Volume 16, Issue 2 (Jun 2022)
payavard 2022, 16(2): 113-122 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Mohammadpour S, Rabiei R, Shabahrami E, Fathisalari K, Khakzad M, Langarizadeh M. The Effects of Implementing Clinical Decision Support Systems on the Diagnosis, Treatment and Management of Cancers: A Systematic Review. payavard 2022; 16 (2) :113-122
URL: http://payavard.tums.ac.ir/article-1-7271-en.html
URL: http://payavard.tums.ac.ir/article-1-7271-en.html
Saman Mohammadpour1 
, Reza Rabiei2 , Elham Shabahrami3 , Kamyar Fathisalari4 , Maryam Khakzad5 , Mostafa Langarizadeh * 6
, Reza Rabiei2 , Elham Shabahrami3 , Kamyar Fathisalari4 , Maryam Khakzad5 , Mostafa Langarizadeh * 6
1- Ph.D. Candidate in Medical Informatics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2- Associate Professor ,Department of Health Information Management and Technology ,School of Allied Medical Sciences ,Shahid Beheshti University of Medical Sciences ,Tehran ,Iran
3- Ph.D. in Industrial Management, School of Management, Kish International Campus, University of Tehran, Tehran, Iran
4- Master of Science in Health Information Technology, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
5- Master of Science in Knowledge and Information Science, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
6- Associate Professor, Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran ,langarizadeh.m@iums.ac.ir
2- Associate Professor ,Department of Health Information Management and Technology ,School of Allied Medical Sciences ,Shahid Beheshti University of Medical Sciences ,Tehran ,Iran
3- Ph.D. in Industrial Management, School of Management, Kish International Campus, University of Tehran, Tehran, Iran
4- Master of Science in Health Information Technology, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
5- Master of Science in Knowledge and Information Science, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
6- Associate Professor, Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran ,
Abstract: (1364 Views)
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.
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.
Keywords: Clinical Decision Support System (CDSS), Clinical Outcome, Effect, Decision Making, Cancer
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
