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jha 2024, 27(3): 54-69 |
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Kheradranjbar M, Khamseh A, Iranban fard S J. Evaluation of health services portfolio management based on information technology using adaptive neuro-fuzzy inference approach. jha 2024; 27 (3) :54-69
URL: http://jha.iums.ac.ir/article-1-4483-en.html
URL: http://jha.iums.ac.ir/article-1-4483-en.html
1- Department of Technology Management, Faculty of Management and Economics, Science and Research Unit, Islamic Azad University, Tehran
2- Department of Industrial Management, Karaj Branch, Islamic Azad University, Karaj ,abbas.khamseh@kiau.ac.ir
3- Department of Management, Shiraz Branch, Islamic Azad University, Shiraz
2- Department of Industrial Management, Karaj Branch, Islamic Azad University, Karaj ,
3- Department of Management, Shiraz Branch, Islamic Azad University, Shiraz
Keywords: Portfolio management, Research and development, Information technology, Adaptive neuro-fuzzy inference system
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| Abstract (HTML) (55 Views)
Figure 1. Input-output calculation method for healthcare service project portfolio management based on inputs
Figure 2. Five input, middle and output layers of ANFIS model
Figure 3. Comparison curve showing impact of micro and macro inputs on output variable
Figure 4. Effects of changes in dimensions based on the impact on the final output (Right: Micro level; Left: Macro level)
Table 2. Impact of inputs on ANFIS model output and importance degree
Discussion
This research provides valuable insights into the key dimensions and components affecting portfolio management in IT-based healthcare service projects. The findings indicate that success in this domain requires simultaneous attention to macro-level factors (such as legal, political, and institutional factors) and micro-level factors (including technology management, intellectual capital, and quality). Sensitivity analysis revealed that macro-level factors (importance score: 0.5) have a greater impact than micro-level factors (importance score: 0.4), highlighting the significance of external environment and macro factors in the success of healthcare projects.
Based on the findings, healthcare organizations should establish strong legal and regulatory frameworks for health IT projects, develop appropriate technological infrastructure, invest in intellectual capital, and implement comprehensive quality management systems. Additionally, organizations must prioritize cultural factor management alongside information technology. Fostering an organizational culture that values innovation, collaboration, and adaptability can foster a supportive environment for successful portfolio management.
For future research, it is recommended to explore the impact of emerging technologies such as artificial intelligence, blockchain, and telehealth on portfolio management practices and healthcare service delivery models. Longitudinal studies are suggested to evaluate the long-term impact of portfolio management strategies on healthcare outcomes and organizational performance. Tracking project outcomes and organizational responses over time can offer researchers a deeper understanding of the dynamics and complexities in healthcare portfolio management. By understanding and prioritizing these identified factors, healthcare organizations can develop more effective portfolio management strategies, ultimately leading to improved project outcomes and higher quality healthcare services.
In the macro dimension, the legal factors (LF) rank first in importance. There are great capacities in Iran’s constitution to promote the use of consultative methods for the regulations, however, to date, no significant legislation has been enacted to facilitate such legal support. To strengthen this component, it is suggested that these capacities should be applied. Political factors (PF) component ranks second in the macro dimension. In this regard, health issues and priorities should be considered by the policy secretariat. Institutional factors component (IF) ranks third in the macro dimension. To strengthen this factor, health-related issues can be communicated by influential and famous figures after any necessary validation.
In the micro dimension, technology management (TM) component ranks as the first and most important factor. In order to strengthen this component, it is suggested that the share of ICT in the health sector should be increased. Intellectual capital management (ICM) and quality management (QM)hold the second and third ranks in the micro dimension. To strengthen these factors, organizations should develop and increase the technical knowledge of health professionals, increase analytical forecasting abilities for events and incidents, develop communication and human skills, improve management and organizational skills, and prepare staff to lead employees and perform executive positions. Quality management (QM) ranks third in the micro dimension.
