the application of business intelligence solutions in a ... · the process of data discovery is...

REMIGIUSZ LEWANDOWSKI, EUGENIA FRONCZAK, KRZYSZTOF WAWRZYNIAK, MARCIN ŁAGODZI�SKI, WOJCIECH CZECHUMSKI

THE APPLICATION OF BUSINESS INTELLIGENCE SOLUTIONS

IN A HEALTH CARE ORGANIZATION

Summary

Implementation of IT solutions in health care, as well as the competent

application of information collected in medical data bases are the key to improving

the standards of treatment and the processes of knowledge management in

organizations. This paper provides an analysis of a concept of the application of

Business Intelligence tools in health care to determine the correlation between

medical events and their results. It describes fundamental elements for the

development of a Data Exchange Structure (DXS) and a Data Warehouse (DW), as

well as for the application of Online Analytical Processing (OLAP)

Keywords: business intelligence, data warehouse, OLAP, data mining, medical data bases

1. Introduction

The global process of informatization has largely contributed to the development of the world

economy and information society; therefore, the possession of comprehensive and current data in

an organization is becoming more and more central to its growth, facilitates the decision-making

processes, and enables the successful undertaking of future projects. The development of

information technology has made it possible to store and process increasing amounts of data.

However, the mere collection of large data bases does not contribute to the development of

a business organization. The quality of data is much more important, as is an ability to take

advantage of its correlations, which makes the data reveal useful knowledge and offer measurable

value for the organization.

Nowadays, in order to survive in the market, one should be skilled at using available

information to draw conclusions which leads to gaining knowledge on the basis of which apt

decisions can be taken [1]. The purpose of knowledge management is to change the attitude from

‘you don’t know what you don’t know’ to ‘you know what you know’ and to apply the knowledge

to improve the efficiency of an organization [2].

Modern database systems are very efficient and capacious; therefore, the actual problem is not

how to store data but how to use it effectively [3]. A massive increase in the number of databases

and data repositories in the field of health service offers ample opportunities for their use to

R. Lewandowski, E. Fronczak, K. Wawrzyniak, M. Łagodzi�ski, W. Czechumski

The application of Business Intelligence solutions in a health care organization

90

explore the data and discover knowledge [4]. Nevertheless, processing information by means of

basic methods is not always enough to provide a satisfactory answer to a formulated hypothesis.

This is why in the realities of today it is becoming more and more important to apply the

technology of data mining.

Data exploration is a process which consists in finding new, previously unknown, potentially

useful, understandable, and correct patterns in extensive data sets [5]. The use of data mining

systems is increasingly vital to the functioning of an organization, because it enables discovering

‘new’ knowledge, which is then used to gain greater advantages. Besides, the process of data

exploration used in business enterprises makes it possible to take suitable decisions in a dynamical

environment [6].

In the data exploration process, new information is mined through mathematical and statistical

analysis of data, by means of database technology, pattern recognition and machine learning

techniques, as well as artificial intelligence. The knowledge mined from large data sets is used to

support decision-making processes, to find solutions to problems, and to make forecasts and plans,

which is particularly important in health service organizations.

Data exploration comprises the following stages:

• Preparation of data – the data is prepared in a way to ensure that it is correct, compliant

with, and significant for the considered problem,

• Data mining – i.e. learning about the features and characteristics of the analyzed data,

• Analysis and evaluation of data – selection of an appropriate method to solve the problem

and obtain useful information (at this stage, thanks to the application of various techniques, rules,

correlations, and algorithms a specific model or models of data are built),

• Model application – the model which gives the best results is applied.

The process of data discovery is very difficult and multifaceted; therefore it often requires

repeating of individual stages or narrowing down the area of exploration due to the complexities of

data and its correlations. Furthermore, in order to achieve the best results in data mining, one

should have specialist knowledge, an ability to understand the specific problems and to identify the

right analytical methods to handle them, and the commitment of all users of the process. Some

measurable benefits of data mining include the finding of certain principles on which the business

operates and a supports in managing public relations [7].

