[IEEE 2013 International Conference on Communications and Information Technology (ICCIT) - Beirut,...

Business Intelligence Solutions in Healthcare A Case Study: Transforming OLTP system to BI Solution

Osama T. Ali, Ali Bou Nassif and Luiz Fernando Capretz

Electrical and Computer Engineering The University of Western Ontario

London, Ontario, Canada, N6A 5B9 {oali8,abounas,lcapretz}@uwo.ca

Abstract – Healthcare environment is growing to include not only the traditional information systems, but also a business intelligence platform. For executive leaders, consultants, and analysts, there is no longer a need to spend hours in design and develop of typical reports or charts, the entire solution can be completed through using Business Intelligence “BI” software. This paper discusses current state-of-the-art B.I components (tools) and outlines hospitals advances in their businesses by using B.I solutions through focusing on inter-relationship of business needs and the IT technologies. We also present a case study that illustrates of transforming a traditional online transactional processing (OLTP) system towards building an online analytical processing (OLAP) solution. Keywords- business intelligence; healthcare informatics; data warehouse; data mining; data analytics; decision support;

I. INTRODUCTION Health Informatics is one of the rapidly growing fields that

focus on applying “Computer Science” and “Information Technology” to medical and health data.

Every day hospitals are collecting huge volumes of data, and the challenge they now have is how to make the raw data inside the relational databases meaningful information for executive leaders in the organization to make effective decisions based on what is going on now and to predict what will happen in the near future.

The implementation of BI solutions within healthcare settings is still one of the controversial issues among healthcare providers, analysts and executive managements’ levels, which executive leaders like to see high level of aggregated data to make strategic decisions. On the other side, analysts need to work with the transactional data to build daily basis operational and monitoring reports.

The hierarchy of different organizational solutions based on their complexity and strategic insight levels can be depicted in the Fig. 1.

Fig. 1: Organizational solutions according to strategy and complexity Eventually, the destination of all healthcare administration

levels is to use analysis service tools in a very efficient way

to make the right decision whether at enterprise or departmental level, and this service requires many other important components, such as clinical/operational data stores (ODS), data warehouse (DW), OLAP, query and reporting, data mining (DM), etc.

II. WHAT IS BUSINESS INTELLIGENCE (BI) BI is the one of hottest buzzword for the last 4-5 years in

the business administration and information management fields. There are a lot of definitions for BI as follows: A “BI is a strategic initiative by which organizations measure and drive the effectiveness of their competitive strategy” [1]. In order to achieve this grand goal, there is need for analysis, software, resources, technical leadership, process specialists, executive leaders and much more. Gartner [2] also defines a BI platform as a “software platform that delivers the 14 capabilities within three main categories of functionality” as follows:

1) Integration: BI infrastructure, Development tools, Metadata management, Collaboration.

2) Information Delivery: Reporting, Ad-hoc query, Dashboards, Data integration, Search-based BI, Mobile BI.

3) Analysis: Online analytical processing (OLAP), Interactive visualization, Data mining and Predictive modelling, Scorecards (Key Performance Indicators -KPIs, Performance Management Methodology) [2].

III. BUSINESS INTELLIGENCE ARCHITECTURE

Fig. 2 represents the essence of BI with the proper workflow of the interdependent components as follows:

A. The Multidimensional Data Warehouse

Data Warehouse (DW) is the core of any solid BI solution. Data Warehouse can be defined as a “repository for keeping data in a subject oriented, integrated, time variant and non-volatile manner that facilitates decision support” [3][4]. Basically it is a big database containing all the data needed for performance management, decision making, and prediction. The multi-dimensional modeling techniques is using facts and dimensions within relational or multi-dimensional databases and it is typically used for the design of corporate data warehouses and departmental data marts.

Such a model can adopt one of the data mart’s building schema whether a star, snowflake, or fact constellation schema [5]. The task of designing and construction of a Data warehouse is very complex, it involves many technical issues related to a number of fields and subfields [6].

