HEALTHCARE BUSINESS INTELLIGENCE: THE CASE OF UNIVERSITYS HEALTH CENTER

Ishola Dada Murainaa*, Azizah Ahmadb ab

ITU-UUM ASP CoE,

Universiti Utara Malaysia, 06010 Sintok Kedah Malaysia

*ishod2001@yahoo.com

azie@uum.edu.my

ABSTRACT

Organizations, private or public, feel increasing pressures, forcing them to respond quickly to changing conditions and be innovative in the way they operate. Such activities require organizations to be agile and make frequent and strategic, tactical, and operational decisions. Making such decision may require considerable amounts of timely and relevant data, information, and knowledge. Every semester university admits new students; they do subject them to medical screening which sometimes includes the staffs and returning students. However, the results of the medical test from the laboratory technologists and the doctors, such as patient diagnosis, treatment and medical prescription are currently kept in the health center data repository for record purposes without being further explored for their managerial activities. Therefore, this paper applies Business Intelligence (BI) method for exploring the university health center database repository. The data warehouse was built for the activities in university health center and a prototype was developed at the end, while the system is evaluated by the prospective users of the system. The result of this research helps the university health center management by simplifying the technique needed for managerial decision making and forecasting future activities that would help the center. Also, the health care BI is also useful to know the medical statistics of the patients in university community and the drugs that need to be frequently ordered for.

Keywords: Business Intelligence (BI), University Utara Malaysia (UUM), University Health Center Business Intelligence (PKUBI), Star Schema

1. Introduction

The business environment in which organizations operate today is becoming more complex and ever-changing. Organizations, private or public, feel increasing pressures, forcing them to respond quickly to changing conditions and be innovative in the way they operate. Such activities require organizations to be agile and make frequent and strategic, tactical, and operational decisions. Making such decision may require considerable amounts of timely and relevant data, information, and knowledge. Processing these in the framework of the required decisions needs quick, frequent and some computerized support which is traced to business intelligence (BI) (Efraim, et al. 2008). Healthcare organizations are swimming in an ever-deeper pool of data. But without a program in place to target, gather, deliver and analyze the most relevant data, these organizations will continue to be data rich but information poor (Eckerson, 2003). Forward-thinking healthcare organizations realize that data and, thus, BI is at the center of informed and precise decision-making will improve patient and service outcomes in ensuring their organizations future (Hyperion Solution Corporation, 2004). To achieve the full benefits of BI in healthcare organizations, there must be a strategic approach to tactical projects and realize that the greatest efficiencies come from integrating data historically in operation and clinical systems (Microsoft, 2009).

University Utara Malaysia (UUM) at Northern part of Malaysia is a public university which is located in a small town called Sintok, Kedah State. The university was officially established on February 16 th, 1984 and its mission is to play leadership role in developing the country by providing high quality management education in the country. On top of that, the university has offered excellent education areas which are represented as College of Business (COB), College of Arts and Sciences (CAS), and College of Law, Government and International Studies (COLGIS).

In addition, UUM exempts itself in such a way that its students and staffs health are guaranteed by conducting medical-up for them at the University Utara Malaysia Health Center (PKU) during the new intake admission and in the time of need to see the medical officers. This medical related information is kept for record purposes in the database repository of UUM. The data in the database can be manipulated using BI techniques and tools in order to provide PKU with faster and more accurate reporting, improved decision making and better customer service, and eventually increased revenue.

Furthermore, BI is operationally described as a collection of data warehousing, data mining, analytics, reporting and visualization technologies, tools, and practices to collect, integrate, cleanse, and mine enterprise information for decision making (Inmon, et al. 2000). Todays BI architecture is usually designed for strategic decision making, where a small number of expert users analyze historical data to prepare reports or build models, and decision making cycles for previous activities (Umeshwar, et al. 2009).

The major objective of BI is to enable interactive access to data, to enable manipulation of data, and to give business managers and analysts the ability to conduct appropriate analysis on the historical and current data, which will reveal the situations and performance for making better decision (Zaman, 2005). Data from distributed sources such as online transaction processing (OLTP) systems is periodically extracted, cleansed,

integrated, transformed, and loaded into a data warehouse (DW), which in turn is queried by analytic applications (Chaudihuri, et al. 2001).

