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Dehkhodaei R, Karamali M, Mohamadian M, Bahadori M, Abbasi Farajzadeh M. Developing a knowledge tree model in health research centers: from theory to practice. jha 2025; 27 (4) :84-100
URL: http://jha.iums.ac.ir/article-1-4602-en.html
URL: http://jha.iums.ac.ir/article-1-4602-en.html
Reza Dehkhodaei1 
, Mazyar Karamali2 , Mohammad Mohamadian1 , MohammadKarim Bahadori1 , Mohsen Abbasi Farajzadeh3
, Mazyar Karamali2 , Mohammad Mohamadian1 , MohammadKarim Bahadori1 , Mohsen Abbasi Farajzadeh3
1- Health Management Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran.
2- Health Management Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran. ,mazyar.karamali@gmail.com
3- Students Research Committee, Baqiyatallah University of Medical Sciences, Tehran, Iran.
2- Health Management Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran. ,
3- Students Research Committee, Baqiyatallah University of Medical Sciences, Tehran, Iran.
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Introduction
Currently, the growth of knowledge does not match the ability of healthcare organizations to disseminate, translate, and use existing knowledge in health and clinical care. Therefore, a large amount of health knowledge is fragmented throughout the organization and scattered in various locations, making it difficult for professionals to use knowledge effectivelly. This issue is important professionals make decisions for patient treatment and care. Therefore, insufficient knowledge or difficult access to it due to its fragmented nature can result in less reliable decisions or incomplete care and services provided to patients [1].
Considering the crucial role of medical universities in the health system and the significant contribution of their research centers to university development, as well as the significance of knowledge management in these institutions, it is essential to develop a knowledge tree model for their research centers. Although some universities have knowledge management departments, no standard model or tool currently exists to measure and evaluate their knowledge status. Developing such a model will help identify the current knowledge, knowledge gaps, and knowledge assets of these centers. This comprehensive assessment can support improved planning and policymaking. Therefore, this study aims to develop a knowledge tree model for the research centers of medical universities.
Methods
This applied study utilized a quantitative approach based on the Delphi method. The Delphi technique was employed to gather and analyze experts’ opinions through a consensus-bulding process among subject-matter specialists. The process began with a literature review and field study using keywords such as "knowledge tree" and "knowledge mapping" to identify stages and components relevant to knowledge map development. Semi-structured interviews with experts were counducted to develop an initial model for designing a knowledge tree in the health domain. Given the limited resources detailing the knowledge tree construction process, general stages of knowledge mapping were used as a foundation for expert surveys, aiming to adapt them to health research centers.
In the first phase, models from various researchers were reviewed and synthesized to create a preliminary framework. These components were organized into an initial 29-item questionnaire with a five-point Likert scale. The questionnaire was distributed electronically or in person. The target population included staff from medical universities’ research centers, research center managers, and experts in health and knowledge management with relevant experience and academic outputs. Selection criteria included managing at least two relevant projects and having a minimum of three scientific publications in the field. The involvement of research center officials aimed to ensure the generalizability of results to such institutions.
Twelve experts participated in the two-round Delphi process. After analyzing the responses, mean scores were calculated for each component. Any component with a score below 3 was subject for elimination, though none fell below this threshold. Experts also provided feedback for minor terminological refinements, which were also integrated into the revised version distributed in the second round. With the achievement of relative consensus, indicated by mean scores above 4 and over 90% agreement (scores of 4 or 5), the proposed knowledge tree model was approved by participants.
To further validate the proposed model, the Content Validity Index (CVI) was utilized. In this phase, eight experts assessed the relevance of each item using a four-point scale: not relevant, major revision needed, minor revision needed, and fully relevant. The ratio of experts selecting option 3 or 4 was calculated for each item. Items with scores below 0.70 were rejected, those between 0.70 and 0.79 were revised, and those above 0.79 were considered valid [2]. This validation process ensured the credibility of the model’s components for the develpment of knowledge tree in health research centers.