Strategic Insight

High

Low

H.W

& S.W

C

omplexity

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ICCIT-2013: Special Session-Computational Intelligence Applications in Software Engineering (CIASE), BeirutICCIT-2013: Special Session-Computational Intelligence Applications in Software Engineering (CIASE), Beirut

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Fig. 2: The main components of BI architecture [7]

A Clinical Data Warehouse (CDW), as defined by Gray

[8], is a “Place where healthcare providers gain access to clinical data gathered in the patient care process”. Some researches consider the extent of the essential background knowledge of the architect is slightly different between DW and CDW which is the CDW is immensely complex to build, and maintain when compared to other business.

B. The source systems:

The source systems are not really considered as a part of the BI environment, but they feed the BI solution and so they are at the basis of whole architecture and should be totally understood by the developers. One of the important things in the set-up of BI environment is that to consider all the types of data that may need to be included in the analysis process. The BI architect should take in his/her consideration to include all information in databases, external data feeds from other stakeholders, e.g. through XML; or connecting to Enterprise Resource Planning (ERP) systems on application server level; or uploading Excel sheets and flat data files (always needed); and many more candidate data sources. C. ETL: Extract, transform and load:

It’s a process of pulling the required data from different data sources and populates the multi-dimensional data warehouse within BI environment, the potential connectivity issue is a keyword here. After extraction task, the data needs to be transformed. The transformation process can mean a lot of things which includes all tasks to make the data match and fit the multi-dimensional model.

Given the significant difference between entity relational and multi-dimensional models, the transformation process may become quite complex specially when it includes extra

work to clean up and harmonize the data coming from different systems, that’s why most of BI professionals describe ETL work as 70% of the IT side of a BI project.

Through working on the development of ETL component, a separate database in the data warehouse is reserved as storage space for intermediate results of the required transformations. This area is called staging area or work area. Once the transformation work is done, the prepared data can be loaded into the multi-dimensional model.

D. Operational Data Store (ODS):

Operational data store is an off-line copy of one or more production source systems which has some characteristics as follows:

1) Has Entity Relational (ER) data model. 2) Has additional functionality of storing historical

versions of the data. 3) Potentially has some or full integration between the ER

models of different applications In any case the ODS is built for supporting the operational

reporting that related to the functional scope of a specific application and it is not to support strategic decision making.

E. Online Analytical Processing (OLAP) cubes:

OLAP is analysis techniques including variety of functionalities such as aggregation, summarization, and consolidation as well as the ability to view information from different angles [5]. OLAP offers high performance in analysis and loading of the data. OLAP cubes have had very high success rates for business environments where the BI solution is used for what-if analysis, financial simulations, budgeting and target setting, etc. Fig. 3 shows the logical representation of multi-dimensional cube.

Fig. 3: A 3-D data cube representation of the data, according to the dimensions time, age groups, and location. The measure displayed is number of patients [9] F. Semantic layer for reporting:

The objective of a BI solution is to offer a tool that enables end users easily access to information for analytical purposes. Unfortunately, databases are often not user-friendly, therefore most vendors of reporting solutions started to create a layer between the database and the reporting


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which made data access easier for end users. In this layer the database fields can be translated into Objects, each object has a business definition. These objects can be easily dragged and dropped onto a report, making report creation easy.

G. BI portal:

When the amount of different reports begins to grow, the best solution is to create a single point of access to information within the organization. Usually the efforts of creating a single point of access is results into building some kind of intuitive portal solution that contains different reports with clear descriptions of the scope of each report, as well as indication who is the business owner of the report.

H. Data mining:

Data mining is “the automated process of discovering previously unknown useful patterns within structured data.” The data warehouse is a perfect environment to conduct data mining exercises along with online analytical processing OLAP in which users can slice and dice, pivot, sort, filter data to discover patterns often using visual data mining form.

Without applying data mining techniques, it is difficult to realize the potential of data collected within healthcare organization as data under analysis is massive, multi-dimensional, distributed and uncertain [10].

