MANAGING MEDICINAL INSTRUCTIONS

Juha Puustjärvi

Helsinki University of Technology, Innopoli 2, Tekniikantie 14, Espoo, Finland

Leena Puustjärvi

The Pharmacy of Kaivopuisto, Neitsytpolku 10, Helsinki, Finland

Keywords: Medicinal documents, Information retrieval, Taxonomies, Ontologies, Business process management.

Abstract: The number of new medications increases every year. As a result also the amount of new instructions

concerning new medication increases rapidly. A problem is how to ensure that the employers of the

medicinal organizations are aware of the relevant medicinal instructions. In this paper, we restrict ourselves

on this problem. In particular, we consider three complementary ways for the dissemination of medicinal

instructions: (i) by providing keyword-based searching of instructions (ii), by providing ontology-based

searching of instructions, and (iii) by integrating the instructions to employers´ day-to-day work tasks. Our

argument is that integration is most preferable as medicinal instructions are provided just-in-time, tailored to

their specific needs, and integrated into day-to-day work patterns. However, automating the integration of

instructions to day-to-day work pattern is not an easy task. In our solution, day-to-day work patterns are

described by BPMN (Business Process Modeling Notation) and BPMN’s association- notation is used for

integrating the instructions to BPMN-processes. The integration of the tasks and instructions is based either

on a medicinal ontology or a taxonomy. The ontology specifies the relationships of the day-to-day tasks and

the medicinal instructions. The taxonomy is used for attaching metadata items for the tasks and instructions,

and so the integration of the tasks and instructions can be done based on the similarity of their metadata

descriptions.

1 INTRODUCTION

Healthcare is a field where the fast development of

drug treatment and technologies requires specialized

skills and knowledge that need to be renewed

frequently (Puustjärvi and Puustjärvi, 2006).

Furthermore, the number of new medications

increases every year. As each drug has its unique

indications, cross-reactivity, complications and costs

also the prescribing medication as well as the

distribution of medicinal products becomes still

more complex (Jung, 2005). As a result also the

amount of new instructions concerning new

medication increases rapidly. An interesting

question arising from this reality is how medicinal

instructions should be organized and retrieved in

order to ensure that the employees are aware of the

relevant medicinal instructions.

In principle, the (medicinal) information

retrieval system should be able to retrieve all the

medicinal instructions, which are relevant while

retrieving as few non-relevant instructions as

possible. This kind of quality of information

retrieval system is usually measured by two

fractions, called recall and precision (Baeza-Yates

and Ribeiro-Neto, 1999). Recall is the fraction of the

relevant documents (e.g., medicinal instructions),

which has been retrieved. Precision is the fraction of

the retrieved documents, which is relevant. The

values of these fractions are highly dependent on the

way the query and the content of medicinal

documents are presented.

In this article, we will first illustrate that by

replacing taxonomy–based searching by ontology-

based searching we can significantly improve both

the recall and precision fractions in searching

medicinal instructions.

On the other, minimizing the extra time required

for retrieving the instructions is turned out to be

crucial. Therefore, in many cases integrating

medicinal instructions to day-to-day work patterns is

105

Puustjärvi J. and Puustjärvi L. (2009).

MANAGING MEDICINAL INSTRUCTIONS.

In Proceedings of the International Conference on Health Informatics, pages 105-110

DOI: 10.5220/0001122401050110

 SciTePress

more preferable. Moreover, in this approach,

medicinal instructions are provided just-in-time, and

tailored to their specific needs.

However, automating the integration of

instructions to day-to-day work pattern is not an

easy task. As we will show, in our solution day-to-

day work patterns are described by BPMN (Business

Process Modeling Notation) (White, 2006) and

BPMN’s association- notation is used for integrating

the instructions to BPMN-processes. The integration

of the tasks and instructions is based either on a

medicinal ontology or a taxonomy. The ontology

specifies the relationships of the day-to-day tasks

and the medicinal instructions. The taxonomy is

used for attaching metadata items for the tasks and

instructions, and so the integration of the tasks and

instructions can be done based on the similarity of

their metadata descriptions.

The rest of the paper is organized as follows.

