نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی دکتری رشته مدیریت فناوری اطلاعات، دانشگاه آزاد اسلامی، قم، ایران
2 دانشیار پژوهشکده فناوری اطلاعات، پژوهشگاه علوم و فناوری اطلاعات ایران (ایرانداک)، تهران، ایران
3 استادیار پژوهشکده فناوری اطلاعات، پژوهشگاه علوم و فناوری اطلاعات ایران (ایرانداک)، تهران، ایران
چکیده
بلوغ هوشمندی کسب‌وکار که هدف اصلی این پژوهش است، نقش مهمی در تصمیم‌گیری، برنامه‌ریزی، کنترل و نظارت هوشمند در حوزه مراقبت‌های سلامت دارد. بصورت کلی، به منظور شناسایی عوامل موثر از روش دلفی و تجمیع نظرات خبرگان استفاده گردید و به منظور تاثیرگذاری و تاثیرپذیری شاخص‌ها و در نهایت اولویت‌بندی آن‌ها از ترکیب روش دیمتل و فرآیند تحلیل شبکه بهره گرفته شد. نمونه آماری شامل 20 نفر از خبرگان هدفمند دانشگاهی و کارشناسان حوزه مراقب‌های سلامت هستند. گردآوری داده‌ها در هر دو بخش از طریق پرسشنامه صورت پذیرفت. طبق نتایج بخش دلفی، 26 شاخص اصلی نهایی شده در تحقیق، شناسایی شد که در سه دسته اصلی شامل معیارهای سازمانی، فرآیندی و فناوری تقسیم بندی می‌شوند. طبق نتایج بخش دیمتل و فرآیند تحلیل شبکه به‌ترتیب معیارهای زیرساخت فنی انعطاف‌پذیر و قابل توسعه (سخت‌افزاری و نرم‌افزاری)، کیفیت داده و سیستم و تعریف درست از مشکلات و فرایندهای هوشمندی کسب‌وکار به‌عنوان سه معیار با رتبه برتر در بلوغ هوشمندی کسب‌وکار اولویت‌دهی شدند. مدل بلوغ هوشمندی کسب‌وکار پیشنهادی پژوهش، می‌تواند نقشه راهی برای پیاده‌سازی موفق بلوغ هوشمندی کسب‌وکار در حوزه مراقبت‌های سلامت باشد.
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
A Business Intelligence Maturity Model in Healthcare Based on the Combination of Delphi and DEMATEL-ANP Methods
نویسندگان [English]
- Mahnaz Saeedi Mamaghani 1
- Mohammad Javad Ershadi 2
- Arman Sajedinejad 3
1 the Student of Ph.D., Information Technology Management, Faculty of Engineering, Islamic Azad University, Qom, Iran. C
2 Associate Professor, Department of Information Technology, Iranian Research Institute for Information Science and Technology (IranDoc), Tehran, Iran.
3 Iranian Research Institute for Information Science and Technology (IranDoc)
چکیده [English]
The maturity of business intelligence, which is the main goal of this research, plays an important role in intelligent decision-making, planning, control and monitoring in the field of health care. In order to identify the effective factors, the Delphi method was used and experts' opinions were, and in order to determine the effectiveness and effectiveness of the indicators and finally to prioritize them, used the DANP method. The statistical sample includes 20 targeted academic experts and health care experts. According to the results of the Delphi section, 26 main indicators finalized in the research were identified, which are divided into three main categories including organizational, process and judgment criteria. According to the results of the DANP process, flexible and expandable technical infrastructure criteria, data and system quality and the correct definition of business intelligence problems and processes were prioritized as the three criteria with the highest ranking in the maturity of business intelligence. The business intelligence maturity model proposed by the research can be a road map for the successful implementation of business intelligence in the field of health care.
Introduction
Business intelligence is one of the most important issues in recent decades as a decision-making system for managers of organizations in order to plan, control and intelligently monitor companies and their subordinate units and measure the achievement of organizational goals. Business intelligence includes a comprehensive set of tools, technologies and products designed to collect, aggregate, analyze and present usable data (Reinschmidt J. & Francoise A., 2000) Introduction of new and complex medical technologies, the global trend of increasing length Longevity, the unexpected development of chronic diseases and emerging diseases (such as Covid-19) can lead to an increase in health care costs to unsustainable levels (Janssen & Moors, 2013; Qaseem et al., 2012). Public or private medical care organizations have focused their efforts on achieving new, cost-effective and efficient levels of care (Romanow et al., 2012). For this purpose, information technologies play a fundamental role by transforming data into knowledge that can improve patient care, medical care facilities, and process management (Behkami & U. Daim, 2012; Li & Mao, 2015; Pai & Huang, 2011). Considering the very important role of data in supporting the improvement of the organizational level, business intelligence is one of the important areas of research for researchers and activists in the treated field (Chen et al., 2012).
The field of business intelligence has improved significantly over the past decade and has promising applications in the health field (El-Gayar & Timsina, 2014; Gandomi & Haider, 2015). Indeed, business intelligence can not only improve outcomes in healthcare organizations but also help them achieve continuous improvement and precision in medicine (Christensen et al., 2008; Gastaldi et al., 2015; Tremblay et al., 2012).