Every semester UUM enrolls the new intakes with the target of having 40% of graduate students and 60% of undergraduate students as a research university, the PKU conducts medical test for the new students before they are allowed to continue with their academic activities. Both the staffs and returning students are subjected to medical screening whenever they need medical attention from the physicians throughout the semesters. The results of the medical test from the laboratory technologists and the doctors, such as patient diagnosis, treatment and medical prescription are currently kept in the data repository for record purposes without being further explored for their managerial activities. However, there have been a fewer research that explores BI for PKU in managerial decision making which should help in speeding-up the processes in dealing with their patients and day-to-day activities of the PKU. Therefore, this research intends to use the medical record of PKU at UUM by performing business analytics which boosts their services.

Finally, this paper produces answers to the following questions like

i. What are the requirements needed for business intelligence in UUM Health Center? ii. What type of model necessary for BI in making decision at UUM Health Center?

iii. Which prototype needed for making decision in UUM Health Center? iv. Does the UUM Health Centers BI system easy to use?

In addition, the application of BI in UUM health center is an important measure which is used as a computerized support for managerial decision making. This is achieved by viewing the performance through the help of visualization of important data, while the services receive by the students and staffs of UUM is also increased. This research is also contributed to the body of knowledge in the areas of healthcare and BI domains (helping the PKU know the common diseases among the patients and medication that need to be supplied frequently).

2. Business Intelligence (BI) Raisinghani (2004) described BI as an umbrella term that includes architectures, tools, databases, application, and methodologies. This means that BI is a content-free expression that reflects different things to different people. He added that BI enables interactive access to data, enables manipulation of data and provides business managers and analysts the ability to conduct appropriate analysis. Zaman (2005) stressed that analysis in BI is based on the historical and current data, situations, and performances which make decision makers to get valuable insights upon which they can base more informed and better decision. Therefore, the process of BI is based on the transformation of data to information, to decisions and finally to actions.

In industry, BI is finally achieving an increased prominence record and existed in many forms for over three decades, initially being called Decision Support Systems (DSS), while the umbrella term is still most widely used in academia. The term Business Intelligence has existed even longer, but in its present form has been attributed to historical data (Watson & Wixom, 2007). In addition, BI has evolved into a managerial philosophy and a business tool, which can be referred to as an organized and systematic process by which organizations acquire, analyze, and disseminate information from both internal and external information sources significant for their business activities and for decision making (Lonnqvist & Pirttimaki, 2006).

3. EVOLUTION OF BUSINESS INTELLIGENCE The BI was brought newly by the Gartner group in the mid 1990s from the area of management information system (MIS) reporting systems of the 1970s. During the introduction of BI, the reporting systems were static, having two-dimensional features and had no analytical capabilities. In early 1980s, the concept of executive information systems (EIS) was emerged by expanding computerized support to top-level managers and executives. The EIS concept consists of dynamic multidimensional reporting systems, forecasting and predicting, trend analysis, drilling down to details, status access and critical success factors, while all of these features leaved to the mid 1990s. The BI evolved with the existing capabilities, but built of the EIS with few features and believes that all the information needed by the executives can be in a BI-based enterprise information system. In the year 2005, BI system started to include artificial intelligence and powerful analytical capabilities (Efraim, et al. 2008). Figure 1 shows the interconnectivities of features that lead to business intelligence (BI).

Fig. 1: Evolution of Business Intelligence (Source: Efraim, et al. 2008)

4. CHARACTERISTICS OF BUSINESS INTELLIGENCE Inmon (2005) submitted that set or tools and methodologies of BI have the following characteristics:

i. Accessibility to Information: The business intelligence is known to be flexible and allows end users to gain access to data regardless of the source of data.

ii. Support in Decision Making: Business intelligence presents the information and gives access to analysis tools that will allow the users to select and manipulate data that are important to them.

iii. Strategic Advantage: The business intelligence creates fewer barriers to entry for new competitors to enter and possess globalization features for readily available supply chain and e-commerce.

5. BENEFITS OF BUSINESS INTELLIGENCE Eckerson (2003) reported that many executives do not insist on a rigorous cost-justification for business intelligence projects due to its numerous intangible benefits, while Thompson (2004) noticed that the most common application areas of BI are general reporting, sales and marketing analysis, planning and forecasting, financial consolidation, statutory reporting, budgeting and profitability analysis. Meanwhile, Eckerson (2003) highlighted the benefits of BI as saves time, improves strategies and plans, and improves tactical decisions, more efficient in processes and cost saving.

