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Creating a Management Dashboard for the Oncology Ward to Monitor Health Data of Children with Cancer
Saber Yazdani Damavandi, Tayebeh Baniasadi, Mohammad Ali Molavi, Farid Khorrami,
Volume 19, Issue 5 (12-2025)
Volume 19, Issue 5 (12-2025)
Abstract
Background and Aim: Cancer has been recognized as the second leading cause of child mortality in recent years. Due to the increasing amount of healthcare data for cancer patients, healthcare providers need a tool to monitor patients for immediate intervention. An intelligent and dynamic information management dashboard is capable of compiling and displaying data using charts and tables. In the present study, a management dashboard was designed for the oncology department of a children’s hospital, and its usability was evaluated.
Materials and Methods: This developmental–applied research was conducted in 2024 at the Educational, Medical, and Research Center for Children in Bandar Abbas. In order to create a management dashboard for the pediatric oncology department, three stages were carried out. In the first stage, all necessary content to be displayed on the dashboard was extracted based on a review of literature and documents from the oncology department of Bandar Abbas Children’s Hospital. This content was then validated by experts using the Delphi method in two rounds. In the second stage, a prototype of dashboard for the oncology ward was designed using Power BI Desktop software. Finally, its usability was evaluated using a SUS questionnaire by 20 users. The data were analyzed using descriptive statistics with SPSS software.
Results: Following the screening of 3,435 initial records and a review of 22 articles alongside 38 patient files, a preliminary set of 104 managerial and 67 clinical indicators was extracted. These indicators were validated through a tworound Delphi process with 12 experts, resulting in the final selection of 105 managerial and 71 clinical indicators for dashboard inclusion. Based on this validated set, a tenpage managerial dashboard was developed to present key performance metrics. Usability assessment using the System Usability Scale (SUS) yielded a mean score of 75.87, which, according to the Bangor scale, is classified as “acceptable” and corresponds to a grade of “excellent.” User feedback informed subsequent refinements to the dashboard’s data visualizations and interface. In summary, the developed dashboard represents an effective and userfriendly tool for monitoring and managing information within a pediatric oncology department.
Conclusion: The pediatric oncology management dashboard facilitates the integration and summarization of essential data for healthcare providers, thereby assisting them in making timely diagnoses and interventions for children with cancer. Additionally, the present dashboard demonstrates appropriate usability, which enhances users’ understanding of health information and leads to more accurate decision-making.
Materials and Methods: This developmental–applied research was conducted in 2024 at the Educational, Medical, and Research Center for Children in Bandar Abbas. In order to create a management dashboard for the pediatric oncology department, three stages were carried out. In the first stage, all necessary content to be displayed on the dashboard was extracted based on a review of literature and documents from the oncology department of Bandar Abbas Children’s Hospital. This content was then validated by experts using the Delphi method in two rounds. In the second stage, a prototype of dashboard for the oncology ward was designed using Power BI Desktop software. Finally, its usability was evaluated using a SUS questionnaire by 20 users. The data were analyzed using descriptive statistics with SPSS software.
Results: Following the screening of 3,435 initial records and a review of 22 articles alongside 38 patient files, a preliminary set of 104 managerial and 67 clinical indicators was extracted. These indicators were validated through a tworound Delphi process with 12 experts, resulting in the final selection of 105 managerial and 71 clinical indicators for dashboard inclusion. Based on this validated set, a tenpage managerial dashboard was developed to present key performance metrics. Usability assessment using the System Usability Scale (SUS) yielded a mean score of 75.87, which, according to the Bangor scale, is classified as “acceptable” and corresponds to a grade of “excellent.” User feedback informed subsequent refinements to the dashboard’s data visualizations and interface. In summary, the developed dashboard represents an effective and userfriendly tool for monitoring and managing information within a pediatric oncology department.
Conclusion: The pediatric oncology management dashboard facilitates the integration and summarization of essential data for healthcare providers, thereby assisting them in making timely diagnoses and interventions for children with cancer. Additionally, the present dashboard demonstrates appropriate usability, which enhances users’ understanding of health information and leads to more accurate decision-making.