Results
The findings from the Delphi panel indicated that the first step in developing a knowledge tree for research centers is to identify the centers’ knowledge areas. According to the findings, understanding the context for organizational knowledge is achieved through three steps: reviewing the organization's strategies and upstream documents, identifying the required knowledge fields needed for the organization, and identifying the types and structure of knowledge needed. After completing the first step and gaining a clear understanding of the organization’s knowledge, the second step involves planning to start the process for constructing the knowledge tree. This step was previously titled “Determining Knowledge Assets and Areas,” but the participants suggested the new title. The second step consists of four steps: determining knowledge assets (including knowledge workers, processes, technology, and resources), identifying organization's stakeholders and knowledge users, analyzing the organization's knowledge needs, assessing the current status, and forming a working group to develop the knowledge
tree.
The third step involves determining the framework for developing the organization's knowledge tree. This step was initially titled “Initial Planning for the Organization's Knowledge Tree” but participants noted that “planning” is too broad term and recommend the current title. In this step, it is necessary to define the scope of the knowledge tree (defining layers and branches), determine the type of knowledge tree (object-oriented-mission-oriented), and select the methodology (Delphi panel, focus group, etc.) to construct the knowledge tree.
After providing the initial framework of the knowledge tree and prerequisites, it is necessary to acquire knowledge from resources and assets and organize it in the tree structure. The fourth step includes four stages: designing a knowledge acquisition tool (e.g. relevant forms or checklists), extracting knowledge representative keywords, interviewing stakeholders and scholars, forming knowledge profiles of individuals, and categorizing and integrating keywords. The fifth and final step consists of two parts. The first part includes three stages: creating a knowledge structure of keywords (domain, branch, sub-branch, etc.), mapping relationships between keywords in a tree structure, and visualizing the knowledge tree using appropriate software. The output of the previous steps can be placed in a visual interface. The second part consists of three stages: validation and verification of the knowledge tree by experts, completion and correction of the knowledge tree by the project team, and periodically updating the knowledge tree to ensure it remains update and valid. These steps of the model are presented in Figure 1.
Figure 1. The template for developing knowledge tree in health research centers
Currently, the growth of knowledge does not match the ability of healthcare organizations to disseminate, translate, and use existing knowledge in health and clinical care. Therefore, a large amount of health knowledge is fragmented throughout the organization and scattered in various locations, making it difficult for professionals to use knowledge effectivelly. This issue is important professionals make decisions for patient treatment and care. Therefore, insufficient knowledge or difficult access to it due to its fragmented nature can result in less reliable decisions or incomplete care and services provided to patients [1].
Considering the crucial role of medical universities in the health system and the significant contribution of their research centers to university development, as well as the significance of knowledge management in these institutions, it is essential to develop a knowledge tree model for their research centers. Although some universities have knowledge management departments, no standard model or tool currently exists to measure and evaluate their knowledge status. Developing such a model will help identify the current knowledge, knowledge gaps, and knowledge assets of these centers. This comprehensive assessment can support improved planning and policymaking. Therefore, this study aims to develop a knowledge tree model for the research centers of medical universities.
Methods
This applied study utilized a quantitative approach based on the Delphi method. The Delphi technique was employed to gather and analyze experts’ opinions through a consensus-bulding process among subject-matter specialists. The process began with a literature review and field study using keywords such as "knowledge tree" and "knowledge mapping" to identify stages and components relevant to knowledge map development. Semi-structured interviews with experts were counducted to develop an initial model for designing a knowledge tree in the health domain. Given the limited resources detailing the knowledge tree construction process, general stages of knowledge mapping were used as a foundation for expert surveys, aiming to adapt them to health research centers.
