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Open Access Highly Accessed Research article

Development of a real-time clinical decision support system upon the web mvc-based architecture for prostate cancer treatment

Hsueh-Chun Lin1*, Hsi-Chin Wu2, Chih-Hung Chang3, Tsai-Chung Li4, Wen-Miin Liang4 and Jong-Yi Wang Wang5

Author Affiliations

1 Department of Health Risk Management, School of Public Health, China Medical University, 91 Hsueh-Shi Road, Taichung 40402, Taiwan

2 Department of Medicine, School of Medicine, China Medical University, Taiwan

3 Buehler Center on Aging, Health & Society and Department of Medicine, Feinberg School of Medicine, Northwestern University, Graduate Institute of Biostatistics, China Medical University, Taiwan

4 Graduate Institute of Biostatistics& Biostatistics Center, China Medical University, Taiwan

5 Department of Health Services Administration, School of Public Health, China Medical University, Taiwan

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BMC Medical Informatics and Decision Making 2011, 11:16  doi:10.1186/1472-6947-11-16

Published: 8 March 2011

Abstract

Background

A real-time clinical decision support system (RTCDSS) with interactive diagrams enables clinicians to instantly and efficiently track patients' clinical records (PCRs) and improve their quality of clinical care. We propose a RTCDSS to process online clinical informatics from multiple databases for clinical decision making in the treatment of prostate cancer based on Web Model-View-Controller (MVC) architecture, by which the system can easily be adapted to different diseases and applications.

Methods

We designed a framework upon the Web MVC-based architecture in which the reusable and extractable models can be conveniently adapted to other hospital information systems and which allows for efficient database integration. Then, we determined the clinical variables of the prostate cancer treatment based on participating clinicians' opinions and developed a computational model to determine the pretreatment parameters. Furthermore, the components of the RTCDSS integrated PCRs and decision factors for real-time analysis to provide evidence-based diagrams upon the clinician-oriented interface for visualization of treatment guidance and health risk assessment.

Results

The resulting system can improve quality of clinical treatment by allowing clinicians to concurrently analyze and evaluate the clinical markers of prostate cancer patients with instantaneous clinical data and evidence-based diagrams which can automatically identify pretreatment parameters. Moreover, the proposed RTCDSS can aid interactions between patients and clinicians.

Conclusions

Our proposed framework supports online clinical informatics, evaluates treatment risks, offers interactive guidance, and provides real-time reference for decision making in the treatment of prostate cancer. The developed clinician-oriented interface can assist clinicians in conveniently presenting evidence-based information to patients and can be readily adapted to an existing hospital information system and be easily applied in other chronic diseases.