DECISION SUPPORT ON THE MOVE

Mobile Decision Making for Triage Management

Julie Cowie, Paul Godley

Department of Computing, University of Stirling, Stirling, UK, FK9 4LA

Keywords: Mobile Decision Support, Multicriteria Decision Analysis.

Abstract: This paper describes research investigating ways in which a mobile decision support system might be

implemented. Our view is that the mobile decision maker will be better supported if he/she is aware of the

Quality of the Data (QoD) used in deriving a decision, and how QoD improves or deteriorates while he/she

is on the move. We propose a QoD model that takes into account static and dynamic properties of the

mobile decision making environment, uses multicriteria decision analysis to represent the user’s decision

model and to derive a single QoD parameter, and investigates the use of powerful graphics to relay

information to the user.

1 INTRODUCTION

In this paper, we address the concept of mobile

decision support. The aim of developing a mobile

decision support system is to provide on the spot

assistance to a mobile decision maker who is forced

to make decisions on the move. Potentially, the

decision maker is away from his desktop PC / office

environment where information might usually reside

which would help in the decision making. The

prototype developed uses multicriteria decision

analysis (MCDA) to model the decision problem,

scenario reasoning to evaluate the alternative

options, and calculation of Quality of Data (QoD) to

indicate the reliability of a recommended solution.

Although the use of mobile computing is not new,

we believe the use of MCDA and mobile decision

support has been little researched. Further details of

comparative studies are provided in Cowie and

Burstein (2006).

Although applicable to many domains (San

Pedro et al., 2004; Hodgkin et al., 2004), we have

chosen to focus the use of the tool on triage

management to illustrate its potential use and

benefits. In this environment, quick and accurate

decisions are imperative. It is hoped that this mobile

decision support tool can aid in achieving this aim.

We begin by providing a brief overview of

multicriteria decision making, explaining the method

and its potential use. In Section 3 we discuss some

of the main features of mobile decision making, in

particular the concept of static and dynamic

decisions. The measure by which we measure the

quality of the data relayed to the mobile decision

maker is discussed in Section 4. In Section 5 we

detail the prototype developed, discussing use of the

tool in a triage setting. The paper concludes by

examining potential avenues for future work.

2 MULTICRITERIA DECISION

ANALYSIS

Multicriteria Decision Analysis (MCDA) solves a

decision problem by evaluating and comparing a

number of alternatives against several, possibly

conflicting, criteria and proposes the best

alternatives based on some aggregation of these

evaluations and comparisons. In a mobile decision

making context, this MCDA model can assist a

triage worker to understand the best course of action

to take in an emergency situation. By facilitating

real-time connectivity to live data, the decision

maker will be able to access crucial information to

aid, for example, in deciding which Accident and

Emergency (A&E) department a casualty should be

sent to, the best mode of transport to use in transiting

patients, or estimating travel time.

296

Cowie J. and Godley P. (2006).

DECISION SUPPORT ON THE MOVE - Mobile Decision Making for Triage Management.

In Proceedings of the Eighth International Conference on Enterprise Information Systems - AIDSS, pages 296-299

DOI: 10.5220/0002441902960299

 SciTePress

Technology-

related contex

User-related

contex

Security

Connectivity

Synergy

User’s Decision

Model

Historical

context

Completeness

Currency

Accuracy

3 STATIC AND DYNAMIC

DECISION MAKING

Much of our previous work using MCDA has

adopted the approach in static decision making. In

static decision making, we can assume that the

evaluation of alternatives with respect to criteria is

constant (over a given period of time), and

evaluations will not fluctuate according to some

external factor. For example, in assessing a suitable

location to conduct a conference, criteria such as

size of rooms, facilities available, and

accommodation costs will have scores that are

unlikely to change from one minute to the next, one

day to the next, or even one week to the next.

There are however some genres of problems

which encompass dynamic as well as static criteria.