Investigating the process of information production and transmission in the field of healthcare is of great importance. Today, organizations active in this field need correct information at the right time, in order to make the best decision by the right person. But many of the systems used by users do not have appropriate and expected performance, and health care organizations need to act smarter, but despite the potential, business intelligence has not been widespread in the field of medical care (Hanson, 2011) and research There are limited studies on how to successfully implement a sample business intelligence solution in the field of medical care (Foshay & Kuziemsky, 2014). This research tries to fill this gap by developing a model that provides maturity levels for evaluating and improving business intelligence solutions in healthcare. Therefore, considering the explanations and issues raised in the field of business intelligence, the present research seeks to answer a main question, what is the maturity model of business intelligence in the field of health care providers? In this regard, two sub-questions are also raised, which are: What are the indicators affecting the maturity model of business intelligence in the field of health care providers, and what is the prioritization of these factors?
Literature Review
According to the studies of Foshay and Kuziemsky, healthcare organizations are under constant pressure to not only achieve more results with fewer resources, but also to gradually transform into information-based systems (Foshay & Kuziemsky, 2014). Considering that the amount of information recorded by electronic health records and medical record centers is growing rapidly, healthcare organizations are trying to use tools such as business intelligence to improve the efficiency and effectiveness of their operations (Kuiler, 2014; Wang et al., 2018).
According to the research conducted by Naqash, business intelligence solutions help decision-makers by providing practical information in the right format, at the right time and in the right place (Negash, 2004). The business intelligence market has grown significantly and has become the first investment priority for CIOs (Gartner, 2015). Also, the awareness of the potential benefits of business intelligence is increasing (Chuah & Wong, 2011), however, the implementation of business intelligence in health and treatment organizations is progressing relatively slowly and in a case-by-case manner (Foshay & Kuziemsky, 2014).
Some studies show the benefits of business intelligence to improve patient care, treatment outcomes, effective use of human resources, lower costs (Borzekowski, 2009), higher revenue (Ayal & Seidman, 2009) and improved productivity (Lucas et al., 2010). have reported As reported in other studies, the successful implementation of business intelligence in healthcare depends on understanding and analyzing the characteristics of this field (Avison & Young, 2007; Mettler & Vimarlund, 2009). Therefore, one of the most important goals of this research is to provide a maturity model for the continuous development and improvement of business intelligence solutions to healthcare professionals. In Table (1), some business intelligence maturity models in the field of health care that have been implemented in the past are reviewed.
Table 1. Some business intelligence maturity models in healthcare
Research results
Research researchers
A framework for defining and prioritizing decision support information needs in the context of specific health care processes is presented.
Foshay & Kuziemsky (2014)
In this study, the subject of comprehensive business intelligence in special care and understanding the basic concepts of business intelligence solutions with comprehensive features have been discussed.
Pereira et al. (2016)
A way to identify the capabilities and weaknesses of the intelligent information system in the hospital has been presented.
Carvalho et al. (2018)
The methodology of implementing the model of hospital information systems is presented.
Carvalho, Rocha, & Abreu (2019)
This article identifies a wide range of maturity models in the health sector and its characteristics and strengthens the belief that the maturity of the hospital information system can contribute to the quality of information and knowledge management in this field.
Gomes & Romão (2018)
The result of this research is the maturity model of the hospital information system based on 6 stages of maturity. The hospital information system maturity model has the feature of collecting a set of key and effective factors of maturity and related characteristics and not only enables the evaluation of the overall maturity of a hospital information system, but also the individual maturity of its different dimensions.
Carvalho, Rocha, Vasconcelos, et al. (2019)
The purpose of this research is to determine how the existing business continuity maturity models conform to the ISO 22301 standard and to map the existing health care model with the business continuity maturity model.
Haidzir et al. (2018)
In this research, while determining organizational maturity levels, effective factors in improving maturity have been identified and prioritized, and a road map for applying business intelligence in this field has been presented.
(Gastaldi et al. (2018)
The result of this research is to present a maturity model including six stages of the growth and maturity sequence of the hospital information system.
Carvalho, Rocha, Vasconcelos, et al. (2019)
The importance of scientific research on business intelligence with a focus on patients has been investigated.
Zheng et al. (2018)
In this research, by providing a maturity assessment framework and infrastructure development based on results, information and digital transformation in health care has been encouraged and guided.
Williams et al. (2019)
In this research, it has been determined that organizational business intelligence application screens at all management levels have a positive and significant effect on measurable performance indicators. In this context, when businesses monitor their operational activities through business intelligence, they have come to the conclusion that performance indicators provide less time wastage, high reliability, integrated data, quality and accurate valuation benefits in the evaluation process.
Işık et al. (2021)
The relevant factors for the adoption of business intelligence system have been established using a systematic literature review and a theoretical structure based on technology, organization, environment and determinants and theories of CEOs. This research deepens the literature of business intelligence system and promotes the understanding of the important decision-making elements of business intelligence system.
Salisu et al. (2021)
The co-creation approach will optimize the currency, accuracy and appropriateness of information in the digital health profile, understanding and use of the digital health profile and the maturity assessment tool to facilitate informed iterative discussions by Pacific Island countries on digital health maturity in order to use digital tools to strengthen use the country's health systems. Digital health profile and maturity assessment tool can rationalize the selection and use of existing tools and reduce cognitive overload.
Liaw et al. (2021)
In this research, an alternative solution with the benefits and possible costs of its implementation in the hospital has been shown, and the proposed initial evaluation method can be used in different health and treatment units after confirming the weight of the criteria based on the adopted strategy.
Wielki & Jurczyk (2019)
The results of the study enrich the recent literature of business intelligence system and improve the understanding of the decision-making processes of practitioners to obtain the maximum value from the adoption of business intelligence system.
Ahmad et al., (2020)
The findings support the argument that the organizational learning culture plays an important role in the business intelligence system and also affects the business performance.