IV. BI APPLICATIONS FOR HEALTHCARE BI applications in healthcare can be categorized in two major sets of solutions as shown in Fig. 4 [11]:

Fig. 4: BI business and technology focused applications [11] A. Technology Solutions: It’s Data & Information Tools and Services, as follows:

1) Decision Support Systems (DSS): Support managerial

decision making, usually day-to-day tactical. 2) Executive Information Systems: Support decision making

at the senior management level which provide and consolidate metrics-based performance information.

3) Online Analytical Processing (OLAP): Support analysts with the capability of perform multi-dimensional analysis of data (i.e rollup, drill down, slice-and-dice, ‘what if’ analysis).

4) Query and Reporting Services: Provide quick and easy access to the data with predefined report design capabilities.

5) Data Mining (Predictive Model): Examines data to discover hidden facts in databases using different techniques (i.e, statistical analysis, machine learning, frequent pattern/relation finding, infer predictive and descriptive information). Examples of successful predictive models include [12][13][14].

6) Operational Data Services: Collect data from end users, organizing data, establishing solid data structures and store them in different databases, retrieve data from multiple databases.

7) Integration Services: Design and implement of process flow of data extracting, transforming, and loading to the data warehouse. B. Business Solutions: Business focused analytical applications, as follows:

1) Patient Analysis: Focuses on analysis of patients’ demographic and satisfaction processes.

2) Electronic Health Record Analysis: Focuses on analysis of the quality of clinical data (illness, diagnosis, medication, etc.)

3) Performance Analysis: Streamline and optimize the way that a business uses its resources (budget, human, equipment).

4) Fund Channel Analysis: Devise, implement, and evaluate fund strategies, then use the corporate metrics to continuously monitor and enhance the fund process

5) Productivity Analysis: Focuses on building business metrics for activities such as quality improvement, risk mitigation, asset management, capacity planning, etc.

6) Behavioural Analysis: Understanding and predicting trends and patterns that provides business advantage.

7) Supply Chain Analysis: Monitor, benchmark, and improve supply chain activities from materials ordering through service delivery.

8) Wait Time Analysis: Focuses on the factors that are associated with longer waiting times and the effects of delays in scheduling and operation. There is a big competition among BI vendors based on many factors, such as performance, customization, risk tolerance, and business improvement.

V. CASE STUDY: TRANSFORMING OLTP RELATIONAL DB TO B.I SOLUTION

A. Corporate Project Purpose London Health Sciences Centre (LHSC) continues to have

significant challenges related to infection rates, reporting rates well above the provincial average and often showing LHSC as one of the poorest performing teaching centres in Ontario. The corresponding costs to the organization (time, energy, supplies and equipment) are extreme. Reducing infections has become a strategic priority for LHSC.

Patients (Demographic and Satisfaction) Analysis

Decision Support Systems Electronic Health Record

Analysis Executive Information SystemsPerformance Analysis

(Staffing & Scheduling, Case Costing) Online Analytical Processing (OLAP) Fund Channel Analysis

Query & Reporting Services Productivity Analysis

Data Mining (Predictive Model)Behavioural Analysis

Operational Data Services Supply Chain Analysis

Integration Services (ETL) Wait Time Analysis

Business Solutions Technology Solutions


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The infection safety solution will consist of multiple projects and strategies to improve infection rates of antibiotic resistant organisms (AROs) across LHSC. Having a solid framework for collecting, managing and reporting data and information will be a key success factor in these projects and overall solution.

The intent of this project is to define and recommend an appropriate data management and reporting structure to support the Infection Safety corporate strategy. This will include (but may not yet be inclusive) the following deliverables:

1) Hand Hygiene Audit Technology 2) Corporate Data Warehouse with Data Integration and

Analysis Tools 3) Infection Prevention and Control (IPaC) Reporting Tool

/ Dashboard

B. Project Scope: This project will cover the approaches of analysis and

implementation of B.I solution for traditional OLTP relational database in order to expand the classical use of data from different perspectives through showing how SQL Server 2008 Integration and Analysis Services solves many business problems in a simple and cost-effective manner via providing a simple, integrated view of data, data mart and specialized application consolidation, intelligent views of data, and real-time business intelligence data.

