Annals of Tropical Medicine and Public Health

: 2017  |  Volume : 10  |  Issue : 4  |  Page : 816--820

A general approach to extract the business intelligence requirements of bio-surveillance systems

Taha Samad-Soltani1, Marjan GhaziSaeedi1, Hossein Masoumi-Asl2, Peyman Rezaei-Hachesu3, Kayvan Mirnia4, Reza Safdari1,  
1 Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
2 Center for Communicable Diseases Control, Ministry of Health and Medical Education, Tehran, Islamic Republic of Iran
3 Department of Health Information Technology, School of Health Management and Informatics, Tabriz University of Medical Sciences, Tabriz, Islamic Republic of Iran
4 Department of Neonatology, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Islamic Republic of Iran

Correspondence Address:
Reza Safdari
Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran
Islamic Republic of Iran


Introduction: A successful surveillance system must consist of business intelligence (BI) modules that refer to applications and technologies used to gather, access, and analyze data. The objective of the current study was to develop a general checklist using a structured method for gathering BI requirements for an antimicrobial resistance surveillance system (AMRSS). Methods: First, a review was conducted to extract BI specific requirements. Final checklist was completed by ten context experts, using a decision Delphi method for two rounds. Items with <50% agreement were excluded in the first round, and those with more than 75% agreement were included in the first round. Those within the range of 50%–75% were surveyed in the next round. Results: The total number of items was 133. After applying the Delphi method in two rounds, the number of items was reduced to 94. The 94 items were divided into 10 classes of items as follows: goals and vision, data inquiries, data manipulation, data analysis, reporting, graphics, data security, documents, automation, and portability and accessibility. Conclusion: Although our specific problem was the design of an AMRSS, the outcome of this study was a general tool that can be used to capture and gather BI requirements in other fields.

How to cite this article:
Samad-Soltani T, GhaziSaeedi M, Masoumi-Asl H, Rezaei-Hachesu P, Mirnia K, Safdari R. A general approach to extract the business intelligence requirements of bio-surveillance systems.Ann Trop Med Public Health 2017;10:816-820

How to cite this URL:
Samad-Soltani T, GhaziSaeedi M, Masoumi-Asl H, Rezaei-Hachesu P, Mirnia K, Safdari R. A general approach to extract the business intelligence requirements of bio-surveillance systems. Ann Trop Med Public Health [serial online] 2017 [cited 2019 Oct 17 ];10:816-820
Available from:

Full Text


Antimicrobial resistance (AMR) is an important public health challenge across the world that could render all the advantages of antimicrobial agents ineffectual in decreasing morbidity and mortality from infectious diseases. Common microbial pathogens have increasingly developed resistance to widely used antibiotics. This challenge, combined with a dry antibiotic pipeline led the WHO to warn the public in a global series of alerts of a “postantibiotic era, in which common infections and minor injuries can kill.”[1],[2],[3],[4],[5],[6] In 2015, it was estimated that AMR causes about 700,000 deaths annually. If no plans are developed to combat AMR, it is forecasted that it would cause 10 million deaths annually by 2050.[7]

The success of a comprehensive AMR plan depends on the availability of accurate, reliable, and comprehensive data, logistics, and knowledge at the point of therapeutic decision-making. In addition, this information can be used to identify the origin and spread patterns of AMR.[8],[9] To obtain this information, surveillance research and methods are needed in critical situations. Surveillance data provide critical information for research in population morbidity and mortality, the spread, and causes of AMR. A demand to monitor, observe, and predict AMR has resulted in the design and implementation of surveillance systems at all geographical levels. Design and development of new surveillance tools are among the most important objectives in the US national action plan for combating AMR. So now more than ever, knowledge, discovery, and visualization methods lie at the interface of statistics, database management, machine learning, data mining, business intelligence (BI), decision support, and expert systems, which are capable of resolving the previous challenges and including new variables and methods.[10],[11],[12],[13],[14] In addition to database technology, a successful AMR surveillance system (AMRSS) must incorporate data mining, knowledge discovery, digital dashboards, decision support systems (DSSs), and geographical information systems (GISs).[15]

BI is a business management concept that refers to applications and technologies that are dedicated to gathering, accessing, and analyzing data about organizational operations. BI systems can help industries have a comprehensive knowledge of the factors affecting their functions. BI is perceived as a critical task for organizations and is increasingly discussed in requirements' analysis. BI task can contribute to the successful design and implementation of BI systems, by supporting the identification and analysis of systems' requirements, and successfully building the requirements into the system.