Limitations
This study had limitations that should be considered. Limited access to relevant literature and data might result in missed some important sources. Due to structural, cultural, and regulatory differences in different healthcare settings, findings may be not generalizable. We also could not examine all aspects of healthcare project portfolio management due to the complexity and breadth of the field. This study focused mainly on aspects related to information technology. Therefore, some other influential factors might be neglected. Although the neuro-adaptive fuzzy inference methodology is a powerful tool for modeling complex relationships, it has its inherent limitations. The limited amount of data available for training the model and the possibility of systematic errors in data collection could affect the accuracy of the results.
Conclusion
Successfull portfolio management in IT-based health services projects require simultaneous attention to macro-level factors (with an emphasis on legal, political, and institutional factors) and micro-level factors (centered on technology management, intellectual capital, and quality). Macro-level factors (significance level of 0.5) have a greater impact than micro-level factors (significance level of 0.4), which highlights the importance of paying attention to the external environment and macro-factors in the success of health services projects.
Health organizations should establish a strong legal and regulatory framework for health IT projects. This requires close cooperation with legislative and regulatory bodies. In addition, it is essential to establish appropriate technology infrastructure and invest in developing the organization’s intellectual capital, which includes continuous training of employees, updating systems, and establishing knowledge management processes. Implementing comprehensive quality control systems to ensure the compliance of projects with technical and clinical standards is also of particular importance. These systems should be regularly evaluated and updated. Healthcare organizations should prioritize managing cultural factors alongside information technology. Fostering an organizational culture that values innovation, collaboration, and adaptability can create an environment conducive to successful portfolio management. This requires promoting open communication channels, encouraging knowledge sharing, and embracing change to effectively integrate technology into healthcare processes. Examining the impact of emerging technologies such as artificial intelligence, blockchain, and telemedicine in shaping portfolio management practices and healthcare delivery models are also recommended.
This research provides a comprehensive framework for assessing and managing the portfolio of healthcare projects, taking an important step towards improving healthcare services and increasing the effectiveness of IT projects in this field. By understanding and prioritizing the identified influencing factors, healthcare organizations can develop more effective portfolio management strategies that ultimately lead to improved project outcomes and increased quality of healthcare services.
Declerations
Ethical considerations: Not applicable
Funding: This research was conducted without financial support
Conflict of interest: The authors declare no conflict of interest
Authors' contributions: Abbas Khamseh: Conceptualization, study design, data management, data analysis, review and editing, final approval; Maryam Kherandranjabr: Study design, methodology, validation, sourcing, data collection, writing-drafting; Seyyed Javad Iranbanfard: Study supervision, study design, data management, review and editing. All authors have read and approved the final text of the article.
Consent for publication: Not applicable
Data availability: The data could not be publically shared.
AI deceleration: None
Acknowledgements: The authors are grateful to all participants in this study.
Full-Text: (12 Views)
Introduction
The effective management of IT-based healthcare service project portfolios plays a vital role in optimizing resource allocation, mitigating risks, and improving outcomes in healthcare organizations. With growing complexity and diversity of technology-driven healthcare projects, there is a growing need for systematic approaches to portfolio management of these project [1]. The integration of healthcare services and information technology has created numerous opportunities for innovation in portfolio management, thereby enhancing the efficiency and effectiveness of healthcare projects [2]. Portfolio management, a key aspect of project management, requires strategic selection, prioritization, and monitoring of projects to meet organizational objectives. In healthcare service projects characterized by complexity and dynamism, efficient portfolio management is essential for effective resource allocation and risk reduction [3].
Previous studies have shown that traditional portfolio management approaches are ineffective for addressing dynamic and uncertain nature of healthcare environments [4]. Specifically, the integration of information technology introduces additional complexities that require innovative methods capable of adapting to changing environments [5]. Furthermore, previous studies have primarily focused on specific aspects such as risk assessment or resource allocation, neglecting the complex interaction between IT integration, organizational dynamics, and strategic alignment [6]. Studies by Alolayyan et al. [7] and Tahvildarzadeh et al. [8] emphasized the importance of improving health information technology quality and the pivotal role of e-health in enhancing management capabilities. Haji Ali Asgari et al. [9] also focused on the necessity of developing IT maturity models within healthcare organizations. However, notable gaps persist in the comprehensive and integrated assessment of the factors affecting IT-based healthcare service project portfolio management.