First, in Section 2, we give a motivating example of

the restrictions that we will encounter in using

keyword-based search in retrieving medicinal

instructions. Then, in Section 3, we illustrate the use

of medicinal ontologies in retrieving medicinal

instructions. How such ontologies can be specified

by the Web Ontology Language (OWL) is illustrated

in Section 4. Then, in Section 5, we illustrate how

day-to-day work patterns can be modeled by

business process modeling language BPMN. In

particular, we present how the modeling primitives

of BPMN can be used in attaching medicinal

instructions to business process tasks which model

the day-to-day work patterns. Finally, Section 6

concludes the paper by discussing the advantages

and disadvantages of our approach.

2 TAXONOMY-BASED

SEARCHING

Documents’ content is traditionally represented

through keywords, which are extracted directly from

the document (Baeza-Yates and Ribeiro-Neto,

1999). However, a reason for missing many relevant

documents is that the keywords used with queries

and documents descriptions are not standardized

(Puustjärvi and Pöyry, 2006). In order to standardize

semantic metadata specific taxonomies are

introduced in many disciplines. To illustrate this, a

simple drug taxonomy is presented in Figure 1. The

idea behind this classification is that the medicinal

instructions can be annotated by the metadata items

(the branching points and the leaves) represented in

the tree.

A user can then query medicinal instructions by

Boolean expressions (Baeza-Yates and Ribeiro-

Neto, 1999) comprising of operands and operations.

The operands are the used keywords (which are

taken from the taxonomy) and the operands are

typically “and”, “or”, and “not”. For example, by

using the taxonomy of Figure 1 the keywords

attached to the medicinal instruction “New warnings

of using pain drugs in topical use with children”

could be “Pain drugs for topical use” and

“Prescription based pain drug”.

Medical product category

Cough drug

Pain drug Fewer drug

Prescription

based pain

drug

Oral pain

drug

Pain drug

for topical

use

Injection

pain drug

Figure 1: Medicinal product categories in a taxomomy.

Now assume that a pharmacist has to check the

instructions concerning pain drugs, and so she enters

the Boolean expression: Prescription based pain

drug and Pain drug for topical use. In our example

the result includes at least the instructions “New

warnings of using pain drugs in topical use with

children”. After reading the instruction the

pharmacist is interested to read the previous

medicinal instruction of the same topic. The

pharmacist may also be interested to know the

medicinal products that are under this new warning.

Unfortunately by using keyword based searching

(i.e., Boolean expressions) the pharmacist has no

hope for finding the answers for such queries.

In the next section we will consider an ontology-

based (Gruber, 1993; Antoniou and Harmelen, 2004)

searching that supports such queries as well as the

queries based on taxonomies.

3 ONTOLOGY-BASED

SEARCHING

In order that the information retrieval system could

answer for the queries presented in previous section

we have to extend the search functionalities by

querying features. This requires the deployment of

an ontology.

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Our developed medicinal ontology models the

medicinal instructions as well as their relationships

to other relevant medicinal concepts such as patient,

physician, patient record, drug, and e-prescriptions.

In addition the ontology models the associations of

medicinal instructions to the tasks of the day-to-day

work tasks. Part of this ontology is graphically

presented in Figure 2. In the figure ellipses represent

classes and boxes represent properties. The ontology

includes for example the following information:

• Medicinal product category is a class and

each instance of the category may have a

parent, which is also an instance of

medicinal product category, i.e., among

other things, the ontology models the

taxonomy presented in Figure 1.

• Each medicinal instruction relates to zero or

more medicinal products (e.g., Aspirin),

and each medicinal product includes one or

more drugs and has one or more

substitutable medicinal products.

• Each medicinal instruction is associated to

zero or more tasks which are parts of a

workflow. That is, each medicinal

instruction is associated to a task and a

workflow which represents functionalities

in a day-to-day work patterns.

medicinal

instruction

replaces

deals

medicinal

product

includes

drug

substitutable

medicinal product

medicinal

product catrgory

deals

parent product category

task

associates

is_part_of

workflow

belongs

price

title

Figure 2: A medicinal ontology.

The ontology of Figure 2 allows making queries

such as:

• Give me medicinal instructions having

keywords Prescription based pain drug and

Pain drug for topical use (i.e., a query that

corresponds to a Boolean expression).

• Give me the medicinal instruction that is

replaced by the medicinal instruction “New

warnings of using pain drugs in topical use

with children”.