In the first phase, models from various researchers were reviewed and synthesized to create a preliminary framework. These components were organized into an initial 29-item questionnaire with a five-point Likert scale. The questionnaire was distributed electronically or in person. The target population included staff from medical universities’ research centers, research center managers, and experts in health and knowledge management with relevant experience and academic outputs. Selection criteria included managing at least two relevant projects and having a minimum of three scientific publications in the field. The involvement of research center officials aimed to ensure the generalizability of results to such institutions.
Twelve experts participated in the two-round Delphi process. After analyzing the responses, mean scores were calculated for each component. Any component with a score below 3 was subject for elimination, though none fell below this threshold. Experts also provided feedback for minor terminological refinements, which were also integrated into the revised version distributed in the second round. With the achievement of relative consensus, indicated by mean scores above 4 and over 90% agreement (scores of 4 or 5), the proposed knowledge tree model was approved by participants.
To further validate the proposed model, the Content Validity Index (CVI) was utilized. In this phase, eight experts assessed the relevance of each item using a four-point scale: not relevant, major revision needed, minor revision needed, and fully relevant. The ratio of experts selecting option 3 or 4 was calculated for each item. Items with scores below 0.70 were rejected, those between 0.70 and 0.79 were revised, and those above 0.79 were considered valid [2]. This validation process ensured the credibility of the model’s components for the develpment of knowledge tree in health research centers.
Results
The findings from the Delphi panel indicated that the first step in developing a knowledge tree for research centers is to identify the centers’ knowledge areas. According to the findings, understanding the context for organizational knowledge is achieved through three steps: reviewing the organization's strategies and upstream documents, identifying the required knowledge fields needed for the organization, and identifying the types and structure of knowledge needed. After completing the first step and gaining a clear understanding of the organization’s knowledge, the second step involves planning to start the process for constructing the knowledge tree. This step was previously titled “Determining Knowledge Assets and Areas,” but the participants suggested the new title. The second step consists of four steps: determining knowledge assets (including knowledge workers, processes, technology, and resources), identifying organization's stakeholders and knowledge users, analyzing the organization's knowledge needs, assessing the current status, and forming a working group to develop the knowledge
tree.
The third step involves determining the framework for developing the organization's knowledge tree. This step was initially titled “Initial Planning for the Organization's Knowledge Tree” but participants noted that “planning” is too broad term and recommend the current title. In this step, it is necessary to define the scope of the knowledge tree (defining layers and branches), determine the type of knowledge tree (object-oriented-mission-oriented), and select the methodology (Delphi panel, focus group, etc.) to construct the knowledge tree.
After providing the initial framework of the knowledge tree and prerequisites, it is necessary to acquire knowledge from resources and assets and organize it in the tree structure. The fourth step includes four stages: designing a knowledge acquisition tool (e.g. relevant forms or checklists), extracting knowledge representative keywords, interviewing stakeholders and scholars, forming knowledge profiles of individuals, and categorizing and integrating keywords. The fifth and final step consists of two parts. The first part includes three stages: creating a knowledge structure of keywords (domain, branch, sub-branch, etc.), mapping relationships between keywords in a tree structure, and visualizing the knowledge tree using appropriate software. The output of the previous steps can be placed in a visual interface. The second part consists of three stages: validation and verification of the knowledge tree by experts, completion and correction of the knowledge tree by the project team, and periodically updating the knowledge tree to ensure it remains update and valid. These steps of the model are presented in Figure 1.