Previous studies have yielded valuable insights into healthcare project portfolio management. For example, Mahdavi et al. [10] identified six key areas including cost-effectiveness of information systems, system success and failure, security and privacy, service quality, interoperability, and future direction. Sittig et al. [11] highlighted important patient safety challenges related to health information technology. Salman et al. [12] emphasized the significance of commercialization and technology-based healthcare services.
The main research question is how portfolio management can be effectively utilized to maximize the impact of IT-based healthcare service projects. This research helps healthcare organizations optimize their portfolio management strategies by identifying and prioritizing key factors and offering a framework for evaluating these factors, ultimately leading to improved healthcare service quality.
Methods
The research employed a meta-synthesis of literature and adaptive neuro-fuzzy inference system. In the first phase, Sandelowski and Barroso's [13] meta-synthesis method was applied to identify key effective dimensions and components. A systematic literature search was conducted across reputable databases using keywords such as project portfolio management, healthcare services, information technology, and healthcare service projects. The inclusion criteria encompassed relevant qualitative studies published between 2014-2023 in international (Elsevier, Wiley, Springer, Taylor & Francis, and Emerald) and domestic (Civilica, SID, and Magiran)databases. Out of 408 identified articles, 35 were selected for final analysis. Research validity was confirmed using Sandelowski and Barroso's method, while reliability was assessed using the Critical Appraisal Skills Program (2018).
In the second phase, a questionnaire developed from the identified dimensions was distributed to 100 experts, with 87 responses received. The statistical population included experts, managers, and policymakers with over 10 years of management experience and holding master's or doctoral degrees, selected through purposive sampling. Questionnaire validity was confirmed through face and content validity, and its reliability through Cronbach's alpha.
Data analysis was conducted using MATLAB software and adaptive neuro-fuzzy inference system (ANFIS), which integrates neural network with fuzzy logic [14]. The data was partitioned into training (60%), testing (25%), and validation (15%) sets. Subtractive clustering was used to design the inference rules. A Gaussian membership function was selected due to its differentiability and flexibility. Model validation was performed using test dataset and boundary condition testing. A hybrid method, combining backpropagation and least squares estimation, was used to train membership function parameters.
The designed ANFIS model included two inputs (micro and macro level dimensions) and three rules in the main processing layer. In subsidiary ANFIS models, each dimension was modeled separately, with seven components at the micro level and eight components at the macro level. This architecture allows for a comprehensive and integrated evaluation of the factors affecting healthcare service project portfolio management [15].
Results
The meta-synthesis process identified 15
components across two levels (micro and macro)
through a taxonomic analysis was shown in Table1.
The effective management of IT-based healthcare service project portfolios plays a vital role in optimizing resource allocation, mitigating risks, and improving outcomes in healthcare organizations. With growing complexity and diversity of technology-driven healthcare projects, there is a growing need for systematic approaches to portfolio management of these project [1]. The integration of healthcare services and information technology has created numerous opportunities for innovation in portfolio management, thereby enhancing the efficiency and effectiveness of healthcare projects [2]. Portfolio management, a key aspect of project management, requires strategic selection, prioritization, and monitoring of projects to meet organizational objectives. In healthcare service projects characterized by complexity and dynamism, efficient portfolio management is essential for effective resource allocation and risk reduction [3].
Previous studies have shown that traditional portfolio management approaches are ineffective for addressing dynamic and uncertain nature of healthcare environments [4]. Specifically, the integration of information technology introduces additional complexities that require innovative methods capable of adapting to changing environments [5]. Furthermore, previous studies have primarily focused on specific aspects such as risk assessment or resource allocation, neglecting the complex interaction between IT integration, organizational dynamics, and strategic alignment [6]. Studies by Alolayyan et al. [7] and Tahvildarzadeh et al. [8] emphasized the importance of improving health information technology quality and the pivotal role of e-health in enhancing management capabilities. Haji Ali Asgari et al. [9] also focused on the necessity of developing IT maturity models within healthcare organizations. However, notable gaps persist in the comprehensive and integrated assessment of the factors affecting IT-based healthcare service project portfolio management.