Arefin et al., (2021)
Methodology
To implement business intelligence in the field of health care, the characteristics of this field must be understood and analyzed; This task has been carried out in three stages. First, the subject literature was analyzed with the "systematic review" method, and in addition to the field of health care, all sectors in which the maturity of business intelligence was evaluated were also considered. In the following, a series of key success factors of business intelligence and maturity components were extracted by examining more than 23 articles in the fields related to business intelligence and further, the steps of implementing the proposed method are also described.
3.1. First stage - knowledge acquisition
At this stage, previous studies in the field of business intelligence maturity model, evaluating the value of key success factors in business intelligence and identifying maturity components were reviewed. In this study, the structured search strategy method was used as data sources from Emerald, Sage, Elsevier, IEEE, Taylor & Francis, and Springer databases in the period from 2000 to the beginning of 2022. At first, this study used the following keywords and search terms, combined and separate: "business intelligence", "factors affecting business intelligence system", "business intelligence maturity", "maturity measurement" and "business intelligence system in health care". The collection of articles presented in this research was consistent with the topic of this research in terms of questions, objectives, adopted frameworks and findings. The definitions used and their alignment with the measurement adopted were evaluated, to ensure that the factors of business intelligence investigated by different researchers are largely similar. Finally, by advancing the previous steps, an initial version of the business intelligence maturity model was adopted, which is significantly different from the final business intelligence maturity model.
3.2. The second stage - identifying and categorizing the criteria
After extracting the main criteria influencing the success of business intelligence in three areas of organization, process and technology, using the Delphi decision-making technique, the key factors and important criteria of business intelligence maturity in health care organizations were determined and categorized, and finally the maturity model of business intelligence in the field of health care, it was confirmed by a survey of experts.
3.3. The third step - determining the criteria
After finalizing the dimensions and criteria of the research with the Delphi method, using pairwise comparisons and the Dimtel method based on the network analysis process method, the internal and external connections of the factors were determined and each of the factors were weighted and prioritized. In this step, a committee evaluation method was used to evaluate the validity of the questionnaire (Harkness & Schoua-Glusberg, 1998). In addition, ANP-DEMATEL combined method was used to evaluate how and how much the components affect each other. Various researchers such as (Đurek et al., 2019; Rasouli et al., 2021) have used this approach in the field of maturity model.
Results
In this research, business intelligence was investigated in three basic areas of organization, process and technology, and each of these areas has criteria. First, articles were comprehensively reviewed in the field of business intelligence maturity in order to determine the dimensions of the goal and criteria. The criteria of the designed research maturity model were finalized using the Delphi method and with the opinion of experts, and then decision-making methods with multiple criteria were used to measure the optimality. The effects of goals, dimensions, and criteria on each other were investigated with the Dimtel method, then the dimensions and criteria were weighted in terms of importance with the network analysis process method. According to the results of Dimtel, the two dimensions of technology and process are effective, and the organizational dimension is effective. The organizational field has a higher relative importance than process and technology and has more interaction with other factors of the system and is affected by two dimensions of technology and process. The results of the analysis of the questionnaires of the network analysis process method answered by the experts show that the organizational factor is the most preferred and heaviest factor in the maturity of business intelligence, and then the process factor has a higher weight and the technology factor has a lower weight than the other two areas. In line with the results of William et al. (2019) and Gastaldi et al. (2018), who have encouraged and guided information and digital transformation in health care by providing a maturity assessment framework and infrastructure development based on results, respectively, two technical infrastructure criteria Flexible and expandable (hardware and software) and data and system quality were obtained from the highest importance compared to other criteria. And in the same way, the criterion of the correct definition of business intelligence problems and processes was prioritized with the third rank compared to other criteria in the maturity of business intelligence, and the rest of the criteria were also ranked in the article. Jayanthi Ranjan (2008) has also achieved this. In this way, a comprehensive and complete business intelligence maturity model was obtained in the field of health care, which can make the path of business intelligence maturity smoother in health care and be a road map for the successful implementation of business intelligence maturity in health care. It is suggested that in future researches, the proposed maturity model should be practically implemented in health care organizations and the maturity level of business intelligence should be evaluated.
Figure 1. The final research model (source: researcher's findings)
Organizational field
• Cooperation between the employees of the organization and the information technology department
• Alignment of business strategies with business intelligence strategies
• Senior management support for the business intelligence project
• Clear goals and vision for business intelligence
• Development of business intelligence strategy
• The ability of the organization to provide sufficient resources and funds needed for business intelligence projects
• Risk-taking of senior managers in investing in new information technologies
• Capabilities of the team/employees/managers
• Monitor information through the Business Intelligence Assessment Center
• Continuous improvement of organizational processes (improvement of competence)
Technology field
· Flexible and expandable technical infrastructure (hardware and software)
· Data and system quality
· Appropriate technology/tools or the use of appropriate technology and tools for hospital conditions
· Business intelligence system architecture
· Integration of business intelligence systems with other systems
· Quality of data analysis
· Connector
Business intelligence in the field of health care
Process area
• Correct definition of business intelligence problems and processes
• Using patterns and repeatable methods in designing business intelligence projects
• Aligning business intelligence solutions with user expectations
• User training and support
• Effective change management
• Balanced and strong composition of the business intelligence project group
• Project planning and management in the implementation of business intelligence
• Measuring business intelligence
• Decision making
Keywords: Business Intelligence, Healthcare, DEMATEL, ANP.