In another contribution by Thompson (2004) shows that the major benefits of BI are; faster, more accurate reporting, improves decision making, improves customer service and increases revenue.

6. TYPES OF BUSINESS INTELLIGENCE DATA SOURCES Adelman & Larissa (2000) emphasized that one of the challenges in building a BI decision-support environment is to merge data from different types of data sources. The authors stated that there are three major types of data sources: operational, private and external. 6.1 Operational Data Source

An operational data source is a database repository which can be used to store and retain runtime data. The operational data source application can read and write data to and from this data source throughout the life of the application unlike the metadata data source. The metadata source is primarily written during the implementation stage of development and read mostly during startup of the server. Also, because data stored in the operational data source is of a transient nature, users do not need to back-up, restore, or transfer the underlying database during upgrade. Both online transaction processing (OLTP) and batch systems provide internal operational data about subject areas, such as financial, logistics, sales, order entry, personnel, billing, research and engineering.

6.2 Private Data Source

Adelman & Larissa (2000) referred to internal departmental data as the data that usually comes from the desktops and workstations of business analysts, knowledge workers, statisticians, and managers which include the Product Analysis Spreadsheets, Regional Product Usage Spreadsheets and Prospective Customer Databases.

6.3 External Data Source

Demarco (2001) submitted that organizations are often purchase external data from vendors that specialize in collecting specific industrial information that are available in the public domain like Healthcare Statistics, Customer Profile Information, Customer Catalog-Ordering Habits and Customer Credit Report.

Therefore, Kimball & Richard (2000) added that external data is usually clustered around the following categories:

i. Sales and Marketing Data: this is the list of prospective customers. ii. Credit Data: individual credit ratings, business viability assessments.

iii. Competitive Data: products, services, prices, sales promotions, mergers, takeovers. iv. Industry Data: technology trends, marketing trends, management science and trade

information.

v. Economic Data: currency fluctuations, political indicators, interest rate movements, stock and bond prices.

vi. Economic Data: income groups and customer behavior. vii. Demographic Data: age profiles and population density.

viii. Community Data: raw material prices. ix. Psychometric Data: consumer profiling. x. Meteorological Data: weather conditions, rainfall, temperature (for agriculture and

travelling industry).

7. BUSINESS INTELLIGENCE (BI) ARCHITECTURE According to Eckerson (2003), BI consists of four major components that are merged together to form BI architecture; a data warehouse (DW), business analytics (BA), business performance management (BPM) and a user interface. The BI architecture with its components is shown in Figure 2 below.

Fig. 2: A high level architecture of BI (Source: Eckerson, W. 2003)

7.1 Data Warehouse

Data warehouse and its variants is the cornerstone of any medium-to-large BI system. Initially, data warehouse included only the historical data that are organized and summarized, and allows end users to easily view or manipulate data and information. While data warehouse in the new versions is known to include current data in order to provide real-time decision support.

7.2 Business Analytics

Efraim, et al. (2008) added that business analysis as one of the components of BI architecture is a collection of tools for manipulating and analyzing the data in the data warehouse without segregating the data mining. The author said business analytic makes it possible for the end users to work with data and information in a data ware house by using a variety of tools and techniques that are classified into three categories below:

i. Reporting and Queries: This includes both static and dynamic reporting, all types of queries, and discovery of information, multidimensional view and drill-down to details.

ii. Advanced Analytics: Advance analytics includes statistical, financial, and mathematical and models used in analyzing data and information.

iii. Data, Text and Web Mining: This is a process of searching for unknown relationships or information in large database or data warehouse, using intelligent tools like neural computing and predictive analytics techniques.

7.3 Business Performance Management (BPM) Business performance management (BMP) is always referred to as corporate performance management (CPM) which is an immerging portfolio of applications and methodology that contains BI architecture and tools. BPM does measure, monitor, comparing of sales, profit, cost and other performance indicators by introducing the concepts of management and feedback. It also performs functions like planning and forecasting as the core belief of a business strategy. Unlike DSS, EIS and BI which support the bottom-top extraction of information from data, BPM provides a top-down enforcement of corporate-wide strategy.

7.4 The User Interface (Dashboards and Other Information Broadcasting Tools) Dashboards provide a comprehensive visual view of corporate performance measures, such as (Key Performance Indicators) trends and exceptions from multiple business areas. The graphs do show actual performance versus desired metrics and provide at-a-glance view of the health of the organization.