Previous studies have yielded valuable insights into healthcare project portfolio management. For example, Mahdavi et al. [10] identified six key areas including cost-effectiveness of information systems, system success and failure, security and privacy, service quality, interoperability, and future direction. Sittig et al. [11] highlighted important patient safety challenges related to health information technology. Salman et al. [12] emphasized the significance of commercialization and technology-based healthcare services.
The main research question is how portfolio management can be effectively utilized to maximize the impact of IT-based healthcare service projects. This research helps healthcare organizations optimize their portfolio management strategies by identifying and prioritizing key factors and offering a framework for evaluating these factors, ultimately leading to improved healthcare service quality.
Methods
The research employed a meta-synthesis of literature and adaptive neuro-fuzzy inference system. In the first phase, Sandelowski and Barroso's [13] meta-synthesis method was applied to identify key effective dimensions and components. A systematic literature search was conducted across reputable databases using keywords such as project portfolio management, healthcare services, information technology, and healthcare service projects. The inclusion criteria encompassed relevant qualitative studies published between 2014-2023 in international (Elsevier, Wiley, Springer, Taylor & Francis, and Emerald) and domestic (Civilica, SID, and Magiran)databases. Out of 408 identified articles, 35 were selected for final analysis. Research validity was confirmed using Sandelowski and Barroso's method, while reliability was assessed using the Critical Appraisal Skills Program (2018).
In the second phase, a questionnaire developed from the identified dimensions was distributed to 100 experts, with 87 responses received. The statistical population included experts, managers, and policymakers with over 10 years of management experience and holding master's or doctoral degrees, selected through purposive sampling. Questionnaire validity was confirmed through face and content validity, and its reliability through Cronbach's alpha.
Data analysis was conducted using MATLAB software and adaptive neuro-fuzzy inference system (ANFIS), which integrates neural network with fuzzy logic [14]. The data was partitioned into training (60%), testing (25%), and validation (15%) sets. Subtractive clustering was used to design the inference rules. A Gaussian membership function was selected due to its differentiability and flexibility. Model validation was performed using test dataset and boundary condition testing. A hybrid method, combining backpropagation and least squares estimation, was used to train membership function parameters.
The designed ANFIS model included two inputs (micro and macro level dimensions) and three rules in the main processing layer. In subsidiary ANFIS models, each dimension was modeled separately, with seven components at the micro level and eight components at the macro level. This architecture allows for a comprehensive and integrated evaluation of the factors affecting healthcare service project portfolio management [15].
Results
The meta-synthesis process identified 15
components across two levels (micro and macro)
through a taxonomic analysis was shown in Table1.
Table 1. Dimensions and components of healthcare service portfolio management
| Main concept | Dimensions | Code | Components | Code | References |
| IT-based healthcare service project portfolio management | Micro level | MI.L | Financial management | FM | [6, 16, 17] |
| Intellectual capital management | ICM | [16, 18] | |||
| Technology management | TM | [9, 19, 20] | |||
| R&D management | RDM | [18, 21, 22] | |||
| Strategic management | SM | [6, 12, 23] | |||
| Project portfolio management | PPM | [18, 21, 23] | |||
| Quality management | QM | [12, 16, 17] | |||
| Macro level | MA.L | Political factors | PF | [17, 26] | |
| Cultural factors | CF | [12, 24] | |||
| Economic factors | EF | [12, 17] | |||
| Legal factors | LF | [6, 24] | |||
| Institutional factors | IF | [9, 17] | |||
| IT management | ITM | [22, 23] | |||
| Health IT management | HTM | [17, 22, 25] | |||
| Healthcare service management | MHS | [17, 22, 25] |
After identifying the components, the fuzzy inference system was designed using two approaches: once involving the two main dimensions and final output, and another using subsidiary ANFIS models based on the components of each dimension. Figure 1 shows how the ANFIS model outputs are calculated based on the changes in the inputs. The five-layer structure of the ANFIS model, including input layers, membership functions, rules, normalization, and output, is shown in Figure 2.