The maturity of business intelligence, which is the main goal of this research, plays an important role in intelligent decision-making, planning, control and monitoring in the field of health care. In order to identify the effective factors, the Delphi method was used and experts' opinions were, and in order to determine the effectiveness and effectiveness of the indicators and finally to prioritize them, used the DANP method. The statistical sample includes 20 targeted academic experts and health care experts. According to the results of the Delphi section, 26 main indicators finalized in the research were identified, which are divided into three main categories including organizational, process and judgment criteria. According to the results of the DANP process, flexible and expandable technical infrastructure criteria, data and system quality and the correct definition of business intelligence problems and processes were prioritized as the three criteria with the highest ranking in the maturity of business intelligence. The business intelligence maturity model proposed by the research can be a road map for the successful implementation of business intelligence in the field of health care.
Introduction
Business intelligence is one of the most important issues in recent decades as a decision-making system for managers of organizations in order to plan, control and intelligently monitor companies and their subordinate units and measure the achievement of organizational goals. Business intelligence includes a comprehensive set of tools, technologies and products designed to collect, aggregate, analyze and present usable data (Reinschmidt J. & Francoise A., 2000) Introduction of new and complex medical technologies, the global trend of increasing length Longevity, the unexpected development of chronic diseases and emerging diseases (such as Covid-19) can lead to an increase in health care costs to unsustainable levels (Janssen & Moors, 2013; Qaseem et al., 2012). Public or private medical care organizations have focused their efforts on achieving new, cost-effective and efficient levels of care (Romanow et al., 2012). For this purpose, information technologies play a fundamental role by transforming data into knowledge that can improve patient care, medical care facilities, and process management (Behkami & U. Daim, 2012; Li & Mao, 2015; Pai & Huang, 2011). Considering the very important role of data in supporting the improvement of the organizational level, business intelligence is one of the important areas of research for researchers and activists in the treated field (Chen et al., 2012).
The field of business intelligence has improved significantly over the past decade and has promising applications in the health field (El-Gayar & Timsina, 2014; Gandomi & Haider, 2015). Indeed, business intelligence can not only improve outcomes in healthcare organizations but also help them achieve continuous improvement and precision in medicine (Christensen et al., 2008; Gastaldi et al., 2015; Tremblay et al., 2012).
Investigating the process of information production and transmission in the field of healthcare is of great importance. Today, organizations active in this field need correct information at the right time, in order to make the best decision by the right person. But many of the systems used by users do not have appropriate and expected performance, and health care organizations need to act smarter, but despite the potential, business intelligence has not been widespread in the field of medical care (Hanson, 2011) and research There are limited studies on how to successfully implement a sample business intelligence solution in the field of medical care (Foshay & Kuziemsky, 2014). This research tries to fill this gap by developing a model that provides maturity levels for evaluating and improving business intelligence solutions in healthcare. Therefore, considering the explanations and issues raised in the field of business intelligence, the present research seeks to answer a main question, what is the maturity model of business intelligence in the field of health care providers? In this regard, two sub-questions are also raised, which are: What are the indicators affecting the maturity model of business intelligence in the field of health care providers, and what is the prioritization of these factors?
Literature Review
According to the studies of Foshay and Kuziemsky, healthcare organizations are under constant pressure to not only achieve more results with fewer resources, but also to gradually transform into information-based systems (Foshay & Kuziemsky, 2014). Considering that the amount of information recorded by electronic health records and medical record centers is growing rapidly, healthcare organizations are trying to use tools such as business intelligence to improve the efficiency and effectiveness of their operations (Kuiler, 2014; Wang et al., 2018).
According to the research conducted by Naqash, business intelligence solutions help decision-makers by providing practical information in the right format, at the right time and in the right place (Negash, 2004). The business intelligence market has grown significantly and has become the first investment priority for CIOs (Gartner, 2015). Also, the awareness of the potential benefits of business intelligence is increasing (Chuah & Wong, 2011), however, the implementation of business intelligence in health and treatment organizations is progressing relatively slowly and in a case-by-case manner (Foshay & Kuziemsky, 2014).
Some studies show the benefits of business intelligence to improve patient care, treatment outcomes, effective use of human resources, lower costs (Borzekowski, 2009), higher revenue (Ayal & Seidman, 2009) and improved productivity (Lucas et al., 2010). have reported As reported in other studies, the successful implementation of business intelligence in healthcare depends on understanding and analyzing the characteristics of this field (Avison & Young, 2007; Mettler & Vimarlund, 2009). Therefore, one of the most important goals of this research is to provide a maturity model for the continuous development and improvement of business intelligence solutions to healthcare professionals. In Table (1), some business intelligence maturity models in the field of health care that have been implemented in the past are reviewed.
Table 1. Some business intelligence maturity models in healthcare
Research results
Research researchers
A framework for defining and prioritizing decision support information needs in the context of specific health care processes is presented.
Foshay & Kuziemsky (2014)
In this study, the subject of comprehensive business intelligence in special care and understanding the basic concepts of business intelligence solutions with comprehensive features have been discussed.
Pereira et al. (2016)
A way to identify the capabilities and weaknesses of the intelligent information system in the hospital has been presented.
Carvalho et al. (2018)
The methodology of implementing the model of hospital information systems is presented.
Carvalho, Rocha, & Abreu (2019)
This article identifies a wide range of maturity models in the health sector and its characteristics and strengthens the belief that the maturity of the hospital information system can contribute to the quality of information and knowledge management in this field.
Gomes & Romão (2018)
The result of this research is the maturity model of the hospital information system based on 6 stages of maturity. The hospital information system maturity model has the feature of collecting a set of key and effective factors of maturity and related characteristics and not only enables the evaluation of the overall maturity of a hospital information system, but also the individual maturity of its different dimensions.
