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meet the industry specific requirements. The results indicate that the framework is able to address the needs of a domain specific Business Intelligence (BI) maturity model, and guide the development of such model that proved acceptable to expert practitioners in the field. The case study instantiation within a healthcare organization helped to demonstrate the process. It used an iterative process of model development performed using Delphi method with Business Intelligence (BI) participants.

Celina and Kornelia (2012), proposed an intelligent technique for an effective computational methods and robust environment for Business Intelligence as applied in the healthcare domain.

In the paper, discussion was made showing the proposal was important as much data storage in all kinds of system used in healthcare organizations resides in proprietary silos which makes access difficult. So the use of Business Intelligence (BI) systems which is worth using is determined by its efficiency of its intelligent techniques, methodologies and tools. The authors’

also discussed the essence of Business Intelligence (BI), characteristics of the healthcare sector and its potential application of Business Intelligence technology, which is so far the only technology that is able to focus on key indicators easily and quickly in providing valuable information for the healthcare organization. In summary, the authors’ proposed a Business Intelligence (BI) healthcare system that would be responsible for collecting, providing and analyzing the most relevant data for improved and better healthcare information, given its’

huge pool of available data, thereby assisting the sector to not just be rich in data, but also in information.

Mihaela and Manole (2015), highlighted the advantages of big data analytics and Business Intelligence in the healthcare industry, by reviewing the Real-Time Healthcare Analytics Solutions for Preventative Medicine that was provided by SAP and the paper further reviewed the different ideas realized by possible customers for new applications in Healthcare industry in order to demonstrate that healthcare system can and should benefit from the new opportunities provided by information technology (IT) in general and big data analytics in particular. It further states that the future of healthcare industry is under construction, but it is so clear the design of the healthcare information technology (IT) platform of tomorrow means imagining not only how data is used but also how healthcare is delivered. The authors’

recommends that business intelligence (BI) and big data analytics can significantly assist healthcare research and ultimately improve the quality of life for patients from any domain.

Hence, it is time for change in the health sector domain, as the use of analytics will enable

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putting the right data at the fingertips of the people with the potential to generate life saving or life style improving insights.

Tobias and Vivian (2008) suggested in their paper the use of Business Intelligence (BI) as possible solution to the challenge of the healthcare sector decision makers that are facing a growing demand for both clinical and administrative information in order to comply with legal and customer-specific requirements. The aim was to contribute to the translating and amending the current findings that Business Intelligence (BI) is primarily focused on the industrial sector, to bringing the findings to be for the health care context. To achieve this, different definitions of Business Intelligence (BI) were examined and condensed in a framework. From the research a pragmatic conclusion therefore is for effective support for evidence-based practice, data management and to understand the correlations among them. This would lead to integration of information, organizations and measurement of outputs in real-time. Hence in the health sector, managers and users need real-time information to better manage data as well as to generate information and knowledge needed to improve health services quality and diminish risks.

Health specific analytical capabilities however, have been built until recently into other core operational applications as well as embedded in medical equipment and devices. But seldom have they been successfully put forth as stand-alone intelligence applications. Such as with significant intelligence built into CPOE (computerized provider order entry) systems, CDS (clinical decision support) applications, telemedicine devices (example, remote vitals sensing appliances) and handheld computing tablets seen everywhere in hospitals, clinics and health care centres. These technologies central function is not analysis; they only employ analysis to make them more valuable.

Guangzhi, et al., (2014) developed a general curriculum framework and exemplar implementation strategies to demonstrate how Business Intelligence can be incorporated into a healthcare information technology (HIT) or health informatics (HI) program. The challenge that led to the framework development was the fact that Business Intelligence (BI) and healthcare analytics are emerging technologies that provide analytical capability to help healthcare industry improve service quality, reduce cost and manage risks, but these component on analytical healthcare data processing is largely missed from current healthcare information technology (HIT) or health informatics (HI) curricula. In conclusion, an educational framework for delivering business intelligence (BI) content in HIT curriculum and programs was developed, which can be used as a model for general HIT curriculum development and improvement.

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Ishola and Azizah (2012), focused on the fact that medical screening being underwent by new students, staffs and returning students, that the result of the medical screening in terms of medical test from laboratory technologists and doctors such as patient diagnosis, treatment and medical prescription are currently kept in the health centre data repository (registry) for record purposes, but it is not explored further for managerial activities. The paper proposed and developed a Business Intelligence (BI) method for the exploration of the university health centre database repository to be applied. The method was a data warehouse which was built for the activities in the university health centre and a prototype was developed at the end of the research, while the system was evaluated by prospective users of the system. The developed Business Intelligence (BI) tagged PKUBI helped the university health centre management by simplifying the technique needed for managerial decision making and forecasting of future activities that would help the centre as well as it is useful to know the medical statistics of the patients in the university community and the drugs that need to be frequently ordered for.

