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Background: This research aims to design and implement a software with the ability to identify patients who are facing the risk of pulmonary embolism and deep venous thrombosis instantly as well as the ability to send timely reminders for any prophylactic action. The main target is introduce a clinical decision- support system which could finally lead to preventing mortality and handicap cases caused by embolism and thromboses in patients who are confined to bed in hospitals. Materials and Methods: The software was designed using the Visual Basic.Net and SQL Server database. Afterwards the software was installed in the largest educational hospital of Kerman and a survey was conducted amongst the physicians using multiple questionnaires and interviews. Finally, the data were analyzed using the SPSS software. Results: The average score was 21.16 for the physicians and 20.76 for the nurses. T-Test results show that there is no significant difference between the total average score of the physicians and that of the nurses. Conclusion: The results have shown that both groups (physicians and nurses) have a positive viewpoint about the software therefore using the clinical decision support system can be effective in reducing the occurrence of pulmonary embolism and deep venous thrombosis through sending timely electronic alerts to the medical staff.

Background: Despite the fact that only one-third of chest pains occur due to heart diseases, still physicians have tendency to admit most of these patients to reduce risk of negligence and its consequences.Clinical decision support systems (CDSS) enable physicians to distinguish better cardiac from non-cardiac chest pain. This study reviewed articles which focused on this issue.

Materials and Methods: Google scholar and PubMed database were targeted for search. Out of ninety primary matching articles based on the title, abstract and keywords, 28 full texts were relevant which were included in this study.

Results: Included articles were classified into two categories such as managing hospital resources and increasing the accuracy of diagnosis. Study results in the first categoryshowed decrease in both reception and referral time up to 30% and length of hospital stay up to 26% using CDSS. In the second category, the highest reported accuracy of diagnosis was 97% and the maximum sensitivity and specificity were 100% and 89.43% respectively. Even though, the results of a study revealed that the accuracy of decision support system in diagnosing cardiac chest pain was better than the compared cardiologists.

Conclusion: Considering the role of CDSS in managing hospital resources and improving accuracy of diagnosing cardiac chest pain, it is suggested that emergency wards and cardiac screening centers equipped by these systems.