John (2007) stated that dashboards provide an at a glance view of business performance for many individuals in an organization. They give companies a factual and timely window into performance and help to identify anomalies that could turn into significant business issues, and therefore provide an entry point for digging deeper into root causes. Meanwhile, data consistency is crucial to the success of any dashboard solution which means that no matter how spectacular the interface is, it has to be fed with trusted data from an enterprise-class platform. Without reliable and consistent data, the value of predictive analysis is limited at best. Moreover, John (2007) added that the following as the characteristics of BI dashboards:

i. BI dashboards deliver a high degree of visualization with graphs, gauges and charts.

ii. BI dashboards offer personalized views of trusted key information. iii. BI dashboards can easily be delivered in multiple formats to suit specific needs of

business users. iv. They are easy to manage from an IT perspective.

8. HEALTHCARE ORGANIZATIONS

Maria & Abdel-Badeeh (2010) stressed that Healthcare organizations (HCOs) are information-intensive enterprises, while Healthcare personnel requires sufficient data and information management tools to make appropriate decisions. Clinicians assess patients status, plan patients care, administer appropriate treatments, and educate patients and families regarding clinical management of various conditions. Primary-care physicians and care managers assess the health status of new members of a health plan. Medical directors evaluate the clinical outcomes, quality, and cost of health services provided. Administrators determine appropriate staffing levels, manage inventories of drugs and supplies, and negotiate payment contracts for services. Governing boards make decisions about investing in new business lines, partnering with other organizations, and eliminating underutilized services. Collectively, healthcare professionals comprise a heterogeneous group with diverse objectives and information requirements.

In addition, the authors submitted that the main objective of HCO in a highly competitive environment is to reduce operating costs while maintaining a consistently acceptable level of patient treatment. Reduce operating costs at all levels, such as:

i. Cost of healthcare Professionals. ii. Cost of lab equipment & consumables.

iii. Cost of pharmaceuticals / medical material. iv. Cost of a treatment per Diagnosis related grouping (DRG). v. Cost per type of medical intervention (specific medical operation).

Meanwhile, an acceptable level of patient treatment involves: i. Evidence based medicine, accurate diagnosis and efficient treatment.

ii. On time admittance in the Hospital and healthcare treatment. iii. Treatment with respect for the Patient- analysis of options. iv. Reduction of risks during treatment. v. Capture of medical history of the patient in to support evidence based medicine.

9. HEALTHCARE ACTIVITIES, SERVICES AND PROCESSES IN THE CONTEXT OF BUSINESS INTELLIGENCE

Health care organizations typically prescribe how their processes have to be performed; especially those processes that represent complex routine work, that involve many persons and organizational units and that are in general frequently performed (Yorozu et al., 1987). In the context of BI, medical processes are those activities and work practices within a HCO and focused on the health services delivery (nursing and medical treatment). Business processes comprise activities that are needed to effectively run the health care organization. Support processes are used from both kinds of processes but only have an indirect impact on medical and business activities (supply of materials) as shown in Figure 3.

Fig. 3: Healthcare Process in the Context of BI

10. TECHNOLOGICAL COMPONENTS OF BI IN HEALTHCARE

Maria & Abdel-Badeeh (2010) submitted that intelligent technologies can be seen as enabler for managing, storing, analyzing, visualizing, and giving access to a great amount of data in the context of BI. For this purpose, a wide range of intelligent technologies such as; expert systems, online analytical processing, data mining and knowledge discovery, grid computing, cloud computing are used in developing BI system in healthcare sector. Technology is required to provide an integrated view of both, internal and external data (data warehouse) which is regarded as the base for BI. 10.1 Types of Operational Databases Maria & Abdel-Badeeh (2010) highlighted the essential parts of database technologies and intelligent technologies from BI perspective, and concluded that three types of operational databases should be created in healthcare organization (HCO) as shown in Figure 4.

i. Clinical Operational Databases (CODB): these include all kind of medical data which is needed for health care service delivery to the patients; such as electronic patient records, and laboratory results.

ii. Administrative Operational Databases (AODB): these contain the entire business data which is required for running the health care organization; like personnel data, and financial data.

iii. External Operational Databases (EODB): these can either be clinical or business data from an external provider (medical reports, insurance forms, and statistical data).

