References

@conference{Rajkovi\'{c}2016,

title = {Adaption of medical information system's e-learning extension to a simple suggestion tool},

author = {Petar Rajkovi\'{c} and Dragan Jankovi\'{c} and Aleksandar Milenkovi\'{c}},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006371699\&doi=10.1109%2fHealthCom.2016.7749473\&partnerID=40\&md5=cf0c405d9e3e593437b8d8fe7360e46e},

doi = {10.1109/HealthCom.2016.7749473},

year  = {2016},

date = {2016-01-01},

journal = {2016 IEEE 18th International Conference on e-Health Networking, Applications and Services, Healthcom 2016},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Developing suggestion tools in the scope of health information systems can be a complex task, followed by a risk of not being accepted by the end users. Thus, we decide to start the implementation around the existing functionality. In this paper we present a case study showing the adaptation of e-learning medical information system extension to a set of simple suggestion tools. While some features of initial system had to be modified, the domain specific knowledge collected for the e-learning extension is used to suppress potential errors. Presented suggestion tool is based on highly configurable lists of pre-defined entities that can be easily selected, and after the verification from the medical practitioner, copied into an active visit. After four years of active use, and several iteration of update, described suggestion tools are mostly accepted among the general practitioners, especially within certain scenarios where faster medication prescription is a must. © 2016 IEEE.},

keywords = {Bioinformatics; E-learning; Health; Health risks; Information systems; Complex task; data suggestion system; Domain-specific knowledge; General practitioners; Health information systems; Medical practitioner; Potential errors; System acceptance; Medical information systems},

pubstate = {published},

tppubtype = {conference}

}