The most challenging part of this project was the analysis and design of data warehouse model by using the Star schema as well as building the cube and KPI. C. Project Outcomes:

The expected outcomes from the project include the following:

1) All Data Management and Reporting • Understanding of our current state regarding how data is

collected, analysed, and disseminated including what infrastructure, tools and support exist.

• Defining standards and processes regarding future collection, analysis and reporting of data related to infection safety.

• Recommending infrastructure, appropriate tools and technology required to support quality data management and reporting.

2) Hand Hygiene System • Clear definition of roles and responsibilities regarding the

auditing and data entry process. • To have a short-term interim solution that will allow us to

deliver monthly audits as soon as possible until a long-term solution has been developed and implemented.

• Inform requirements and support ITS as they consider an appropriate long term solution for the auditing.

3) Data Warehouse Building

• Creating a standard data dictionary for all heterogeneous information

• Creating a solid data infrastructure which is made up by a collection of heterogeneous systems

• Automating the process of extraction, transformation, and loading in order to integrate all the different data sources in central repository.

• Building multi-dimensional cubes to provide with all the required information to different level of users (managers, specialists, consultants, analysts, etc.).

4) Infection Prevention and Control (IPaC) Reporting Tool/Dashboard • Review existing Internal and External reporting

processes, tools and reports for ARO and Safer healthcare Now (SHN) indicators and evaluate effectiveness and efficiency of data analysis and review.

• Obtain feedback from IPC and identify internal departmental reporting needs.

• Solicit clinical director and manager level feedback re current ARO reports, formats, delivery.

• Consider dashboard/ business intelligence approach; benchmark with other healthcare organizations regarding software tools, review potential vendors in collaboration with ITS and make recommendations

D. Technology used for design and implementation: • For Data Mart Building: Microsoft SQL Server 2008 R2

Management Studio • For Data Transformation (ETL): SSIS packages

(Business Intelligence Development Studio with Visual Studio 2008

• For Reporting: SSRS or MS Excel 2010

E. Traditional System’s Diagram: Fig. 5 illustrates the traditional solution architecture and

workflow.

Fig. 5: Diagram of data systems and infrastructure of the existing traditional

systems [15]


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F. Project Methodology: In this project as a mid-sized solution, the BI developer

was applying the following development life cycle phases: 1) Design the data warehouse first through using SQL

Server Management Studio. In particular, design the tables (dimensions and fact) that are needed as part of the DW, ignoring any staging tables as shown in the Fig. 6.

Fig. 6: Star schemal data mart design (dimensions and fact tables)

2) Design the ETL by using SQL Server Integration Services project which contains a lot of data integration components as shown in the Fig. 7, also sometimes the stored procedures have been used within the production databases: • Its fine if any staging tables are required as part of the

ETL, but at the same time they should get cleaned up. A staging table used only as part of a single series of ETL steps should be truncated after those steps are completed.

• SSIS packages refer to the OLTP database at least to pull data into the staging tables. Depending on the situation, they may process the OLTP tables directly into the data warehouse.

• Documenting and make it clear what inputs are used by each package, where the output goes, and the criteria by which the input are selected (i.e. last 24 hours? Since last success? New identity values? All rows?)

3) Designing Analysis Service project by using VS 2008 SSAS as follows: • Create Data Source. • Create Data Source View. • Create associated dimensions plus DateTime Dim. • Create multi-dimensional cube. • Create KPI’s (Key Performance Indicators) to display

monthly metrics.

•

Fig. 7: Building the ETL framework by using SQL server integration

services project

4) Build required reports by using SSRS or Excel 2010 pivot table.

5) Data mining component

VI. CONCLUSIONS In this paper, the general architectural approaches for BI

solution have been outlined and its major development


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components have been introduced in order to give the reader a high level picture with some important details regarding healthcare informatics.

Many healthcare organizations struggle with the lack of utilization of data collected through non-integrated OLTP system which have been used for decision making and data mining. For successful healthcare organization it is important to empower the staff and management with data warehousing based on critical thinking and knowledge management tools for strategic decision making.