Fig. 4: Technology of Business Intelligence in Healthcare

11. BENEFITS OF BI IN HEALTHCARE INDUSTRY

The benefits from applying BI in the healthcare environment can be tremendous. BI serves an increasingly wide variety of departments in the provider market with an assortment of unique reporting and analysis applications (Kornack & Rakic, 2001). Thus, the authors added that a robust BI environment offers healthcare organizations a host of business benefits including, which include the following:

i. The ability to optimize resources (including physical space, equipment and devices, staff and supplies) in individual departments such as Surgical Services.

ii. The ability to develop and monitor key performance indicators and clinical indicators to improve performance and quality.

iii. The ability to conduct planning, budgeting, and forecasting more efficiently and accurately across large organizations.

iv. The ability to effectively understand and manage the supply chain and logistics to contain costs and ensure consistent supply.

v. The ability to better ensure patient safety through efficient diagnostics and the identification and enforcement of best practice treatment protocols.

vi. The ability to contain costs and improve performance and quality through human resources management and physician profiling.

12. RELATED WORK ON BI

Diana et al. (2010) submitted that a disorder characterized by an excessive sweating was treated by endoscopic thoracic sympathectomy which improved the patient overall quality of life. Therefore, the patients daily activities are not affected, or are less affected, by this disorder, and their emotional state verifies a significant improvement, from a situation of shame and self-punishing to what we could say a normal life. The authors presented the analysis of the quality of life of 227 patients that were treated by an endoscopic thoracic sympathectomy, using business intelligence technologies which allowed the storage, the analysis and the reporting of all the relevant findings. Meanwhile, the authors illustrated that database and data analysis developments were needed in a specific healthcare application domain. Such as, a data mart (data storage) which was designed to address the relevant attributes. Also, On-line analytical processing and data mining (data analysis) technologies were used to show the evolution of the patients health conditions.

Furthermore, Maria & Abdel-Badeeh (2010) stressed that Business intelligence is a new methodology to maximize the benefits for healthcare organization. Business intelligence also provides an integrated view of data that can be used to monitor, key performance indicators, identify hidden patterns in diagnosis and identify variations in cost factors. Therefore, intelligent techniques provide an effective computational methods and robust environment for business intelligence in the healthcare domain.

Christos et al. (2008) stressed that On-Line Analytical Processing (OLAP) tools use multidimensional views to provide quick access to information. Therefore, these have become the existing standard in the business world for analytical databases. In health care, care givers and managers could benefit from being able to perform interactive data exploration, while ad-hoc analysis and possibly discover hidden trends and patterns in health data.

Jim & Achim (2008) argued that paper still plays an important role in todays and future BI-lifecycle. This makes the authors to hypothesis that working with a digital pen together with the specially designed graphical elements and forms can enhance work in the healthcare domain, because of the digitalization of the contents. The investigations were operated in the context of a Business Intelligence (BI) lifecycle, where content is captured, analyzed, utilized, and re-annotated on paper printouts. The authors developed architecture for acoustic digital pen integration into a healthcare environment and implemented a prototype for evaluation reasons. Form elements were classified while current processing of digital pen forms was also analyzed. The whole system was evaluated to gather first impressions from end users. The basic question arises, how forms can be designed that the process of note taking is getting faster, lesser and well arranged so that nurses and physicians have more time for the patients. In addition, the authors came out that when designing forms for healthcare, three parties need to be involved; the nurses or whoever has to work with the form to assure ergonomic and usability requirements, the management or whoever has knowledge about data integration and process optimizing opportunities and the physician or whoever is responsible for quality management and legal concerns. This will make the acoustic digital pens seem to provide a cost-effective opportunity to bridge the gap between physical paper records and the digital representation of them.

Lihong, et al (2010) submitted that Extract-Transform-Loading (ETL) tools integrate data from source side to target in building data warehouse. However data structure and semantic heterogeneity exits widely in the enterprise information systems. On the purpose of eliminate data heterogeneity so as to construct data warehouse, the authors introduced domain ontology into ETL process of finding the data sources, defining the rules of data transformation, and eliminating the heterogeneity. They embedded domain ontology in the metadata of the data warehouse which led to data record mapped from data bases to ontology classes of Web Ontology Language (OWL). This resulted to access information resources more efficiently. The authors tested the method in a hospital data warehouse project, and the result shows that ontology method plays an important role in the process of data integration by providing common descriptions of the concepts and relationships of data items, and medical domain ontology in the ETL process is of practical feasibility.