Figure 1. Input-output calculation method for healthcare service project portfolio management based on inputs
Figure 2. Five input, middle and output layers of ANFIS model
The analysis revealed that macro-level factors (importance degree 0.5) ranked first, followed by micro-level factors (importance degree 0.4) among the main dimensions ( Table 2). Figure 3 shows the curve comparing the impact of two micro and macro level input dimensions on the output variable. Moreover, the impact of changes in each dimension on the final output is shown in Figure 4 which shows a decreasing trend in the relationship between input and output variables.
Figure 3. Comparison curve showing impact of micro and macro inputs on output variable
Figure 4. Effects of changes in dimensions based on the impact on the final output (Right: Micro level; Left: Macro level)
Table 2. Impact of inputs on ANFIS model output and importance degree
| Dimensions | code | Importance | Rank | Components | Code | Importance | Rank |
| Micro level | MI.L | 1 | 0.5 | Financial management | FM | 0.16 | 4 |
| Intellectual capital management | ICM | 0.09 | 5 | ||||
| Technology management | TM | 0.01 | 7 | ||||
| R&D management | RDM | 0.19 | 3 | ||||
| Strategic management | SM | 0.26 | 1 | ||||
| Project portfolio management | PPM | 0.16 | 4 | ||||
| Quality management | QM | 0.03 | 6 | ||||
| Macro level | MA.L | 2 | 0.4 | Political factors | PF | 0.21 | 2 |
| Cultural factors | CF | 0.17 | 3 | ||||
| Economic factors | EF | 0.05 | 6 | ||||
| Legal factors | LF | 0.14 | 4 | ||||
| Institutional factors | IF | 0.01 | 7 | ||||
| IT management | ITM | 0.24 | 1 | ||||
| Health IT Management | HTM | 0.18 | 2 | ||||
| Healthcare service management | MHS | 0.10 | 5 |
Figure 5 shows the agreement of the ANFIS model validation mean errors of 5.0198 × 10^-7 for
training data and 3.365 × 10^-7 for validation data.
Figure 5. Comparison between ANFIS output and data: Right) Training data; Left) Validation data
training data and 3.365 × 10^-7 for validation data.
Figure 5. Comparison between ANFIS output and data: Right) Training data; Left) Validation dataDiscussion
This research provides valuable insights into the key dimensions and components affecting portfolio management in IT-based healthcare service projects. The findings indicate that success in this domain requires simultaneous attention to macro-level factors (such as legal, political, and institutional factors) and micro-level factors (including technology management, intellectual capital, and quality). Sensitivity analysis revealed that macro-level factors (importance score: 0.5) have a greater impact than micro-level factors (importance score: 0.4), highlighting the significance of external environment and macro factors in the success of healthcare projects.
Based on the findings, healthcare organizations should establish strong legal and regulatory frameworks for health IT projects, develop appropriate technological infrastructure, invest in intellectual capital, and implement comprehensive quality management systems. Additionally, organizations must prioritize cultural factor management alongside information technology. Fostering an organizational culture that values innovation, collaboration, and adaptability can foster a supportive environment for successful portfolio management.
For future research, it is recommended to explore the impact of emerging technologies such as artificial intelligence, blockchain, and telehealth on portfolio management practices and healthcare service delivery models. Longitudinal studies are suggested to evaluate the long-term impact of portfolio management strategies on healthcare outcomes and organizational performance. Tracking project outcomes and organizational responses over time can offer researchers a deeper understanding of the dynamics and complexities in healthcare portfolio management. By understanding and prioritizing these identified factors, healthcare organizations can develop more effective portfolio management strategies, ultimately leading to improved project outcomes and higher quality healthcare services.
