Volume 20, Issue 67 (4-2017)                   jha 2017, 20(67): 75-88 | Back to browse issues page

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Langarizadeh M, Sadr-ameli S, Soleymani M. Development of Vital Signs Monitoring Decision Support System for Coronary Care Unit Inpatients. jha. 2017; 20 (67) :75-88
URL: http://jha.iums.ac.ir/article-1-2154-en.html
1- Assistant Professor of Medical Informatics Iran University of Medical Sciences (IUMS)
2- Professor of Cardiology and Cardiac Electrophysiologist Iran University of Medical Sciences (IUMS)
3- Master sutudent of medical informatics Iran University of Medical Sciences (IUMS) , Soleymani.m@tak.iums.ac.ir
Abstract:   (1070 Views)

Introduction: Big volume of patient’s medical data is one of the medical error reasons in coronary care unit (CCU). The purpose of this study was the designing a system that can monitored the patient’s vital sign continuously and when there are abnormal, producing alarms and proposed appropriate medical interventions according to the patient’s conditions in CCU.

 Methods: This was application-development study that done in cross-sectional method in Shahid Rajai hospital at Tehran in 2015. 15 physicians and 15 nurses of CCU were considered as non-random purposively sampling. MEAN.js technology and MIMIC II Physionet’s database were used for system designing.

Results: Normal and abnormal ranges of Vital signs were assessed according to the environmental and population conditions in this study. Variety of therapeutic interventions due to the patients’ vital signs changing was identified with their priorities. The results showed that the clinical decision support system (CDSS) had accuracy (94/68 %), sensitivity (82/60 %) and specificity (100 %) in proposing of proper interventions and had (92/92 %) accuracy, (80 %) sensitivity and (100 %) specificity in producing of timely alarms.

Conclusion: There are several factors that impact on determining of normal and abnormal ranges of vital signs and interventions priorities. The results showed that CDSS can help professionals in appropriate medical interventions selecting in unanticipated conditions at clinical care processes. At clinical point, this system can improve the understanding of vital signs, patient health conditions and decision-making process that can help in reducing of medical errors.

Full-Text [PDF 1484 kb]   (1523 Downloads)    
Type of Study: Research | Subject: General
Received: 2016/06/18 | Accepted: 2017/02/18 | Published: 2017/02/18

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