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
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].
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.
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.
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).
• 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).
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.
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.
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
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
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.
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ZASTOSOWANIE NARZĉDZI BUSINESS INTELLIGENCE W ORGANIZACJI SŁUĩBY ZDROWIA
Streszeczenie
Informatyzacja słuĪby zdrowia jak teĪ umiejĊtne wykorzystywanie informacji zgromadzonych w medycznych bazach danych odgrywa kluczową rolĊ w podwyĪszaniu standardów leczenia jak równieĪ w procesie zarządzania wiedzą w organizacji. W pracy przeanalizowano koncepcjĊ zastosowania narzĊdzi Business Intelligence w słuĪbie zdrowia w celu ustalenia relacji miĊdzy 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
