Erratum to “Human-centred design processes for clinical decision support: A pulmonary embolism case study” [Int. J. Med. Inf. (2020) 104196]

Author(s):  
Julie N. Babione ◽  
Wrechelle Ocampo ◽  
Sydney Haubrich ◽  
Connie Yang ◽  
Torre Zuk ◽  
...  
2020 ◽  
Vol 142 ◽  
pp. 104196
Author(s):  
Julie N. Babione ◽  
Wrechelle Ocampo ◽  
Sydney Haubrich ◽  
Connie Yang ◽  
Torre Zuk ◽  
...  

10.2196/25046 ◽  
2020 ◽  
Author(s):  
Safiya Richardson ◽  
Katherine Dauber-Decker ◽  
Thomas McGinn ◽  
Douglas Barnaby ◽  
Adithya Cattamanchi ◽  
...  

2020 ◽  
Author(s):  
azita yazdani ◽  
Reza Safdari ◽  
Roxana Sharifian ◽  
maryam zahmatkeshan

Abstract Background: One of the most important types of information systems that play important role today in providing quality health care services are clinical decision support systems (CDSSs). These systems are effective in overcoming human resource constraint and intelligent analysis of information generated by Tele-monitoring systems. In spite of the many advantages of this architectures, these are single-purpose, meaning that only the CDSS of a disease is located on them. If we want to use the same model of architecture in the decision-making process of another disease, all the components of this architecture should be redevelopment with a new CDSS, which is time-consuming and costly. Due to the increasing demand for health information technology at low cost and mobile access in the health care industry, in this article, a scalable software platform(Patient Tele monitoring: PATEL) based on SOA for implementing and use different CDSSs on a common platform, for use in Tele-monitoring Systems, was created.Implementation: To develop PATEL platform, the component-based software development approach and hybrid programming approach to implementing various components used. In the evaluation phase of the proposed platform, the case study, accuracy and performance evaluation (transmission delays, patient data fetch, parsing overhead and inference time) used.Results: The results of the case study evaluation confirmed the scalability and interoperability between CDSSs on the platform. Based on performance evaluation, the proposed platform has responded to 89% of the requests in less than one second. Also, based on accuracy evaluation, the platform presented in this article was successful in diagnosing 91.6% of the cases.Conclusion: The proposed platform can support CDSSs of various diseases simultaneously and provides the necessary scalability to add a new CDSS. Tele-monitoring systems will be capable of service by connecting to this platform. Using this infrastructure is expected to be a lot of duplication in the implementation of tele-monitoring systems based CDSSs will be reduced.


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