Augmenting Guideline Knowledge with Non-compliant Clinical Decisions: Experience-Based Decision Support

Tipo

Inproceedings

Fecha

2017-05-21

Autores

Nekane Larburu Rubio, Naiara Muro Amuchastegui, Jacques Bouaud, Jon Belloso, Gerardo Cajaraville, Ander Urruticoechea, Brigitte Séroussi

Libro

InMed 2017: Innovation in Medicine and Healthcare 2017

@Inproceedings{
author ={Nekane Larburu Rubio, Naiara Muro Amuchastegui, Jacques Bouaud, Jon Belloso, Gerardo Cajaraville, Ander Urruticoechea, Brigitte Séroussi},
title ={Augmenting Guideline Knowledge with Non-compliant Clinical Decisions: Experience-Based Decision Support},
booktitle ={InMed 2017: Innovation in Medicine and Healthcare 2017},
publisher ={Springer International Publishing},
address ={Vilamoura, Portugal},
date ={2017-05-21},
year ={2017},
pages ={217-226},
volume ={71},
series ={SIST},
keys ={

Experience-based clinical decision support system; Data mining techniques; Clinical guidelines evolution; Breast cancer; DESIREE

},
abstract ={

Guideline-based clinical decision support systems (CDSSs) are expected to improve the quality of care by providing best evidence-based recommendations. However, because clinical practice guidelines (CPGs) may be incomplete and often lag behind the publication time of very last scientific results, CDSSs may not provide up-to-date treatments. It happens that clinical decisions made for specific patients do not comply with CDSS recommendations, whereas they comply with the state of the art. They may also be non-compliant because they rely on some implicit knowledge not covered by CPGs. We propose to capitalize the clinical know-how built from such non-compliant decisions and allow physicians to use it in future similar cases by the development of a decisional event structure that allows the modelling, storage, processing, and reuse of all the information related to a decision-making process. This structure allows the analysis of non-compliant decisions, which generates new experience-based rules. These new rules augment the knowledge embedded in CPGs supporting clinician decision for specific patients poorly covered by CPGs. This work is applied to the management of breast cancer within the EU Horizon 2020 project DESIREE.

},
ISBN ={978-3-319-59396-8},
}