Figure 1. The template for developing knowledge tree in health research centers
Discussion
In this study, based on expert opinions and two Delphi rounds, it was found that the first step in developing a knowledge tree for health-related research centers is identifying the knowledge areas of the research center. In this regard, Sadeghi et al. [3], Akhavan and Pezeshkan [4], and Javaheri et al. [5] also introduced the identification of knowledge areas as an important step for knowledge mapping. However, the present study found that this step is prioritized over all other steps and is a prerequisite for proceeding to the next steps. This is because any knowledge management activities must begin with a clear understanding of the current situation, so that planning can be made. It should be noted that the analyzing knowledge areas helps determine both the existing and needed knowledge areas in the organization. Furthermore, determining the types of knowledge in research centers helps policymakers understand the focus of past outputs, their needs for tacit or explicit knowledge, and recognize the types of knowledge that require further development and dissemination. In the second step, it is necessary to develop a plan for creation of the knowledge tree. The studies by Sadeghi et al. [3], Varnaseri et al. [6], Haghighi Borujeni and Tavallaei [7], Ahmadvand and Junaidi [8], and Vestal [9] align with the present study. They similarly considered the determination of knowledge assets and knowledge resources as essential steps for knowledge mapping. Knowledge assets and resources may include people, procedures, instructions, etc. Recognizing and determining these items helps research centers locate and manage their knowledge. Moreover, Sadeghi et al. [3], Haghighi Borujeni and Tavallaei [7] have considered the importance of identifying supporters, stakeholders, and users, as well as establishing a working group or committee for knowledge tree development. This is because this group of individuals can directly and indirectly influence the production and dissemination of knowledge resources. At this stage, the knowledge management team and the knowledge tree working group should be formed to coordinate activities.
In the third step, the framework of the organization's knowledge tree should be established by determining the knowledge domains, identifying the type of knowledge tree, and selecting a methodological approach, which is in line with the research by Sadeghi et al. [3], the Knowledge Management System of the Iran Ministry of Health [10], Lu et al. [11], Wu et al. [12], Barros et al. [13]. These measures serve as a clear roadmap and basis to help the executive team to proceed. They should understand which scientific and valid methods to use for developing their knowledge tree. In the fourth step, namely developing the knowledge tree, studies by Najafi et al. [14], and Sadeghi et al. [3] has also considered the importance of designing a tool and framework for knowledge acquisition. In this study, it was found that designing a knowledge acquisition tool precedes any other activity in this step. After establishing the roadmap, acquiring knowledge should begin. The second action after knowledge acquisition is the extraction of knowledge representative keywords. Studies by Sadeghi et al. [3], Kiani et al. [15], Haghighi Borujeni and Tavallaei [7], Moomivand et al. [16], Alipourhafezi et al. [17], and Akbari Javid and Ghaffari [18] have emphasized the extraction of knowledge representative keywords at this step. This is because knowledge must be simplified for visualization, which is achieved by assigning knowledge representative keywords. In the present study, it was also found that after extracting keywords, classification and profiling of these keywords should be done.
In the fifth and final step (design and validation of the knowledge tree), it was determined that knowledge must first be organized in a specific and tree-like structure and visualized using an appropriate tool, which is in line with the research counducted by Akhavan and Joudi [19], Haghighi Borujeni and Tavallaei [7], Driessen et al. [20], Vernaseri et al. [6], Huosong et al. [21], vail [22], Davenport and Prusak [23], Eppler [24], Wang [25], Kim et al. [26], Li et al. [27], Ebener et al. [28], Yang [29], Rao et al. [30], Akhavan and Pezeshkan [4], Ronda-Pupo [31], Sadeghi et al. [3], and Zahedi et al. [32]. In the second stage of the last step, the validation of the knowledge tree is carried out in several steps, consistent with the research of Sadeghi et al. [3], Hossein Gholizadeh [33], Eppler [24], Wang [25], Kim et al. [26], Hellstrom and Husted [34], Lin and Hsueh [35], Driessen et al. [20], Rao et al. [30], and Zahedi et al. [32]. These researchers have emphazied on the validation, accessibility, and also updating of the knowledge maps. Validation is an important step that ensures the knowledge tree is referable.
Limitations
This study faced limitations due to limited access to specialists and low response rate, which limited the generalizability of the results at the national evel. It is recommended that the proposed model be tested and validated across various geographic regions and organizations.