Some leading business companies and organizations have developed checklists and questionnaires to gather and analyze BI systems' requirements, such as M87systems, Microstrategy, SelectHub, BIPortal, informationbuilders, wunderdata, and the University of Michigan BI.[16] Several checklists were developed that capture BI-related requirements in international business organizations. However, there has been no formal study that vouches for the evaluation validity and reliability of a local checklist in Iran or that has gathered local requirements in the Persian language. The objective of the current study was developing a checklist to satisfy local needs, using a structured method for gathering the BI requirements for a bio-surveillance system such as AMRSS.


This descriptive study was performed in 2016. The data were collected from international scientifically based companies, which had formalized BI activities in requirements' analysis and a checklist or strategy to perform the requirement analysis (M87systems, Microstrategy, SelectHub, BIPortal, information builders, wunderdata, University of Michigan BI, and business objects) by searching the World Wide Web using keywords such as “BI OR BI” AND “requirement OR needs” AND “checklist OR questionnaire” across Google and Bing search engines; databases, including Google scholar, Cochrane, PubMed, and Magiran; and companies' websites. At this stage, a checklist was used to extract relevant items. Data were collected from published documents and webpages with contents related to BI requirements. All retrieved documents and webpages were studied, and BI requirements' items were extracted. Sampling was not applied in this study, and all relevant documents were examined based on the inclusion criteria.

Inclusion and exclusion criteria

The search was limited to English and Persian documents. Unavailable premium and for-fee documents were excluded as were documents before 2010 due to the rapid and recent development of BI. Review of the literature was performed until data saturation was satisfied. A checklist was used to collect the requirements' items. Next, the content of a final checklist was formed by mixing items extracted from the source documents. The requirement items from the checklist were then used to develop a questionnaire. Three columns were added to each requirement item including a description to clarify data elements, “Yes” (obligatory or optional), and “No.” At the end of each section, some blank rows were provided as placeholders for experts' opinions. The content validity of the constructed checklist was evaluated using a focus group discussion with experts in the field of medical informatics, health information management, and software engineers. A total of six persons consisting of one expert representing each stakeholder group were selected. The reliability of the checklist was assessed and completed by the six experts. After a week, a retest was performed, and the results were analyzed by SPSS 20 developed by IMB company in London. Spearman's rank correlation coefficient was used to evaluate the checklist's reliability, which presented a coefficient of 88%.

To determine the BI requirements' items for the AMRSS, using a specific system design to be applied to pediatrics' Intensive Care Unit wards, the final checklist was completed using ten AMRSS experts [demographics characteristics are described in [Table 1] through using two rounds of the Delphi decision method. Selection criteria were based on the agreement level. Items with <50% agreement were excluded in the first round, and those with more than 75% agreement were included in the first round. Those items within a range from 50% to 75% were surveyed in the next round; if an item reached at least 75% agreement in the second round, it was included in the final requirement items.{Table 1}


The AMRSS BI requirements' items were assigned to ten classes. The total number of items was 133. After applying the Delphi method in two rounds, the number of items was reduced to 94 [Table 2]. The ten classes of items were as follows: goals and vision, data inquiries, data manipulation, data analysis, reporting, graphics, data security, documents, automation, and portability and accessibility. Final inclusion items determined according to the agreement level were accessible through in English and Persian languages.{Table 2}