Carvalho, Rocha, Vasconcelos, et al. (2019)
The purpose of this research is to determine how the existing business continuity maturity models conform to the ISO 22301 standard and to map the existing health care model with the business continuity maturity model.
Haidzir et al. (2018)
In this research, while determining organizational maturity levels, effective factors in improving maturity have been identified and prioritized, and a road map for applying business intelligence in this field has been presented.
(Gastaldi et al. (2018)
The result of this research is to present a maturity model including six stages of the growth and maturity sequence of the hospital information system.
Carvalho, Rocha, Vasconcelos, et al. (2019)
The importance of scientific research on business intelligence with a focus on patients has been investigated.
Zheng et al. (2018)
In this research, by providing a maturity assessment framework and infrastructure development based on results, information and digital transformation in health care has been encouraged and guided.
Williams et al. (2019)
In this research, it has been determined that organizational business intelligence application screens at all management levels have a positive and significant effect on measurable performance indicators. In this context, when businesses monitor their operational activities through business intelligence, they have come to the conclusion that performance indicators provide less time wastage, high reliability, integrated data, quality and accurate valuation benefits in the evaluation process.
Işık et al. (2021)
The relevant factors for the adoption of business intelligence system have been established using a systematic literature review and a theoretical structure based on technology, organization, environment and determinants and theories of CEOs. This research deepens the literature of business intelligence system and promotes the understanding of the important decision-making elements of business intelligence system.
Salisu et al. (2021)
The co-creation approach will optimize the currency, accuracy and appropriateness of information in the digital health profile, understanding and use of the digital health profile and the maturity assessment tool to facilitate informed iterative discussions by Pacific Island countries on digital health maturity in order to use digital tools to strengthen use the country's health systems. Digital health profile and maturity assessment tool can rationalize the selection and use of existing tools and reduce cognitive overload.
Liaw et al. (2021)
In this research, an alternative solution with the benefits and possible costs of its implementation in the hospital has been shown, and the proposed initial evaluation method can be used in different health and treatment units after confirming the weight of the criteria based on the adopted strategy.
Wielki & Jurczyk (2019)
The results of the study enrich the recent literature of business intelligence system and improve the understanding of the decision-making processes of practitioners to obtain the maximum value from the adoption of business intelligence system.
Ahmad et al., (2020)
The findings support the argument that the organizational learning culture plays an important role in the business intelligence system and also affects the business performance.
Arefin et al., (2021)
Methodology
To implement business intelligence in the field of health care, the characteristics of this field must be understood and analyzed; This task has been carried out in three stages. First, the subject literature was analyzed with the "systematic review" method, and in addition to the field of health care, all sectors in which the maturity of business intelligence was evaluated were also considered. In the following, a series of key success factors of business intelligence and maturity components were extracted by examining more than 23 articles in the fields related to business intelligence and further, the steps of implementing the proposed method are also described.
3.1. First stage - knowledge acquisition
At this stage, previous studies in the field of business intelligence maturity model, evaluating the value of key success factors in business intelligence and identifying maturity components were reviewed. In this study, the structured search strategy method was used as data sources from Emerald, Sage, Elsevier, IEEE, Taylor & Francis, and Springer databases in the period from 2000 to the beginning of 2022. At first, this study used the following keywords and search terms, combined and separate: "business intelligence", "factors affecting business intelligence system", "business intelligence maturity", "maturity measurement" and "business intelligence system in health care". The collection of articles presented in this research was consistent with the topic of this research in terms of questions, objectives, adopted frameworks and findings. The definitions used and their alignment with the measurement adopted were evaluated, to ensure that the factors of business intelligence investigated by different researchers are largely similar. Finally, by advancing the previous steps, an initial version of the business intelligence maturity model was adopted, which is significantly different from the final business intelligence maturity model.
3.2. The second stage - identifying and categorizing the criteria
After extracting the main criteria influencing the success of business intelligence in three areas of organization, process and technology, using the Delphi decision-making technique, the key factors and important criteria of business intelligence maturity in health care organizations were determined and categorized, and finally the maturity model of business intelligence in the field of health care, it was confirmed by a survey of experts.
3.3. The third step - determining the criteria
After finalizing the dimensions and criteria of the research with the Delphi method, using pairwise comparisons and the Dimtel method based on the network analysis process method, the internal and external connections of the factors were determined and each of the factors were weighted and prioritized. In this step, a committee evaluation method was used to evaluate the validity of the questionnaire (Harkness & Schoua-Glusberg, 1998). In addition, ANP-DEMATEL combined method was used to evaluate how and how much the components affect each other. Various researchers such as (Đurek et al., 2019; Rasouli et al., 2021) have used this approach in the field of maturity model.
