Clinical decision support system for dental treatment

Mago, Vijay; Bhatia, Nitin; Bhatia, Ajay; Mago, Anjali

doi:10.1016/j.jocs.2012.01.008

Cited by 20 publications

(9 citation statements)

References 31 publications

(36 reference statements)

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“…For example, a fuzzy cognitive map is used to help the dentist decide on a suitable implant abutment for patients , combining expert knowledge from dentists and suppliers in the decision making process. Similarly, fuzzy logic is used by the system proposed by Mago et al (2012) to identify symptoms from patients, which are usually vague, making it difficult for the dentist to reach a detailed and definitive diagnosis.…”

Section: Decision Support Systemsmentioning

confidence: 99%

“…Comments Selection of Implant Abutments Uses expert knowledge from dentists and abutment suppliers Treatment of Tooth Fracture (Mago et al, 2012) Uses fuzzy logic to help identify complaints from patients Treatment of Cavitated Lesions (Park et al, 2012) Considers the patient's preference Management of Dental Caries (Bessani et al, 2014) Considers the patient's oral history and health risk factors …”

Section: Systemsmentioning

confidence: 99%

“…Unlike binary logic methods such as the above described ANN or SVM where the output is either true or false, fuzzy logic allows for different degrees of truth. In Mago et al (2012)'s design of a DSS for dental treatment, fuzzy logic was used to accept inaccurate and vague values of dental signs and symptoms associated with fractured teeth to produce possible treatment plans. Under rigorous testing conditions, the system was found to be similar to the dentist's professional predictions with respect to treatment for such situations.…”

Section: Knowledge Representationmentioning

confidence: 99%

“…Poor usability is often cited as a reason for slow adoption of IS as it "makes it difficult for providers to navigate through the information and obtain an integrated view of patient data" (Thyvalikakath et al, 2012). Besides, most systems only support a particular kind of treatment, such as treatment planning for dental caries (Park et al, 2010;Mago et al, 2012) or the selection of implant abutments .…”

Section: Lack Of Perceived Usefulnessmentioning

confidence: 99%

“…To enhance patient satisfaction and effective treatment, the clinical DSS not only stores expert knowledge but also generates treatment options using ontology that contains the patient's profile and their preference of options (Park et al, 2012). Mago et al (2012) also developed a system to reduce inconsistencies in treatment planning for a fractured tooth. Fuzzy logic, first introduced by Takagi and Sugeno (1985), was used for its strength in dealing with imprecision pertaining to dental disease and symptoms.…”

Section: Current Clinical Dss Design Features Expected Design Featuresmentioning

confidence: 99%

See 4 more Smart Citations

Decision support systems for adoption in dental clinics: A survey

Goh

Tao

et al. 2016

Knowledge-Based Systems

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While most dental clinicians use some sort of information system, they are involved with administrative functions, despite the advisory potential of some of these systems. This paper outlines some current decision support systems (DSS) and the common barriers facing dentists in adopting them within their workflow. These barriers include lack of perceived usefulness, complicated social and economic factors, and the difficulty for users to interpret the advice given by the system. A survey of current systems found that although there are systems that suggest treatment options, there is no real-time integration with other knowledge bases. Additionally, advice on drug prescription at point-of-care is absent from such systems, which is a significant omission, in consideration of the fact that disease management and drug prescription are common in the workflow of a dentist. This paper also addresses future trends in the research and development of dental clinical DSS, with specific emphasis on big data, standards and privacy issues to fulfil the vision of a robust, user-friendly and scalable personalised DSS for dentists. The findings of this study will offer strategies in design, research and development of a DSS with sufficient perceived usefulness to attract adoption and integration by dentists within their routine clinical workflow, thus resulting in better health outcomes for patients and increased productivity for the clinic.

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Section: Decision Support Systemsmentioning

confidence: 99%

Section: Systemsmentioning

confidence: 99%

Section: Knowledge Representationmentioning

confidence: 99%

Section: Lack Of Perceived Usefulnessmentioning

confidence: 99%