preventive checkup process as shown in Figure 3. In

this first solution we modeled non interacting agents,

because it was not necessary for the concerning

question.

Figure 3: Population of agents with migration effects.

Before implementing the PCCP into our agent

framework we had to calibrate our agents to build up

a population that was quite similar to the real one in

each age group. That’s why we had to add

immigration, migration and fertility data to each

agent. In this case we took statistical data rates from

the past decades and added them to the framework.

This data can now be loaded from several input files

into the framework. Furthermore this attributes can

be changed in time to get a similar characteristic as

data from the past. Mortality is divided into the main

parts of the ICD-10 (International Classification of

Diseases endorsed by the WHO in 1990) code and

can also be changed in time. Due to this

classification the framework is able to handle all

different types of classified diseases. To add a

specific prevention model one first has to define the

ICD-10 category it belongs to and then add the

needed attributes to an agent. In our case this new

“disease data sheet” that is connected to an agent

contains the number of performed interventions, the

waiting period till next check is performed, the new

death probability, and so forth. Depending on the

input data that is linked to the agents they act on

probabilities each simulation period. Because this

paper is about how to model a PCCP and not about

the whole ABM framework we will not go into deep

detail this time.

Since we are looking for population effects the

number of agents that make up this population has to

be sufficiently high. There is obviously a tradeoff

between accuracy and computational effort. Agent

Based simulation can be seen as a numerical solver

to Dynamic System’s system of differential

equations. The more agents the smoother is the

integration.

In the following we will show the first results

from the ABM model to illustrate the great level of

detail our framework is able to handle. We used 1.5

AGENT BASED MODELING AND SYSTEM DYNAMICS IN HEALTHCARE - Modeling Two Stage Preventive

Medical Checkup Systems

419

million agents and 50 simulation runs to get a robust

estimate of mean and standard deviation.

The output for the PCCP with the given values

for the colon carcinoma was really astonishing for us

and is shown in the Figures 3 and 4. Although more

people are entering than leaving the risk group the

demand for preventive checkup will not grow when

we assume that the same percentage of people as

today will go to checkup in the future. This is

because most of the demand is already generated by

the people in this two stage cycle. The demand will

not grow until the prevention percentage is set up to

more than 60% and this is in fact a relatively

unrealistic scenario for the future. In Figure 4 we see

the absolute difference of people dieing from colon

cancer per year. The absolute amount of people that

could be saved due to more preventive checkups will

not dramatically fall just by doing 55% more of

these checkups. This output is really crucial when

we think about investing more money in these

preventive checkups or advertisement to increase the

amount of people going to cancer prevention.

Figure 4: ABM-Framework output for the PCCP, showing

medical checkups as a consequence of different policies.

Figure 5: ABM-Framework output for the PCCP, showing

PCCP death rates as a consequence of different policies.

3 DISCUSSION

During our modeling sessions we were able to

produce the needed output data with both modeling

techniques. Building a SD model with realistic real

life behavior was really a hard challenge, because of

the averaging effect within stocks. Despite all

difficulties we found a solution by transferring the

initial model into an "Array Model". Due to the

specialization of this model it is not possible to

simulate more than one PCCP as mentioned above.

That’s why we had to switch the modeling approach

to implement the given PCCP with ABM. After

defining the attributes and rules of an agent we

implemented our own arbitrary extendable

framework. Because of the astonishing answers for

the future demand in specialists for colon carcinoma

the framework will now be object of further

research. Integrating more cancer prevention

models, interactions between the agents like

transmissibility of diseases, word of mouth

advertising for preventive medical checkup, are just

a few work packages for the future.

In general both techniques can not be

differentiated just by modeling size because both are

capable to model small and large-scale systems.

They can rather be classified by the problem or

perspective and the required output information. One

fact that should be considered when deciding for one

technique is that with today’s modeling tools it is

much more complicated to implement a solution in

an ABM Framework, when you are not experienced

in programming, than implementing a model in one

of the intuitive graphic oriented SD tools.

Quantifying the parameters of a model is the main

difficulty both approaches have in common. In

ABM it is tough defining the rules for the agent’s

behavior and their attributes and in SD it is

sometimes quite hard to quantify or find the

correlation function between the connections of

variables. In contrast to SD ABM allows increasing

the level of detail as long as relevant data is

available but will not work when this required data

does not exist at that level of detail. The next factors

to be concerned with are computational effort,

memory management, and simulation time. SD

provides the output within a few runs lasting only

seconds depending on the method that is used to

solve the differential equations. When trying to

solve the same problem with agents one first has to

define the width of the confidence interval and then

calculate the needed runs to hit that spread. The

simulation time with our model in SD is just a few

seconds on an ordinary office computer and there is

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no need to worry about memory management

contrary to our ABM solution.

In general picking one or the other modeling

approach depends on the system to be simulated.

There are lots of applications where it is much easier

and efficient to solve given problems with SD but if

you want to capture more realistic real-life

phenomena you have to choose the ABM approach.

A general decision for one of the two techniques

always deals with a trade-off between efficiency and

significance.

4 CONCLUSIONS

As we could see from our simulation System

Dynamics is useful to model the basic system’s

behavior. With the causal loop diagram SD provides

a powerful tool for modeling, to describe a model

and its interactions. Combined with Vesters

sensitivity analysis

(Vester, 2005) one can easily

extract the different kinds of elements in the system

(active, reactive, buffering, critical, and neutral) to

make steering actions more efficient. A substantial

advantage of SD is the big number of available

Simulation Software and their intuitive and easy use,

when needing quick answers about a systems

behavior. Generating realistic quantitative output

data was quite a challenging problem with SD and

we could just manage it by transferring the original

model into an “Array Model” but due to the

specialization of this model it is not able to cope

with more details or other preventive checkups and

therefore we had to switch the modeling approach to

ABM.

The ABM approach took much more time to

implement, but now agents, the primary building

block, can easily be extended with more and more

details. That is why the ABM approach and our

framework can get beyond the limits of SD,

especially when the system contains active objects.

However it is difficult to decide on attributes and

rules of agents in order to get a behavior that is

sufficiently similar to the real system and it is much

more difficult to get all the data at the needed level

of detail for the simulation than just modeling the

structure of the system which is where SD ends.

Memory management restrictions still become a big

issue for the future of our framework when

simulating with millions of agents as we experienced

it in our simulation.

With the existing framework we are now able to

answer questions for the future demand of several

preventive checkup systems and we will extend the

model as mentioned above to address more crucial

questions concerning futures healthcare

management.

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