Ahmad Negahban, Azam Salehzadeh, Razieh Farrahi, Alireza Nourozi, Sina Tavakoli,
Volume 19, Issue 6 (3-2026)
Volume 19, Issue 6 (3-2026)
Abstract
Background and Aim: With the digitalization of healthcare, hospital information systems handle vast amounts of sensitive data, making their protection crucial. This study aimed to assess the compliance of these systems in hospitals affiliated with Birjand University of Medical Sciences with the physical and technical safeguard standards of Health Insurance Portability and Accountability Act (HIPAA) in 2024.
Materials and Methods: This cross-sectional descriptive study was conducted in 15 hospitals affiliated with Birjand University of Medical Sciences. The study population consisted of Information Technology (IT) unit managers, who were selected using a census method (15 individuals). The research instrument was a researcher-developed checklist consisting of 56 items based on the physical and technical standards of HIPAA. The face validity of the checklist was confirmed by five experts in Health Information Management, Medical Informatics, and Health Policy, and its reliability was verified with a Cronbach’s alpha coefficient of 0.84. Data were analyzed using SPSS software and descriptive statistics, including frequency, percentage, mean, and standard deviation.
Results: A total of 15 information technology managers (14 men and 1 woman) from 15 hospitals, including 8 teaching and 7 non-teaching hospitals, participated in the study. The findings showed that the hospital information systems of Birjand University of Medical Sciences complied with the HIPAA physical and technical safeguard standards at rates of 81.7% and 86.7%, respectively. In the domain of physical safeguards, the workstation security standard demonstrated the highest level of compliance, with a mean score of 89.3%. Full compliance (100%) was observed for certain indicators, including emergency access procedures for facilities and physical access control procedures. In contrast, the lowest compliance in this domain was related to the device and media controls standard, with a mean score of 74.9%, particularly in the identification and tracking of hardware and electronic media. In the domain of technical safeguards, the overall mean compliance rate was 86.7%. Among these standards, person or entity authentication achieved the highest level of compliance, with all hospitals demonstrating full compliance (100%). In addition, access control (93.3%), audit controls (86.7%), and transmission security (85.3%) were all at desirable levels. However, the lowest compliance was observed for the integrity standard (50%), highlighting the need to strengthen technical infrastructure and implement more advanced electronic mechanisms to ensure data accuracy and integrity.
Conclusion: Although the overall level of compliance in the hospitals under study is satisfactory, significant gaps remain, particularly in device and media control and data integrity. These deficiencies may lead to breaches of patient privacy and undermine public trust in the healthcare system. It is recommended that senior hospital managers and health policymakers address these deficiencies by developing and implementing clear internal guidelines, investing in appropriate supportive technologies, and conducting continuous, targeted training programs for all personnel. In addition, periodic compliance monitoring is essential to ensure continuous improvement.
Materials and Methods: This cross-sectional descriptive study was conducted in 15 hospitals affiliated with Birjand University of Medical Sciences. The study population consisted of Information Technology (IT) unit managers, who were selected using a census method (15 individuals). The research instrument was a researcher-developed checklist consisting of 56 items based on the physical and technical standards of HIPAA. The face validity of the checklist was confirmed by five experts in Health Information Management, Medical Informatics, and Health Policy, and its reliability was verified with a Cronbach’s alpha coefficient of 0.84. Data were analyzed using SPSS software and descriptive statistics, including frequency, percentage, mean, and standard deviation.
Results: A total of 15 information technology managers (14 men and 1 woman) from 15 hospitals, including 8 teaching and 7 non-teaching hospitals, participated in the study. The findings showed that the hospital information systems of Birjand University of Medical Sciences complied with the HIPAA physical and technical safeguard standards at rates of 81.7% and 86.7%, respectively. In the domain of physical safeguards, the workstation security standard demonstrated the highest level of compliance, with a mean score of 89.3%. Full compliance (100%) was observed for certain indicators, including emergency access procedures for facilities and physical access control procedures. In contrast, the lowest compliance in this domain was related to the device and media controls standard, with a mean score of 74.9%, particularly in the identification and tracking of hardware and electronic media. In the domain of technical safeguards, the overall mean compliance rate was 86.7%. Among these standards, person or entity authentication achieved the highest level of compliance, with all hospitals demonstrating full compliance (100%). In addition, access control (93.3%), audit controls (86.7%), and transmission security (85.3%) were all at desirable levels. However, the lowest compliance was observed for the integrity standard (50%), highlighting the need to strengthen technical infrastructure and implement more advanced electronic mechanisms to ensure data accuracy and integrity.