Xuezhong, et al (2008) suggested that the clinical data from the daily clinical process, which keeps to traditional Chinese medicine (TCM) theories and principles, is the core empirical knowledge source for TCM researches. The authors introduced a data warehouse system, which is based on the structured electronic medical record system and daily clinical data, for TCM clinical researches and medical knowledge discovery. The system consists of several key components: clinical data schema, extraction-transformation-loading tool, online analytical analysis (OLAP) based on Business Objects (a commercial business intelligence software), and integrated data mining functionalities. Their data warehouse is currently contained 20,000 inpatient data of diabetes, coronary heart disease and stroke, and more than 20,000 outpatient data. Conclusively, their analysis applications showed that the developed clinical data warehouse platform is promising to build the bridge for TCM clinical practice and theoretical research, which will promote the related TCM researches.

13. METHODS Therefore, paper uses the Business Intelligence Roadmap methods in designing the Healthcare BI for PKU in UUM (Larissa & Shaku, 2003). The adopted method consists of six stages; Justification Stage, Planning Stage, Business Analysis Stage, Design Stage, Construction Stage and Deployment Stage, shown in Figure 5.

Fig. 5: Business Intelligence Roadmap (Sources: Larissa & Shaku, 2003)

The PKUBI system starts with investigation of the highlighted problem of making decision by the policy makers (Chief Medical Officer, Doctors, matrons and medical laboratory officers) in UUM and business opportunity that need BI solution which was discovered during the interview with the chief medical officer of PKU, Dr. Sanuri. Each BI application should be cost-justified and should clearly define the benefits of either solving a business problem or taking advantage of a business opportunity in PKU. This method proceeds to the certification of infrastructure that PKU in UUM has on ground for the development of BI and preparation of the needs for the application. The infrastructure may include hardware, software, middleware, Meta data repository and network components. In addition, organizations of the staffs, budgets, and technologies, business representatives of PKU in UUM which must be in detail are closely reported.

Furthermore, the business analysis stage for PKU in UUM has four phases, such as project requirement definition, data analysis, and application prototyping and Meta data repository analysis. Moreover, the design stage consists of database design, extract transform load (ETL) design and Meta data repository design for the PKU BI system. The design has to meet the requirements of the logical Meta model and take processes of SQL Server Integration Services (SSIS), SQL Server Analytical Services (SSAS) and SQL Server Report Services (SSRS). The conclusion part has to do with the development of ETL, application, data mining and the Meta data repository for the PKU BI application in UUM. Therefore, the BI system then deploys for evaluation by the doctors, matrons and the laboratory technologists during the deployment period.

14. ANALYSIS AND DESIGN OF PKUBI This is the processes of analyzing and designing BI system for PKU in UUM. It displays stepwise development of BI application and answers the measures from the fact table of the dimension tables. Therefore, System analysis is the dissection of a system into its components for the purposes of studying how those components interact and work.

14.1 Dimensional Model IBM (2006) submitted that to overcome performance issues for large queries in the data warehouse, we use dimensional models. The dimensional modeling approach provides a way to improve query performance for summary reports without affecting data integrity. A dimensional model is also commonly called a star schema. This type of model is very popular in data warehousing because it can provide much better query performance, especially on very large queries, than an E/R model. However, it also has the major benefit of being easier to understand. It consists, typically, of a large table of facts (known as a fact table), with a number of other tables surrounding it that contain descriptive data, called dimensions. When it is drawn, it resembles the shape of a star. The dimensional model consists of two types of tables having different characteristics like Fact table and Dimension table.

14.2 Dimensional Tables and Models for PKUBI Data warehouses are built using dimensional data models which consist of fact and dimension tables. Dimension tables are used to describe dimensions; they contain dimension keys, values and attributes. For example, the time dimension would contain every hour, day, week, month, quarter and year that has occurred since the beginning of business operations. Product dimension could contain a name and description of products you sell, their unit price, color, weight and other attributes as applicable. Meanwhile, Dimension tables are typically small, ranging from a few to several thousand rows which can grow fairly large (sqlserverpedia.com). Therefore, the dimensional tables for PKUBI in UUM are students

patient, staffs patient, familys patient, publics patient and diseases patient, time, doctor and laboratory technologist.

Student Patient Dimensional Table Table 1: Student Patient Dimensional Model Definition Attribute Description

Student_patient_id The unique identification for the students patient. Student_patient_name This is the name of students patient. Student_patient_age This is the age of students patient.

To generate the report of students patient based on age. To give a correct treatment based on age.

