Knowledge-based Decision Support in Healthcare via Near Field Communication

Knowledge-based Decision Support in Healthcare via Near Field Communication

Sensors - -2020

Authors

Loseto Giuseppe, Scioscia Floriano, Ruta Michele, Gramegna Filippo, Ieva Saverio, Pinto Agnese, Scioscia Crescenzio

Abstract

The benefits of automatic identification technologies in healthcare have been largely recognized. Nevertheless, unlocking their potential to support the most knowledge-intensive medical tasks requires to go beyond mere item identification. This paper presents an innovative Decision Support System (DSS), based on a semantic enhancement of Near Field Communication (NFC) standard. Annotated descriptions of medications and patient’s case history are stored in NFC transponders and used to help caregivers providing the right therapy. The proposed framework includes a lightweight reasoning engine to infer possible incompatibilities in treatment, suggesting substitute therapies. A working prototype is presented in a rheumatology case study and preliminary performance tests are reported. The approach is independent from back-end infrastructures. The proposed DSS framework is validated in a limited but realistic case study, and performance evaluation of the prototype supports its practical feasibility. Automated reasoning on knowledge fragments extracted via NFC enables effective decision support not only in hospital centers, but also in pervasive IoT-based healthcare contexts such as first aid, ambulance transport, rehabilitation facilities and home care.

Download: sensors-20-04923.pdf

DOI

https://doi.org/10.3390/s20174923

BibTex references

@Article{LSRGIPS20,
  author       = "Loseto, Giuseppe and Scioscia, Floriano and Ruta, Michele and Gramegna, Filippo and Ieva, Saverio and Pinto, Agnese and Scioscia, Crescenzio",
  title        = "Knowledge-based Decision Support in Healthcare via Near Field Communication",
  journal      = "Sensors",
  year         = "2020",
  note         = "to appear",
  url          = "http://sisinflab.poliba.it/Publications/2020/LSRGIPS20"

}