HCR.20.048 – How can we use Big Data and e-health to prevent over- and undertreatment in dental healthcare.

Route: Health care research, sickness prevention and treatment

Cluster question: 105 How can Big Data and technological innovation (e-health) contribute to health care?

It is estimated that roughly 20% of all medical care is unnecessary. Within dental care estimations of under and overtreatment roughly vary and are reported up to 40% for certain interventions. Overtreatment in the form of diagnosis/tests, medication and interventions create an increasing burden on the annual dental and healthcare costs. Earlier studies have shown that in the United States third molar extractions are performed preventively in up to 59% of the cases. Furthermore, the diagnostic value of routine check-ups using Conebeam CT-scans, orthopantomography or dental X-rays varies and relies on the experience and knowledge of the dental professional. Overtreatment and overdiagnosis is well recognized and acted upon within the field of medicine and dental care. However, despite the recognition of over- and undertreatment within dental care only limited solutions of reducing these issues are reported. The reasons for this mismatch in treatment are widely reported from financial incentives, preventive measures, uncertainty, patient and dentist preferences, (the lack of) clinical evidence, to overdiagnosis with new or better imaging modalities. Artificial intelligence (AI) within (dental) healthcare has accelerated new breakthroughs within diagnostics and predictions in the past few years thanks to the increase in available (Big) data and computing power. Dentistry has historically collected enormous amounts of clinical imaging making it a perfect field for AI applications. With improved diagnostics and predictions better informed decisions can be made on treatment within dental healthcare. For instance, predictions on which third molar will become symptomatic or remain life-long asymptomatic can reduce the need for unnecessary removal. We therefor propose to implement AI, Big Data and better imaging modalities for the reduction in over- and undertreatment to reduce the burden within dental healthcare.

Keywords

Artificial Intelligence, Big Data, dental healthcare, e-health, imaging, overtreatment, undertreatment

Submitter

Organisation Radboudumc (RUMC)
Name Prof. Dr. Thomas Maal
E-mail Thomas.maal@radboudumc.nl
Website https://www.radboudumc.nl/en/people/thomas-maal