Dental Extractions under General Anesthesia: New Insights from Process Mining.

Area of Science:

• Public Health
• Data Science
• Dental Medicine

Background:

• Tooth extraction under general anesthetic (GA) is a significant public health concern due to its high costs, risks, and resource demands.
• Reducing the prevalence of GA extractions is a key objective for healthcare systems globally.

Purpose of the Study:

• To apply process mining techniques to dental electronic health record (EHR) data to analyze treatment pathways preceding GA extractions.
• To identify key metrics and issues within these pathways to inform policy decisions and improve public health outcomes.

Main Methods:

• Anonymized dental EHR data from 231,760 patients (aged 0-16 years) in the Irish public healthcare system (2000-2014) were analyzed.
• Process mining methods were adapted to analyze treatment sequences, waiting times, visit frequency, and interventions prior to GA extractions.

Main Results:

• Analysis of 5,563 patients undergoing 26,115 GA extractions revealed complex pathways.
• Significant findings include high rates of emergency appointments (11,867), prescriptions (4,370), and unsuccessful restorative treatments prior to extraction.
• A notable percentage (9%) received tooth dressings with a 6-month lag before extraction.

Conclusions:

• Process mining provides valuable insights into the complexity of dental treatment pathways leading to GA extractions.
• The findings highlight the need for enhanced screening, preventive care, and alternative treatment strategies to reduce the burden of GA extractions.