• Give me the names of the medicinal

products that relate to the medicinal

instruction “New warnings of using pain

drugs in topical use with children”.

• Assuming that the result includes the

medicinal products A and B, then it allows

querying (browsing by clicking the edges)

the substitutable medicinal products and

prices for A and B, as well as the drugs that

are included to these medicinal products.

In the next section we illustrate how our used

medicinal ontology can be specified by an ontology

language.

4 USING OWL FOR

PRESENTING MEDICINAL

INFORMATION

Web Ontology Language (OWL) (Daconta, Obrst

and Smith, 2003; OWL, 2006) has more facilities for

expressing meaning and semantics than XML, RDF

and RDF Schema, and thus OWL goes beyond these

languages in its ability to represent machine

interpretable content of the ontology (Mattocks,

2005). In particular, it adds more semantics for

describing properties and classes, for example

relations between classes, cardinality of

relationships, and equality of classes and instances.

The instances in OWL-ontologies are presented

by RDF-descriptions. RDF (Resource Description

Framework) (Davies, Fensel and Harmelen, 2002) is

essentially a data model. There are various ways in

capturing knowledge with RDF, e.g., as natural

language sentence, in a simple triple notation called

N3, in RDF/XML serialization format, and by as a

graph of the triples (Daconta, Obrst and Smith,

2003).

RDF’s modeling primitive is an object-attribute-

value triple, which is called a statement. For

example, the medicinal instruction titled “New

warnings of using pain drugs in topical use with

children” deals the medicinal products “Pain drugs

for topical use” and “Pain drugs for topical use” is

a natural language sentence that can be presented by

RDF/XML serialization format (Figure 3) by using

the vocabulary (ontology) presented in Figure 2.

However note that in capturing knowledge the

MANAGING MEDICINAL INSTRUCTIONS

107

designers are not burdened by using RDF/XML

serialization format (Antoniou and Harmelen, 2004)

as there are graphical editors that are used in design

and which automatically produce the descriptions in

RDF/XML and OWL-format (e.g., the Protégé

editor (Protégé, 2007)).

<rdf:RDF

xmlns : rdf=”http://www.w3.org/1999/02/22-rdf-syntax-ns#”

xmlns : xsd=”http://www.w3.org/2001/XMLSchema#”

xmlns : mo=“http://www.lut.fi/ontologies/medicinal_ontology#”>

<rdf:Description rdf:about=” # Instruction123”>

<rdf:type rdf:resource=“&mo;medicinal_instruction”/>

<mo : title> New warnings of using pain drugs

in topical use with children </mo : title>

<mo : deals>Pain drugs for topical use</mo : deals>

<mo : deals> Prescription based pain drugs</mo : deals>

<mo : replaces> rdf: resource

Instruction122</mo : replaces>

<mo : assocoates> rdf: resource

Check_the_dose</mo : replaces>

</rdf : Description>

</rdf:RDF

Figure 3: An RDF-statement in a medical ontology.

Note that the above illustrative RDF-description

uses the vocabulary (named medicinal_ontology)

having url

http://www.lut.fi/ontologies/medicinal_ontology#.

The rdf-description also states that the new

instruction identified by Instruction123 replace the

instruction identified by Instruction122. In addition

the description associates the instruction to a task

named “Check_the_dose”. How this association is

presented in our used business process specification

language is the topic of the next section.

5 ATTACHING INSTRUCTIONS

TO D AY-TO -D AY WO RK

PATTERNS

Though the ultimate goal of using Business Process

Modeling Notation (BPMN) (White, 2006; BPMN,

2005) is the automation of the coordination of

business processes we use BPMN to model

medicinal processes and for attaching medicinal

instructions to day-to-day work patterns which are

presented in BPMN.

The BPMN is a standard for modeling business

process flows and web services. Basically BPMN

and the UML 2.0 Activity Diagram from the OMG

(White, 2006) are rather similar in their presentation.

However, the Activity diagram has not adequate

graphical presentation of parallel and interleaved

processes, which are typical in workflow

specifications.

The BPMN defines a Business Process Diagram

(BPD), which is based on a flowcharting technique

tailored for creating graphical models of business

process operations. These elements enable the easy

development of simple diagrams that will look

familiar to most analysts. In addition BPMN allows

an easy way to connect documents and other

artifacts to flow objects, and so narrows the gap

between process models and conceptual models.

