Background: Patients may be inadvertently harmed while undergoing dental treatments. To improve care, we must first determine the types and frequency of harms that patients experience, but identifying cases of harm is not always straightforward for dental practices. Mining data from electronic health records is a promising means of efficiently detecting possible adverse events (AEs). Methods: We developed 7 electronic triggers (electronic health record based) to flag patient charts that contain distinct events common to AEs. These electronic charts were then manually reviewed to identify AEs. Results: Of the 1,885 charts reviewed, 16.2% contained an AE. The positive predictive value of the triggers ranged from a high of 0.23 for the 2 best-performing triggers (failed implants and postsurgical complications) to 0.09 for the lowest-performing triggers. The most common types of AEs found were pain (27.5%), hard tissue (14.8%), soft tissue (14.8%), and nerve injuries (13.3%). Most AEs were classified as temporary harm (89.2%). Permanent harm was present in 9.6% of the AEs, and 1.2% required transfer to an emergency room. Conclusion: By developing these triggers and a process to identify harm, we can now start measuring AEs, which is the first step to mitigating harm in the future. Knowledge Transfer Statement: A retrospective review of patients’ health records is a useful approach for systematically identifying and measuring harm. Rather than random chart reviews, electronic health record–based dental trigger tools are an effective approach for practices to identify patient harm. Measurement is one of the first steps in improving the safety and quality of care delivered.

This study assessed contributing factors associated with dental adverse events (AEs).Methods: Seven electronic health record-based triggers were deployed identifying potential AEs at 2 dental institutions. From 4106 flagged charts, 2 reviewers examined 439 charts selected randomly to identify and classify AEs using our dental AE type and severity classification systems. Based on information captured in the electronic health record, we analyzed harmful AEs to assess potential contributing factors; harmful AEs were defined as those that resulted in temporary moderate to severe harm, required hospitalization, or resulted in permanent moderate to severe harm. We classified potential contributing factors according to (1) who was involved (person), ( 2) what were they doing (tasks), ( 3) what tools/ technologies were they using (tools/technologies), ( 4) where did the event take place (environment), ( 5) what organizational conditions contributed to the event? (organization), ( 6) patient (including parents), and (7) professional-professional collaboration. A blinded panel of dental experts conducted a second review to confirm the presence of an AE.Results: Fifty-nine cases had 1 or more harmful AEs. Pain occurred most frequently (27.1%), followed by nerve injury (16.9%), hard tissue injury (15.2%), and soft tissue injury (15.2%). Forty percent of the cases were classified as "temporary not moderate to severe harm." Person (training, supervision, and fatigue) was the most common contributing factor (31.5%), followed by patient (noncompliance, unsafe practices at home, low health literacy, 17.1%), and professional-professional collaboration (15.3%).Conclusions: Pain was the most common harmful AE identified. Person, patient, and professional-professional collaboration were the most frequently assessed factors associated with harmful AEs.

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