In the macro dimension, the legal factors (LF) rank first in importance. There are great capacities in Iran’s constitution to promote the use of consultative methods for the regulations, however, to date, no significant legislation has been enacted to facilitate such legal support. To strengthen this component, it is suggested that these capacities should be applied. Political factors (PF) component ranks second in the macro dimension. In this regard, health issues and priorities should be considered by the policy secretariat. Institutional factors component (IF) ranks third in the macro dimension. To strengthen this factor, health-related issues can be communicated by influential and famous figures after any necessary validation.
In the micro dimension, technology management (TM) component ranks as the first and most important factor. In order to strengthen this component, it is suggested that the share of ICT in the health sector should be increased. Intellectual capital management (ICM) and quality management (QM)hold the second and third ranks in the micro dimension. To strengthen these factors, organizations should develop and increase the technical knowledge of health professionals, increase analytical forecasting abilities for events and incidents, develop communication and human skills, improve management and organizational skills, and prepare staff to lead employees and perform executive positions. Quality management (QM) ranks third in the micro dimension.
Limitations
This study had limitations that should be considered. Limited access to relevant literature and data might result in missed some important sources. Due to structural, cultural, and regulatory differences in different healthcare settings, findings may be not generalizable. We also could not examine all aspects of healthcare project portfolio management due to the complexity and breadth of the field. This study focused mainly on aspects related to information technology. Therefore, some other influential factors might be neglected. Although the neuro-adaptive fuzzy inference methodology is a powerful tool for modeling complex relationships, it has its inherent limitations. The limited amount of data available for training the model and the possibility of systematic errors in data collection could affect the accuracy of the results.
Conclusion
Successfull portfolio management in IT-based health services projects require simultaneous attention to macro-level factors (with an emphasis on legal, political, and institutional factors) and micro-level factors (centered on technology management, intellectual capital, and quality). Macro-level factors (significance level of 0.5) have a greater impact than micro-level factors (significance level of 0.4), which highlights the importance of paying attention to the external environment and macro-factors in the success of health services projects.
Health organizations should establish a strong legal and regulatory framework for health IT projects. This requires close cooperation with legislative and regulatory bodies. In addition, it is essential to establish appropriate technology infrastructure and invest in developing the organization’s intellectual capital, which includes continuous training of employees, updating systems, and establishing knowledge management processes. Implementing comprehensive quality control systems to ensure the compliance of projects with technical and clinical standards is also of particular importance. These systems should be regularly evaluated and updated. Healthcare organizations should prioritize managing cultural factors alongside information technology. Fostering an organizational culture that values innovation, collaboration, and adaptability can create an environment conducive to successful portfolio management. This requires promoting open communication channels, encouraging knowledge sharing, and embracing change to effectively integrate technology into healthcare processes. Examining the impact of emerging technologies such as artificial intelligence, blockchain, and telemedicine in shaping portfolio management practices and healthcare delivery models are also recommended.
This research provides a comprehensive framework for assessing and managing the portfolio of healthcare projects, taking an important step towards improving healthcare services and increasing the effectiveness of IT projects in this field. By understanding and prioritizing the identified influencing factors, healthcare organizations can develop more effective portfolio management strategies that ultimately lead to improved project outcomes and increased quality of healthcare services.
Declerations
Ethical considerations: Not applicable
Funding: This research was conducted without financial support
Conflict of interest: The authors declare no conflict of interest
Authors' contributions: Abbas Khamseh: Conceptualization, study design, data management, data analysis, review and editing, final approval; Maryam Kherandranjabr: Study design, methodology, validation, sourcing, data collection, writing-drafting; Seyyed Javad Iranbanfard: Study supervision, study design, data management, review and editing. All authors have read and approved the final text of the article.
Consent for publication: Not applicable
Data availability: The data could not be publically shared.
AI deceleration: None
Acknowledgements: The authors are grateful to all participants in this study.
Type of Study: Research |
Subject:
Health Information Management
Received: 2024/03/29 | Accepted: 2025/05/22 | Published: 2025/06/8
Received: 2024/03/29 | Accepted: 2025/05/22 | Published: 2025/06/8
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