Conclusion: Although the overall level of compliance in the hospitals under study is satisfactory, significant gaps remain, particularly in device and media control and data integrity. These deficiencies may lead to breaches of patient privacy and undermine public trust in the healthcare system. It is recommended that senior hospital managers and health policymakers address these deficiencies by developing and implementing clear internal guidelines, investing in appropriate supportive technologies, and conducting continuous, targeted training programs for all personnel. In addition, periodic compliance monitoring is essential to ensure continuous improvement.
Reza Safdari, Arash Mansourian, Shahram Tahmasebian, Niloofar Mohammadzadeh, Hamideh Ehtesham,
Volume 19, Issue 6 (3-2026)
Volume 19, Issue 6 (3-2026)
Abstract
Background and Aim: Artificial intelligence-based systems can facilitate data management and interpretation in various dental specialties and can be used as auxiliary tools in diagnosis and education. Case-based reasoning is a promising artificial intelligence method for implementing decision support systems in medical sciences. In the current research, this technique has been used to design an intelligent system for the differential diagnosis of oral diseases.
Materials and Methods: This research is a developmental study and is applied in terms of results. To create a database of cases, patient data was collected by referring to the specialized polyclinic of the Faculty of Dentistry at Tehran University of Medical Sciences and through clinical interviews. The [feature-value] collection was used to display the cases. The weight of the features was determined through a specialized Delphi survey conducted at the national level and as an online study. The determined weights were stored in the case database and used as similarity evaluation parameters. Then, the similarity index was calculated for each case.
Results: The intelligent system designed in this research has been developed based on web technologies. Problem-solving in the case-based reasoning method is done in a cycle and includes four main stages: recovery, reuse, review, and maintenance. The input parameters of the system include clinical indicators, paraclinical indicators, historical data, and management data affecting the diagnosis process. The system provides a prioritized list of differential diagnoses of oral diseases across six main axes as output including Ulcerative, vesicular, and bullous lesions, Red and white lesions of the oral mucosa, Pigmented lesions of the oral mucosa, Benign lesions of the oral cavity, Oral cancer, Salivary gland diseases.
Conclusion: The development of the system utilizing case-based reasoning techniques and clinical data processing has the potential to assist dentists in achieving differential diagnosis across six main areas of oral diseases.
Materials and Methods: This research is a developmental study and is applied in terms of results. To create a database of cases, patient data was collected by referring to the specialized polyclinic of the Faculty of Dentistry at Tehran University of Medical Sciences and through clinical interviews. The [feature-value] collection was used to display the cases. The weight of the features was determined through a specialized Delphi survey conducted at the national level and as an online study. The determined weights were stored in the case database and used as similarity evaluation parameters. Then, the similarity index was calculated for each case.
Results: The intelligent system designed in this research has been developed based on web technologies. Problem-solving in the case-based reasoning method is done in a cycle and includes four main stages: recovery, reuse, review, and maintenance. The input parameters of the system include clinical indicators, paraclinical indicators, historical data, and management data affecting the diagnosis process. The system provides a prioritized list of differential diagnoses of oral diseases across six main axes as output including Ulcerative, vesicular, and bullous lesions, Red and white lesions of the oral mucosa, Pigmented lesions of the oral mucosa, Benign lesions of the oral cavity, Oral cancer, Salivary gland diseases.
Conclusion: The development of the system utilizing case-based reasoning techniques and clinical data processing has the potential to assist dentists in achieving differential diagnosis across six main areas of oral diseases.
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