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A collaborative web service exploiting collective rules and evidence integration to support sustainable orthodontic decisions
Despite the growing demand for orthodontic care, a framework to support sustainable orthodontic decision-making is lacking, even if scientific literature offers several attempts to deal with this issue. As well known, dentistry generates solid health residues that include heavy metals and biomedical waste, that asks for a professional duty and a social responsibility of the orthodontist that should transform, more and more, his daily practice to a sustainable one, by adopting environmental oriented measures and, at the same time, cutting the overall costs of his professional performance while keeping the performance standards high. This work aims at filling such a gap in knowledge by proposing a decision tree algorithm that, besides increasing the level of agreement within and between orthodontists, allows for the adoption of a framework of sustainable orthodontic best practices, using a dataset of 290 randomly selected patients generated from 2011 medical records of patients of the orthodontic School at the University of Napoli “Federico IIâ€. The best practices framework, provided as if-then rules which can be easily inspected by orthodontists, represents a sustainable model in that it minimizes the time and resources employed for dentistry decision-making, dramatically reduce the environmental impact in terms of waste and use of electric equipment and tools, and increases patient satisfaction by delivering quick and appropriate treatment, thus meeting the economic, environmental and social pillars of sustainability in health care.
A collaborative web service exploiting collective rules and evidence integration to support sustainable orthodontic decisions
Despite the growing demand for orthodontic care, a framework to support sustainable orthodontic decision-making is lacking, even if scientific literature offers several attempts to deal with this issue. As well known, dentistry generates solid health residues that include heavy metals and biomedical waste, that asks for a professional duty and a social responsibility of the orthodontist that should transform, more and more, his daily practice to a sustainable one, by adopting environmental oriented measures and, at the same time, cutting the overall costs of his professional performance while keeping the performance standards high. This work aims at filling such a gap in knowledge by proposing a decision tree algorithm that, besides increasing the level of agreement within and between orthodontists, allows for the adoption of a framework of sustainable orthodontic best practices, using a dataset of 290 randomly selected patients generated from 2011 medical records of patients of the orthodontic School at the University of Napoli “Federico IIâ€. The best practices framework, provided as if-then rules which can be easily inspected by orthodontists, represents a sustainable model in that it minimizes the time and resources employed for dentistry decision-making, dramatically reduce the environmental impact in terms of waste and use of electric equipment and tools, and increases patient satisfaction by delivering quick and appropriate treatment, thus meeting the economic, environmental and social pillars of sustainability in health care.
A collaborative web service exploiting collective rules and evidence integration to support sustainable orthodontic decisions
D'Avanzo, Ernesto (author) / D'Antò, Vincenzo (author) / Michelotti, Ambrosina (author) / Martina, Roberto (author) / Adinolfi, Paola (author) / Pango Madariaga, Ada C. (author) / Zanoli, Roberto (author) / D'Avanzo, Ernesto / D'Antò, Vincenzo / Michelotti, Ambrosina
2018-01-01
Article (Journal)
Electronic Resource
English
Policy for sustainability , Class I malocclusion , Sustainable decision-making , Education for sustainable development , 2300 , Evidence-based decision-making (EBDM) , Strategy and Management1409 Tourism , Industrial and Manufacturing Engineering , Sustainability and the Environment , Leisure and Hospitality Management , Renewable Energy , Decision tree
DDC:
690
Collective, collaborative, corporate
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