Conclusion
This research identified the key stages for developing a knowledge tree, which plays a vital role in assessing the current knowledge status. Steps such as identifying knowledge areas, structuring the research center’s knowledge, gathering expert knowledge, profiling, categorizing, and visualizing organizational knowledge support this process. The
In this study, based on expert opinions and two Delphi rounds, it was found that the first step in developing a knowledge tree for health-related research centers is identifying the knowledge areas of the research center. In this regard, Sadeghi et al. [3], Akhavan and Pezeshkan [4], and Javaheri et al. [5] also introduced the identification of knowledge areas as an important step for knowledge mapping. However, the present study found that this step is prioritized over all other steps and is a prerequisite for proceeding to the next steps. This is because any knowledge management activities must begin with a clear understanding of the current situation, so that planning can be made. It should be noted that the analyzing knowledge areas helps determine both the existing and needed knowledge areas in the organization. Furthermore, determining the types of knowledge in research centers helps policymakers understand the focus of past outputs, their needs for tacit or explicit knowledge, and recognize the types of knowledge that require further development and dissemination. In the second step, it is necessary to develop a plan for creation of the knowledge tree. The studies by Sadeghi et al. [3], Varnaseri et al. [6], Haghighi Borujeni and Tavallaei [7], Ahmadvand and Junaidi [8], and Vestal [9] align with the present study. They similarly considered the determination of knowledge assets and knowledge resources as essential steps for knowledge mapping. Knowledge assets and resources may include people, procedures, instructions, etc. Recognizing and determining these items helps research centers locate and manage their knowledge. Moreover, Sadeghi et al. [3], Haghighi Borujeni and Tavallaei [7] have considered the importance of identifying supporters, stakeholders, and users, as well as establishing a working group or committee for knowledge tree development. This is because this group of individuals can directly and indirectly influence the production and dissemination of knowledge resources. At this stage, the knowledge management team and the knowledge tree working group should be formed to coordinate activities.
In the third step, the framework of the organization's knowledge tree should be established by determining the knowledge domains, identifying the type of knowledge tree, and selecting a methodological approach, which is in line with the research by Sadeghi et al. [3], the Knowledge Management System of the Iran Ministry of Health [10], Lu et al. [11], Wu et al. [12], Barros et al. [13]. These measures serve as a clear roadmap and basis to help the executive team to proceed. They should understand which scientific and valid methods to use for developing their knowledge tree. In the fourth step, namely developing the knowledge tree, studies by Najafi et al. [14], and Sadeghi et al. [3] has also considered the importance of designing a tool and framework for knowledge acquisition. In this study, it was found that designing a knowledge acquisition tool precedes any other activity in this step. After establishing the roadmap, acquiring knowledge should begin. The second action after knowledge acquisition is the extraction of knowledge representative keywords. Studies by Sadeghi et al. [3], Kiani et al. [15], Haghighi Borujeni and Tavallaei [7], Moomivand et al. [16], Alipourhafezi et al. [17], and Akbari Javid and Ghaffari [18] have emphasized the extraction of knowledge representative keywords at this step. This is because knowledge must be simplified for visualization, which is achieved by assigning knowledge representative keywords. In the present study, it was also found that after extracting keywords, classification and profiling of these keywords should be done.
In the fifth and final step (design and validation of the knowledge tree), it was determined that knowledge must first be organized in a specific and tree-like structure and visualized using an appropriate tool, which is in line with the research counducted by Akhavan and Joudi [19], Haghighi Borujeni and Tavallaei [7], Driessen et al. [20], Vernaseri et al. [6], Huosong et al. [21], vail [22], Davenport and Prusak [23], Eppler [24], Wang [25], Kim et al. [26], Li et al. [27], Ebener et al. [28], Yang [29], Rao et al. [30], Akhavan and Pezeshkan [4], Ronda-Pupo [31], Sadeghi et al. [3], and Zahedi et al. [32]. In the second stage of the last step, the validation of the knowledge tree is carried out in several steps, consistent with the research of Sadeghi et al. [3], Hossein Gholizadeh [33], Eppler [24], Wang [25], Kim et al. [26], Hellstrom and Husted [34], Lin and Hsueh [35], Driessen et al. [20], Rao et al. [30], and Zahedi et al. [32]. These researchers have emphazied on the validation, accessibility, and also updating of the knowledge maps. Validation is an important step that ensures the knowledge tree is referable.