For example, suppose we are considering the best

mode of transport to our work place. Travel time is

a very dynamic attribute in that it can change

frequently. We may have an idea of approximate

travel times for different modes of transport (e.g.

train, bus, car), however, this information may

change after listening to travel reports on the radio

prior to setting off for work, causing us to re-rank

which mode of transport will get us to work in the

shortest time. This scenario is still not static, as 5

minutes into our journey the travel situation may

change again, such that perhaps a different mode of

transport would have been preferable. In such

situations, it is important to have some indication of

how static or dynamic our decision is, and some

estimation of the time period over which the stability

of the data is likely to be maintained. So for

example, if we choose to travel by train as it is

currently the best option, is it likely that this will

remain the optimum option for a given time period,

say 30 minutes. It is here we see the need to not

only provide decision support in the more traditional

sense as seen with static decisions, but also to give

some indication of the quality of the decision or

quality of data being received.

4 QUALITY OF DATA

Our measure of quality is not reflective of the

standard of the information received, but of the

robustness, recency, and stability of the data. Recent

research defines QoD as a score that allows the user

to appreciate the uncertainties inherent to a mobile

computing environment (Mihaila et al, 2000). By

using QoD, appropriate sensitivity analysis can be

performed and results used to better inform the user

as to the suitability of choices. This additional

information allows the decision maker to make their

decision with confidence as not only are they

presented with the pertinent data, they are also aware

of changes in the environment and the potential

impact this could have on the decisions made.

Figure 1: Quality of Data Model.

In Figure 1 we illustrate how QoD can be

represented as an aggregate measure of technology-

related parameters (e.g., synergy, security and

connectivity), user-related parameters (e.g., stability

of scores in user’s decision model), and data quality

parameters that are related to historical context (e.g.,

completeness, currency and accuracy of historical

data). This aggregate measure can be represented as

a weighted sum, to allow the decision maker to

choose the importance associated with the various

quality measures. Further details of the QoD

measure can be found in Cowie and Burstein (2006).

5 THE PROTOTYPE

5.1 Setting Up the Model

The prototype was developed using an Object

Oriented (OO) methodology, where unified

modelling language (UML) was used to assist in

conceptualising the design of the prototype.

Although the Decision Support System (DSS)

proposed is designed to operate on a number of

mobile devices, this initial design is for use on a

PDA. The technologies used comprised of Java ME,

Java SE, Excel, MySQL and the multicriteria

decision analysis software V•I•S•A.

In order to use the prototype, an appropriate

MCDA model must be created and imported onto

the mobile decision device (such as a PDA).

Through access to the web, the PDA can then keep

the decision maker up-to-date with the latest

information pertinent to the problem domain being

addressed.

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5.2 Use of the Model-Triage

Initial real-time assessment of emergency situations

(triage) has to be accurate and quick. Triage is used

in a variety of different scenarios: on the battlefield,

at disaster sites, and in hospital emergency rooms

when limited medical resources must be allocated.

There is an obvious need to optimise the triage

process and outcomes in order to satisfy the

demands for high quality and responsiveness of

contingency management.

In order to demonstrate the potential of the

mobile decision support system, we use the example

of a triage decision maker who is first on the scene

to an accident. In such a scenario, various criteria

would need to be considered in assessing what the

appropriate course of action would be. For example,

supposing the triage worker has to choose between

the following options: Calling for an ambulance,

calling for an airlift, calling for both ambulance and

airlift, and treating injured parties on-site. Factors

that he/she may consider could include time: both

time taken to administer treatment to an individual,

and how critical this time is; also, number of

casualties: where the triage worker needs to assess

the collective number of casualties and differentiate

between the number of major and minor injured

parties.

Assuming the triage accident model is already in

existence, the decision maker at the scene of the

accident would select the “accident model” on his

mobile decision device to aid them in assessing the

most appropriate course of action. The mobile

support system will have been accessing appropriate

websites for up-to-date information pertaining to

accident scenarios over a given time period. The

information collated is assessed and results relayed

back to the mobile decision maker. A typical screen

from the model is depicted in Figure 2a.

Figure 2a and 2b: Example of Weights.

It is clear to see from the figure that alternative C

(in our example, “Treat Onsite”) is currently the best

option. However, it is also crucial that the decision

maker (DM) takes into account the QoD score (top

left Figure 2a) and the predicted QoD score over

time (top right Figure 2a). The current QoD

indicates that the quality of the data is quite high.

