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Showing 4 results for Mohammadpour
Determining the Information Requirements of Electronic Prescribing and Medication Administration System for Cardiovascular Patients
Reza Safdari, Farnoosh Larti, Kamyar Fathi Salari, Saman Mohammadpour,
Volume 14, Issue 3 (Aug & Sep 2020)
Abstract
Background and Aim: Cardiovascular diseases and medication errors are among the leading causes of morbidity and mortality around the world. Electronic prescribing and Medication Administration(ePMA) systems can prevent medication errors to some extent. This study aimed to determine the information requirements of ePMA systems.
Materials and Methods: This descriptive study was conducted in Imam Khomeini Hospital of Tehran and School of Allied Medical Sciences affiliated to Tehran University of Medical Sciences (TUMS) in the summer of 2019 in two phases: literature review and survey-based questionnaire. Information items obtained from reviewing the texts of 100 articles were organized in three questionnaires. In the survey phase, questionnaires were distributed among physicians, nurses, and the experts of health information management(HIM) and medical informatics, using census sampling method. The reliability of the questionnaires was measured using Cronbach's coefficient alpha. Statistical analysis was done using SPSS.
Results: The findings showed that based on specialists’ point of view, patients' demographic information items and unique identifiers gained the highest average, above 4.7. Physicians agreed most with clinical information, including medication history and generic names. From the nurses’ point of view, the information items of the patients’ problems and the procedures performed and the types of drug doses obtained a complete average of 5.
Conclusion: The need for information items varies among different users of ePMA systems, but there may be items that are common for them. Future studies should further investigate financial and pharmaceutical information requirements based on the perspectives of other hospital pharmacy and accounting staff.

The Effects of Implementing Clinical Decision Support Systems on the Diagnosis, Treatment and Management of Cancers: A Systematic Review
Saman Mohammadpour, Reza Rabiei, Elham Shabahrami, Kamyar Fathisalari, Maryam Khakzad, Mostafa Langarizadeh,
Volume 16, Issue 2 (Jun 2022)
Abstract
Background and Aim: Cancer is the second leading cause of death in the world, which leads to the death of more than 10 million people in the world every year. Its early diagnosis, management and proper treatment play an important role in reducing complications and mortality. One of the support tools in early diagnosis, treatment and management of this disease are Clinical Decision Support System (CDSS), which are divided into two groups, rule-based and non-rule-based. Rule-based decision support systems are created based on clinical guidelines, while non-rule-based decision support systems use machine learning. In this research, the effects of decision support systems, rule-based and non-rule-based, on cancer diagnosis, treatment and management were measured.
Materials and Methods: The present study was conducted using a systematic review method, which was conducted by searching the Web of Science, Scopus, IEEE and PubMED databases until 12/31/2021. After removing duplicates and evaluating the characteristics of the inclusion and exclusion criteria, studies related to the goal were selected. The selection of articles was based on the title, abstract and full text The data collection tool was the data extraction form, which included year of study, type of study, system of body, organ of body, the service provided by the decision support system, type of decision support system, effect, effect index and the score of effect index. Narrative synthesis were used for data analysis.
Results: Out of 768 articles, 16 articles related to the objectives of the study were identified. Studies were presented in two categories of clinical decision-support systems: Rule-based and non-Rule based. The effects evaluated in the clinical decision support systems were Rule-based, dose adjustment, symptoms, adherence to treatment guidelines, care time, smoking, need for chemotherapy and pain management, all of which except pain management were significant and positive. The effects evaluated were in the category of non-Rule based clinical decision support systems, diagnostic and therapeutic decisions, controlling neutropenia, all of which were significant and positive except controlling neutropenia.
Conclusion: The results obtained for the effectiveness of both Rule-based and non-Rule-based decision support systems indicated different benefits of these two categories. Therefore, using their combination in the field of cancer can bring very useful results.

Educational Needs Assessment of Health Information Technology Professionals: A Cross-Sectional Study in Hospitals in Western Iran
Taleb Khodaveisi, Hamid Bouraghi, Tooba Mehrabi, Javad Faradmal, Mahdiye Shojaei Baghini, Ali Mohammadpour,
Volume 18, Issue 5 (11-2024)
Abstract
Background and Aim: Identifying the educational needs of health information technology staff is essential before implementing any continuous education programs. This comprehensive study investigates these needs among health information technology personnel working in hospitals in the Hamadan province, considering both the general and specialized aspects of the field.
Materials and Methods: This descriptive cross-sectional study was conducted across 11 hospitals affiliated with Hamadan University of Medical Sciences. The study population comprised staff from the reception, medical records, statistics, and coding departments. Data were gathered using a validated and reliable standardized questionnaire. Collection methods included both in-person and remote approaches. Data analysis was performed using SPSS software, with results reported through descriptive and inferential statistics, specifically utilizing the Kruskal-Wallis test.
Results: The results of this study showed that among the generally accepted needs, items such as information technology (96.7%), legal aspects of medical records (87.6%), and communication skills (76.7%) had the highest percentage. Additionally, educational needs varied across different units: Coding unit staff required more training in the principles of diagnosis documentation (92.9%), familiarity with the coding guidelines for causes of death (85.7%), and familiarity with the coding guidelines for procedures (85.7%), statistics unit staff needed training in statistical software, and reception and medical records staff required education on relevant regulations. There was also a significant correlation between educational needs and certain individual characteristics such as work experience, education level, gender, and field of study.
Conclusion: The study results indicate that designing effective educational programs for health information technology staff requires consideration of individual characteristics, such as gender, work experience, and education level. Additionally, the training should be continuous, tailored to the distinct needs of each group, and delivered at appropriate intervals.

Applications and Role of Artificial Intelligence in the Management of Cardiac Diseases: A Systematic Review of Persian Research Studies
Mozhgan Farazmand, Mandana Asgari, Hamid Bouraghi, Taleb Khodaveisi, Ali Mohammadpour, Soheila Saeedi,
Volume 19, Issue 3 (9-2025)
Abstract
Background and Aim: Cardiovascular diseases have a very high prevalence globally and are recognized as one of the main causes of mortality worldwide. Artificial intelligence, as a novel technology, has garnered attention in recent years in Iran and other parts of the world for the management of a wide variety of diseases. The present study aimed to systematically review research studies conducted in the field of applying artificial intelligence in cardiovascular diseases.
Materials and Methods: To investigate research studies conducted in the field of cardiovascular diseases utilizing artificial intelligence, the Persian language databases SID, Google Scholar, and Magiran were searched. This search was conducted without time limitations on April 3, 2024 and included all research studies that, up to this date, had used various artificial intelligence methods in the field of cardiovascular diseases in the present systematic review.
Results: The results of the search in the aforementioned three databases led to the retrieval of 17,819 research studies, of which 46 research studies met the inclusion and exclusion criteria of the study. These research studies had used artificial intelligence in three areas: prediction, treatment, and diagnosis. Neural networks (n=22), support vector machines (n=20), and decision trees (n=16) were the algorithms that were used more than other techniques. The data sources of the included research studies were mainly patient medical records and the UCI database. Additionally, MATLAB software was used more than other software. The most frequently mentioned limitations in the research studies included not considering all factors, limited access to data, insufficient data, the presence of noise in signals or images, and the presence of outliers, missing values, and non-normality of data.
Conclusion: The systematic review of research studies conducted in the field of cardiovascular diseases utilizing artificial intelligence showed that this technology has been used in a wide range of cardiovascular diseases, and most of the conducted research studies confirmed its effectiveness and successful performance.

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