Limitations
This study faced limitations due to limited access to specialists and low response rate, which limited the generalizability of the results at the national evel. It is recommended that the proposed model be tested and validated across various geographic regions and organizations.
Conclusion
This research identified the key stages for developing a knowledge tree, which plays a vital role in assessing the current knowledge status. Steps such as identifying knowledge areas, structuring the research center’s knowledge, gathering expert knowledge, profiling, categorizing, and visualizing organizational knowledge support this process. The
knowledge tree is a crucial tool in knowledge management, enabling organizations to analyze their knowledge assets before initiating management management actions, thereby guiding future decisions. Research centers, in particular, benefit from using the knowledge tree as a roadmap for evaluating their current status and forecasting future directions for effective knowledge management.
Declerations
Ethical considerations: This article is based on research approved by Baqiyatallah University of Medical Sciences with the ethics code IR.BMSU.REC.1402.027. In this study, the principles of honesty were observed throughout the research.
Funding: This research was conducted without financial support.
Conflict of interest: The authors declare that there is no conflict of interest in this study.
Authors contributions: R.D: Data curation, software, data analysis, resources, writing– original draft, visualization, final approval; M.K: conceptualization, validation, writing-reviewing and editing, project administration, final approval; M.M: data management, project administration, final approval; MK. B: study design, methodology, validation, supervision; M.AF: Data curation, Resources, final approval. All final authors participated in the initial writing of this article, or its revision, and all accept responsibility for the accuracy and correctness of the contents contained in it.
Consent for publication: Not applicable.
Data availability: Access to data is available through the corresponding author with reasonable cause.
AI decleration: No artificial intelligence was used in writing this article.
Acknowledgements: This article is part of a research project titled "Mapping the Knowledge Tree of Research Centers at Baqiyatallah University of Medical Sciences," approved by Baqiyatallah University of Medical Sciences in 2022, with the project code 401000288. We would like to express our deepest gratitude to all the staff and officials of the Research Center of Health Management of Baqiyatallah University of Medical Sciences who assisted the authors in preparing and producing the article.
Declerations
Ethical considerations: This article is based on research approved by Baqiyatallah University of Medical Sciences with the ethics code IR.BMSU.REC.1402.027. In this study, the principles of honesty were observed throughout the research.
Funding: This research was conducted without financial support.
Conflict of interest: The authors declare that there is no conflict of interest in this study.
Authors contributions: R.D: Data curation, software, data analysis, resources, writing– original draft, visualization, final approval; M.K: conceptualization, validation, writing-reviewing and editing, project administration, final approval; M.M: data management, project administration, final approval; MK. B: study design, methodology, validation, supervision; M.AF: Data curation, Resources, final approval. All final authors participated in the initial writing of this article, or its revision, and all accept responsibility for the accuracy and correctness of the contents contained in it.
Consent for publication: Not applicable.
Data availability: Access to data is available through the corresponding author with reasonable cause.
AI decleration: No artificial intelligence was used in writing this article.
Acknowledgements: This article is part of a research project titled "Mapping the Knowledge Tree of Research Centers at Baqiyatallah University of Medical Sciences," approved by Baqiyatallah University of Medical Sciences in 2022, with the project code 401000288. We would like to express our deepest gratitude to all the staff and officials of the Research Center of Health Management of Baqiyatallah University of Medical Sciences who assisted the authors in preparing and producing the article.
Type of Study: Research |
Subject:
Medical Librarianship and Information Science
Received: 2025/01/4 | Accepted: 2025/07/16 | Published: 2025/07/27
Received: 2025/01/4 | Accepted: 2025/07/16 | Published: 2025/07/27
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