However the QoD score over time shows that the

quality of the data is likely to deteriorate. Should the

DM wish to find out more about the QoD score, by

clicking on the QoD info button they are taken to the

screen depicted in Figure 2b. This screen allows the

DM to analyse the quality of the data concerning the

main three criteria used in constructing the QoD

score (as shown in Figure 1). From Figure 2b we

can clearly see that it is the technology-related issues

that have poor quality (so for example, perhaps we

are unable to connect to the specified URLs as

frequently as requested due to poor network

connection). The user also has the ability to drill

down further and analyse the factors contributing to

the QoD scores for user-related, technology-related,

and historical contexts.

Figures 3a and 3b: Further Data Analysis.

By clicking on any of the score bars associated

with an alternative (shown in Figure 2a) the decision

maker can view information relating to the stability

of the scores of the alternative. In Figure 3a we see

the stability scores for the “Treat Onsite” option.

Currently, the interface shows the previous scores

(shown in pink) achieved by the option at five

minute intervals. It is evident from Figure 3a that

“Treat Onsite” appears to be a consistently high

scoring option. This may help the decision maker in

deciding whether the option is suitable. Had the

score for “Treat Onsite” been less stable, the

decision maker may feel more cautious about

choosing an option which scores well at the current

time, but may score badly in the next five minutes.

The interface also shows a blue dot which depicts

the predicted score of the alternative in the next five

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minutes. A simple forecasting technique is used to

achieve this value. This predicted score value

provides additional information to enable the

decision maker to assess the stability of the

alternative by the decision maker.

A further results screen available to the decision

maker is to view comparative scores for each

alternative over a given time period. This is depicted

in Figure 3b. Again the scores are shown at

intervals of 5 minutes. This allows the decision

maker to assess the stability of each option, and how

the ranking of options changes over time. Again, it

is evident that “TreatOnsite” is consistently the best

alternative. Option “Airlifting” the casualties and

“calling for ambulance and airlift” remain fairly

stable options over the displayed time period, ranked

and 2

respectively. “Calling the ambulance

only” in this scenario is consistently the lowest

ranked option, and over time the suitability of this

option deteriorates.

6 FUTURE WORK

6.1 Evaluation of System

Current work is focused on evaluating the usefulness

of the tool developed. Initial research has indicated

that mobile decision support is of use to decision

makers on the go (Cowie and Burstein, 2006),

however more rigorous evaluation is due to begin

investigating how such a system is used in different

application areas. In addition, we hope to identify

whether such a mobile device is restricted to only

certain types of decisions, and whether there are

some areas where the quality of a decision made in

this way is degraded. For example, it could be the

case that facilitating mobile decision making

encourages rushed, ill-thought out decisions. Such a

finding would impact greatly on the potential use of

the tool in areas such as triage management, where

the quality of the decision made is paramount.

6.2 Interface Improvements

The current interface, although facilitating mobile

decision support, is undergoing continual

improvement. We hope to run some evaluation

workshops in the near future with potential users of

the system. These workshops will allow us to assess

the usefulness of the tool and the usability of the

interface.

6.3 Prediction Capabilities

One facility that is regarded as highly important

when trying to assess the suitability and stability of

an option is forecasting. Currently, the tool uses a

very simple weighted averages approach to predict

the next score value for an alternative. We hope to

incorporate more sophisticated techniques to enable

a greater amount of prediction ability for future

score values.

REFERENCES

Cowie, J., Burstein, F., 2006. Quality of Data Model for

Supporting Mobile Decision Making. Accepted by

Journal of Decision Support Systems.

Hodgkin, J., San Pedro, J., Burstein, F., 2004. Quality of

Data Model For Supporting Mobile Decision Making.

In Proceedings of Decision Support Systems

Conference, July 1-3, Italy.

Mihaila, G. , Rashid, L. , Vidal, M., 2000. Using Quality

of Data Metadata for Source Selection and Ranking.

In Proceedings of the Third International Workshop

on the Web and Databases, WebDB 2000

San Pedro, J., Burstein, F., Zaslavsky, A., Hodgkin, J.,

2004.

Pay by Cash, Credit or EFTPOS? Supporting the User

with Mobile Accounts Manager. In Proceedings of

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Mobile Business Conference, MBusiness

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V•I•S•A. Simul8, Glasgow, UK.

http://www.simul8.com/products/visa.htm

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