Student_patient_gender This is the gender of students patient. To generate the report of students patient based on gender. To give a correct treatment based on gender.

Student_patient_college This is the college of study of students patient. To ease the contact of students patient in terms of need.

Student_patient_natinality This is the country o origin of studentpatient. To know if the students patient ailment is peculiar to his or her country of origin.

Student_patient_contactnum Contact number of students patient. To contact students patient for further treatment through telephone.

Student_patient_treatment To identify number of students patient with similar diseases and current syndrome. Student_patient_appointment To record any appointment.

To easily track the health history of students patient. Student_patient_department This department o the students patient.

Staffs Patient Dimensional Table Table 2: Staffs Patient Dimensional Model Definition Attribute Description

Staff_patient_id The unique identification for the staffs patient. Staff_patient_name This is the name of Staffs patient. Staff_patient_address This is the address of staffs patient in order to follow-up through hard copy. Staff_patient_age This is the age of staffs patient.

To generate the report of staffs patient based on age.

To give a correct treatment based on age.

Staff_patient_gender This is the gender of staffs patient. To generate the report of staffs patient based on gender. To give a correct treatment based on gender.

Staff_patient_contactnum Contact number of staffs patient. To contact staffs patient for further treatment through telephone.

Staff_patient_treatment To identify number of patient with similar diseases and current syndrome.

Staff_patient_appointment To record any appointment.

To easily track the health history of staffs patient.

Familys Patient Dimensional Table Table 3: Familys Patient Dimensional Model Definition Attribute Description

family_patient_id The unique identification for the familys patient. family_patient_name This is the name of familys patient. family_patient_address This is the address of familys patient in order to follow-up through hard copy. family_patient_age This is the age of familys patient.

To generate the report of familys patient based on age. To give a correct treatment based on age.

family_patient_gender This is the gender of familys patient. To generate the report of familys patient based on gender. To give a correct treatment based on gender.

family_patient_contactnum Contact number of familys patient. To contact familys patient for further treatment through telephone.

family_patient_treatment To identify number of patient with similar diseases and current syndrome.

family_patient_appointment To record any appointment.

To easily track the health history of familys patient.

Publics Patient Dimensional Table Table 4: Publics Patient Dimensional Model Definition Attribute Description

public_patient_id The unique identification for the publics patient. public_patient_name This is the name of publics patient. public_patient_address This is the address of publics patient in order to follow-up through hard copy.

public_patient_age This is the age of publics patient. To generate the report of publics patient based on age. To give a correct treatment based on age.

public_patient_gender This is the gender of publics patient. To generate the report of publics patient based on gender. To give a correct treatment based on gender.

public_patient_contactnum Contact number of publics patient. To contact publics patient for further treatment through telephone.

public_patient_treatment To identify number of patient with similar diseases and current syndrome.

public_patient_appointment To record any appointment.

To easily track the health history of publics patient.

Disease Dimensional Table Table 5: Disease Dimensional Model Definition

Attribute Description

Disease_id The unique identification for the diseases.

Disease_name The name of the diseases for proper identification.

Disease_type To know the group the diseases belong to.

Disease_class To know the class that diseases belong to.

To know either the disease is major or minor.

Disease_control To know the specific drug for controlling the diseases.

Time Dimensional Table Table 6: Time Dimensional Model Definition

Attribute Description

Time _id The unique identification for each patient that receives treatment.

Year The year that the patient receives treatment.

To generate report based on year.

Month The month that the patient receives treatment.

To generate report based on month.

Day The day that the patient receives treatment.

To generate report based on day.

Doctor Dimensional Table Table 7: Doctor Dimensional Model Definition

Attribute Description

Doctor_id Unique identification for each doctor.

Doctor_name Name of doctor.

Doctor_gender To assign doctor for each patients treatment. (a special case which is requested by patient)

Doctor_department To know the department of each doctor.

Doctor_specialisation To know the specialization of each doctor.

To assign a correct doctor to a correct patient.

Laboratory Technologist Dimensional Table Table 8: Laboratory Technologist Dimensional Model Definition

Attribute Description

technologist_id Unique identification for each technologist.

technologist_name Name of the technologist that attend to patient.

technologist_gender To assign technologist for each patients diagnosis. technologist_specialisation To know the specialization of each technologist.

To assign the right technologist to the right patient.