Iran is among the countries with a high rate of antibiotic misuse through prescriptions and general overuse of antibiotics. Self-medication remains a challenging factor in increasing AMR. Unfortunately, this factor has been increasing in Iran during recent years.[17],[18],[19] To encourage the rational use of antimicrobial agents, stewardship programs were developed. With AMR on the rise worldwide and slow development of new antimicrobial agents, antimicrobial stewardship programs (ASPs) are more critical than ever in ensuring the continued efficacy of existing antimicrobials. To support ASPs, electronic surveillance systems were designed and implemented by various countries and organizations. These systems collect, analyze, and report AMR data in an effective manner.[20] Studies mentioned using informatics and technology, such as electronic health records (EHR), clinical DSSs (CDSSs), and social media, to enable practitioners to improve antimicrobial stewardship. Clinical dashboards and CDSSs are among the core functions of a standard EHR. Extension of these core functions in public health can lead to powerful bio-surveillance systems.[21],[22] To develop a successful BI project within an organization, specifically within the health-care industry, collecting, and prioritizing BI requirements remains a critical step. Requirement analysis effects every decision throughout the implementation of BI systems.[23],[24] There are no local or standard user requirement tools available to capture an organization's BI needs. However, this current study led to a reliable and valid checklist to aid the analysis of BI system characteristics to fit core functions, such as CDSSs and dashboards, within a national AMR surveillance system. This tool identified knowledge and visualization methods based on experts' opinions. Our designed checklist was customized for a surveillance system, specifically for an AMRSS, but the same checklist can be used for all surveillance systems because it embodies a general approach, capturing BI requirements through extracting elements from reviewed resources.

Stakeholders from different contexts and organizations have a wide variety of requirements. AMRSS can encompass forecasting while policy makers perform association analysis or evaluate a predefined set of key performance indicators (KPI) required by end users.[24],[25] No single BI system can fulfill all the required items due to wide variety of requirements and novel technologies and technics. In this study, we accounted for the diversity of BI requirements based on function. For every important element, experts recommended KPIs and visualization methods; they selected the type of queries, graphs, and database elements. From these elements, a checklist was formulated.

Requirement gathering forms an essential phase in software engineering methodologies. Stakeholders and requirements' analysts work together to identify data source capabilities to provide the foundation of BI, knowledge discovery, and visualization. Selecting the correct BI tools and indicators were key steps in the fulfillment of BI systems' development.[26] In this study, a previously designed tool for BI requirement analysis satisfied knowledge discovery and visualization using analysis, reporting, data inquiry, graphs, and documents. Each class included a single aspect of BI characteristics. Nearly, all of the items from the M87systems (Ontario, Canada) BI requirements checklist were selected by our experts. Other items from various checklists comprised a small portion in our final checklist.

Various studies demonstrated that data mining, dashboards, and DSSs are significant modules of every AMRSS. To develop effective tracking and monitoring for AMRs, computational methods can be applied in the form of BI toolboxes, such as dashboards and DSSs.[19],[26] The current study defined the most important aspects of BI in the form of a requirements' checklist that identified dashboard and DSS characteristics for every system and project, particularly in AMR surveillance systems. However, there was no limited scope, and the checklist included a general approach with application to other business areas. Experts can recommend the use cases for each item in their system using the expert recommendation column. We developed this tool to capture business requirements because of the need to design an AMRSS based on formal BI characteristics. By formalizing the checklist with the participation of context experts, software engineers gathered their requirements concerning the requested system without ambiguity. Although the need for such a checklist arose from a specific problem (AMRSS), the outcome can be applied to all BI systems in healthcare industries.

Operational BI systems are gaining popularity within organizations because this system handled tactical objectives, as well as historical, strategic goals. Operational systems analyze real-time data to discover the latest changes in knowledge of a business problem. Key requirements of operational BI systems include the business context (services, stakeholders, and mapping), operational requirements, functional and user requirements, informational requirements, and knowledge requirements.[25] In the current study, the designed checklist covered operational BI requirements through a logical classification of requirements items, which consisted of functional, informational, and knowledge requirements. Reporting, analysis, graphics, data inquiry, and data manipulation item sets fulfilled the wide variety of requirements in the BI system.


This paper presented a collaborative method for developing a BI requirements' checklist that addressed the key characteristics of a comprehensive system demanded by stakeholders. Although the specific problem was the design of an AMRSS, the outcome was a general tool that captured BI requirements. The approach was based on the Delphi method with the participation of context experts. The proposed checklist gathered BI necessities covering business needs, and operational, functional, informational, and knowledge requirements. For the future research, we have suggested applying the designed checklist to develop a dashboard and DSS for the Iranian National AMRSS. Designed checklist helps stakeholders of BI systems to analysis and design novel modules such as CDSS and dashboards.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.