Results
In this research, business intelligence was investigated in three basic areas of organization, process and technology, and each of these areas has criteria. First, articles were comprehensively reviewed in the field of business intelligence maturity in order to determine the dimensions of the goal and criteria. The criteria of the designed research maturity model were finalized using the Delphi method and with the opinion of experts, and then decision-making methods with multiple criteria were used to measure the optimality. The effects of goals, dimensions, and criteria on each other were investigated with the Dimtel method, then the dimensions and criteria were weighted in terms of importance with the network analysis process method. According to the results of Dimtel, the two dimensions of technology and process are effective, and the organizational dimension is effective. The organizational field has a higher relative importance than process and technology and has more interaction with other factors of the system and is affected by two dimensions of technology and process. The results of the analysis of the questionnaires of the network analysis process method answered by the experts show that the organizational factor is the most preferred and heaviest factor in the maturity of business intelligence, and then the process factor has a higher weight and the technology factor has a lower weight than the other two areas. In line with the results of William et al. (2019) and Gastaldi et al. (2018), who have encouraged and guided information and digital transformation in health care by providing a maturity assessment framework and infrastructure development based on results, respectively, two technical infrastructure criteria Flexible and expandable (hardware and software) and data and system quality were obtained from the highest importance compared to other criteria. And in the same way, the criterion of the correct definition of business intelligence problems and processes was prioritized with the third rank compared to other criteria in the maturity of business intelligence, and the rest of the criteria were also ranked in the article. Jayanthi Ranjan (2008) has also achieved this. In this way, a comprehensive and complete business intelligence maturity model was obtained in the field of health care, which can make the path of business intelligence maturity smoother in health care and be a road map for the successful implementation of business intelligence maturity in health care. It is suggested that in future researches, the proposed maturity model should be practically implemented in health care organizations and the maturity level of business intelligence should be evaluated.
Figure 1. The final research model (source: researcher's findings)
Organizational field
• Cooperation between the employees of the organization and the information technology department
• Alignment of business strategies with business intelligence strategies
• Senior management support for the business intelligence project
• Clear goals and vision for business intelligence
• Development of business intelligence strategy
• The ability of the organization to provide sufficient resources and funds needed for business intelligence projects
• Risk-taking of senior managers in investing in new information technologies
• Capabilities of the team/employees/managers
• Monitor information through the Business Intelligence Assessment Center
• Continuous improvement of organizational processes (improvement of competence)
Technology field
· Flexible and expandable technical infrastructure (hardware and software)
· Data and system quality
· Appropriate technology/tools or the use of appropriate technology and tools for hospital conditions
· Business intelligence system architecture
· Integration of business intelligence systems with other systems
· Quality of data analysis
· Connector
Business intelligence in the field of health care
Process area
• Correct definition of business intelligence problems and processes
• Using patterns and repeatable methods in designing business intelligence projects
• Aligning business intelligence solutions with user expectations
• User training and support
• Effective change management
• Balanced and strong composition of the business intelligence project group
• Project planning and management in the implementation of business intelligence
• Measuring business intelligence
• Decision making
Keywords: Business Intelligence, Healthcare, DEMATEL, ANP.
کلیدواژه‌ها [English]
- Business Intelligence
- Healthcare
- DEMATEL
- ANP
- حسینی، ف.، عباس نژاد، ط.، و بانشی، ع. (2015). شناسایی و رتبه‌بندی عوامل حیاتی موفقیت سیستم‌های هوشمند کسب‌وکار در صنعت درمان با رویکرد آمیخته (مطالعه موردی بیمارستان‌های شهر بندرعباس). مطالعات مدیریت کسب‌وکار هوشمند، 3(11)، 47–70. https://ims.atu.ac.ir/article_1625.html
- خجسته، ن.، عابدی شربیانی، ع. ا.، و انصاری، ر. (2014). بررسی عوامل تکنولوژیک، سازمانی، فرآیندی و کسب‌وکار مؤثر بر پیاده‌سازی موفق سیستم هوشمند کسب‌وکار در شرکت‌های خدمات اینترنتی (موردمطالعه: شرکت شاتل). تحقیقات بازاریابی نوین، 4(4)، 143–166. https://nmrj.ui.ac.ir/article_17751.html
- رضایی، ص.، میر عابدینی، س. ج.، و ابطحی، ع. (2018). عوامل مؤثر بر پیاده‌سازی هوشمندی کسب‌وکار در صنعت بانکداری ایران. مطالعات مدیریت کسب‌وکار هوشمند، 6(23)، 33–81. https://doi.org/10.22054/ims.2018.8851
- نوری،، ا.، و تقوا،، م. (1395). هوشمندی کسب‌وکار (مفاهیم و طراحی و توسعه سیستم) (1st ed.). دانشگاه علامه طباطبایی.
- Ahmad, S., Miskon, S., Alkanhal, T. A., & Tlili, I. (2020). Modeling of Business Intelligence Systems Using the Potential Determinants and Theories with the Lens of Individual, Technological, Organizational, and Environmental Contexts-A Systematic Literature Review. In Applied Sciences (Vol. 10, Issue 9). https://doi.org/10.3390/app10093208
- Anjariny, A. H., Zeki, A. M., & Hussin, H. (2012). Assessing Organizations Readiness toward Business Intelligence Systems: A Proposed Hypothesized Model. 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT), 213–218. https://doi.org/10.1109/ACSAT.2012.57
- Arefin, M. S., Hoque, M. R., & Rasul, T. (2021). Organizational learning culture and business intelligence systems of health-care organizations in an emerging economy. Journal of Knowledge Management, 25(3), 573–594. https://doi.org/10.1108/JKM-09-2019-0517
- Avison, D., & Young, T. (2007). Time to Rethink Health Care and ICT? Communications of the ACM, 50(6), 69–74. https://doi.org/10.1145/1247001.1247008
- Ayal, M., & Seidman, A. (2009). An Empirical Investigation of the Value of Integrating Enterprise Information Systems: The Case of Medical Imaging Informatics. Journal of Management Information Systems, 26(2), 43–68. https://doi.org/10.2753/MIS0742-1222260203
- Behkami, N. A., & U. Daim, T. (2012). Research Forecasting for Health Information Technology (HIT), using technology intelligence. Technological Forecasting and Social Change, 79(3), 498–508. https://doi.org/https://doi.org/10.1016/j.techfore.2011.08.015
- Borzekowski, R. (2009). Measuring the cost impact of hospital information systems: 1987-1994. Journal of Health Economics, 28(5), 938–949. https://doi.org/10.1016/j.jhealeco.2009.06.004
- Brooks, P., El-Gayar, O., & Sarnikar, S. (2015). A framework for developing a domain specific business intelligence maturity model: Application to healthcare. International Journal of Information Management, 35(3), 337–345. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2015.01.011
- Carvalho, J. V., Rocha, Á., & Abreu, A. (2019). Maturity Assessment Methodology for HISMM - Hospital Information System Maturity Model. Journal of Medical Systems, 43(2), 35. https://doi.org/10.1007/s10916-018-1143-y
- Carvalho, J. V., Rocha, Á., Vasconcelos, J., & Abreu, A. (2019). A health data analytics maturity model for hospitals information systems. International Journal of Information Management, 46, 278–285. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2018.07.001
- Carvalho, J. V., Rocha, Á., Vasconcelos, J., & Abreu, A. (2018). Health Data Analytics: A Proposal to Measure Hospitals Information Systems Maturity BT - Trends and Advances in Information Systems and Technologies (Á. Rocha, H. Adeli, L. P. Reis, & S. Costanzo (eds.); pp. 1071–1080). Springer International Publishing.
- Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503
- Christensen, C. M., Grossman, J. H., & Hwang, J. (2008). The Innovator’s Prescription: A Disruptive Solution for Health Care. McGraw Hill LLC. https://books.google.co.uk/books?id=x8KFZD_pnH4C
- Chuah, M.-H., & Wong, K.-L. (2011). A review of business intelligence and its maturity models. African Journal of Business Management, 5(9), 3424–3428. https://doi.org/10.5897/AJBM10.1564
- El-Gayar, O., & Timsina, P. (2014). Opportunities for Business Intelligence and Big Data Analytics in Evidence Based Medicine. 2014 47th Hawaii International Conference on System Sciences, 749–757. https://doi.org/10.1109/HICSS.2014.100
- Foshay, N., & Kuziemsky, C. (2014). Towards an implementation framework for business intelligence in healthcare. International Journal of Information Management, 34(1), 20–27. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2013.09.003
- Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2014.10.007
- Gastaldi, L., Mangiaracina, R., Miragliotta, G., Perego, A., & Tumino, A. (2015). Measuring the benefits of tracking medical treatment through RFId. International Journal of Productivity and Performance Management, 64(2), 175–193. https://doi.org/10.1108/IJPPM-10-2013-0171
- Gastaldi, L., Pietrosi, A., Lessanibahri, S., Paparella, M., Scaccianoce, A., Provenzale, G., Corso, M., & Gridelli, B. (2018). Measuring the maturity of business intelligence in healthcare: Supporting the development of a roadmap toward precision medicine within ISMETT hospital. Technological Forecasting and Social Change, 128, 84–103. https://doi.org/https://doi.org/10.1016/j.techfore.2017.10.023
- Gluchowski, P., Dinter, B., & Schieder, C. (2011, August 4). Towards a Life Cycle Oriented Business Intelligence Success Model. Proceedings of the Americas Conference on Information Systems (AMCIS2011).
- Gomes, J., & Romão, M. (2018). Information System Maturity Models in Healthcare. Journal of Medical Systems, 42(12), 235. https://doi.org/10.1007/s10916-018-1097-0
- Grublješič, T., Coelho, P. S., & Jaklič, J. (2014). The Importance and Impact of Determinants Influencing Business Intelligence Systems Embeddedness. Issues in Information Systems, 15, 106–117. https://doi.org/10.48009/1_iis_2014_106-117
- Habibi, A., Sarafrazi, A., & Izadyar, S. (2014). Delphi Technique Theoretical Framework in Qualitative Research. The International Journal Of Engineering And Science (IJES), 3(4), 8–13.
- Haidzir, H., Othman, S. H., & Kutty Mammi, H. (2018). Evaluation of Business Continuity Plan Maturity Level Using Business Continuity Maturity Model. International Journal of Innovative Computing, 8(1). https://doi.org/https://doi.org/10.11113/ijic.v8n1.163
- Hanson, R. M. (2011). Good health information--an asset not a burden! Australian Health Review : A Publication of the Australian Hospital Association, 35(1), 9–13. https://doi.org/10.1071/AH09865
- Harkness, J., & Schoua-Glusberg, A. (1998). Questionnaires in translation. In J. Harkness (Ed.), Cross-cultural survey equivalence (Vol. 3, pp. 87–126). Zentrum f., Umfragen, Methoden und Analysen -ZUMA-.
- Hawking, P. (2013). Factors Critical To The Success of Business Intelligence Systems. Victoria University.
- Howson, C. (2006). The seven pillars of BI success. Intelligent Enterprise. Tata McGraw-Hill Education.
- Imhoff, C. (2004). Business intelligence–Five factors for success. Retrieved June, 22.