Information technology is finding more and more applications in healthcare facilities, whether

it involves various aspects of therapy, or the management, e.g. of resources, deliveries, logistics,

medical supplies, according to standards specified by the National Health Fund.

Opinions that data mining supplements the current functionalities of database systems are

becoming increasingly common [8]. Collected data is stored in databases, which should be used

directly by analytical systems to manage the knowledge of an organization. It is essential to

combine the process of exploration with a database administration system [9]. However, the next

step in the informatization of health services is the growing number of implementations of data

warehouses with Business Intelligence (BI) applications. This enables the integration of diverse

information coming from various sources, such as medical equipment, diagnostic procedures,

patient demographics, as well as details connected with the costs of treatment. It should be noted

that it is particularly important that the resources available to health service units are reasonably

managed [10].

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The implementation of a Data Warehouse (DW) at the Oncology Centre in Bydgoszcz is

another stage of its informatization, as well as a key element in the concept of knowledge

management in that organization [11]. In this paper, an attempt has been made to introduce the

outlooks of establishing correlations between medical events and their results using a Business

Intelligence tool kit. The paper outlines the process of sourcing knowledge from large sets of

medical data of patients from the Oncology Centre.

2. Results

a. Information systems in medicine

The number of IT products for the health service sector offered on the market is continu-ously

increasing and now nearly every healthcare facility uses a whole range of specialist field-specific

systems. Nevertheless, subsequent implementations of new systems often fail to yield expected

results for the health service unit due to fundamental problems with integration of the information

handled by the systems. The effects of the implementation of electronization tools vary that the

probability of failure is higher than that of the success [12]. The most critical factors that impact

the effectiveness of a BI system are: output information accuracy, conformity to the requirements,

and support of organizational efficiency [13]. One of the ways to optimize internal processes in

hospitals is to introduce a standardized format of data recording and automation of data acquisition

and exchange methods that would not require human intervention [14].

Investments in IT systems usually have a clear priority – to introduce additional

functionalities. However, the functionality of a platform on which data could be exchanged among

different sys-tems (or their modules) is not readily available in the market of IT solutions. This is

where a need arose for the introduction of our own method to solve the problems connected with

such integration of information processed by IT systems operated now and in the future. The

solution was to launch a Data Exchange Structure (DXS), which enables the multi-party exchange

of data and organized collection of data transferred through the DXS. The acquisition of collected

data for the Data Warehouse, as another party in the data exchange system, was just the logical

next step.

b. Principles of interaction between data exchange systems and the DXS

The following principles were established for the systems which were to exchange data with

the Data Exchange Structure:

• Each system operates using its own native data structures.

• Due to the conditions in which the systems operated in the organization, in the event of

any change in the data exchanged between the systems, the change must be propagated

immediately.

• Each system handling data acquired from an external system makes its own data reception

mechanism accessible to receive notifications of changes in the data. The mechanism should

provide the functionality of recording data acquired from external systems in its native structures.

• In order to notify an external system of a change in the data of interest to the external

system, each system, having modified its internal data set, initiates the reception mechanism of the

external system.

Studies & Proceedings of Polish Association for Knowledge Management No. 58, 2012



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For example, if an X system is interested in data from an external Y system, a change made in

the data will require the Y system to notify the X system of the change by triggering the reception

mechanism of the X system.

The role of the Y system is to prepare its native data correctly for the reception mechanism of

the X system and to initiate the mechanism. If the data is accepted by the X system, the role of the

Y system ends when it registers the delivery of the information in the log. The information may

only be rejected by the X system if an irregularity in the data exchange is detected. In a situation

like this, the X system is required to communicate the reason for rejection. Both X and Y systems

must register all errors.

The principles above refer to the interaction of any data-exchange parties (shown as X and Y

systems in Fig. 1) and of the Data Exchange Structure.