Loewen (2017), the author’s research study proposed a “Business Intelligence Benefits Model for Health” derived from frameworks used in other sectors and establishes health sector measures for two foundational constructs; Business Intelligence (BI) Assimilation and Health system organizational performance. This was achieved through an online Delphi consensus process involving 25 Canadian health leadership panellists from four provinces; the study establishes a total of 30 concept measures for the constructs. The model validated the need for sector specific measures. Its contribution to knowledge has to do with establishing that these Business Intelligence (BI) constructs for healthcare is a precursor to measuring Business Intelligence (BI) success and informs priorities and approaches for Business Intelligence (BI) implementation as well as further instrument development.

Wilfred (2013); this paper explored the importance of integrating the Business Intelligence (BI) technology with electronic health record (EHR) and electronic medical record (EMR). The benefit of the research is that it has the potential to benefit healthcare providers and stakeholders in determining the applicability of Business Intelligence (BI) technology in integrated healthcare information systems. So it is very important that healthcare providers and information technology (IT) vendors become aware of the wealth of information contained in the electronic health record (EHR) and take full advantage of the BI technology to assist in the knowledge discovery process and as an investment strategy to focus on maximizing evidence-based practice. Furthermore, the paper proposed a Business Intelligence (BI) solution to the corresponding need to apply data mining technologies which is a tool of Business Intelligence

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(BI), to extract quality data and inference rules from the information stored in the electronic records so as to provide real-time decision supports and evidence-based practice to clinicians and healthcare providers. In exploring the key benefits, challenges and obstacles of incorporating the Business Intelligence (BI) technology into electronic health record (EHR), a literature review was used. With this review it is seen that Business Intelligence (BI) technology in electronic health record (EHR) would help in improving the quality and safety of healthcare delivery.

Thodoros and Daniele (2015), the paper focused on using Business Intelligence (BI) to deliver relevant and actionable information to the front-line staff of hospitals in order to assist them in their work. It was achieved via two story lines of the value of information and evidence in solving business problems and about the information systems methods toolbox utilized in establishing an effective BI program in an operating organization. It was found that through business modeling the information technology team converted from implementers to business problem solvers. A big enabler of delivering useful information was to fit data into a concept model that matches that of the information consumer using methods from business modeling, data semantics, data integration and data quality. In concluding the work, it was opinioned that the frameworks are broadly applicable to organizations that rely on evidence-based decision making in resource constrained environments.

Hence, the fact that Business Intelligence is aimed at turning information into action and action into improved performance, the research was geared towards ensuring that complex business process environment which is a driving force to improving Business Intelligence (BI) process of disease control procedure in the health sector which is an example of such complex process environment was achieved in real-time. Despite the existence of various health information systems, there is still a huge number of health care data to be accessed for intelligent and real-time decision making purpose, but accessibility is still a challenge to doctors and healthcare managers or administrators who need seamless access to the huge disease control registry data.

Also, the health sector has a wealth of information that is contained in its electronic health records (EHR) therefore, they should take full advantage of the research enhanced data integration process of Business Intelligence (BI) to assist them in knowledge discovery process, and so be able to use the Business Intelligence (BI) technology as an investment strategy to focus on maximizing evidence-based practice. This is where the CBR becomes handy in performing effective, predictive and prescriptive analysis in the research developed hybrid model for the enhanced Business Intelligence (BI) process.

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In conclusion, it is a fact that Business Intelligence (BI) is fast becoming inevitable in most field of endeavor especially the health sector, thereby leading to the demand for real-time, flexible, adaptable, and intelligent Business Intelligence (BI) solutions. As already enumerated by various authors’ it is pertinent that data integration is of great importance in any Business Intelligence solution, so the application and implementation of ontology-based and virtual data integration techniques, as well as the use of case-based reasoning (CBR) as agent technology in a Business Intelligence solution as the research adopted in developing an expert hybrid model for enhancing Business Intelligence process using the health sector domain to demonstrate its applicability; the model would be beneficial to the users of Business Intelligence (BI) in the health sector for an integrated disease control procedure. The reason goes way back to the already discussed benefits of ontology-based data integration technique and that of virtual data integration technique as well as agent (case-based reasoning - CBR) technology.