Nationality Dimensional Table Table 9: Nationality Dimensional Model Definition

Attribute Description

Nationality_id Unique identification for each nationality

Nationality_name Name of country of origin of the patient

College Dimensional Table Table 10: College Dimensional Model Definition

Attribute Description

College_id Unique identification for each college

College_name Name of college of the patient

Drug Dimensional Table Table 11: Drug Dimensional Model Definition

Attribute Description

Drug_id Unique identification for drug given to the patient

Drug_name Name of the recommended drug

Drug_expiringdate The period the drug will be expired

14.3 Requirements for PKUBI An interview was granted the chief medical officer, Dr. Sanuri and Madam Ashia of PKU University Utara Malaysia about their expectation on the requirements of the PKUBI. These requirements are the measures of the PKUBI for making a managerial decision on the important records in the data warehouse repository of PKU. The following are the requirements gathered from the management of the PKU:

Table 12: Requirements Detail for PKUBI

No Analysis Pass Fail

1 Which of the drug consume most by the patient? 1 0

2 Which of the drug is expiring in the next three months? 1 0

3 Which set of patients patronize PKU most? 1 0

4 What is the department of the most patronized patient in PKU? 1 0

5 What is the nationality of the most patronized patient in PKU? 1 0

6 What is the college of the most patronized patient in PKU? 1 0

7 How many patients have skin disease in a period of time? 1 0

8 Which of the diseases is most common among the patients in PKU? 1 0

14.4 PKUBI Star Schema A Star schema has one fact table and several dimension tables based on the PKUBI requirements.

Fig. 6: A PKU Star Schema

15. RESULTS

Fig. 7: Distribution of Patients According To the Nationality

The Figure 7 above shows the distribution of patients that have visited the PKU according to their nationality from 2006 to 2010. It shows that Nigerians visited the PKU 32, 20, 24, 43 and 60 times from the year 2006, 2007, 2008 and 2010 respectively, while Malaysian visited PKU in 68, 46, 56, 65, and 80 times from 2006 to 2010. Also, Thai students visited PKU for treatment in 56, 23, 56, 76 and 87 times in the year 2006, 2007, 2008, 2009 and 2010 respectively.

Fig. 8: The Cube for the Patients According To the Nationality in PKU

The Figure 8 shows how the important data like patients and nationality distribution are filled in the cube. This is generated from the reporting tool of the SQL server 2008 used for this research.

Drug According to the Nationality

Fig. 9: Distribution of Use of Drug According To the Nationality

Fig. 10: The Cube for the Use of Drug According To the Nationality in PKU

communicable disease Non communicable disease

Fig. 11: Distribution of College According To the Diseases

Fig. 12: the Cube for the college according To the Diseases in PKU

16. THE PKUBI DEPLOYMENT The PKUBI System has been successfully implemented. All the functional requirements described before have been fully achieved. The prototype initially developed for testing has been fully converted to a working system. Front Page 2003 is used as the Integrated Development Environment (IDE) and the back end database was developed using Microsoft SQL Server 2008. The Figures 13-17 (screenshots) show a sample of user interface.

Fig. 13: Login page of PKUBI

Fig. 14: Login page of PKUBI

The Figures 13 and 14 display the login page of the PKUBI. This page allows the officer and the administrator in PKU to access some functions of the PKUBI. Once the login button has been clicked, the user will move to the home page of the system.

Fig. 15: Home page of the PKUBI

The home page of the PKUBI contains the pages that the users can have access to; view page, report page and update page.

Fig. 16: Viewing page of the PKUBI

This page allows the administrator and the officer (authorized) to view the record or profile of the patients through the PKUBI system.

Fig. 17: Update page of the PKUBI

Figure 17 shows the update page which allows the user (database officer) in PKU to update records of the patients in order for the system to be effective.

17. RECOMMENDATION The importance of the PKUBI in decision making cannot be overemphasized in achieving effective healthcare set-up. Therefore, this calls for immediate recommendation of this research in PKU. This research has helped in the style of service delivery to the patients in PKU and helps in forecasting and drilling of the drugs that need to be ordered for in large quantity for pharmacy department at PKU.

18. CONCLUSION The design of BI system for PKU in UUM helps the management by simplifying the technique needed for managerial decision making and forecasting future activities that would help the PKU. The PKUBI will also be useful to know the medical statistics of the patients in UUM and the drugs that need to be frequently ordered for. Moreover, this research has helped to determine the diseases that require a crucial attention among the patients at PKU in UUM.

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