Figure 1. Standard data exchange between individual systems and the DXS

Source: own study.

The DXS makes it possible to exchange data down to the detail of data elements supported by

individual parties, which participate in the exchange. In this way, external conditions and

changeable standards do not require any modifications of the internal structures of the data

handled by the parties. As a result, for example, medical procedures can be defined to a greater

degree of detail than in standard classification methods.

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c. Databases

The environment in which the analyzed data is stored and processed is based on the MS SQL

2008 Database Engine. A few dozen databases from the production server were analyzed, and the

number of patients registered in the system exceeded four hundred thousand. More than four

million internal procedures were registered for the patients.

d. Business Intelligence (BI) tools

In this project, the data was analyzed in more than sixty data bases on an OLTP (OnLine

Transaction Processing) server operating in the MS SQL 2005 environment. In order to improve

the usefulness of the databases, the following BI services were applied: MS SQL 2008 SSIS (SQL

Server Integration Services), SSAS (SQL Server Analysis Services), and – for presentations –

Microsoft PowerPivot. The choice of BI tools was determined by the desire to ensure coherence of

the environment for OLTP and OLAP servers, as well as by the price factor. The OLAP is a new

model of data processing, which is designed to support data analysis processes. In this model, it is

possible to analyze data in multiple user-defined ‘dimensions’ (time, place, product classification,

etc.). The analysis consists in calculating aggregates for defined dimensions. It should be

underlined that the whole analytical process is controlled by the user [15].

Despite the substantial degree of integration of the production server, a problem of data

heterogeneity occurred in the project, which principally resulted from diverse needs and

applications of the specialist systems. Another difficulty was encountered because of continuous

changes in medical and auxiliary systems. The application of a special analytical tool, dedicated to

the problem, enabled the smooth acquisition of data and the feeding of OLAP cubes from various

heterogeneous data sources.

2.1. Categories of fundamental problems and applied solutions

a. Patients’ survival as a measure of successful treatment and a consequence of medical

events

• The survival time is calculated as the time from the day on which the cancer was first

diagnosed to the date of the last recorded medical event (including the patient’s death, if

applicable). There is also an option to use an automatic time marker, as in the paragraph below.

• In the case that a patient does not report for check-ups and has not died, and the time from

the patient’s last visit to the Oncology Centre to the date of the current analysis is longer than the

assumed >t, an automatic time marker is used (current date – >t), equivalent to a medical event.

• The assumed >t should ensure that the death of a patient is properly reflected in the Data

Warehouse (DW).

b. Comparability of analyzed relations

• The principal requirement for the analysis, provided the data is correct, is to maintain its

comparability. This should be ensured through a selection of resources in terms of the kind and

stage of disease at the time when treatment is attempted.

For example, for the purpose of presentation, a group of female patients with diagnosed C50.*

(all forms of breast cancer) was selected at the OC. They were divided by years (2006–2009) and

stage of cancer development (0 – IV).




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• The use of a relative time scale

In order to maintain the comparability of events concerning different patients who start

treatment in different times, a relative time scale was introduced.

o n – number of days (n = 50 in the presentation), which enables an analysis of the actions

preceding the TNM classification (e.g. surgery and pathomorphological examinations).

o The first day on this scale is a relative start date, defined as the day on which the first

medical event took place, recorded from day n, being earlier than the date of the identification of

the TNM code, which determines the stage of development of the cancer in the OC medical

system.

o If the assumed start date is the date of a patient’s first visit to the OC, these could be the

dates of examinations or procedures, which are unrelated to the cancer, much earlier than the

period of our interest. A similar situation happens if the start date is the day on which C50.* was

diagnosed (e.g. occurring earlier than the date of the patient’s first visit to the OC).

o All dates are referenced as days calculated from the start date (including the date of death,

if applicable).

o Wherever the patient’s age is indicated in the analysis, it is related to the date.

c. Method of presentation of analyzed relations

• Event categories

For analytical purpose,s the following categories of medical events (actions) were identified:

– Check-up and/or diagnosis,

– Surgical procedures,

– Chemotherapy,

– Radiotherapy,

– Other medical actions, not listed above, mainly including rehabilitation.