- Işık, M.., Yarar, O., & Söylemez Sur, D.. (2021). Measurement of the Effects of Business Intelligence Applications on Performance in Hospitals According to the Managerial Levels: A Chain Hospital Application. Journal of International Health Sciences and Management, 7(13), 97–108. https://doi.org/10.48121/jihsam.776109
- Janssen, M., & Moors, E. H. M. (2013). Caring for healthcare entrepreneurs — Towards successful entrepreneurial strategies for sustainable innovations in Dutch healthcare. Technological Forecasting and Social Change, 80(7), 1360–1374. https://doi.org/https://doi.org/10.1016/j.techfore.2012.12.003
- Li, M., & Mao, J. (2015). Hedonic or utilitarian? Exploring the impact of communication style alignment on user’s perception of virtual health advisory services. International Journal of Information Management, 35(2), 229–243. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2014.12.004
- Liaw, S.-T., Zhou, R., Ansari, S., & Gao, J. (2021). A digital health profile & maturity assessment toolkit: cocreation and testing in the Pacific Islands. Journal of the American Medical Informatics Association : JAMIA, 28(3), 494–503. https://doi.org/10.1093/jamia/ocaa255
- Lucas, F. L., Sirovich, B. E., Gallagher, P. M., Siewers, A. E., & Wennberg, D. E. (2010). Variation in cardiologists’ propensity to test and treat: is it associated with regional variation in utilization? Cardiovascular Quality and Outcomes, 3(3), 253–260. https://doi.org/10.1161/CIRCOUTCOMES.108.840009
- Mettler, T., & Vimarlund, V. (2009). Understanding business intelligence in the context of healthcare. Health Informatics Journal, 15(3), 254–264. https://doi.org/10.1177/1460458209337446
- Negash, S. (2004). Business Intelligence. Communications of the Association for Information Systems, 13, 177–195. https://doi.org/10.17705/1CAIS.01315
- Ojeda-Castro, Á., & Ramaswamy, M. (2014). Best Practices for Successful Development of Data Warehouses for Small Businesses. Issues in Information Systems, 15, 277–284.
- Pai, F.-Y., & Huang, K.-I. (2011). Applying the Technology Acceptance Model to the introduction of healthcare information systems. Technological Forecasting and Social Change, 78(4), 650–660. https://doi.org/https://doi.org/10.1016/j.techfore.2010.11.007
- Pereira, A., Portela, F., Santos, M. F., Machado, J., & Abelha, A. (2016). Pervasive Business Intelligence: A New Trend in Critical Healthcare. Procedia Computer Science, 98, 362–367. https://doi.org/https://doi.org/10.1016/j.procs.2016.09.055
- Popovič, A., Hackney, R., Coelho, P., & Jaklič, J. (2012). Towards business intelligence systems success: Effects of maturity and culture on analytical decision making. Decision Support Systems, 54, 729–739. https://doi.org/10.1016/j.dss.2012.08.017
- Qaseem, A., Alguire, P., Dallas, P., Feinberg, L. E., Fitzgerald, F. T., Horwitch, C., Humphrey, L., LeBlond, R., Moyer, D., Wiese, J. G., & Weinberger, S. (2012). Appropriate use of screening and diagnostic tests to foster high-value, cost-conscious care. Annals of Internal Medicine, 156(2), 147–149. https://doi.org/10.7326/0003-4819-156-2-201201170-00011
- Ranjan, Jayanthi. (2008). Hurdles and opportunities for Indian firms adopting business intelligence. Journal of Advances in Management Research, 5(1), 56–62. https://doi.org/10.1108/97279810880001267
- Ranjan, JAYANTHI. (2009). Business intelligence: Concepts, components, techniques and benefits. Journal of Theoretical and Applied Information Technology, 9(1), 60–70.
- Romanow, D., Cho, S., & Straub, D. (2012). Editor’s Comments: Riding the Wave: Past Trends and Future Directions for Health IT Research. MIS Q., 36(3), III–A18.
- Salisu, I., Bin Mohd Sappri, M., & Bin Omar, M. F. (2021). The adoption of business intelligence systems in small and medium enterprises in the healthcare sector: A systematic literature review. Cogent Business & Management, 8(1), 1935663. https://doi.org/10.1080/23311975.2021.1935663
- Tremblay, M. C., Hevner, A. R., & Berndt, D. J. (2012). Design of an information volatility measure for health care decision making. Decision Support Systems, 52(2), 331–341. https://doi.org/https://doi.org/10.1016/j.dss.2011.08.009
- Wielki, J., & Jurczyk, M. (2019). Evaluation of Healthcare Business Intelligence Using the Fuzzy TOPSIS Method. In Informatyka i zarządzanie na przełomie wieków. Metody, narzędzia, systemy. Wydawnictwo Naukowe Wydziału Zarządzania Uniwersytetu Warszawskiego. https://books.google.com/books?id=ao5wzgEACAAJ
- Williams, P. A., Lovelock, B., Cabarrus, T., & Harvey, M. (2019). Improving Digital Hospital Transformation: Development of an Outcomes-Based Infrastructure Maturity Assessment Framework. JMIR Medical Informatics, 7(1), e12465. https://doi.org/10.2196/12465.
- Wixom, B. H., & Watson, H. J. (2001). An empirical investigation of the factors affecting data warehousing success. Management Information Systems Quarterly, 25(1), 17–41.
- Yeoh, W., & Koronios, A. (2010). Critical success factors for business intelligence systems. Journal of Computer Information Systems, 50.
- Yeoh, W., Koronios, A., & Gao, J. (2007). Critical Success Factors for Implementation of Business Intelligence Systems: A Study of Engineering Asset Management Organizations BT - Decision Support for Global Enterprises (U. Kulkarni, D. J. Power, & R. Sharda (eds.); pp. 33–49). Springer US. https://doi.org/10.1007/978-0-387-48137-1_3
- Zheng, W., Wu, Y.-C. J., & Chen, L. (2018). Business intelligence for patient-centeredness: A systematic review. Telematics and Informatics, 35(4), 665–676. https://doi.org/https://doi.org/10.1016/j.tele.2017.06.015
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