1Lila G, Mulliqi-Osmani G, Bajrami R, Kurti A, Azizi E, Raka L. Antimicrobial resistance profile and serotyping of Pseudomonas aeruginosa in university clinical centre of Kosovo. Acta Med 2016;32:829.
2Coculescu BI. Antimicrobial resistance induced by genetic changes. J Med Life 2009;2:114-23.
3World Health Organization. Antimicrobial Resistance: Global Report on Surveillance. Texas, America: World Health Organization; 2014.
4Alsan M, Schoemaker L, Eggleston K, Kammili N, Kolli P, Bhattacharya J. Out-of-pocket health expenditures and antimicrobial resistance in low-income and middle-income countries: An economic analysis. Lancet Infect Dis 2015;15:1203-10.
5Hoffman SJ, Outterson K, Røttingen JA, Cars O, Clift C, Rizvi Z, et al. An international legal framework to address antimicrobial resistance. Bull World Health Organ 2015;93:66.
6Felmingham D. The need for antimicrobial resistance surveillance. J Antimicrob Chemother 2002;50 Suppl S1:1-7.
7Amábile-Cuevas CF. Antimicrobial Resistance in Bacteria. Mexico City, Mexico: Horizon Bioscience; 2007.
8Critchley IA, Karlowsky JA. Optimal use of antibiotic resistance surveillance systems. Clin Microbiol Infect 2004;10:502-11.
9Stedtfeld RD, Williams MR, Fakher U, Johnson TA, Stedtfeld TM, Wang F, et al. Antimicrobial resistance dashboard application for mapping environmental occurrence and resistant pathogens. FEMS Microbiol Ecol 2016;92:202-10.
10Obenshain MK. Application of data mining techniques to healthcare data. Infect Control Hosp Epidemiol 2004;25:690-5.
11Poupard JA, Gagnon RC, Stanhope MJ. Data mining to discover emerging patterns of antimicrobic resistance. Antibiotic Policies: Theory and Practice. New York: Plenum Publishers; 2005. p. 421-46.
12Galvin S, Bergin N, Hennessy R, Hanahoe B, Murphy AW, Cormican M, et al. Exploratory spatial mapping of the occurrence of antimicrobial resistance in E. coli in the community. Antibiotics (Basel) 2013;2:328-38.
13Shebl NA, Franklin BD, Barber N. Clinical decision support systems and antibiotic use. Pharm World Sci 2007;29:342-9.
14Bălăceanu D. Components of a business intelligence software solution. Inform Econ 2007;2:42.
15Burnay C, Jureta IJ, Linden I, Faulkner S. A framework for the operationalization of monitoring in business intelligence requirements engineering. Softw Syst Model 2016;15:531-52.
16M87Systems. BI Requirments Checklist; 2015, 2016. Available from: [Last accessed on 2017 Jul 10]
17Chee T, Chan LK, Chuah MH, Tan CS, Wong SF, Yeoh W, editors. Business intelligence systems: State-of-the-art review and contemporary applications. Symposium on Progress in Information & Communication Technology 2009;12:23-27.
18Website SO. SelectHub; 2016. Available from: [Last accessed on 2017 Jul 19].
19Sarahroodi S, Arzi A. Self medication with antibiotics, is it a problem among Iranian college students in Tehran. J Biol Sci 2009;9:829-32.
20MacDougall C, Polk RE. Antimicrobial stewardship programs in health care systems. Clin Microbiol Rev 2005;18:638-56.
21Kukafka R, Ancker JS, Chan C, Chelico J, Khan S, Mortoti S, et al. Redesigning electronic health record systems to support public health. J Biomed Inform 2007;40:398-409.
22Kullar R, Goff DA. Transformation of antimicrobial stewardship programs through technology and informatics. Infect Dis Clin North Am 2014;28:291-300.
23Abai NH, Yahaya JH, Deraman A. User requirement analysis in data warehouse design: A review. Procedia Technol 2013;11:801-6.
24Dumitrescu SR, Popescu D, Purcarea VL, Albu LC. The benefits of using Sentinel WebDashboard in medicine: IT solution for monitoring and treatment of patient with liver cirrhosis. J Med Life 2014;7:205-10.
25Gangadharan GR, Swami SN, Business Intelligence Systems: Design and Implementation Strategies. Information Technology Interfaces, 26th International Conference on 2004, IEEE; 2004; p. 43-7.
26Britos P, Dieste O, García-Martínez R. Requirements elicitation in data mining for business intelligence projects. Advances in Information Systems Research, Education and Practice. Berlin, Germany: Springer; 2008. p. 139-50.