The events were defined on a time scale with an accuracy of one day of the patient’s stay at

the OC.

The date of determination of the stage of cancer was also marked on the time scale along with

the date of the patient’s death, if applicable.

• Frequency of occurrence

In order to analyze the accomplishment of specific categories of medical events by the

hospital, the presentation includes the number of days on which the events took place during the

patient’s stay at the OC.

The number of days can be represented as the length of the green bar on the charts, or the days

can be directly marked on the time scale.

• Time frame of the events

For the purpose of presenting the time frame in which medical events of a specific category

took place, a red horizontal bar was used on the time scale, the length of which represents the

interval in days between the first and the last event in the given category over the considered

period.

• Time scale

The time scale is determined according to needs; therefore it is possible to use a time scale

with the days in the treatment process, on which specific medical procedures were carried out.

Also, an analysis of the sequence of their occurrence is possible (i.e. the procedures that precede

and follow a given procedure).

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2.2. Selected presentations

a. Patient’s survival rates – a possibility to use statistical measures

a) – (upper chart) the use of the median and quartiles for the number of survival days in

patients with C50.* and stage IIa

b) – (lower chart) the use of the median to compare patients with C50.* at different stages of

development (except for stage 0 which was hardly represented)

Figure 2. Selected presentations: Patient’s survival rates – a possibility to use statistical

measures a) – (upper chart) the use of the median and quartiles for the number of survival days in

patients with C50.* and Stage IIa. b) – (lower chart) the use of the median to compare patients

with C50.* at different stages of development.

Source: own study.

b. Presentation of the course of treatment of female patients diagnosed with a C50.9 tumor

on a synchronized relative timeline.




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Figure 3. Presentation of the course of treatment of female patients diagnosed

with a C50.9 tumor on a synchronized relative timeline

Source: own study.

A selection of treatment courses of three patients diagnosed with C50.9, presented on

a synchronized relative timeline, show different patterns of the course of treatment, rehabilitation,

and the time during which the patients remained under medical care (on the diagnostics bar).

In the first case, surgical events occurred on the time scale for the total of approximately 300

days. This kind of information can be identified by selecting the option of the span of specific

medical events (surgical procedures in the example above).

c. Example presentation of the quantity of medical events (various categories of actions)

with a consideration for the stage of development of C50.* (Fig. 4.)

The presentation enables the assessment of the level of involvement of the hospital in different

categories of medical actions, taking account of the stage of cancer.

The number of medical events (different categories of medical actions), represented by the

number of days on which these occurred, clearly shows the efficiency of treatment of breast cancer

at different stages of spread (the height of the blue bars versus the height of the yellow bars). It

also gives an idea of the involvement of the resources available to the OC in the process of

treatment of breast cancer at different stages.

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Figure 4. Example presentation of the quantity of medical events (various categories of

actions) with a consideration for the stage of development of C50.*

Source: own study.

d. Presentation of the involvement of OC resources in f 2006–2011 in the process of

treatment of patients diagnosed with C50.*, divided into surviving (blue bar) and deceased patients

(red bar) and different stages of cancer (Fig. 5.)

Figure 5. Presentation of the involvement of OC resources in 2006–2011 in the process of

treatment of patients diagnosed with C50.*, divided into surviving (blue bar) and deceased

patients (yellow bar) and different stages of cancer

Source: own study.




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2.3. Types of analyses practicable using the currently functioning solution

a. Medical analysis

? The information resources of the Data Warehouse make it possible to narrow down

analyzed medical events to the applied medical procedures. This means that it is possible to select

– for defined conditions (dimensions) – all cases where:

– a specific procedure was applied,

– the procedure was applied within a time interval (Tx) following a significant event on the

time scale (including after another specific procedure),

– a criterion of no later than or no earlier than is used.