Decision support tools such as data mart, OLAP and data mining techniques can support on building a solid foundation for clinical data warehouse.

A data mart is a subset of data warehouse. It focuses on selected business subjects. OLAP solutions provide a multi-dimensional view of the data found in relational databases. Storing and presenting data in three dimensional format as a OLAP cube makes it possible to analyze potentially large amount of data with very fast response times and provides the ability for users to go through the data and drill down, roll up, or slice-and-dice through various dimensions as defined by the data structure.

The primary motivations of the case study in this paper have been to expand the horizon of a software engineer to mix the business acumen with the technical expertise in order to maximize the strategic thinking through moving from classical information systems towards enterprise BI solutions.

VII. FUTURE WORK

Within big healthcare organizations there is vast potential for implementation of BI solutions (Clinical Data Warehouse, Information Visualization, and Data Mining Applications), such as, operational performance, quality and risk management, case costing, patient safety factors, wait time management, staffing and scheduling, evaluation of effective treatment and best practices, predictive medicine, image and pattern recognition, etc.

Through data mining and predictive analytics, historical data can reveal patterns that are used to predict trends. Historical data analysis and predictive analytics, together with expert knowledge will effectively assist in the diagnosis and treatments of numerous diseases.

REFERENCES

[1] Business intelligence – an endless story. MAIA Intelligence [Online]. pp. 1-14. 2011. Available: http://www.maia-intelligence.com/pdf/BI-An-endless-story-wp.pdf. [2] J. Hagerty, R. L. Sallam and J. Richardson. Magic quadrant for business intelligence platforms. Gartner [Online]. 2012. Available: http://www.microstrategy.com/download/files/whitepapers/open/gartner-magic-quadrant-for-bi-platforms-2012.pdf. [3] W. H. Inmon. What is a data warehouse? Prism Solutions, Inc [Online]. 1995. Available: www.cait.wustl.edu/cait/papers/prism/vol1_no1/. [4] Y. Naddaf. Data mining in health informatics. [Online]. Available: http://yavar.naddaf.name/downloads/Data%20Mining%20in%20Health%20Informatics.pdf. [5] J. Han and M. Kamber, Data Mining: Concepts and Techniques. Kaufmann, 2006. [6] A. Sen and A. P. Sinha, "A Comparison of Data Warehousing Methodologies," Communications of the ACM, vol. 48, pp. 79-84, 2005.

[7] R. Guro. Components of business intelligence. The Business Intelligence Guy [Online]. 2011. Available: http://www.the-business-intelligence-guy.com/components-of-business-intelligence-bi/. [8] G. W. Gray, "Challenges of Building Clinical Data Analysis Solutions," Journal of Critical Care, vol. 19, pp. 264-270, 2004. [9] Information technology for students and professionals. United States Information Source [Online]. Available: http://www.info-source.us/. [10] H. Kaur and S. K. Wasan, "Empirical Study on Applications of Data Mining Techniques in Healthcare," Journal of Computer Science, vol. 2, pp. 194-200, 2006. [11] M. Peco, "TDWI Business Intelligence Fundamentals: From Data Warehousing to Business Impact," The Data Warehouse Institute, 2011. [12] A. B. Nassif, D. Ho and L. F. Capretz, "Towards an Early Software Estimation Using Log-linear Regression and a Multilayer Perceptron Model," Journal of Systems and Software, vol. 86, pp. 144-160, 1, 2013. [13] A. B. Nassif, L. F. Capretz and D. Ho, "Estimating software effort based on use case point model using sugeno fuzzy inference system," in 23rd IEEE International Conference on Tools with Artificial Intelligence, Florida, USA, 2011, pp. 393-398. [14] A. B. Nassif, L. F. Capretz and D. Ho, "Software effort estimation in the early stages of the software life cycle using a cascade correlation neural network model," in 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel & Distributed Computing (SNPD), Kyoto, Japan, 2012, pp. 589-594. [15] A project charter, "Data Management System for Infection Prevention and Control," IPAC Project Team, London Health Sciences Centre, 2011.


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