Also, a notable gain of BPMN specification is that it

can be used for generating executable BPEL

(Business Process Execution Language) (BPEL,

2004) code.

We now give an overview of the BPMN. We

first shortly describe the types of graphical objects

that comprise the notation, and then we show how

they work together as part of a Business Process

Diagram (BPD) (White, 2006). After it, we give a

simplified pharmaceutical process description using

BPD.

In BPD there are tree Flow Objects: Event,

Activity and Gateway:

• An Event is represented by a circle and it

represents something that happens during

the business process, and usually has a

cause or impact.

• An Activity is represented by a rounded

corner rectangle and it is a generic term for

a task that is performed in companies. The

types of tasks are Task and Sub-Process.

So, activities can be presented as

hierarchical structures.

• A Gateway is represented by a diamond

shape, and it is used for controlling the

divergence and convergence of sequence

flow.

In BPD there are also three kind of connecting

objects: Sequence Flow, Message Flow and

Association.

• A Sequence Flow is represented by a solid

line with a solid arrowhead.

• A Message Flow is represented by a dashed

line with an open arrowhead and it is used

to show the flow of messages between two

separate process participants.

• An Association is represented by a dotted

line with a line arrowhead, and it used to

associate data and text with flow objects.

In Figure 4, we have presented how the process

of producing electronic prescription can be

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represented by a BPD. As illustrated in the figure we

use Association to attach instructions to Activities

and Gateways. For example, Instruction A is

associated to activity “Produce prescription”, and

Instruction B is associated to gateway “Check

negative effects”.

Produce a prescription

Check

negative

effects

Check

the dose

Check

the prices

Sign the prescription

Send the prescription to

prescription holding store

Send to expert database system

Send to medical database

Send to pricing authority

Yes

Instruction

Figure 4: Attaching medicinal instructions to a BPD.

We can support automatic integration of

medicinal instructions to business processes in two

ways. If the relationship of the medicinal instruction

and the task of a workflow (Figure 2) are presented

in the medicinal ontology, then this information can

be used.

Otherwise the integration can be made by

comparing the similarities of the metadata

descriptions of the instructions and the tasks. This

requires that the metadata items of the workflow

tasks are picked up from the same taxonomy that is

used for annotating medicinal instructions. Hence

we can conclude that an instruction is relevant for

the task, if they have similar metadata description.

That is, it is appropriate to integrate Instruction I to

Task T, if they have somehow similar keywords. To

illustrate this, assume that Instruction I has m

keywords and Task T has m keywords, then they

have at most min{m,n} common keywords. So we

can assume that the higher the number of the

common keywords is, the better the Instruction I

match for the Task T. Hence, we order the

instructions of the Task T according to the number of

their common keywords.

6 CONCLUSIONS

Healthcare is a field where the fast development of

drug treatment and technologies requires specialized

skills and knowledge. At the same time the amount

of new instructions concerning new medication

increases rapidly. How to ensure that healthcare staff

is aware of the new instructions is not an easy task.

However, applying computing technology for

retrieving and disseminating medicinal information

this complexity can be alleviated in many ways.

Particularly, we have considered taxonomy-

based and ontology-based retrieving of medicinal

instructions. It is turned out that by deploying

ontology-based retrieving method the expression

power of searching expressions can be considerably

increased. On the other hand, the drawback of

ontology-based searching is that the ontology must

be updated whenever a new medicinal instruction is

published. However, such an update can be done by

medicinal authorities, and thus it does not burden the

medicinal organizations that use the ontology.

We have also presented how the dissemination

of medicinal instructions can be carried out by

integrating the instructions to daily tasks. The gain

of this approach is that dissemination processes are

integrated in a natural way into day-to-day work

patterns, and thereby minimize the extra time

required for retrieving the instructions.

The introduction of a new technology in

retrieving and disseminating medicinal instructions

is also an investment. The investment on new ICT-

technology includes a variety of costs including

software, hardware and training costs. Introducing

and training the staff on new technology is a notable

investment, and hence many organizations like to

cut on this cost as much as possible. However, the

incorrect usage and implementation of a new

technology, due to lack of proper training, might

turn out to be more expensive in the long run.

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