? Each case selected for analysis can be evaluated from the point of view of the outcome

(patient survival, reoperations and recurrences).

? It is also possible to select combined treatment, which however requires a precise

identification of the course of events pertinent to the case.

? For selected events (e.g. the patient’s visit to the clinic for a routine checkup), it is

possible to determine whether patients report for checkups with the frequency recommended from

the medical perspective and what the consequences are.

? With this kind of information in hand, the hospital could control the patients and its own

medical staff, as medically unjustified visits prevents the patients who are in a real threat from the

disease from access to the services.

b. Economic aspects of treatments

• One of the basic problems faced by hospitals are the difficulties connected with the

accounting and settlement of medical services with the National Health Fund.

Allocating events related to individual patients for these purposes with the use of DW

resources and the functionalities of a BI solution is possible and already applied at the Oncology

Centre.

• The process of cost analysis at hospitals, similarly to the settlement of services with the

NHF, is another one of the fundamental problems of the administrative nature. The availability of

economic information in the DW makes it possible to analyze costs (including e.g. the problem of

underestimating costs of procedures, averaged costs of treatment of a patient in a defined time

frame, etc.).

• Besides cost control, it is possible to carry out different ongoing analyses, e.g. of the

loading (utilization) of specific resources, such as diagnostic equipment or beds.

3. Conclusions

The presentation of the implemented concept of the application of Business Intelligence

solutions in a health service organization, intended to determine the correlations between medical

events and their results, allows us to formulate a basic conclusion that such solutions, structurally

adapted to provide business analysis, may well be applied in the medical sector, and even more so

where business and health services overlap. It also concerns the data structures in transactional

data sets, which occur on the borderline of medical and economic and organizational problems.

The condition precedent for the task identified in the title is a competent preparation of the Data

Warehouse environment, which would guarantee that the information resources are reliable and

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proper for the intended analyses. The systems from which the information would be acquired by

the DW should meet these requirements as well.

The use of DW resources and taking advantage of the possibilities offered by the BI tool

implemented at the Oncology Centre in Bydgoszcz have only been hinted at. The tool was

developed to link individual classifications, to visualize correlations, and to find common indirect

connections. The authors plan as a next step to develop a tool so-called “knowledge cube” which

will register, integrate, and present knowledge to the user. At the same time, it will minimize

integration services and enable finding the shortest links or the kind of lack of them for a minimal

path between individual objects. An organized register of knowledge is indispensable for

successful knowledge management.

The use of a data warehouse and a BI solution, suitably to the needs and possibilities of any

other kind of hospital is also practicable, offering advantages and prospects of development.

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ZASTOSOWANIE NARZ�DZI BUSINESS INTELLIGENCE

W ORGANIZACJI SŁU�BY ZDROWIA

Streszeczenie

Informatyzacja słu�by zdrowia jak te� umiej�tne wykorzystywanie informacji

zgromadzonych w medycznych bazach danych odgrywa kluczow� rol�

w podwy�szaniu standardów leczenia jak równie� w procesie zarz�dzania wiedz�

w organizacji. W pracy przeanalizowano koncepcj� zastosowania narz�dzi Business

Intelligence w słu�bie zdrowia w celu ustalenia relacji mi�dzy zdarzeniami

medycznymi, a ich skutkami. Opisano kluczowe elementy tworzenia Struktury

Wymiany Danych (SWD), Hurtowni Danych (HD) oraz stosowania technologii

OLAP (On Line Analytical Processing).

Słowa kluczowe: Business Intelligence, Hurtownia Danych, OLAP, Data Mining, Medyczne Bazy

Danych

Remigiusz Lewandowski

Management Information Systems

University of Technology and Life Sciences in Bydgoszcz

ul. Fordo%ska 430, 85-790 Bydgoszcz, Poland

e-mail: [email protected]

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